EP3468457A1 - Rapid detection of bleeding following injury - Google Patents
Rapid detection of bleeding following injuryInfo
- Publication number
- EP3468457A1 EP3468457A1 EP17813880.6A EP17813880A EP3468457A1 EP 3468457 A1 EP3468457 A1 EP 3468457A1 EP 17813880 A EP17813880 A EP 17813880A EP 3468457 A1 EP3468457 A1 EP 3468457A1
- Authority
- EP
- European Patent Office
- Prior art keywords
- cri
- patient
- bleeding
- data
- probability
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Withdrawn
Links
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Classifications
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- A61B5/02042—Determining blood loss or bleeding, e.g. during a surgical procedure
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- A61B5/026—Measuring blood flow
Definitions
- the '888 Application may also be related to U.S. Patent Application No. 14/542,423, filed November 14, 2014 by Mulligan et al. and titled, “Noninvasive Monitoring for Fluid Resuscitation” (attorney docket no. 0463.11, referred to herein as the " '423 Application”), which is incorporated herein by reference in its entirety and which claims priority to provisional U.S. Patent Application No.
- Patent Application No. 13/028, 140 filed February 15, 2011 by Grudic et al. and entitled "Statistical, Noninvasive Measurement of Intracranial Pressure” (attorney docket no. 0463.03, referred to herein as the " ⁇ 40 Application”; now issued U.S. Patent No. 8,512,260), which is hereby incorporated by reference in its entirety and which claims priority to provisional U.S. Patent Application No. 61/305, 110, filed February 16, 2010, by Moulton et al. and titled “Statistical, Noninvasive Method for Measuring Intracranial Pressure” (attorney docket no. 0463.03PR), which is hereby incorporated by reference in its entirety.
- the present disclosure relates, in general, tools and techniques for medical monitoring, and more particularly, to tools and techniques that can provide rapid detection of bleeding following injury.
- Hemorrhagic shock induced by traumatic injury is a leading cause of mortality.
- the first hour following injury has been termed the "golden hour,” because there is a short interval of time during which recognition and proper management of a patient with significant, ongoing bleeding can make the difference between life and death.
- Significant bleeding is not always clinically evident.
- Many severely injured patients have intracavitary bleeding, which means that bleeding from a major organ or vessel is contained within the thorax or abdomen. There is no external evidence of bleeding and as a result, suspicion and clinical signs of bleeding must be sought by the practitioner. In the field, where imaging and laboratory tests are generally not available, a change in vital signs over time may be the only indication that a patient is bleeding.
- the "golden hour” one must learn to recognize the signs and symptoms of acute blood loss, then initiate fluid resuscitation and frequently estimate the patient's fluid needs in an ongoing fashion.
- Fig. 1 A is a schematic diagram illustrating a system for estimating compensatory reserve, in accordance with various embodiments.
- Fig. IB is a schematic diagram illustrating a sensor system that can be worn on a patient's body, in accordance with various embodiments.
- FIG. 2A is a process flow diagram illustrating a method of assessing blood loss, in accordance with various embodiments.
- Fig. 2B illustrates a technique for assessing blood loss, in accordance with various embodiments.
- FIG. 3 A is a process flow diagram illustrating a method estimating a patient's compensatory reserve and/or dehydration state, in accordance with various embodiments.
- Fig. 3B illustrates a technique for estimating and/or predicting a patient's compensatory reserve index, in accordance with various embodiments.
- FIG. 4 is a process flow diagram illustrating a method of generating a model of a physiological state, in accordance with various embodiments.
- FIG. 5 is a process flow diagram illustrating a method of implementing rapid detection of bleeding before, during, and after fluid resuscitation, in accordance with various embodiments.
- Figs. 6-8 are exemplary screen captures illustrating display features of a compensatory reserve monitor showing assessments of blood loss before, during, and/or after fluid resuscitation, in accordance with various techniques.
- Figs. 9A-9H are graphical diagrams illustrating rapid detection of bleeding before, during, and after fluid resuscitation of patients in a multi-trauma clinical study at Denver Health Medical Center, in accordance with various embodiments.
- Fig. 10 is a schematic diagram illustrating Compensatory Reserve
- Fig. 11 is a graphical diagram illustrating receiver operating characteristic ("ROC") area under the curve (“AUC”) for CRI and classification of actively bleeding versus not actively bleeding, in accordance with various embodiments.
- ROC receiver operating characteristic
- AUC area under the curve
- Fig. 12 is a set of graphical diagrams illustrating CRI response in bleeding patients and in non-bleeding patients one hour after infusion of 1 liter of crystalloid or 1 unit of blood product, in accordance with various embodiments.
- Figs. 13 A and 13B are graphical diagrams illustrating CRI changes over time for two example gunshot patients in response to varying resuscitation techniques.
- Figs. 14A and 14B are graphical diagrams illustrating Fluid Volume
- FVR Fluorescence Volts
- Fig. 15 is a generalized schematic diagram illustrating a computer system, in accordance with various embodiments.
- Various embodiments can detect bleeding in patients following injury, in some instances, in real-time before, during, and after fluid resuscitation.
- detection can be performed noninvasively.
- the detection can be based on a calculation (or estimation) of a patient's compensatory reserve index ("CRI," also referred to herein and in the Related Applications as "cardiac reserve index” or “hemodynamic reserve index” (“HDRI”)).
- CRI patient's compensatory reserve index
- HDRI hemodynamic reserve index
- the assessments might be based on raw waveform data (e.g., PPG waveform data) captured by a sensor on the patient (such as the sensors described in the Related Applications, for example).
- a combination of waveform data and calculated/estimated CRI can be used to calculate the effectiveness of resuscitation and/or the amount of fluid needed for effective resuscitation.
- functionality can be provided by and/or integrated with systems and devices (such as a cardiac reserve monitor), tool, techniques, methods, and software described in the Related Applications, including in particular the '483 Application.
- systems and devices such as a cardiac reserve monitor
- various operations described in accordance with the methods disclosed by the Related Applications can be employed in a method of assessing effectiveness of resuscitation and/or calculating an amount of fluid needed for effective resuscitation.
- such techniques can be performed by the systems and/or embodied by the software products described in the Related Applications.
- An embodiment can include a system that comprises one or more sensors placed on the patient and a computer system (such as those described in the Related Applications) that performs a method for using sensor data for estimating and predicting (in real-time, after every heartbeat, or as the information is needed) one or more of the relevant parameters outlined above.
- Other embodiments can comprise the computer system programmed to perform such a method, an apparatus comprising instructions to program a computer to perform such a method, and/or such a method itself.
- a sensor may include, but is not limited to, any of the following: a noninvasive blood pressure sensor such as the Nexfin (BMEYE, B.V.) or Finometer (Finapres Medical Systems B.V.); invasive arterial blood pressure, using an arterial catheter; invasive central venous pressure; invasive or noninvasive intracranial pressure monitor; electroencephalograph ("EEG”); cardiac monitor ("EKG”);
- a noninvasive blood pressure sensor such as the Nexfin (BMEYE, B.V.) or Finometer (Finapres Medical Systems B.V.)
- invasive arterial blood pressure using an arterial catheter
- invasive central venous pressure using an arterial catheter
- invasive or noninvasive intracranial pressure monitor invasive or noninvasive intracranial pressure monitor
- electroencephalograph EEG
- cardiac monitor EKG
- transcranial Doppler sensor transthoracic impedance plethysmography; pulse oximetry; a sensor generating a photoplethysmograph ("PPG") waveform; near infrared spectroscopy; electronic stethoscope; and/or the like.
- PPG photoplethysmograph
- the '809 Application describes several exemplary embodiments, but various embodiments are not limited to those described in the '809 Application.
- Fig. 1 of the '809 Application illustrates an exemplary sensor that can be used to collect waveform data for analysis, but other sensors could be used as well.
- the '809 Application describes several techniques for estimating probability of blood loss. Many such techniques depend on an estimate of a patient's CRI, which can be calculated using the techniques described in the '483 Application. It should be appreciated, however, that other embodiments of estimating a probability of bleeding and/or of estimating CRI can be employed in various embodiments.
- a method can include receiving data from such a sensor and analyzing such data using techniques including, but not limited to, analyzing the data using models described in the Related Applications.
- a model might be constructed using test subject data from a study, such as the LB P study, which can be used to predict or estimate a CRI (or HDRI) value, as described in the Related Applications, and in particular in the '483
- a method might comprise capturing waveform data from a patient with the sensor before, during, and/or after fluid resuscitation and/or calculating a CRI value for the patient at these times.
- the variation in CRI values obtained during the procedure can be used to estimate a probability that the patient is bleeding.
- the standard deviation of the CRI values during the recording and/or the difference in CRI values before, during, and/or after fluid resuscitation can be used to estimate probability of bleeding, as described more fully with regard to the clinical study detailed in the '809
- Some embodiments further comprise normalizing an estimated probability of bleeding against a scaling. For example, in some cases, an index from 0 to 1 could be used, with 0 indicating that the patient is not bleeding, 1 indicating that the patient is bleeding, and values between 0 and 1 indicating relative
- a node could be "virtual" or supported on a hypervisor or Host system, or could be a physical node or network device within a network.
- the figures illustrate bridging a virtual path and possibly a node (virtual machine) across the path or between two physical nodes.
- the "swapping" of paths via orchestration can occur in any
- the tools provided by various embodiments include, without limitation, methods, systems, and/or software products.
- a method might comprise one or more procedures, any or all of which are executed by a computer system.
- an embodiment might provide a computer system configured with instructions to perform one or more procedures in accordance with methods provided by various other embodiments.
- a computer program might comprise a set of instructions that are executable by a computer system (and/or a processor therein) to perform such operations.
- software programs are encoded on physical, tangible, and/or non-transitory computer readable media (such as, to name but a few examples, optical media, magnetic media, and/or the like).
- a system might be provided that comprises one or more sensors to obtain physiological data from a patient and a computer system in communication with the one or more sensors.
- the computer system might comprise one or more processors and a non-transitory computer readable medium in communication with the one or more processors.
- the computer readable medium might have encoded thereon a set of instructions executable by the one or more processors to cause the computer system to receive the physiological data from the one or more sensors before, during, and after resuscitation following injury, analyze the physiological data, estimate a probability that the patient is bleeding, and display, on a display device, at least one of an assessment, prediction, or estimate indicating a probability that the patient is bleeding.
- a method might be provided that comprises monitoring, with one or more sensors, physiological data of a patient before, during, and after resuscitation following injury, analyzing, with a computer system, the physiological data, and estimating, with the computer system, a probability that the patient is bleeding, based at least in part on the analyzed physiological data.
- the method might further comprise displaying, with the computer system and on a display device, an indication of at least one of an assessment, prediction, or estimate of a probability that the patient is bleeding.
- resuscitation might comprise at least one of infusing crystalloid in the patient, infusing blood products in the patient, or infusing intravenous fluid in the patient, and/or the like.
- the intravenous fluid might comprise one or more of saline solution or lactated ringer's ("LR”) solution, and/or the like.
- the blood products might comprise one or more of infusion of packed red blood cells ("PRBC") in the patient, infusion of fresh frozen plasma (“FFP”) in the patient, infusion of platelets (“PLTs”) in the patient, or infusion of cryoprecipitated antihaemophilic factor (“cryo”) in the patient, and/or the like.
- one or more of monitoring the physiological data, analyzing the physiological data, estimating the probability that the patient is bleeding, or displaying the indication of at least one of an assessment, prediction, or estimate of the probability that the patient is bleeding are performed in real-time.
- estimating a probability that the patient is bleeding might comprise estimating, with the computer system, a probability that the patient is bleeding, based at least in part on one or more values of compensatory reserve index ("CRI") estimated based on the received physiological data (i.e., the physiological data that are received by the computer system from the one or more sensors).
- CRI compensatory reserve index
- the one or more values of CRI are estimated based on physiological data that are at least one of received before, received during, or received after a fluid resuscitation procedure.
- the one or more values of CRI might comprise a plurality of values of CRI.
- estimating a probability that the patient is bleeding might comprise estimating, with the computer system, the probability that the patient is bleeding based at least in part on one or more of an average value of CRI over a particular period of time, a standard deviation of at least some of the plurality of values of CRI, a skewness of at least some of the plurality of values of CRI, a rate of change of at least some of the plurality of values of CRI, a rate of rate change of at least some of the plurality of values of CRI, and/or a difference between at least some of the plurality of values of CRI.
- the indication is a value between 0 and 1. According to some embodiments, a value of 1 might indicate that the patient is not bleeding, while a value of 0 might indicate that the patient is bleeding.
- estimating a CRI of the patient comprises estimating a compensatory reserve index by comparing the physiological data to a model constructed using the following formula:
- the physiological data comprises waveform data and wherein estimating the CRI comprises comparing, with the computer system, the waveform data with one or more sample waveforms generated by exposing one or more test subjects to state of hemodynamic decompensation or near hemodynamic decompensation, or a series of states progressing towards hemodynamic decompensation, and monitoring
- the physiological data might comprise waveform data
- estimating the CRI might comprise comparing, with the computer system, the waveform data with a plurality of sample waveforms, each of the sample waveforms corresponding to a different value of the CRI to produce a similarity coefficient expressing a similarity between the waveform data and each of the sample waveforms; normalizing, with the computer system, the similarity coefficients for each of the sample waveforms; and summing, with the computer system, the normalized similarity coefficients to produce an estimated CRI value for the patient.
- estimating a probability that the patient is bleeding is based at least in part on a fixed time history of monitoring the physiological data of the patient.
- estimating a probability that the patient is bleeding is based at least in part on a dynamic time history of monitoring the physiological data of the patient.
- at least one of the one or more sensors each comprises at least one of a blood pressure sensor, an intracranial pressure monitor, a central venous pressure monitoring catheter, an arterial catheter, an electroencephalograph, a cardiac monitor, a transcranial Doppler sensor, a
- transthoracic impedance plethysmograph a pulse oximeter, a near infrared
- spectrometer a ventilator, an accelerometer, an electrooculogram, a transcutaneous glucometer, an electrolyte sensor, or an electronic stethoscope, and/or the like.
- physiological data might comprise at least one of blood pressure waveform data, plethysmograph waveform data, or photoplethysmograph (“PPG”) waveform data.
- PPG photoplethysmograph
- analyzing the physiological data might comprise analyzing, with the computer system, the physiological data against a pre-existing model.
- the method might further comprise generating, with the computer system, the pre-existing model prior to analyzing the physiological data.
- generating the pre-existing model might comprise receiving, with the computer system, data pertaining to one or more physiological parameters of a test subject to obtain a plurality of physiological data sets, directly measuring one or more physiological states of the test subject with a reference sensor to obtain a plurality of physiological state measurements, and correlating, with the computer system, the received data with the physiological state measurements of the test subject.
- the one or more physiological states comprise reduced circulatory system volume.
- the method might further comprise inducing the physiological state of reduced circulatory system volume in the test subject.
- inducing the physiological state might comprise at least one of subjecting the test subject to lower body negative pressure ("LB P"), subjecting the test subject to dehydration, and/or the like.
- the one or more physiological states might comprise at least one of a state of cardiovascular collapse or near- cardiovascular collapse, a state of euvolemia, a state of hypervolemia, a state of dehydration, and/or the like.
- correlating the received data with the physiological state measurements of the test subject might comprise identifying, with the computer system, a most predictive set of signals S k out of a set of signals
- the most-predictive set of signals S k corresponds to a first data set representing a first physiological parameter
- each of the one or more outcomes o k represents a physiological state measurement
- b k is a prediction of outcome o k derived from a model M k that uses as inputs values obtained from the set of signals S k .
- an apparatus might be provided that comprises a non-transitory computer readable medium that has encoded thereon a set of instructions executable by one or more computers to cause the apparatus to receive physiological data from one or more sensors before, during, and after resuscitation following injury, analyze the physiological data, estimate a probability that the patient is bleeding, and display, on a display device, at least one of an assessment, prediction, or estimate indicating a probability that the patient is bleeding.
- Various embodiments can assess the effectiveness of fluid intake hydration, where effectiveness can be defined as, but not limited to, leading to a better hydration state or maintain an optimal hydration state.
- optimal hydration might be defined as a fluid state that maximized some performance index/measure, perhaps indicated by the patient's compensatory reserve index ("CRI,” also referred to herein and in the Related Applications as "cardiac reserve index” or “hemodynamic reserve index” (“HDRI”), all of which should be considered synonymous for purposes of this disclosure).
- CRMI compensatory reserve index
- HDRI hemodynamic reserve index
- the term, "patient,” is used herein for convenience, that descriptor should not be considered limiting, because various embodiments can be employed both in a clinical setting and outside any clinical setting, such as by an athlete before, during, or after an athletic contest or training, a person during daily activities, a soldier on the battlefield, etc.
- the term, "patient,” as used herein, should be interpreted broadly and should be considered to be synonymous with "person.”
- the assessments might be based on raw waveform data (e.g., PPG waveform data) captured by a sensor on the patient (such as the sensors described below and the Related Applications, for example).
- a combination of waveform data and calculated/estimated CRI can be used to calculate the effectiveness of hydration and/or the amount of fluid needed for effective hydration.
- such functionality can be provided by and/or integrated with systems, devices (such as a cardiac reserve monitor and/or wrist-worn sensor device, or the like), tools, techniques, methods, and software described below and in the Related Applications.
- one set of embodiments provides methods.
- An exemplary method might comprise monitoring, with one or more sensors,
- the method might further comprise analyzing, with a computer system, the physiological data.
- Many different types of physiological data can be monitored and/or analyzed by various embodiments, including, without limitation, blood pressure waveform data, plethysmograph waveform data, photoplethysmograph ("PPG") waveform data (such as that generated by a pulse oximeter), and/or the like.
- analyzing the physiological data might comprise analyzing the data against a pre-existing model.
- the method can further comprise assessing the effectiveness of hydration efforts, and/or displaying (e.g., on a display device) an assessment of the effectiveness of the hydration efforts.
- Such an assessment can include, without limitation, an estimate of the effectiveness at a current time, a prediction of the effectiveness at some point in the future, an estimate and/or prediction of a volume of fluid necessary for effective hydration, an estimate of the probability a patient requires fluids, etc.
- An apparatus in accordance with yet another set of embodiments, might comprise a computer readable medium having encoded thereon a set of instructions executable by one or more computers to perform one or more operations.
- the set of instructions might comprise instructions for performing some or all of the operations of methods provided by certain
- a system in accordance with yet another set of embodiments, might comprise one or more processors and a computer readable medium in communication with the one or more processors.
- the computer readable medium might have encoded thereon a set of instructions executable by the computer system to perform one or more operations, such as the set of instructions described above, to name one example.
- the system might further comprise one or more sensors and/or a therapeutic device, either or both of which might be in
- Such sensors can include, but are not limited to, a blood pressure sensor, an intracranial pressure monitor, a central venous pressure monitoring catheter, an arterial catheter, an electroencephalograph, a cardiac monitor, a transcranial Doppler sensor, a transthoracic impedance plethysmograph, a pulse oximeter, a near infrared spectrometer, a ventilator, an accelerometer, an electrooculogram, a transcutaneous glucometer, an electrolyte sensor, and/or an electronic stethoscope.
- a blood pressure sensor an intracranial pressure monitor, a central venous pressure monitoring catheter, an arterial catheter, an electroencephalograph, a cardiac monitor, a transcranial Doppler sensor, a transthoracic impedance plethysmograph, a pulse oximeter, a near infrared spectrometer, a ventilator, an accelerometer, an electrooculogram, a transcutaneous glucometer, an electrolyte sensor, and
- a set of embodiments provides methods, systems, and software that can be used, in many cases noninvasively, to quickly and accurately assess blood loss in a patient (e.g., before, during, and/or after fluid resuscitation).
- Such an assessment can include, without limitation, an estimate of the effectiveness at a current time, a prediction of the effectiveness at some point in the future, an estimate and/or prediction of a volume of fluid necessary for effective hydration, an estimate of the probability a patient requires fluids, an estimate and/or prediction of blood loss (e.g., before, during, and/or after fluid resuscitation), etc.
- an estimate of the effectiveness at a current time e.g., a prediction of the effectiveness at some point in the future
- an estimate and/or prediction of a volume of fluid necessary for effective hydration e.g., an estimate of the probability a patient requires fluids
- an estimate and/or prediction of blood loss e.g., before, during, and/or after fluid resuscitation
- a device which can be worn on the patient's body, can include one or more sensors that monitor a patient's physiological parameters.
- the device (or a computer in communication with the device) can analyze the data captured by the sensors and compare such data with a model (which can be generated in accordance with other embodiments) to assess the effectiveness of hydration, as described in further detail in the '426 Application, and/or to assess blood loss (e.g., before, during, and/or after fluid resuscitation).
- physiological parameters from the patient can vary according to which parameters are measured (and which, according to the generated model, are found to be most predictive of the effectiveness of hydration, including the probability of the need for hydration and/or the volume of fluids needed, or most predictive of blood loss).
- the parameters themselves e.g., continuous waveform data captured by a photoplethysmograph
- assessments of hydration effectiveness or assessments of blood loss e.g., before, during, and/or after fluid resuscitation).
- physiological parameters can be derived from the captured data, and these parameters can be used Merely by way of example, as described further below and the '483 Application (already incorporated by reference), direct physiological data (captured by sensors) can be used to estimate a value of CRI, and this value of CRI can be used to assess the effectiveness of hydration and/or to assess blood loss (e.g., before, during, and/or after fluid resuscitation).
- direct physiological data captured by sensors
- this value of CRI can be used to assess the effectiveness of hydration and/or to assess blood loss (e.g., before, during, and/or after fluid resuscitation).
- the derived CRI values and raw sensor data can be used together to perform such assessments.
- the '483 Application describes a compensatory reserve monitor (also described as a cardiac reserve monitor or hemodynamic reserve monitor) that is able to estimate the compensatory reserve of a patient.
- this monitor quickly, accurately, and/or in real-time can determine the probability of whether a patient is bleeding.
- the device can simultaneously monitor the patient' s compensatory reserve by tracking the patient's CRI, to appropriately and effectively guide hydration and ongoing patient care.
- the same device can also include advanced functionality to assess the effectiveness of hydration, based on the monitored CRI values, as explained in further detail in the '426 Application, and/or to rapidly assess blood loss (e.g., before, during, and/or after fluid resuscitation).
- CRI is a hemodynamic parameter that is indicative of the individual- specific proportion of intravascular fluid reserve remaining before the onset of hemodynamic decompensation.
- CRI has values that range from 1 to 0, where values near 1 are associated with normovolemia (normal circulatory volume) and values near 0 are associated with the individual specific circulatory volume at which
- BLV(t) is the intravascular volume loss (“BLV,” also referred to as “blood loss volume” in the Related Applications) of a person at time “t,” the intravascular volume loss of a person when they enter hemodynamic decompensation (“HDD”). Hemodynamic decompensation is generally defined as occurring when the systolic blood pressure falls below 70 mmHg. This level of intravascular volume loss is individual specific and will vary from subject to subject.
- BLV ⁇ ⁇ LBNP
- LBNP(t) is the LBNP level that the individual is experiencing at time "t”
- LBNP HDD is the LNPB level that the individual will enter hemodynamic
- BL blood loss
- BL blood loss
- BL f BL ⁇ CRl t , FV t , S t )
- BL is a measure or an estimate of blood loss
- sz is an algorithm embodied by a model generated empirically, e.g., using the techniques described with respect to Fig.
- CRI t is a time history of CRI values (which can range from a single CRI value to many hours of CRI values)
- FV t is a time history of fluid volume being given to the patient (which can range from a single value to many hours of values)
- S t is a time history of raw sensor values, such as physiological data measured by the sensors, as described elsewhere herein (which can range from one value to many hours of values).
- Eq. 4 The functional form of Eq. 4 is similar to but not limited to the form of the CRI model in the sense that time histories of (CRI t , FV t , S t ) data gathered from human subjects at various levels of BL are compared to time histories of
- Eq. 4 is the general expression for BL, various embodiments might use subsets of the parameters considered in Eq. 4. For instance, in one
- a model might consider only the volume of fluid and CRI data, without accounting for raw sensor input.
- BL can be calculated as follows:
- BL can be expressed thusly:
- the effectiveness of hydration can be assessed by estimating or predicting the volume, V, of fluid necessary for effective hydration of the patient.
- This volume, V can indicate a volume of fluid needed for full hydration if therapy has not yet begun, and/or it can indicate a volume remaining for fully
- J 7 can be expressed as the following:
- V f v (CRI t , FV t , S t ) (Eq. 7)
- V is an estimated volume of fluid needed by a patient need to prevent over or under hydration
- f v (CRI t , FV t , S t ) is an algorithm embodied by a model generated empirically, e.g., using the techniques described with respect to Fig. 4 below, and/or in the Related Applications
- CRI t is a time history of CRI values
- FV t is a time history of fluid volume being given to the patient
- S t is a time history of physiological data received from the one or more sensors.
- Yet another way of assessing effectiveness of hydration is estimating the probability /that the patient requires fluids; this probability can estimate the likelihood that the patient requires hydration if therapy has not been initiated, and/or, if hydration therapy is underway, the probability can estimate the likelihood that further hydration is
- this probability which can be expressed, e.g., as a
- various sensor data can be collected from test subjects before, during, and/or after hydration efforts, during hemorrhaging, or under other conditions that might simulate such situations. This sensor data can be analyzed to develop models, using techniques similar to those of Fig. 4 below, which can then be used to estimate various assessments of hydration effectiveness, using, e.g., the methods described below with respect to Figs. 2 and 3.
- a measure of CRI, BL, V, and/or Pf can be useful in a variety of clinical settings, including, but not limited to: 1) acute blood loss volume due to injury or surgery; 2) acute circulatory volume loss due to hemodialysis (also called
- CRI chronic myelolism
- a change in CRI can also herald other conditions, including, without limitation, changes in blood pressure, general fatigue, overheating, and/or certain types of illnesses. Accordingly, the tools and techniques for estimating and/or predicting CRI can have a variety of applications in a clinical setting, including, without limitation, diagnosing such conditions.
- measures of CRI, BL, V, and/or Pf can have applicability outside the clinical setting.
- an athlete can be monitored (e.g., using a wrist-wearable hydration monitor) before, during, or after competition or training to ensure optimal performance (and overall health and recovery).
- a person concerned about overall wellbeing can employ a similar hydration monitor to ensure that he or she is getting enough (but not too much) fluid, ill infants or adults can be monitored while ill to ensure that symptoms (e.g., vomiting, diarrhea, etc.) do not result in dehydration, and the like.
- soldiers in the field can be monitored to ensure optimal operational readiness.
- a hydration monitor, compensatory reserve monitor, a wrist-wearable sensor device, and/or another integrated system can include, but is not limited to, some or all of the following functionality, as described in further detail herein and in the Related Applications:
- compensatory reserve as an index that is proportional to an approximate measure of intravascular volume loss to CV collapse, recognizing that each patient has a unique reserve capacity.
- the patient's normalized compensatory reserve can be displayed on a continuum between the minimum and maximum values (perhaps labeled by different symbols and/or colors depending on where the patient falls on the continuum).
- F Estimating a patient's current blood pressure and/or predicting a patient's future blood pressure.
- K Estimating a hydration state of a patient or user.
- M Estimating and/or predicting a volume of fluid intake necessary for adequate hydration of a patient or user.
- N Estimating a probability that a patient is dehydrated.
- CRI, BL, V, and/or 5 / estimates can be (i) based on a fixed time history of patient monitoring (for example a 30 second or 30 heart beat window); (ii) based on a dynamic time history of patient monitoring (for example monitoring for 200 minutes, the system may use all sensor information gathered during that time to refine and improve CRI estimates, hydration
- Certain embodiments can also recommend treatment options, based on the analysis of the patient's condition (including the estimated/predicted blood pressure, probability of bleeding, state of dehydration, and/or the patient's estimated and/or predicted CRI).
- Treatment options can include, without limitation, such things as optimizing hemodynamics, ventilator adjustments, IV fluid adjustments (e.g., controlling the flow rate of an IV pump or the drip rate of an IV drip), transfusion of blood or blood products, infusion of volume expanders, medication changes, changes in patient position, and/or surgical therapy, or the like.
- certain embodiments can be used to control an IV drip, IV pump, or rapid infuser. For instance, an embodiment might estimate the probability that a patient requires fluids and might activate such a device in response to that estimate (or instruct a clinician to attach such a device to the patient and activate the device). The system might then monitor the progress of the hydration effort (through continual or periodic assessment of the effectiveness of hydration) and increase/decrease drip or flow rates accordingly.
- certain embodiments can be used as an input for a hemodialysis procedure. For example, certain embodiments can predict how much intravascular (blood) volume can be safely removed from a patient during a hemodialysis process. For example, an embodiment might provide instructions to a human operator of a hemodialysis machine, based on estimates or predictions of the patient's CRI. Additionally and/or alternatively, such embodiments can be used to continuously self-adjust the ultra-filtration rate of the hemodialysis equipment, thereby completely avoiding intradialytic hypotension and its associated morbidity.
- certain embodiments can be used to estimate and/or predict a dehydration state (and/or the amount of dehydration) in an individual (e.g., a trauma patient, an athlete, an elder living at home, etc.) and/or to provide treatment (either by providing recommendations to treating personnel or by directly controlling appropriate therapeutic equipment).
- an analytical model indicates a relationship between CRI (and/or any other physiological phenomena that can be measured and/or estimated using the techniques described herein and in the Related Applications) and dehydration state
- an embodiment can apply that model, using the techniques described herein, to estimate a dehydration state of the patient.
- FIG. 1-15 illustrate some of the features of the method, system, and apparatus for implementing rapid detection of bleeding of a patient following injury, in some cases, before, during, and after fluid resuscitation, as referred to above.
- Figs. 1-8 illustrate some of the specific (although non-limiting) exemplary features of the method, system, and apparatus for implementing rapid detection of bleeding before, during, and after fluid resuscitation
- Figs. 9A-9H illustrate implementing rapid detection of bleeding before, during, and after fluid resuscitation of patients in a clinical trial
- Figs. 10-14B illustrate implementing rapid detection of bleeding of patients following injury in another clinical trial.
- Fig. 15 illustrates exemplary system and hardware implementation.
- the methods, systems, and apparatuses illustrated by Figs. 1-15 refer to examples of different embodiments that include various
- Fig. 1 A provides a general overview of a system 100 provided by certain embodiments.
- the system includes a computer system or computational device 105 in communication with one or more sensors 110 (which might include sensors 110a, 110b, and 110c, or the like), each of which is configured to obtain physiological data from the subject (e.g., animal or human test subject or patient) 120.
- the computer system 105 comprises a Lenovo THINKPAD X200, 4GB of RAM with Microsoft WF DOWS 7 operating system and is programmed with software to execute the computational methods outlined herein.
- the computational methods can be implemented in MATLAB 2009b and C++ programming languages.
- the computer system 105 can be any system of one or more computers that are capable of performing the techniques described herein.
- the computer system 105 is capable of reading values from the physiological sensors 110; generating models of physiological state from those sensors; employing such models to make individual-specific estimations, predictions, or other diagnoses; displaying the results; recommending and/or implementing a therapeutic treatment as a result of the analysis; and/or archiving (learning) these results for use in future, model building and predictions; or the like.
- the sensors 110 can be any of a variety of sensors (including without limitation those described herein) for obtaining physiological data from the subject.
- An exemplary sensor suite might include a Finometer sensor for obtaining a noninvasive continuous blood pressure waveform, a pulse oximeter sensor, an Analog to Digital Board (National Instruments USB-9215A 16-Bit, 4 channel) for connecting the sensors (either the pulse oximeter and/or the finometer) to the computer system 105.
- one or more sensors 110 might obtain, e.g., using one or more of the techniques described herein, continuous physiological waveform data, such as continuous blood pressure.
- Input from the sensors 110 can constitute continuous data signals and/or outcomes that can be used to generate, and/or can be applied to, a predictive model as described below.
- the structure or system might include a therapeutic device 115 (also referred to herein as a "physiological assistive device”), which can be controlled by the computer system 105 to administer therapeutic treatment, in accordance with the recommendations developed by analysis of a patient's physiological data.
- the therapeutic device might comprise hemodialysis equipment (also referred to as a hemodialysis machine), which can be controlled by the computer system 105 based on the estimated CRI of the patient, as described in further detail below.
- therapeutic devices in other embodiments can include a cardiac assist device, a ventilator, an automatic implantable cardioverter defibrillator ("AICD”), pacemakers, an extracorporeal membrane oxygenation circuit, a positive airway pressure (“PAP") device (including, without limitation, a continuous positive airway pressure (“cPAP”) device, or the like), an anesthesia machine, an integrated critical care system, a medical robot, intravenous and/or intra-arterial pumps that can provide fluids and/or therapeutic compounds (e.g., through intravenous injection), intravenous drips, a rapid infuser, a heating/cooling blanket, and/or the like.
- AICD automatic implantable cardioverter defibrillator
- PAP positive airway pressure
- cPAP continuous positive airway pressure
- Fig. IB illustrates in more detail an exemplary sensor device 110, which can be used in the system 100 described above.
- the illustrated sensor device 110 is designed to be worn on a patient's wrist and therefore can be used both in clinical settings and in the field (e.g., on any person for whom monitoring might be beneficial, for a variety of reasons, including, without limitation, assessment of blood pressure and/or hydration during athletic competition or training, daily activities, military training or action, etc.).
- the sensor device 110 can serve as an integrated hydration monitor, which can assess hydration as described herein, display an indication of the assessment, recommend therapeutic action based on the assessment, or the like, in a form factor that can be worn during athletic events and/or daily activities.
- the exemplary sensor 110 device (e.g., hydration monitor or the like) includes a finger cuff 125 and a wrist unit 130.
- the finger cuff 125 includes a fingertip sensor 135 (in this case, a PPG sensor) that captures data based on physiological conditions of the patient, such as PPG waveform data.
- the sensor 135 communicates with an input/output unit 140 of the wrist unit 130 to provide output from the sensor 135 to a processing unit 145 of the wrist unit 130.
- communication can be wired (e.g., via a standard— such as USB— or proprietary connector on the wrist unit 130) and/or wireless (e.g., via Bluetooth, such as
- Bluetooth Low Energy BTLE
- NFC near field connection
- WiFi WiFi
- the processing unit 145 can have different types of functionality. For example, in some cases, the processing unit 145 might simply act to store and/or organize data prior to transmitting the data through the I/O unit 140 to a monitoring computer 105, which might perform data analysis, to control a therapeutic device 115, etc. In other cases, however, the processing unit 145 might act as a specialized computer (e.g., with some or all of the components described in connection with Fig. 10, below and/or some or all of the functionality ascribed to the computer 105 of Figs. 1A and IB), such that the processing unit 145 can perform data analysis onboard, e.g., to estimate and/or predict a patient's current and/or future blood pressure.
- a specialized computer e.g., with some or all of the components described in connection with Fig. 10, below and/or some or all of the functionality ascribed to the computer 105 of Figs. 1A and IB
- the wrist unit 130 might include a display 150, which can display any output described herein, including, without limitation, estimated and/or predicted values (e.g., of CRI, blood pressure, hydration status, etc.), data captured by the sensor (e.g., heart rate, pulse oximetry data, etc.), and/or the like.
- the wrist unit 130 might include a wrist strap 155 that allows the unit to be worn on the wrist, similar to a wrist watch.
- the sensor device 110 might not include all of the
- Fig. IB should be considered only illustrative, and not limiting, in nature.
- FIGs. 2A, 2B, 3A, 3B, 4, and 5 illustrate methods in accordance with various embodiments. While the methods of Figs. 2A, 2B, 3 A, 3B, 4, and 5 are illustrated, for ease of description, as different methods, it should be appreciated that the various techniques and procedures of these methods can be combined in any suitable fashion, and that, in some embodiments, the methods depicted by Figs. 2A, 2B, 3 A, 3B, 4, and 5 can be considered interoperable and/or as portions of a single method. Similarly, while the techniques and procedures are depicted and/or described in a certain order for purposes of illustration, it should be appreciated that certain procedures may be reordered and/or omitted within the scope of various
- Figs. 2A, 2B, 3 A, 3B, 4, and 5 can be implemented by (and, in some cases, are described below with respect to) the system 100 of Fig. 1 (including computational device or monitoring computer 105, or other components of the system, such as the sensor 110 of Figs. 1 A and IB), these methods may also be implemented using any suitable hardware implementation.
- the system 100 of Fig. 1 (and/or other components of such a system) can operate according to the methods illustrated by Figs. 2A, 2B, 3 A, 3B, 4, and 5 (e.g., by executing instructions embodied on a computer readable medium), the system 100 of Fig. 1 can also operate according to other modes of operation and/or perform other suitable procedures.
- a method might comprise one or more procedures, any or all of which are executed by a computer system.
- an embodiment might provide a computer system configured with instructions to perform one or more procedures in accordance with methods provided by various other embodiments.
- a computer program might comprise a set of instructions that are executable by a computer system (and/or a processor therein) to perform such operations.
- software programs are encoded on physical, tangible and/or non-transitory computer readable media (such as, to name but a few examples, optical media, magnetic media, and/or the like).
- various embodiments can comprise a method for using sensor data to assess blood loss in a patient.
- Fig. 2 illustrates an exemplary method 200 in accordance with various embodiments.
- the method 200 might comprise generating a model, e.g., with a computer system, against which patient data can be analyzed to estimate and/or predict various physiological states (block 205).
- generating the model can comprise receiving data pertaining to a plurality of more physiological parameters of a test subject to obtain a plurality of physiological data sets.
- Such data can include PPG waveform data to name one example, and/or any other type of sensor data including, without limitation, data captured by other sensors described herein and in the Related Applications.
- Generating a model can further comprise directly measuring one or more physiological states of the test subject with a reference sensor to obtain a plurality of physiological state measurements.
- the one or more physiological states can include, without limitation, states of various volumes of blood loss and/or fluid resuscitation, and/or various states of hydration and/or dehydration.
- different states can include a state of hypervolemia, a state of euvolemia, and/or a state of cardiovascular collapse (or near-cardiovascular collapse), and/or can include states that have been simulated, e.g., through use of an LB P apparatus).
- Other physiological states that can be used to generate a model are described elsewhere herein and in the Related Applications.
- Generating the model can further comprise correlating the
- Fig. 4 provides a technique using a machine-learning algorithm to optimize the correlation between measured physiological parameters (such as PPG waveform data, to name one example) and physical states (e.g., various blood volume states, including states where a known volume of blood loss has occurred and/or a known volume of fluid resuscitation has been administered, various states of hydration and/or dehydration, etc.).
- measured physiological parameters such as PPG waveform data, to name one example
- physical states e.g., various blood volume states, including states where a known volume of blood loss has occurred and/or a known volume of fluid resuscitation has been administered, various states of hydration and/or dehydration, etc.
- any suitable technique or model may be employed in accordance with various embodiments.
- physiological states can be modeled, and a number of different conditions can be imposed on test subjects as part of the model generation.
- physiological states that can be induced (or monitored when naturally occurring) in test subjects include, without limitation, reduced circulatory system volume, known volume of blood loss, specified amounts of fluids added to blood volume, dehydration, cardiovascular collapse or near-cardiovascular collapse, euvolemia, hypervolemia, low blood pressure, high blood pressure, normal blood pressure, and/or the like.
- a number of physiological parameters of a plurality of test subjects might be measured.
- a subject might undergo varying, measured levels of blood loss (either real or simulated) or intravenous fluid addition.
- the system can determine which sensor information most effectively differentiates between subjects at different blood loss/addition volume levels.
- some embodiments might construct a model based on data that is derived from sensor data.
- one such model might use, as input values, CRI values of test subjects in different blood loss and/or volume addition conditions. Accordingly, the process of generating a model might first comprise building a model of CRI, and then, from that model, building a model of hydration effectiveness. (In other cases, a hybrid model might consider both raw sensor data and CRI data.)
- a CRI model can be generated in different ways. For example, in some cases, one or more test subjects might be subjected to LBNP.
- LBNP data is collected from human subjects being exposed to progressively lower levels of LBNP, until hemodynamic decompensation, at which time LBNP is released and the subject recovers. Each level of LBNP represents an additional amount of blood loss.
- physiological data including, without limitation, waveform data, such as continuous non-invasive blood pressure data
- waveform data such as continuous non-invasive blood pressure data
- a relationship (as expressed by Equation 2) can be identified between LBNP and intravascular volume loss, and this relationship can be used to estimate CRI.
- Equation 2 a relationship for the development of the hemodynamic parameter referred to herein as CRI and can be used to generate models of this parameter.
- physiological state of reduced volume in the circulatory system e.g., to a point of cardiovascular collapse (hemodynamic decompensation) or to a point near
- LBNP can be used to induce this condition, as noted above.
- dehydration can be used to induce this condition as well.
- Other techniques are possible as well.
- data collected from a subject in a state of euvolemia, dehydration, hypervolemia, and/or other states might be used to generate a CRI model in different embodiments.
- the method 200 comprises monitoring, with one or more sensors, physiological data of a patient.
- physiological data a variety of physical parameters can be monitored, invasively and/or non-invasively, depending on the nature of the anticipated physiological state of the patient.
- monitoring the one or more physical parameters might comprise receiving, e.g., from a physiological sensor, continuous waveform data, which can be sampled as necessary.
- continuous waveform data can include, without limitation, plethysmograph waveform data, PPG waveform data (such as that generated by a pulse oximeter), and/or the like.
- the method 200 might further comprise analyzing, with a computer system (e.g., a monitoring computer 105 and/or a processing unit 135 of a sensor unit, as described above), the physiological data (block 215).
- a computer system e.g., a monitoring computer 105 and/or a processing unit 135 of a sensor unit, as described above
- the physiological data is analyzed against a pre-existing model (which might be generated as described above and which in turn, can be updated based on the analysis, as described in further detail below and in the Related Applications).
- sensor data can be analyzed directly against a generated model to assess the effectiveness of hydration (which can include estimating current values, and/or predicting future values for any or all of BL, V, and/or Pf, as expressed above.
- the sensor data can be compared to determine similarities with models that estimate and/or predict any of these values.
- an input waveform captured by a sensor from a patient might be compared with sample waveforms generated by models for each of these values.
- the technique 200' of Fig. 2B provides one method for deriving an estimate of BL in accordance with some embodiments.
- the technique 200' is presented as an example only, and that while this technique 200' estimates BL from raw sensor data, similar techniques can be used to estimate or predict BL, V, and/or /from raw sensor data, CRI data, and/or a combination of these.
- one model might produce a first estimate of BL from raw sensor data, produce a second estimate of BL from estimated CRI values, and then combine those estimates (in either weighted or unweighted fashion) to produce a hybrid BL estimate.
- the illustrated technique 200' comprises sampling waveform data (e.g., any of the data described herein and in the Related Applications, including, without limitation, arterial waveform data, such as continuous PPG waveforms and/or continuous noninvasive blood pressure waveforms) for a specified period, such as 32 heartbeats (block 270). That sample is compared with a plurality of waveforms of reference data corresponding to BL values (block 275), which in this case range from 0 to 1 using the scale described above (but alternatively might use any appropriate scale). These reference waveforms are derived as part of the model developed using the algorithms described in this and the Related Applications, might be the result of experimental data, and/or the like. In effect, these reference waveforms reflect the relationship / from Eq. 6, above.
- any number of sample waveforms can be used for the comparison; for example, if there is a nonlinear relationship between the measured sensor data and the BL values, more sample waveforms might provide for a better comparison.
- a similarity coefficient is calculated (e.g., using a least squares or similar analysis) to express the similarity between the sampled waveform and each of the reference waveforms (block 280).
- These similarity coefficients can be normalized (if appropriate) (block 285), and the normalized coefficients can be summed (block 290) to produce an estimated BL value of the patient (block 295).
- Similar techniques can be used to analyze data against a model based on parameters derived from direct sensor measurements.
- such operations can be iterative in nature, by generating the derived parameters—such as CRI, to name one example— by analyzing the sensor data against a first model, and then analyzing the derived parameters against a second model.
- Fig. 3A illustrates a method 300 of calculating a patient's
- the method 300 includes generating a model of CRI (block 305), monitoring physiological parameters (block 310) and analyzing the monitored physical parameters or data (block 315), using techniques such as those described above and/or in the '483 Application, for example.
- the method 300 includes estimating, with the computer system, a compensatory reserve (or CRI value) of the patient, based on analysis of the physiological data (block 320).
- the method might further comprise predicting, with the computer system, the compensatory reserve (or CRI value) of the patient at one or more time points in the future, based on analysis of the physiological data (block 325).
- the operations to predict a future value of a parameter can be similar to those for estimating a current value; in the prediction context, however, the applied model might correlate measured data in a test subject with subsequent values of the diagnostic parameter, rather than contemporaneous values. It is worth noting, of course, that in some embodiments, the same model can be used to both estimate a current value and predict future values of a physiological parameter.
- the estimated and/or predicted compensatory reserve of the patient can be based on several factors.
- the estimated/predicted compensatory reserve can be based on a fixed time history of monitoring the physiological data of the patient and/or a dynamic time history of monitoring the physiological data of the patient. In other cases, the
- estimated/predicted compensatory reserve can be based on a baseline estimate of the patient's compensatory reserve established when the patient is euvolemic.
- the estimate and/or prediction might not be based on a baseline estimate of the patient's compensatory reserve established when the patient is euvolemic, but rather based on a baseline estimate of the patient's compensatory reserve established when the patient is in another physiological state or condition.
- Fig. 3B illustrates one technique 300' for deriving an estimate of CRI in accordance with some embodiments, similar to the technique 200' described above with respect to Fig. 2B for deriving an assessment of hydration effectiveness and/or deriving an assessment of blood loss (e.g., before, during, and/or after fluid resuscitation) directly from sensor data (and, in fact, CRI can be derived as described herein, and that derived value can be used, alone or with raw sensor data, to assess such effectiveness).
- the illustrated technique comprises sampling waveform data (e.g., any of the data described herein and in the Related Applications, including, without limitation, arterial waveform data, such as continuous PPG waveforms and/or continuous noninvasive blood pressure
- waveforms, or the like for a specified period, such as 32 heartbeats (block 370). That sample is compared with a plurality of waveforms of reference data corresponding to different CRI values (block 375).
- These reference waveforms which might be derived using the algorithms described in the Related Applications, might be the result of experimental data, and/or the like).
- the sample might be compared with waveforms corresponding to a CRI of 1 (block 375a), a CRI of 0.5 (block 375b), and a CRI of 0 (block 375c), as illustrated.
- a CRI of 1 block 375a
- CRI of 0.5 block 375b
- CRI of 0 block 375c
- any number of sample waveforms can be used for the comparison; for example, if there is a nonlinear relationship between the measured sensor data and the CRI estimates, more sample waveforms might provide for a better comparison.
- a similarity coefficient is calculated (e.g., using a least squares or similar analysis) to express the similarity between the sampled waveform and each of the reference waveforms (block 380).
- These similarity coefficients can be normalized (if appropriate) (block 385), and the normalized coefficients can be summed (block 390) to produce an estimated value of the patient's CRI (block 395).
- the method 300 can comprise estimating and/or predicting a patient's dehydration state (block 330).
- the patient's state of dehydration can be expressed in a number of ways. For instance, the state of dehydration might be expressed as a normalized value (for example, with 1.0 corresponding to a fully hydrated state and 0.0 corresponding to a state of morbid dehydration). In other cases, the state of dehydration might be expressed as a missing volume of fluid or as a volume of fluid present in the patient's system, or expressed using any other appropriate metric.
- estimating a dehydration state of the patient might comprise estimating the compensatory reserve (e.g., CRI) of the patient, and then, based on that estimate and the known relationship, estimating the dehydration state.
- CRI compensatory reserve
- a predicted value of compensatory reserve at some point in the future can be used to derive a predicted dehydration state at that point in the future.
- Other techniques might use a parameter other than CRI to model dehydration state.
- the method 300 might further comprise normalizing the results of the analysis (block 335), such as the compensatory reserve, dehydration state, and/or probability of bleeding, to name a few examples.
- the estimated/predicted compensatory reserve of the patient can be normalized relative to a normative normal blood volume value corresponding to euvolemia, a normative excess blood volume value corresponding to circulatory overload, and a normative minimum blood volume value corresponding to cardiovascular collapse. Any values can be selected as the normative values.
- the normative excess blood volume value is > 1
- the normative normal blood volume value is 1
- the normative minimum blood volume value is 0.
- the normative excess blood volume value might be defined as 1, the normative normal blood volume value might be defined as 0, and the normative minimum blood volume value at the point of cardiovascular collapse might be defined as -1.
- different embodiments might use a number of different scales to normalize CRI and other estimated parameters.
- normalizing the data can provide benefits in a clinical setting, because it can allow the clinician to quickly make a qualitative judgment of the patient's condition, while interpretation of the raw estimates/predictions might require additional analysis.
- that estimate might be normalized relative to a normative normal blood volume value corresponding to euvolemia and a normative minimum blood volume value corresponding to cardiovascular collapse.
- any values can be selected as the normative values.
- the normative normal blood volume is defined as 1, and the normative minimum blood volume value is defined as 0, the normalized value, falling between 0.0 and 1.0 can quickly apprise a clinician of the patient's location on a continuum between euvolemia and cardiovascular collapse. Similar normalizing procedures can be implemented for other estimated data (such as probability of bleeding, dehydration, and/or the like).
- the method 300 might further comprise displaying data with a display device (block 340).
- data might include an estimate and/or prediction of the compensatory reserve of the patient, an estimate and/or prediction of the patient's dehydration state, and/or the like.
- a variety of techniques can be used to display such data.
- displaying the estimate of the compensatory reserve of the patient might comprise displaying the normalized estimate of the compensatory reserve of the patient.
- displaying the normalized estimate of the compensatory reserve of the patient might comprise displaying a graphical plot showing the normalized excess blood volume value, the normalized normal blood volume value, the normalized minimum blood volume value, and the normalized estimate of the compensatory reserve (e.g., relative to the normalized excess blood volume value, the normalized normal blood volume value, the normalized minimum blood volume value), and/or the like.
- the method 300 might comprise repeating the operations of monitoring physiological data of the patient, analyzing the physiological data, and estimating (and/or predicting) the compensatory reserve of the patient, to produce a new estimated (and/or predicted) compensatory reserve of the patient.
- displaying the estimate (and/or prediction) of the compensatory reserve of the patient might comprise updating a display of the estimate of the compensatory reserve to show the new estimate (and/or prediction) of the compensatory reserve, in order to display a plot of the estimated compensatory reserve over time.
- the patient's compensatory reserve can be repeatedly estimated and/or predicted on any desired interval (e.g., after every heartbeat, every n number of seconds, etc.), on demand, before fluid resuscitation, during fluid resuscitation, after fluid resuscitation, etc., or a combination of one or more of these.
- the method 300 can comprise determining a probability that the patient is bleeding, and/or displaying, with the display device, an indication of the probability that the patient is bleeding (block 345). For example, some embodiments might generate a model based on data that removes fluid from the circulatory system (such as LB P, dehydration, etc.). Another embodiment might generate a model based on fluid removed from a subject voluntarily, e.g., during a blood donation, based on the known volume (e.g., 500cc) of the donation. Based on this model, using techniques similar to those described above, a patient's
- physiological data can be monitored and analyzed to estimate a probability that the patient is bleeding (e.g., internally, or the like).
- the probability that the patient is bleeding can be used to adjust the patient's estimated CRI.
- the adjusted value of CRI can be expressed as:
- the estimated CRI can be adjusted to produce a more accurate diagnosis of the patient's condition at a given point in time (block 350).
- the method 300 might comprise selecting, with the computer system, a recommended treatment option for the patient, and/or displaying, with the display device, the recommended treatment option (block 355).
- the recommended treatment option can be any of a number of treatment options, including, without limitation, optimization of hemodynamics of the patient, a ventilator adjustment, an intravenous fluid adjustment, transfusion of blood or blood products to the patient, infusion of volume expanders to the patient, a change in medication administered to the patient, a change in patient position, surgical therapy, and/or the like.
- the method 300 might comprise controlling operation of hemodialysis equipment (block 360), based at least in part on the estimate of the patient's compensatory reserve.
- a computer system that performs the monitoring and estimating functions might also be configured to adjust an ultra-filtration rate of the hemodialysis equipment in response to the estimated CRI values of the patient.
- the computer system might provide instructions or suggestions to a human operator of the hemodialysis equipment, such as instructions to manually adjust an ultra-filtration rate, etc.
- the method 300 might include assessing the tolerance of an individual to blood loss, general volume loss, and/or dehydration (block 365). For example, such embodiments might include estimating a patient's CRI based on the change in a patient's position (e.g., from lying prone to standing, from standing to lying prone, from lying prone to sitting, from sitting to lying prone, from standing to sitting, and/or from sitting to standing, or the like). Based on changes to the patient's CRI in response to these maneuvers, the patient's sensitivity to blood loss, volume loss, and/or dehydration can be measured.
- a patient's CRI based on the change in a patient's position (e.g., from lying prone to standing, from standing to lying prone, from lying prone to sitting, from sitting to lying prone, from standing to sitting, and/or from sitting to standing, or the like). Based on changes to the patient's CRI in response to these maneuvers, the patient's sensitivity to blood loss, volume loss, and
- this measurement can be performed using a CRI model generated as described above; the patient can be monitored using one or more of the sensors described above, and the changes in the sensor output when the subject changes position can be analyzed according to the model (as described above, for example) to assess the tolerance of the individual to volume loss.
- the model as described above, for example
- embodiments can be performed in real time.
- the method 200 can include assessing the blood loss of the patient (block 220), based on analysis of the patient's physiological data against the model.
- assessing blood loss can include estimating or predicting a number of values, such as the estimated effectiveness, BL, of the hydration effort, the volume, J 7 , of fluid necessary for effective hydration, the probability, Pf, that the patient needs fluids, and/or the like.
- the assessment of the blood loss will be based on the analysis of a plurality of measured (or derived) values of a particular physiological parameter (or plurality of parameters).
- the analysis of the data might be performed on a continuous waveform, either during or after measurement of the waveform with a sensor (or both), and the assessment of the blood loss can be updated as hydration efforts and/or fluid resuscitation efforts continue.
- the amount of fluids added to the patient's blood volume can be measured directly, and these direct measurements can be fed back into the model to update the model (at block 225) and thereby improve performance of the algorithms in the model (e.g., by refining the weights given to different parameters in terms of estimative or predictive value).
- the updated model can then be used to continue assessing the treatment (in the instant patient and/or in a future patient), as shown by the broken lines on Fig. 2A.
- the method 200 comprises displaying data (block 230) indicating the assessment of the effectiveness of hydration.
- the data might be displayed on a display of a sensor device (such as the device 110 illustrated by Fig. IB).
- the data might be displayed on a dedicated machine, such as a compensatory reserve monitor, or on a monitor of a generic computer system.
- the data might be displayed alphanumerically, graphically, or both.
- the method 200 can include selecting and/or displaying treatment options for the patient (block 235) and/or controlling a therapeutic device (block 240), based on the assessment of the blood loss of the patient.
- a display might indicate to a clinician or the patient himself or herself that the patient is losing (or has lost) blood; that fluid resuscitation therapy should be initiated or continued; an estimated volume of fluid to drink, infuse, or otherwise consume; a drip rate for an IV drip; a flow rate for an IV pump or infuser; or the like.
- the system might be configured to control operation of a therapeutic device, such as dispensing a fluid to drink from an automated dispenser, activating or adjusting the flow rate of an IV pump or infuser, adjusting the drip rate of an IV drip, and/or the like, based on the assessment of the effectiveness of hydration.
- a therapeutic device such as dispensing a fluid to drink from an automated dispenser, activating or adjusting the flow rate of an IV pump or infuser, adjusting the drip rate of an IV drip, and/or the like, based on the assessment of the effectiveness of hydration.
- a water bladder e.g., a backpack-based hydration pack, such as those available from Camelbak Products LLC
- the hydration monitor could communicate with and/or control operation of such a dispensing device (e.g., to cause the device to dispense a certain amount of fluid, to cause the device to trigger an audible alarm, etc.).
- the method 200 can include functionality to help a clinician (or other entity) to monitor hydration, fluid resuscitation, and/or blood volume status.
- a clinician or other entity
- any measure of effectiveness outside of the normal range would set off various alarm conditions, such as an audible alarm, a message to a physician, a message to the patient, an update written automatically to a patient's chart, etc.
- Such messaging could be accomplished by electronic mail, text message, etc., and a sensor device or monitoring computer could be configured with, e.g., an SMTP client, text messaging client, or the like to perform such messaging.
- a hydration monitor might be configured to send monitoring results (e.g., any of the assessments, estimates and/or predictions described herein) to another device or computer, either for personal monitoring by the patient or for monitoring by another. Examples could include transmitting such alarms or data (e.g., by Bluetooth, NFC, WiFi, etc.) to a wireless phone, wearable device (e.g., smart watch or glasses) or other personal device of the patient, e.g., for inclusion in a health monitoring application.
- monitoring results e.g., any of the assessments, estimates and/or predictions described herein
- Examples could include transmitting such alarms or data (e.g., by Bluetooth, NFC, WiFi, etc.) to a wireless phone, wearable device (e.g., smart watch or glasses) or other personal device of the patient, e.g., for inclusion in a health monitoring application.
- such information could be sent to a specified device or computer (e.g., via any available IP connection), for example to allow a parent to monitor a child's (or a child to monitor an elderly parent's) hydration remotely, to allow a coach to monitor a player's hydration remotely, and/or to allow a superior officer to monitor a soldier's hydration remotely, or the like.
- a specified device or computer e.g., via any available IP connection
- an application might aggregate results from a plurality of hydration monitors, to allow the supervisor to view (e.g., in a dashboard-type configuration), hydration effectiveness and/or blood loss (and/or any other data, such as CRI, blood pressure, etc.) for a group of people.
- a display might employ, for example, a plurality of "fuel gauge” displays, one (or more) for each person in the group, allowing the supervisor to quickly ascertain any unusual results (e.g., based on the color of the gauge, etc.).
- an alarm condition were met for another physiological parameter (such as blood pressure, which can be estimated as described in the '171 Application, for example), that alarm could trigger an assessment of hydration effectiveness via the method 200, to determine whether the first alarm condition has merit or not. If not, perhaps there could be an automated silencing of the original alarm condition, since all is well at present. More generally, the assessment techniques could be added to an ecosystem of monitoring algorithms (including, without limitation, those described in the Related Applications), which would inform one another or work in combination, to inform one another about how to maintain optimal physiological stability.
- another physiological parameter such as blood pressure, which can be estimated as described in the '171 Application, for example
- Fig. 4 illustrates a method 400 of employing a self-learning predictive model (or machine learning) technique, according to some embodiments.
- the method 400 can be used to correlate physiological data received from a subject sensor with a measured physiological state. More specifically, with regard to various embodiments, the method 400 can be used to generate a model for assessing, predicting, and/or estimating various physiological parameters, such as blood loss volume, effectiveness of hydration or fluid resuscitation efforts, estimated and/or predicted blood pressure, CRI, the probability that a patient is bleeding, a patient's dehydration state, and/or the like, from one or more of a number of different physiological parameters, including, without limitation, those described above and in the Related Applications.
- various physiological parameters such as blood loss volume, effectiveness of hydration or fluid resuscitation efforts, estimated and/or predicted blood pressure, CRI, the probability that a patient is bleeding, a patient's dehydration state, and/or the like, from one or more of a number of different physiological parameters, including,
- the method 400 begins at block 405 by collecting raw data
- measurements that may be used to derive a set of D data signals s t , s D , as indicated at block 410 (each of the data signals s being, in a particular case, input from one or many different physiological sensors).
- Embodiments are not constrained by the type of measurements that are made at block 405 and may generally operate on any data set.
- data signals can be retrieved from a computer memory and/or can be provided from a sensor or other input device.
- the data signals might correspond to the output of the sensors described above (which measure the types of waveform data described above, such as continuous, non-invasive PPG data and/or blood pressure waveform data, or the like).
- a set of K current or future outcomes 6 ... , o K ) is hypothesized at block 415 (the outcomes o being, in this case, past and/or future physiological states, such as probability that fluids are needed, volume of fluid needed for effective hydration or fluid resuscitation, BL, CRI, dehydration state, probability of bleeding, etc.).
- the method autonomously generates a predictive model that relates the derived data signals s with the outcomes 6.
- “autonomous” means "without human intervention. "
- this is achieved by identifying the most predictive set of signals S k , where S k contains at least some (and perhaps all) of the derived signals ... , s D for each outcome ⁇ 3 ⁇ 4, where k E ⁇ 1, ... , K ⁇ .
- a probabilistic predictive model 6 k M k (S k ) is learned at block 425, where 6 k is the prediction of outcome o t derived from the model Mk that uses as inputs values obtained from the set of signals S k , for all k E ⁇ 1, ... , K ⁇ .
- the method 400 can learn the predictive models
- 6 k M k (S k ) incrementally (block 430) from data that contains example values of signals ... , s D and the corresponding outcomes o , ... , o K .
- the method 400 loops so that the data are added incrementally to the model for the same or different sets of signals S k , for all k E ⁇ 1, ... , K ⁇ .
- a linear model framework may be used to identify predictive variables for each new increment of data.
- the framework used to derive the linear model coefficients may estimate which signals s, ... , s d are not predictive and accordingly sets the
- a prediction system can be implemented that can predict future results from previously analyzed data using a predictive model and/or modify the predictive model when data does not fit the predictive model.
- the prediction system can make predictions and/or adapt the predictive model in real-time.
- a prediction system can use large data sets not only to create the predictive model, but also to predict future results as well as to adapt the predictive model.
- a self-learning, prediction device can include a data input, a processor, and an output.
- Memory can include application software that when executed can direct the processor to make a prediction from input data based on a predictive model. Any type of predictive model can be used that operates on any type of data.
- the predictive model can be implemented for a specific type of data.
- the predictive model when data is received the predictive model can determine whether it understands the data according to the predictive model. If the data is understood, a prediction is made and the appropriate output is provided based on the predictive model. If the data is not understood when received, then the data can be added to the predictive model to modify the model.
- the device can wait to determine the result of the specified data and can then modify the predictive model accordingly. In some embodiments, if the data is understood by the predictive model and the output that is generated using the predictive model is not accurate, then the data and the outcome can be used to modify the predictive model. According to some embodiments, modification of the predictive model can occur in real-time.
- Particular embodiments can employ the tools and techniques described in the Related Applications in accordance with the methodology described herein perform the functions of a cardiac reserve monitor, a wrist-wearable sensor device, and/or a monitoring computer, as described herein (the functionality of any or all of which can be combined in a single, integrated device, in some embodiments).
- These functions include, but are not limited to, assessing fluid resuscitation of a patient; assessing hydration of a patient; monitoring, estimating, and/or predicting a subject's (including, without limitation, a patient's) current or future blood pressure and/or compensatory reserve; estimating and/or determining the probability that a patient is bleeding (e.g., internally) and/or has been bleeding; recommending treatment options for such conditions; and/or the like.
- Such tools and techniques include, in particular, the systems (e.g., computer systems, sensors, therapeutic devices, etc.) described in the Related Applications, the methods (e.g., the analytical methods for generating and/or employing analytical models, the diagnostic methods, etc.), and the software programs described herein and in the Related Applications, which are incorporated herein by reference.
- Fig. 5 illustrates a method 500 of implementing rapid detection of bleeding before, during, and after fluid resuscitation, in accordance with various embodiments.
- method 500 at block 505, comprises estimating a patient's CRI before, during, and/or after resuscitation (e.g., fluid resuscitation, or the like). Estimation of the patient's CRI may be performed, for example, using the techniques described above with respect to Figs. 3 A and 3B, or using other techniques described above and/or in the Related Applications.
- method 500 might comprise recording the patient's CRI, before, during, and/or after resuscitation.
- the CRI may be recorded or stored on one or more of a data storage device that is part of processing unit 145 and/or a memory device that is part of the monitoring computer 105 of Fig. 1, or the like.
- Method 500 might further comprise calculating an average CRI over a period of K seconds (where K > 1), before, during, and/or after resuscitation (block 515), calculating a standard deviation or variance of CRI over a period of K seconds (where K > 1), before, during, and/or after resuscitation (block 520), calculating Pearson's moment coefficient of skewness of CRI over a period of K seconds (where K > 1), before, during, and/or after resuscitation (block 525), calculating a rate of change of CRI over a period of K seconds (where K > 1), before, during, and/or after resuscitation (block 530), calculating a rate of rate change (or a rate of change of rate change) of CRI (also referred to herein as "acceleration of CRI”) over a period of K seconds (where K > 1), before, during, and/or after resuscitation (block 535).
- method 500 might further comprise, at block 540, determining probability of bleeding, based on one or more of the calculations in blocks 515-535 (which may be referred to herein as "variation results").
- variation results might be used to estimate one or more states of bleeding - namely, a (certain) non-bleeding state (perhaps designated by a symbol, "0"), a (certain) bleeding state (perhaps designated by a symbol, " 1 "), and some probability of bleeding state (perhaps designated by a symbol between "0" and
- CRI value sample (ii) a set of values of CRI, (iii) average CRI, (iv) median CRI, (v) standard deviation of CRI, (vi) rate of change of CRI, (vii) rate of change of rate
- A is a matrix defined by: [0171] (vii) Rate of change of rate change of CRI, denoted by r K , over a set of CRI values ⁇ CRI ⁇ ), CRI(t 2 ), . . . , CRI(t K ) ⁇ , where the rate of change of rate
- change measures some rate of change of increase or decrease of CRI over a specific period of time, and, for example, may be calculated as a second order increase or
- B (B t B) ⁇ 1 B t (Eq. 22) b CRKt K )
- B is a matrix defined by:
- ⁇ g 1 (CR/(t fe )-CR/ K ) 3
- S K is some other measure of skewness, possibly Galton skewness (or Bowley's skewness), as defined by:
- a method for estimating a (certain) non-bleeding state might include, but is not limited to, one of the following calculations or a combination of two or
- CRI BR Average of CRI before resuscitation
- CRI AR resuscitation
- each of, or one or more of, NB l through NB23 might either be estimated experimentally or set by the user.
- the number K > 0 may be different in each instance of the calculations (1) through (23), may be chosen by the user, or may be experimentally determined.
- CRI(t 2 ), ⁇ ⁇ , CRI(t K ) ⁇ may be any set of points sampled at times before resuscitation, and CRI BR may be the average value of those points. Accordingly, for example, a classification of no bleeding may be made by choosing a threshold, either experimentally or user set, denoted by NB CRI BR (e.g., NB l above), and classifying non-bleeding may be determined if:
- CRI(t 2 ), ⁇ ⁇ , CRI(t K ) ⁇ may be any set of points sampled at times during resuscitation, and CRI DR may be the average value of those points. Accordingly, a classification of no bleeding may be made by choosing a threshold, either
- NB CRI DR e.g., NB2 above
- classifying non-bleeding may be determined if:
- CRI(t 2 ), ⁇ ⁇ , CRI(t K ) ⁇ may be any set of points sampled at times after resuscitation, and CRI AR may be the average value of those points. Accordingly, a classification of no bleeding may be made by choosing a threshold, either experimentally or user set, denoted by CRI AR (e.g., B3 above), and classifying non-bleeding may be determined if:
- CRI DR and CRI AR may be as defined above.
- a classification of no bleeding may be made by choosing a threshold, either experimentally or user set, denoted by AR CRI DR (e.g., B4 above), and
- classifying non -bleeding may be determined if:
- CRI BR and CRI DR may be as defined above.
- a classification of no bleeding may be made by choosing a threshold, either experimentally or user set, denoted by R CRl BR (e.g., B5 above), and
- classifying non -bleeding may be determined if:
- CRI BR and CRI AR may be as defined above.
- a classification of no bleeding may be made by choosing a threshold, either experimentally or user set, denoted by AR CRI BR (e.g., B6 above), and
- classifying non -bleeding may be determined if:
- CRI BR CRI(t 2 ), ⁇ ⁇ , CRI(t K ) ⁇ may be any set of points sampled at times before resuscitation, and [SD (CRI)] BR may be the variation of those values
- classification of no bleeding may be made by choosing a threshold, either
- classifying non -bleeding may be determined if:
- CRI(t 2 ), ⁇ ⁇ , CRI(t K ) ⁇ may be any set of points sampled at times during resuscitation, and [SD (CRI)] DR may be the variation of those values (perhaps the
- a classification of no bleeding may be made by choosing a threshold, either experimentally or user set, denoted by NB [SD (CRI)] DR (e.g., NB8 above), and classifying non-bleeding may be determined if:
- CRI(t 2 ), ⁇ ⁇ , CRI(t K ) ⁇ may be any set of points sampled at times after resuscitation, and [SD (CRI)] AR may be the variation of those values (perhaps the standard deviation as defined above). Accordingly, a classification of no bleeding may be made by choosing a threshold, either experimentally or user set, denoted by NB [SD (CRI)] AR (e.g., NB9 above), and classifying non-bleeding may be determined if:
- [SD (CRI)] BR and [SD (CRI)] AR may be as defined above. Accordingly, a classification of no bleeding may be made by choosing a threshold, either experimentally or user set, denoted by AR [SD (CRI)] BR (e.g., B 10 above), and classifying non-bleeding may be determined if:
- CRI(t 2 ), ⁇ ⁇ , CRI(t K ) ⁇ may be any set of points sampled at times during resuscitation, and S DR may be a measure of skewness of those points (perhaps as defined above). Accordingly, a classification of no bleeding may be made by
- NB S DR e.g., NB 12 above
- classifying non-bleeding may be determined if: ⁇ S DR ⁇ ⁇ N "S DR . (Eq.
- CRI(t 2 ), ⁇ ⁇ , CRI(t K ) ⁇ may be any set of points sampled at times after resuscitation, and S AR may be a measure of skewness of those points (perhaps as defined above). Accordingly, a classification of no bleeding may be made by
- NB S AR e.g., NB 13 above
- CRI BR CRI(t 2 ), ⁇ ⁇ , CRI(t K ) ⁇ may be any set of points sampled at times before resuscitation, and m BR may be a measure of rate of change of those points (perhaps as defined above). Accordingly, for example, a classification of no bleeding may be made by choosing a threshold, either experimentally or user set, denoted by NB m BR (e.g., B 14 above), and classifying non-bleeding may be
- CRI DR CRI(t 2 ), ⁇ ⁇ , CRI(t K ) ⁇ may be any set of points sampled at times during resuscitation, and m DR may be a measure of rate of change of those points (perhaps as defined above). Accordingly, a classification of no bleeding may be made by choosing a threshold, either experimentally or user set, denoted by
- N B m DR (e.g., NB 15 above), and classifying non-bleeding may be determined if: m DR > NB m DR . (Eq.
- CRI(t 2 ), ⁇ ⁇ , CRI(t K ) ⁇ may be any set of points sampled at times after resuscitation, and m AR may be a measure of rate of change of those points (perhaps as defined above). Accordingly, a classification of no bleeding may be made by
- NB m AR e.g., NB m AR
- m AR NB m AR .
- m BR and m AR may be as defined above.
- a classification of no bleeding may be made by choosing a threshold, either experimentally or user set, denoted by AR m BR ( e -g-, NB 17 above), and
- classifying non -bleeding may be determined if: m AR - m BR > N A m BR . (Eq.
- m BR and m DR may be as defined above.
- a classification of no bleeding may be made by choosing a threshold, either experimentally or user set, denoted by R m BR (e.g., B 18 above), and
- classifying non -bleeding may be determined if:
- CRI BR ⁇ CRI ⁇
- CRI(t 2 ), ⁇ ⁇ , CRI(t K ) ⁇ may be any set of points sampled at times before resuscitation
- r BR may be a measure of rate of rate change of those points (perhaps as defined above). Accordingly, for example, a classification of no bleeding may be made by choosing a threshold, either experimentally or user set, denoted by NB r BR (e.g., B 19 above), and classifying non-bleeding may be determined if: r BR > NB r BR - (Eq.
- CRI AR CRI(t 2 ), ⁇ ⁇ , CRI(t K ) ⁇ may be any set of points sampled at times after resuscitation, and r AR may be a measure of rate of rate change of those points (perhaps as defined above). Accordingly, a classification of no bleeding may be made by choosing a threshold, either experimentally or user set, denoted by r AR (e.g., NB21 above), and classifying non-bleeding may be determined if:
- r BR and r AR may be as defined above.
- a classification of no bleeding may be made by choosing a threshold, either experimentally or user set, denoted by AR r BR ( e -g-, NB22 above), and
- classifying non -bleeding may be determined if:
- m BR and m DR may be as defined above.
- a classification of no bleeding may be made by choosing a threshold, either experimentally or user set, denoted by R m BR (e.g., B23 above), and
- classifying non -bleeding may be determined if:
- a method for estimating a (certain) bleeding state might include, but is not limited to, one of the following calculations or a combination of two or more such calculations, perhaps within a statistical and/or machine learning framework, or the like: (1) Average of CRI before resuscitation
- CRI(t 2 ), ⁇ ⁇ , CRI(t K ) ⁇ may be any set of points sampled at times before resuscitation, and CRI BR may be the average value of those points. Accordingly, for example, a classification of bleeding may be made by choosing a threshold, either experimentally or user set, denoted by B CRl BR (e.g., BLl above), and classifying
- CRI(t 2 ), ⁇ ⁇ , CRI(t K ) ⁇ may be any set of points sampled at times during resuscitation, and CRI DR may be the average value of those points. Accordingly, a classification of bleeding may be made by choosing a threshold, either experimentally or user set, denoted by B CRI DR (e.g., BL2 above), and classifying certain bleeding may be determined if:
- CRI(t 2 ), ⁇ ⁇ , CRI(t K ) ⁇ may be any set of points sampled at times after resuscitation, and CRI AR may be the average value of those points. Accordingly, a classification of bleeding may be made by choosing a threshold, either experimentally or user set,
- B CRI AR e.g., BL3 above
- CRI DR and CRI AR may be as defined above.
- a classification of bleeding may be made by choosing a threshold, either experimentally or user set, denoted by AR CRI DR (e.g., BL4 above), and classifying certain bleeding may be determined if:
- CRI BR and CRI DR may be as defined above.
- a classification of bleeding may be made by choosing a threshold, either experimentally or user set, denoted by DR CRI BR (e.g., BL5 above), and classifying certain bleeding may be determined if:
- CRI BR and CRI AR may be as defined above.
- a classification of bleeding may be made by choosing a threshold, either experimentally or user set, denoted by AR CRI BR (e.g., BL6 above), and classifying certain bleeding may be determined if:
- CRI BR CRI(t 2 ), ⁇ ⁇ , CRI(t K ) ⁇ may be any set of points sampled at times before resuscitation, and [SD (CRI)] BR may be the variation of those values (perhaps the standard deviation as defined above). Accordingly, for example, a classification of bleeding may be made by choosing a threshold, either experimentally or user set, denoted by B [SD (CRI)] BR (e.g., BL7 above), and classifying certain bleeding may be determined if:
- CRI(t 2 ), ⁇ ⁇ , CRI(t K ) ⁇ may be any set of points sampled at times during resuscitation, and [SD (CRI)] DR may be the variation of those values (perhaps the standard deviation as defined above). Accordingly, a classification of bleeding may be made by choosing a threshold, either experimentally or user set, denoted by
- B [SD (CRI)] DR (e.g., BL8 above), and classifying certain bleeding may be determined if:
- CRI(t 2 ), ⁇ ⁇ , CRI(t K ) ⁇ may be any set of points sampled at times after resuscitation, and [SD (CRI)] AR may be the variation of those values (perhaps the standard deviation as defined above). Accordingly, a classification of bleeding may be made by choosing a threshold, either experimentally or user set, denoted by
- B [SD (CRI)] AR e.g., BL9 above
- classifying certain bleeding may be determined if:
- [SD (CRI)] BR and [SD (CRI)] AR may be as defined above. Accordingly, a classification of bleeding may be made by choosing a
- AR SD (CRI)] BR (e.g., BL10 above)
- classifying certain bleeding may be determined if:
- CRI BR CRI(t 2 ), ⁇ ⁇ , CRI(t K ) ⁇ may be any set of points sampled at times before resuscitation, and S BR may be a measure of skewness of those points (perhaps as defined above). Accordingly, for example, a classification of bleeding may be made by choosing a threshold, either experimentally or user set, denoted by B S BR (e.g., BL1 1 above), and classifying certain bleeding may be determined if:
- CRI(t 2 ), ⁇ ⁇ , CRI(t K ) ⁇ may be any set of points sampled at times during resuscitation, and S DR may be a measure of skewness of those points (perhaps as defined above). Accordingly, a classification of bleeding may be made by choosing a threshold, either experimentally or user set, denoted by B S DR (e.g., BL12 above), and classifying certain bleeding may be determined if:
- CRI(t 2 ), ⁇ ⁇ , CRI(t K ) ⁇ may be any set of points sampled at times after resuscitation, and S AR may be a measure of skewness of those points (perhaps as defined above). Accordingly, a classification of bleeding may be made by choosing a threshold, either experimentally or user set, denoted by B S AR (e.g., BL13 above), and classifying certain bleeding may be determined if: [0211] With reference to (14), the rate of change of CRI before resuscitation,
- CRI BR CRI(t 2 ), ⁇ ⁇ , CRI(t K ) ⁇ may be any set of points sampled at times before resuscitation, and m BR may be a measure of rate of change of those points (perhaps as defined above). Accordingly, for example, a classification of bleeding may be made by choosing a threshold, either experimentally or user set, denoted by B m BR (e.g., BL14 above), and classifying certain bleeding may be
- CRI DR CRI(t 2 ), ⁇ ⁇ , CRI(t K ) ⁇ may be any set of points sampled at times during resuscitation, and m DR may be a measure of rate of change of those points (perhaps as defined above). Accordingly, a classification of bleeding may be made by choosing a threshold, either experimentally or user set, denoted by B m DR
- CRI(t 2 ), ⁇ ⁇ , CRI(t K ) ⁇ may be any set of points sampled at times after resuscitation, and m AR may be a measure of rate of change of those points (perhaps as defined above). Accordingly, a classification of bleeding may be made by choosing a threshold, either experimentally or user set, denoted by B m AR (e.g., BL16 above), and classifying certain bleeding may be determined if:
- m BR and m AR may be as defined above.
- a classification of bleeding may be made by choosing a threshold, either experimentally or user set, denoted by AR m BR (e.g., BL17 above), and classifying certain bleeding may be determined if:
- m BR and m DR may be as defined above.
- a classification of bleeding may be made by choosing a threshold, either experimentally or user set, denoted by D fifi (e.g., BL18 above), and classifying certain bleeding may be determined if:
- CRI BR ⁇ CRI ⁇
- CRI(t 2 ), ⁇ ⁇ , CRI(t K ) ⁇ may be any set of points sampled at times before resuscitation
- r BR may be a measure of rate of rate change of those points (perhaps as defined above). Accordingly, for example, a classification of bleeding may be made by choosing a threshold, either experimentally or user set, denoted by B r BR (e.g., BL19 above), and classifying certain bleeding may be
- CRI DR CRI(t 2 ), ⁇ ⁇ , CRI(t K ) ⁇ may be any set of points sampled at times during resuscitation, and r DR may be a measure of rate of rate change of those points (perhaps as defined above). Accordingly, a classification of bleeding may be made by choosing a threshold, either experimentally or user set, denoted by B r DR
- CRI AR CRI(t 2 ), ⁇ ⁇ , CRI(t K ) ⁇ may be any set of points sampled at times after resuscitation, and r AR may be a measure of rate of rate change of those points (perhaps as defined above). Accordingly, a classification of bleeding may be made by choosing a threshold, either experimentally or user set, denoted by B r AR
- r BR and r AR may be as defined above.
- a classification of bleeding may be made by choosing a threshold, either experimentally or user set, denoted by AR r BR (e.g., BL22 above), and classifying certain bleeding may be determined if: rAR ⁇ r BR ⁇ AR r BR - (Eq. [0220] Referring to (23), m BR and m DR may be as defined above.
- a classification of bleeding may be made by choosing a threshold, either experimentally or user set, denoted by DR m BR (e.g., BL23 above), and classifying may be determined if: rDR ⁇ r BR ⁇ DR r BR - (Eq.
- a method for estimating a probability of bleeding might include, but is not limited to, one of the above calculations or a combination of two or more such calculations, perhaps within a statistical and/or machine learning framework, or the like, to estimate the probability of bleeding.
- the method might include, without limitation, empirical estimations of probability density functions, cumulative distribution functions using graphical and/or nonparametric models, and/or the like.
- Other methods might include, but are not limited to: (i) probability of bleeding being proportional to the number of times the bleeding threshold is achieved; (ii) probability of no bleeding being proportional to the number of times the no bleeding threshold is achieved; (iii) probability of bleeding being proportional to the number of times the bleeding threshold is achieved minus the number of times the no bleeding threshold is achieved; (iv) probability of bleeding being expressed as
- Vr(bleeding) f(CRI BRl CRl DR , CRl AR , [SD (CRI)] BR , [SD (CRI)] DR , [SD (CRI)] AR r A R)> (Eq. where /is some empirical estimation of the probability density function and/or cumulative distribution functions using graphical and/or nonparametric models.
- estimated CRI values might include, but are not limited to, one or more of CRI values estimated or measured after every heartbeat, CRI values averaged over the preceding or last N seconds (where N > 1), and/or the median value of CRI over the preceding or last N seconds (where N > 1), or the like. According to some embodiments, the calculations described above with respect to blocks 515-535 might utilize these estimated CRI values. According to some embodiments, the calculations described above with respect to blocks 515-535 might utilize these estimated CRI values. According to some embodiments,
- Figs. 6-8 illustrate exemplary screen captures from a display device of a compensatory reserve monitor, showing various features that can be provided by one or more embodiments. Similar screens could be shown by other monitoring devices, such as a display of a wrist-wearable sensor device, a display of a monitoring computer, and/or the like. While Figs. 6-8 use BL or CRI as an example condition for illustrative purposes, other embodiments might also display values for the volume, J 7 , the volume of fluid necessary for effective hydration, or the probability, Pf, that the patient needs fluid (including additional fluid, if hydration efforts already are underway).
- Fig. 6 illustrates an exemplary display 600 of a compensatory reserve monitor implementation where a normalized CRI estimate of "0" implies that blood loss is certain, and " 1 " implies that there is no blood loss. Values in between “0” and “ 1 " imply a continuum of a probability of no blood loss.
- the normalized CRI estimates can be reversed, whereby a normalized CRI estimate of " 1 " implies that blood loss is certain, and "0” implies that there is no blood loss. Values in between "0” and “ 1 " imply a continuum of a probability of blood loss.
- Fig. 7 A illustrates four screen captures 700 of a display of a compensatory reserve monitor implementation that displays BL as a "fuel gauge" type bar graph for a person undergoing central volume blood loss and subsequent hydration efforts, or for a person who is about to, is undergoing, or has undergone fluid resuscitation. While Fig. 6 illustrates a trace of CRI over time, the bar graphs of Figure 7A provide snapshots of BL at the time of each screen capture corresponding to the CRI of Fig. 6. (In the illustrated implementation, the bar graphs are
- the fuel gauge can be configured to show probability of no blood loss (in which a value of " 1 " implies no blood loss, a value of "0” implies certain blood loss, and a value between "0” and “ 1 " implies a continuum of a probability of no blood loss), similar to Fig. 6.
- the fuel gauge can be configured to show probability of blood loss (in which a value of "0” implies no blood loss, a value of " 1 " implies certain blood loss, and a value between "0” and “ 1 " implies a continuum of a probability of blood loss).
- Fig. 7B illustrates similar "fuel gauge” type displays, but the displays feature bars of different colors - for example, green (illustrated by diagonal cross-hatching), yellow (illustrated by a checked pattern) and red (illustrated by gray shading) corresponding to different levels of CRI, along with arrows 710 indicating trending in the CRI values (e.g., rising, declining, or remaining stable), the CRI values and trends being indicative of blood loss occurring and/or resuscitation efforts being active.
- green illustrated by diagonal cross-hatching
- yellow illustrated by a checked pattern
- red illustrated by gray shading
- a "fuel gauge” display (or other indicator of BL or CRI and/or different physiological parameters) can be incorporated in a more comprehensive user interface.
- Fig. 8 illustrates an exemplary display 800 of a monitoring system.
- the display 800 includes a graphical, color-coded "fuel gauge” type display 805 of the current estimated BL (similar to the displays illustrated by Fig. 7B), along with a historical display 810 of recent CRI estimates; in this example, each bar on the historical display 810 might correspond to an estimate performed every minute, but different estimate frequencies or time intervals are possible, and in some embodiments, the operator can be given the option to specify a different frequency or time interval.
- the display 800 also includes numerical display 815 of the current BL as well as a trend indicator 820 (similar to that indicated above).
- the display 800 can include additional information (and, in some cases, the types of information displayed and/or the type of display can be configured by the operator).
- the exemplary display 800 includes an indicator 825 of the patient's current heart rate and an indicator 830 of the patient's blood oxygen saturation level (Sp02).
- the exemplary display 800 also includes an indicator of the estimated volume, J 7 , necessary for effective hydration, as well as an numerical indicator 840, a trend indicator 845, and a similar color coded "fuel gauge" display 850 of the current CRI.
- Other monitored parameters might be displayed as well, such as an ECG tracing, blood pressure, probability of bleeding estimates, and/or the like.
- Figs. 9A-9H are graphical diagrams 900 illustrating rapid detection of bleeding before, during, and after fluid resuscitation of patients in a multi -trauma clinical study at Denver Health Medical Center (“DHMC”), in accordance with various embodiments.
- DHMC Denver Health Medical Center
- 50 patients were enrolled, of which 45 patients met required criteria while 5 were excluded (as having incomplete data and/or device).
- Fig. 9A illustrates a receiver operating characteristic ("ROC") curve that is used for classification of bleeding using compensatory reserve.
- the sensitivity is 0.93, with specificity of 0.92, and area under the curve (“AUC”) of 0.97.
- Fig. 9B illustrates the CRI for the non-bleeding patients (indicated in the graph as "Trauma No Hemorrhage”) and for the bleeding patients (indicated in the graph as “Trauma + Hemorrhage”). As shown in Fig. 9B, CRI values are low during bleeding.
- Figs. 9C-9E illustrate line tracings of actual CRI curves for three representative patients among the non-bleeding group.
- the average CRI value for the non-bleeding patients before infusing of intravenous fluid ("IVF") is 0.56 ⁇ 0.17.
- Fig. 9C depicts the CRI curves for non-bleeding trauma patient 003, who had a CRI of > 0.3 before infusion of ⁇ , and with IVF containing 2L of saline solution. There was no sustained drop in CRI in this patient during or after infusion of IVF.
- Fig. 9C-9E illustrate line tracings of actual CRI curves for three representative patients among the non-bleeding group.
- the average CRI value for the non-bleeding patients before infusing of intravenous fluid ("IVF") is 0.56 ⁇ 0.17.
- Fig. 9C depicts the CRI curves for non-bleeding trauma patient 003, who had a CRI of > 0.3 before infusion of ⁇ , and with
- FIG. 9D depicts the CRI curves for non-bleeding trauma patient 042, who had a CRI of 0.4 before infusion of IVF, and with IVF containing 1L of saline solution. There was no wound exploration and no sustained drop in CRI in this patient during or after infusion of IVF.
- Fig. 9E depicts the CRI curves for non-bleeding trauma patient 018, who had a CRI of 0.65 before infusion of IVF, and with IVF containing 2L of saline solution, 1L of lactated ringer's (“LR”) solution, and 2 packets of packed red blood cells ("PRBC"). There was no sustained drop in CRI in this patient during or after infusion of IVF.
- CRI is high or generally increasing during and after fluid resuscitation for each patient in the non-bleeding group.
- Figs. 9F-9H illustrate line tracings of actual CRI curves for three representative patients among the bleeding group.
- the average CRI value for the non-bleeding patients before infusing of intravenous fluid (“IVF") is 0.17 ⁇ 0.07.
- Fig. 9F depicts the CRI curves for bleeding trauma patient 019, who had a CRI of 0.15 before infusion of IVF (at time 905), and with an infusion of a first IVF (at time 910), the first IVF containing 7L of saline solution, 3 packets of PRBC, 1 packet of platelets ("PLTs"), and 3 packets of fresh frozen plasma (“FFP").
- the CRI dropped after initial increase (as shown at time 915).
- a second IVF was infused, the second IVF containing 4L of saline solution, 3 packets of PRBC, and 3 packets of fresh frozen plasma ("FFP").
- Fig. 9G depicts the CRI curves for bleeding trauma patient 006, who had a CRI of 0.15 before infusion of IVF (at time 925), and with an infusion of a first IVF (at time 930), the first IVF containing 2L of saline solution. The CRI dropped after initial increase (as shown at time 935).
- a second IVF was infused, the second IVF containing 1L of saline solution. Again, the CRI dropped after initial increase (as shown at time 945).
- Fig. 9G depicts the CRI curves for bleeding trauma patient 006, who had a CRI of 0.15 before infusion of IVF (at time 925), and with an infusion of a first IVF (at time 930), the first IVF containing 2L of saline solution. The C
- FIG. 9H depicts the CRI curves for bleeding trauma patient 012, who had a CRI of 0.15 before infusion of IVF, with infusions of a first IVF (at time 950) and a second IVF (at time 955), the first IVF containing 1L of saline solution and the second IVF containing 2.25L of saline solution.
- CRI drops after an initial increase (during and after fluid resuscitation) for each patient in the bleeding group.
- CRI had a higher sensitivity than heart rate (9%), systolic blood pressure (36%), base deficit (29%)), hemoglobin (50%>), and hematocrit (50%>).
- CRI on average decreased following fluid resuscitation, while on average increased for patients that did not have ongoing bleeding.
- the novel CRI computational algorithm that recognizes subtle changes in PPG waveforms can quickly and non-invasively discern which patients are actively bleeding and continuing to bleed with high sensitivity and specificity in acutely injured patients.
- Figs. 10-13B are results obtained from this different clinical study that evaluated CRI as a basis for rapid detection of bleeding in patients following injuries.
- Fig. 10 is a schematic diagram illustrating Compensatory Reserve ("CR")
- Fig. 11 is a graphical diagram illustrating receiver operating characteristic ("ROC") area under the curve (“AUC") for CRI and classification of actively bleeding versus not actively bleeding
- Fig. 12 is a set of graphical diagrams illustrating CRI response in bleeding patients and in non-bleeding patients one hour after infusion of 1 liter of crystalloid or 1 unit of blood product.
- Figs. 13 A and 13B (collectively, "Fig.
- FIG. 13 are graphical diagrams illustrating CRI changes over time for two gunshot patients in response to varying resuscitation techniques.
- Fig. 13 A is a graphical diagram illustrating CRI change over time for a patient with a negative laparotomy after a gunshot wound to the abdomen
- Fig. 13B is a graphical diagram illustrating CRI change over time for a patient with substantial blood loss and solid organ injury after a gunshot wound to the abdomen.
- BLVHDD is the blood loss volume at which the subject will experience hemodynamic decompensation (defined in LBNP experiments as a systolic blood pressure ⁇ 80 mmHg, loss of vision, or discomfort resulting in subject termination).
- CRI estimates this value by analyzing waveform features within a sliding 30-heartbeat window, and compares them to a library of waveform features originally obtained in the above LBNP experiments. By matching waveform features in the current subject to subjects from the LBNP experiments, CRI is able to determine when a patient will experience hemodynamic collapse in near real-time. With beat-to-beat re-calculation of CRI, this value can be trended over time.
- CRI values range from 1 to 0, and can be thought of as a percentage (100% - 0%) of physiologic reserve remaining, where " 1 " represents supine normovolemia and "0” implies hemodynamic decompensation (as shown in Fig. 10). Values between “ 1 " and “0” indicate the proportion of reserve available to compensate for further volume loss.
- DataOxTM devices continuously recorded and time stamped each patient's PPG waveforms. Due to the limited number of devices, patients were enrolled each night until all devices were in use; at that point no more patients could be enrolled until the next evening.
- EBL estimated blood loss
- active bleeding defined as EBL > 500 mL (known femur or pelvic fracture, known solid organ injury > grade 3, and/or intraoperative EBL of > 500 mL); (2) indeterminate bleeding (known fractures other than femur or pelvis, grade 1 - 2 solid organ injuries, intraoperative EBL ⁇ 500 mL); or (3) not actively bleeding (no evidence of bleeding, or minor bleeding ⁇ 100 mL).
- Values between bleeding and non-bleeding patients were compared using two-tailed Student's t-test and Chi square analysis.
- Receiver operating characteristic (“ROC") area under the curve analysis was performed with identification of cut-off values that maximized the sensitivity and specificity for the identification of bleeding.
- Table 2 Demographics and clinical characteristics. Significant values are bolded with an asterisk.
- Table 3 Fluid administration in the first hour of treatment.
- the average initial CRI was calculated over the first 5 minutes of data collection and was compared to the pre-hospital HR and SBP (since these values are used to triage patients), the initial HR and SBP in the trauma bay, the initial shock index ("SI"), and labs obtained within the first hour of admission including base deficit, lactate, hemoglobin, and hematocrit (as shown in Table 4 below).
- Receiver operating characteristic area under the curve (“ROC AUC") analysis was performed on each metric, using a cut-off value that maximized sensitivity and specificity for identifying actively bleeding patients.
- a threshold value of 0.21 the receiver operating characteristic area under the curve for CRI was 0.97, yielding a sensitivity of 0.83 and a specificity of 0.97 for identifying acutely bleeding patients (as shown in Fig. 11).
- a threshold value of 0.37 had a sensitivity of 1.00, but a specificity of 0.81.
- CRI had the highest AUC of all metrics examined (as shown in Table 5 below). Metrics including lactate and initial heart rate also had relatively high sensitivity but had lower specificity compared to CRI.
- ROC AUC ROC AUC
- the threshold value is the optimal value for discriminating between bleeding and non-bleeding patients.
- HR heart rate
- SBP systolic blood pressure
- SI shock index
- CRI CRI on average decreased the hour after administration of each liter of crystalloid (with CRI of -0.02, 95% CI -0.05 - 0.01), and with each unit of blood product (with CRI of -0.04, 95% CI -0.08 - -0.01).
- CRI on average increased the hour after each liter of crystalloid (with CRI of 0.05, 95% CI -0.05 - 0.14), and after each unit of blood product (with CRI of 0.003, 95%) CI -0.26 - 0.26).
- Base deficit is a rapidly and widely available serum laboratory marker of systemic acidosis that increases with hypoxemia and/or shock. In trauma settings, the degree of base deficit correlates with blood transfusion requirement, risk of multi- organ failure, and mortality. Unfortunately, base deficit is non-specific and can rise due to any derangement causing metabolic acidosis, including, but not limited to, intravascular volume loss, or the like. Serum lactate has similarly been used as a marker of acute blood loss, but is also non-specific. Furthermore, intoxication and chronic alcohol abuse, which are relatively common amongst the traumatically injured, are known causes of lactic acidosis. These facts underscore the importance of identifying new physiological metrics that can reliably detect volume loss in traumatically injured patients.
- CRI hemodynamic reserve
- PVI pleth variability index
- FVR Fluid Volume Requirements
- PPG photoplethysmogram
- a simple monitor that can accurately estimate volume needs can prevent under resuscitation - which poses the risk of hypotension and end organ damage - and over-resuscitation - which may dislodge clots from vascular injuries, resulting in further blood loss, hemodilution, and/or possibly death.
- the second is Detection of Ongoing Fluid Loss ("DOFL”; "OFL”; “DOFL Algorithm”; or “OFL Algorithm”), which monitors patient's PPG signal and directly identifies whether the patient is experiencing ongoing blood loss.
- DOFL Ongoing Fluid Loss
- OFL OFL Algorithm
- a simple monitor e.g., standalone hand held monitor, or a monitor that is integrated into existing monitors, or the like
- that can automatically and accurately detect ongoing bleeding can save lives and improve patient outcomes.
- the third is Rapid Estimation (or Determination) of Immediate Fluid Requirements ("REIFR”; “RDIFR”; “IFR”; “REIFR Algorithm”; “RDIFR Algorithm”; or “IFR Algorithm”), which monitors the patient's PPG signal and flags the need for additional fluid (this classification is made within one minute of the start of monitoring, and gives real-time indication of over- and under- resuscitation).
- REIFR Rapid Estimation (or Determination) of Immediate Fluid Requirements
- a monitor that can quickly detect when a patient does not immediately require fluids can prevent over-resuscitation, which can save lives and improve patient outcomes, as aggressive fluid resuscitation may dislodge clots from vascular injuries, resulting in further blood loss, hemodilution, and/or possibly death.
- the FVR monitor receives as inputs CRI estimates and fluid administration history for a patient, and outputs the real-time estimates of patient fluid requirements (in mL).
- the DOFL or OFL algorithm was applied to 5-minute moving time windows during the study and gave real-time classification of ongoing bleeding or non-bleeding.
- the OFL monitor receives as input CRI estimates for a patient, and outputs an indication of whether the patient is bleeding or not bleeding (in some cases, in a real-time manner, or after every heartbeat, or after a predetermined interval, or the like).
- the subject was classified as needing fluid once 5% volume loss was achieved.
- the REIFR or IFR Algorithm was applied to 1 -minute moving time windows and gave real-time classification of (yes/no determinations of) needing fluids.
- the IFR monitor receives as input CRI estimates for a patient, and outputs an indication of whether the patient needs more fluids or does not need any more fluids (in some cases, in a real-time manner, or after every heartbeat, or after a predetermined interval, or the like).
- FIGs. 14A and 14B illustrate the correspondence between Fluid Volume Requirements ("FVR") estimation and actual volume requirements for two patients in the clinical trial.
- FVR Fluid Volume Requirements
- Both the DOFL and REIFR Algorithms achieved ROC area under the curve of better than 0.9 (sensitivity and specificity of > 0.9) in identifying ongoing bleeding and flagging when no additional fluids were needed.
- the three algorithms for monitoring fluid resuscitation were evaluated on subjects undergoing 20% blood volume loss. These algorithms showed accurate results in estimation of blood volume requirements, classifying ongoing bleeding, and classifying when no additional whole blood was needed.
- These algorithms can be implemented on standard medical monitors ranging from the Nonin 9550 used by medics, to monitors used in hospitals.
- Fig. 15 is a block diagram illustrating an exemplary computer or system hardware architecture, in accordance with various embodiments.
- Fig. 15 provides a schematic illustration of one embodiment of a computer system 1500 that can perform the methods provided by various other embodiments, as described herein, and/or can function as a monitoring computer, a CRI monitor, a processing unit of a sensor device, and/or the like, as described above.
- Fig. 15 is meant only to provide a generalized illustration of various components, of which one or more (or none) of each may be utilized as appropriate.
- Fig. 15, therefore, broadly illustrates how individual system elements may be implemented in a relatively separated or relatively more integrated manner.
- the computer or hardware system 1500 is shown comprising hardware elements that can be electrically coupled via a bus 1505 (or may otherwise be in communication, as appropriate).
- the hardware elements may include one or more processors 1510, including, without limitation, one or more general -purpose processors and/or one or more special-purpose processors (such as digital signal processing chips, graphics acceleration processors, and/or the like); one or more input devices 1515, which can include, without limitation, a mouse, a keyboard and/or the like; and one or more output devices 1520, which can include, without limitation, a display device, a printer, and/or the like.
- processors 1510 including, without limitation, one or more general -purpose processors and/or one or more special-purpose processors (such as digital signal processing chips, graphics acceleration processors, and/or the like)
- input devices 1515 which can include, without limitation, a mouse, a keyboard and/or the like
- output devices 1520 which can include, without limitation, a display device, a printer, and/or the
- the computer or hardware system 1500 may further include (and/or be in communication with) one or more storage devices 1525, which can comprise, without limitation, local and/or network accessible storage, and/or can include, without limitation, a disk drive, a drive array, an optical storage device, solid-state storage device such as a random access memory (“RAM”) and/or a read-only memory (“ROM”), which can be programmable, flash-updateable, and/or the like.
- RAM random access memory
- ROM read-only memory
- Such storage devices may be configured to implement any appropriate data stores, including, without limitation, various file systems, database structures, and/or the like.
- the computer or hardware system 1500 might also include a communications subsystem 1530, which can include, without limitation, a modem, a network card (wireless or wired), an infra-red communication device, a wireless communication device and/or chipset (such as a BluetoothTM device, an 802.11 device, a WiFi device, a WiMax device, a WW AN device, cellular communication facilities, etc.), and/or the like.
- the communications subsystem 1530 may permit data to be exchanged with a network (such as the network described below, to name one example), with other computer or hardware systems, and/or with any other devices described herein.
- the computer or hardware system 1500 will further comprise a working memory 1535, which can include a RAM or ROM device, as described above.
- the computer or hardware system 1500 also may comprise software elements, shown as being currently located within the working memory 1535, including an operating system 1540, device drivers, executable libraries, and/or other code, such as one or more application programs 1545, which may comprise computer programs provided by various embodiments (including, without limitation, hypervisors, VMs, and the like), and/or may be designed to implement methods, and/or configure systems, provided by other embodiments, as described herein.
- an operating system 1540 including, device drivers, executable libraries, and/or other code
- application programs 1545 may comprise computer programs provided by various embodiments (including, without limitation, hypervisors, VMs, and the like), and/or may be designed to implement methods, and/or configure systems, provided by other embodiments, as described herein.
- one or more procedures described with respect to the method(s) discussed above might be implemented as code and/or instructions executable by a computer (and/or a processor within a computer); in an aspect, then, such code and/or instructions can be used to configure and/or adapt a general purpose computer (or other device) to perform one or more operations in accordance with the described methods.
- a set of these instructions and/or code might be encoded and/or stored on a non-transitory computer readable storage medium, such as the storage device(s) 1525 described above.
- the storage medium might be incorporated within a computer system, such as the system 1500.
- the storage medium might be separate from a computer system (i.e., a removable medium, such as a compact disc, etc.), and/or provided in an installation package, such that the storage medium can be used to program, configure, and/or adapt a general purpose computer with the instructions/code stored thereon.
- These instructions might take the form of executable code, which is executable by the computer or hardware system 1500 and/or might take the form of source and/or installable code, which, upon compilation and/or installation on the computer or hardware system 1500 (e.g., using any of a variety of generally available compilers, installation programs, compression/decompression utilities, etc.) then takes the form of executable code.
- some embodiments may employ a computer or hardware system (such as the computer or hardware system 1500) to perform methods in accordance with various embodiments of the invention.
- some or all of the procedures of such methods are performed by the computer or hardware system 1500 in response to processor 1510 executing one or more sequences of one or more instructions (which might be incorporated into the operating system 1540 and/or other code, such as an application program 1545) contained in the working memory 1535.
- Such instructions may be read into the working memory 1535 from another computer readable medium, such as one or more of the storage device(s) 1525.
- execution of the sequences of instructions contained in the working memory 1535 might cause the processor(s) 1510 to perform one or more procedures of the methods described herein.
- machine readable medium and “computer readable medium,” as used herein, refer to any medium that participates in providing data that causes a machine to operate in a specific fashion.
- various computer readable media might be involved in providing instructions/code to processor(s) 1510 for execution and/or might be used to store and/or carry such instructions/code (e.g., as signals).
- a computer readable medium is a non-transitory, physical, and/or tangible storage medium.
- a computer readable medium may take many forms, including, but not limited to, non-volatile media, volatile media, or the like.
- Non-volatile media includes, for example, optical and/or magnetic disks, such as the storage device(s) 1525.
- Volatile media includes, without limitation, dynamic memory, such as the working memory 1535.
- a computer readable medium may take the form of transmission media, which includes, without limitation, coaxial cables, copper wire and fiber optics, including the wires that comprise the bus 1505, as well as the various components of the communication subsystem 1530 (and/or the media by which the communications subsystem 1530 provides communication with other devices).
- transmission media can also take the form of waves (including, without limitation, radio, acoustic and/or light waves, such as those generated during radio- wave and infra-red data communications).
- Common forms of physical and/or tangible computer readable media include, for example, a floppy disk, a flexible disk, a hard disk, magnetic tape, or any other magnetic medium, a CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave as described hereinafter, or any other medium from which a computer can read instructions and/or code.
- Various forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to the processor(s) 1510 for execution.
- the instructions may initially be carried on a magnetic disk and/or optical disc of a remote computer.
- a remote computer might load the instructions into its dynamic memory and send the instructions as signals over a transmission medium to be received and/or executed by the computer or hardware system 1500.
- signals which might be in the form of electromagnetic signals, acoustic signals, optical signals, and/or the like, are all examples of carrier waves on which instructions can be encoded, in accordance with various
- the communications subsystem 1530 (and/or components thereof) generally will receive the signals, and the bus 1505 then might carry the signals (and/or the data, instructions, etc. carried by the signals) to the working memory 1535, from which the processor(s) 1505 retrieves and executes the instructions.
- the instructions received by the working memory 1535 may optionally be stored on a storage device 1525 either before or after execution by the processor(s) 1510.
Abstract
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US11395634B2 (en) | 2008-10-29 | 2022-07-26 | Flashback Technologies, Inc. | Estimating physiological states based on changes in CRI |
US11395594B2 (en) | 2008-10-29 | 2022-07-26 | Flashback Technologies, Inc. | Noninvasive monitoring for fluid resuscitation |
US11406269B2 (en) | 2008-10-29 | 2022-08-09 | Flashback Technologies, Inc. | Rapid detection of bleeding following injury |
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US11659023B2 (en) * | 2017-12-28 | 2023-05-23 | Cilag Gmbh International | Method of hub communication |
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US20110282169A1 (en) * | 2008-10-29 | 2011-11-17 | The Regents Of The University Of Colorado, A Body Corporate | Long Term Active Learning from Large Continually Changing Data Sets |
US8512260B2 (en) * | 2008-10-29 | 2013-08-20 | The Regents Of The University Of Colorado, A Body Corporate | Statistical, noninvasive measurement of intracranial pressure |
US11857293B2 (en) * | 2008-10-29 | 2024-01-02 | Flashback Technologies, Inc. | Rapid detection of bleeding before, during, and after fluid resuscitation |
WO2011050066A2 (en) * | 2009-10-20 | 2011-04-28 | The Research Foundation Of State University Of New York | Apparatus and method for respiratory rate detection and early detection of blood loss volume |
EP2734103B1 (en) * | 2011-07-22 | 2020-12-23 | Flashback Technologies, Inc. | Hemodynamic reserve monitor and hemodialysis control |
EP3207479A4 (en) * | 2014-10-16 | 2018-05-02 | Flashback Technologies, Inc. | Rapid detection of bleeding before, during, and after fluid resuscitation |
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US11389069B2 (en) | 2008-10-29 | 2022-07-19 | Flashback Technologies, Inc. | Hemodynamic reserve monitor and hemodialysis control |
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