WO2020030455A1

WO2020030455A1 - Method and system for cardiovascular risk assessment for patients

Info

Publication number: WO2020030455A1
Application number: PCT/EP2019/070300
Authority: WO
Inventors: Mirela Alina Weffers-Albu
Original assignee: Koninklijke Philips N.V.
Priority date: 2018-08-09
Filing date: 2019-07-29
Publication date: 2020-02-13

Abstract

A system for assessing cardiovascular risk of patients in an emergency department is provided. The system includes a patient carrier comprising a transparent structure, one or more optical or radar sensors configured to measure respiration information of the patient via light or radio waves traveling through at least a portion of the transparent structure of the patient carrier, one or more additional sensors configured to measure cardiac information of the patient, and a computer system comprising one or more physical processors that are programmed with computer program instructions that, when executed cause the computer system to: determine a cardiovascular risk parameter for the patient from cardiac information and respiration information of the patient obtained from the one or more sensors. The cardiovascular risk parameter for the patient indicates that the patient requires medical intervention within a specified time period.

Description

METHOD AND SYSTEM FOR CARDIOVASCULAR RISK ASSESSMENT FOR PATIENTS

BACKGROUND

1. Field

[01] The present disclosure pertains to a method and a system for assessing

cardiovascular risk for patients, for example, at an emergency medicine facility.

2. Description of the Related Art

[02] As indicated by the Agency for Healthcare Research and Quality in the U.S.

(https ://www.ahrq gov/)“The purpose of triage in the emergency department (ED) is to prioritize incoming patients and to identify those who cannot wait to be seen. The triage nurse performs a brief, focused assessment and assigns the patient a triage acuity level, which is a proxy measure of how long an individual patient can safely wait for a medical screening examination and treatment. In 2008, there were 123.8 million visits to U.S. emergency departments. Of those visits, only 18% of patients were seen within 15 minutes, leaving the majority of patients waiting in the waiting room. The Institute of Medicine (IOM) published the landmark report,“The Future of Emergency Care in the United States,” and described the worsening crisis of crowding that occurs daily in most emergency departments. With more patients waiting longer in the waiting room, the accuracy of the triage acuity level is even more critical. Under-categorization (undertriage) leaves the patient at risk for deterioration while waiting. This is particularly relevant in the case of patient with cardiovascular risk as it can result in the patient death. Over categorization (over-triage) uses scarce resources, limiting availability of an open ED bed for another patient who may require immediate care. And rapid, accurate triage of the patient is important for successful ED operations. Triage acuity ratings are useful data that can be used to describe and benchmark the overall acuity of an individual EDs' case mix. This is possible only when the ED is using a reliable and valid triage system, and when every patient, regardless of mode of arrival or location of triage (i.e. at the bedside) is assigned a triage level. By having this information, difficult and important questions such as,“Which EDs see the sickest patients?” and “How does patient acuity affect ED overcrowding?” can then be answered. There is also growing interest in the establishment of standards for triage acuity and other ED data elements in the United States to support clinical care, ED surveillance, benchmarking, and research activities.”

[03] The current state of art in triaging patients coming to the Emergency Department presents some disadvantages. For example, triaging is based on human assessment that can be subjective assessment. That is, the assessment can be incomplete, as the assessment does not involve scans to understand what happened internally (e.g., fractures, internal bleeding etc.). Also, the assessment requires time and expertise and the assessment can be incorrect.

SUMMARY

[04] Accordingly, it is an object of one or more embodiments of the present patent application to provide a system for assessing cardiovascular risk of patients in an emergency department. The system includes a patient carrier comprising a transmissive structure. The patient carrier is configured to support a patient to lay over the transmissive structure of the patient carrier. The system also includes one or more optical or radar sensors configured to measure respiration information of the patient via light or radio waves traveling through at least a portion of the transmissive structure of the patient carrier. The system further includes one or more additional sensors configured to measure cardiac information of the patient. The cardiac information of the patient includes blood pressure information of the patient,

Electrocardiography (ECG) information of the patient and/or heart rate information of the patient. The system also includes a computer system comprising one or more physical processors. The one or more physical processors are programmed with computer program instructions that, when executed cause the computer system to: determine a cardiovascular risk parameter for the patient based on the cardiac information and the respiration information of the patient obtained from the one or more sensors, the cardiovascular risk parameter for the patient indicating that the patient requires medical intervention within a specified time period.

[05] It is yet another aspect of one or more embodiments of the present patent

application to provide a method for assessing cardiovascular risk of patients in an emergency department. The method is implemented by a computer system comprising one or more physical processors executing computer program instructions that, when executed, perform the method.

The method comprises obtaining, from one or more optical or radar sensors disposed in a transmissive structure of a patient carrier, respiration information of the patient, the patient carrier being configured to support a patient to lay over the transmissive structure of the patient carrier; obtaining, from one or more additional sensors, cardiac information of the patient, the cardiac information of the patient comprising blood pressure information of the patient,

Electrocardiography (ECG) information of the patient and/or heart rate information of the patient; and determining, using the computer system, a cardiovascular risk parameter for the patient based on the cardiac information and the respiration information of the patient obtained from the one or more sensors, the cardiovascular risk parameter for the patient indicating that the patient requires medical intervention within a specified time period.

[06] It is yet another aspect of one or more embodiments to provide a system for

assessing cardiovascular risk of patients in an emergency department. The system comprises a means for executing machine-readable instructions with at least one processor. The machine- readable instructions comprise obtaining, from one or more optical or radar sensors disposed in a transmissive structure of a patient carrier, respiration information of the patient, the patient carrier being configured to support a patient to lay over the transmissive structure of the patient carrier; obtaining, from one or more additional sensors, cardiac information of the patient including blood pressure information of the patient, Electrocardiography (ECG) information of the patient and/or heart rate information of the patient; and determining a cardiovascular risk parameter for the patient based on the cardiac information and the respiration information of the patient obtained from the one or more sensors, the cardiovascular risk parameter for the patient indicating that the patient requires medical intervention within a specified time period.

[07] These and other objects, features, and characteristics of the present patent

application, as well as the methods of operation and functions of the related elements of structure and the combination of parts and economies of manufacture, will become more apparent upon consideration of the following description and the appended claims with reference to the accompanying drawings, all of which form a part of this specification, wherein like reference numerals designate corresponding parts in the various figures. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended as a definition of the limits of the present patent application.

BRIEF DESCRIPTION OF THE DRAWINGS

[08] FIG. 1 shows a system for assessing cardiovascular risk for a patient in

accordance with an embodiment of the present patent application; [09] FIG. 2 shows a system for cardiovascular risk assessment for the patient in accordance with another embodiment of the present patent application;

[10] FIG. 3 shows exemplary ECG pattern characteristic of myocardial infraction;

[11] FIG. 4 shows exemplary respiration signal data (e.g., normal vs dyspnea);

[12] FIG. 5 shows exemplary PQRST wave;

[13] FIG. 6 shows exemplary ECG signal data (e.g., at moderate hyperkalemia)

showing distinct characteristic features;

[14] FIG. 7 shows exemplary ECG QRS signal data pattern (e.g., at mild, moderate and severe hyperkalemia);

[15] FIG. 8 shows exemplary ECG sinusoidal signal data pattern (e.g., at final stage hyperkalemia);

[16] FIG. 9 shows exemplary ECG signal features characteristic for each hyperkalemia phase;

[17] FIG. 10 shows exemplary ECG signal wave changes at hypokalemia compared to the normal ECG signal wave at normokalemia;

[18] FIG. 11 shows exemplary ECG signal features characteristic for each

hypokalemia phase;

[19] FIG. 12 shows an exemplary method for assessing cardiovascular risk of patients in an emergency department in accordance with an embodiment of the present patent application.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

[20] As used herein, the singular form of“a”,“an”, and“the” include plural references unless the context clearly dictates otherwise. As used herein, the statement that two or more parts or components are“coupled” shall mean that the parts are joined or operate together either directly or indirectly, i.e., through one or more intermediate parts or components, so long as a link occurs. As used herein,“directly coupled” means that two elements are directly in contact with each other. As used herein,“fixedly coupled” or“fixed” means that two components are coupled so as to move as one while maintaining a constant orientation relative to each other. As used herein, the term“or” means“and/or” unless the context clearly dictates otherwise.

[21] As used herein, the word“unitary” means a component is created as a single piece or unit. That is, a component that includes pieces that are created separately and then coupled together as a unit is not a“unitary” component or body. As employed herein, the statement that two or more parts or components“engage” one another shall mean that the parts exert a force against one another either directly or through one or more intermediate parts or components. As employed herein, the term“number” shall mean one or an integer greater than one (i.e., a plurality).

[22] Directional phrases used herein, such as, for example and without limitation, top, bottom, left, right, upper, lower, front, back, and derivatives thereof, relate to the orientation of the elements shown in the drawings and are not limiting upon the claims unless expressly recited therein.

[23] The present patent application provides an automatic solution for the assessment of cardiovascular risk of patients coming to the Emergency Department. The solution provides an objective assessment that is correct and complete (in the aspects it monitors). The solution also allows internal scanning and supports the triage process/procedure enabling patients to be treated within optimal time within optimal medical staff resources.

[24] In some embodiments, a system 100 for assessing cardiovascular risk of patients in an emergency department. System 100 includes a patient carrier 202 comprising a

transmissive structure. Patient carrier 202 is configured to support a patient to lay over the transmissive structure of patient carrier 202. System 100 also includes one or more optical or radar sensors l06a .. 106h. In some embodiments, one or more optical or radar sensors l06a .. 106h may be embedded in the transmissive structure of patient carrier 202. One or more optical or radar sensors l06a .. 106h are configured to measure respiration information of the patient via light or radio waves traveling through at least a portion of the transmissive structure of patient carrier 202. System 100 further includes one or more additional sensors l07a .. 107h configured to measure cardiac information of the patient. The cardiac information of the patient includes blood pressure information of the patient, Electrocardiography (ECG) information of the patient and/or heart rate information of the patient. System 100 also includes a computer system 102 comprising one or more physical processors. The one or more physical processors are programmed with computer program instructions which, when executed cause the computer system to: determine a cardiovascular risk parameter for the patient based on the cardiac information and the respiration information of the patient obtained from the one or more sensors. The cardiovascular risk parameter for the patient indicates that the patient requires medical intervention within a specified time period.

[25] Emergency department, as used herein, refers to any medical treatment and health care facility that specializes in emergency medicine. Emergency department may also be referred to as accident and emergency department, emergency room, and casualty department. In some embodiments, emergency department may be part of a hospital system. In some embodiments, system 100 may also be used in an urgent medical treatment and health care facility. In some embodiments, system 100 may also be used in other primary care medical treatment and health care facilities.

[26] Triage, as used herein, refers to a process/procedure of determining the priority of patients' treatments based on the severity of their condition. For example, triage may result in determining the order and priority of emergency treatment. Triage may also result in the order and priority of emergency transport, or the transport destination for the patient. Triage also rations treatment to the patients efficiently when available resources are insufficient to treat all the patients immediately.

[27] As will be clear from the discussions below, in some embodiments, system 100 includes computer system 102 that has one or more physical processors programmed with computer program instructions that, when executed cause computer system 102 to obtain information or data from one or more sensors l06a..l06n; l07a..l07n associated with patient P or with patient carrier or support 202 (see FIG. 2). In some embodiments, patient carrier or support 202 is configured to support patient P in the emergency department. As shown in FIG.

1, system 100 for assessing cardiovascular risk of patients in the emergency department may comprise server 102 (or multiple servers 102). Server 102 may comprise myocardial infarction risk assessment subsystem 112, hyperkalemia risk assessment subsystem 116, hypokalemia risk assessment subsystem 114, cardiovascular system analysis subsystem 1 18 or other components or subsystems.

[28] In some embodiments, one or more sensors l06a..l06n; l07a..l07n are

operatively associated with the patient and/or operatively connected with a patient support configured to support the patient. In some embodiments, one or more sensors l07a.T07n include a blood pressure sensor for measuring the blood pressure information of the patient, an Electrocardiography (ECG) sensor for measuring the Electrocardiography (ECG) information of the patient, and a heart rate sensor for measuring the heart rate information of the patient. In some embodiments, one or more sensors l06a..l06n, include a respiration sensor for measuring the respiration information of the patient. Each of these sensors l06a..l06n; l07a.. l07n are described in detail in the discussion below.

[29] In some embodiments, one or more sensors l06a..l06n ; l07a..l07n may

include sensor systems and/or other medical devices that are configured for monitoring patient health information, such as heart rate, ECG waves, respiration waves, blood pressure, breathing or respiration rate, etc.

[30] In some embodiments, one or more sensors l06a..l06n include optical or radar sensors. In some embodiments, the optical or radar sensors l06a..l06n are embedded in the transmissive structure of patient carrier 202. In some embodiments, one or more sensors l06a..l06n are configured to measure respiration information of the patient via light or radio waves traveling through at least a portion of the transmissive structure of patient carrier 202. In some embodiments, one or more optical or radar sensors l06a..l06n comprises one or more radar sensors (i) embedded in the transmissive portion of the patient carrier and (ii) configured to measure the respiration information of the patient via radio waves traveling through at least a portion of the transmissive structure of patient carrier 202. In some embodiments, one or more optical or radar sensors l06a..l06n comprises one or more optical sensors (i) embedded in the transmissive portion of the patient carrier and (ii) configured to measure the respiration information of the patient via light traveling through at least a portion of the transmissive structure of patient carrier 202.

[31] In some embodiments, one or more optical sensors l06a..l06n are partially

embedded in the transmissive portion of patient carrier 202. In some embodiments, one or more optical sensors l06a..l06n are fully embedded in the transmissive portion ofpatient carrier 202. In some embodiments, one or more radar sensors l06a..l06n are partially embedded in the transmissive portion of patient carrier 202. In some embodiments, one or more radar sensors l06a..l06n are fully embedded in the transparent portion ofpatient carrier 202.

[32] In some embodiments, the transmissive portion of patient carrier 202 is

configured to allow sensor (signal) waves to travel through at least a portion of the transparent structure of patient carrier 202.

[33] In some embodiments, one or more sensors l07a..l07n include a heart

rate sensor for measuring the heart rate information of patient P. In some embodiments, the heart rate sensor is implemented in a wrist device (like a smart watch) or other wearable devices, i.e., devices that can be attached or arranged on the skin of patient P. In some embodiments, the heart rate sensor can be an optical sensor for determining the heart rate data of patient P. In some embodiments, the heart rate sensor is a non-invasive heart rate sensor that is able to measure the electrical, acoustical or optical activity of the heart or the cardio-respiratory system. In some embodiments, the heart rate sensor is configured to communicate wirelessly with computer system 102. In some embodiments, the wearable heart rate sensor is applied to patient’s wrist, allowing measurement of heart rate, along with the analysis of the PQRST complex. In some embodiments, the heart rate sensor includes an electrode operatively associated with patient P and to measure the heart rate information of patient P, and a transmitter for sending the heart rate information to computer system 102.

[34] In some embodiments, one or more sensors l06a..l06n include a radar

sensor 204 (as shown in FIG. 2) for measuring respiration information of patient P. In some embodiments, the radar sensor includes a transmitter for sending signals to patient P and a receiver for receiving the signals from patient P. In some embodiments, the radar sensor is partially or fully embedded in patient support 202. In some embodiments, the radar sensor is configured to communicate wirelessly with computer system 102. In some embodiments, one or more sensors l06a.. 106h include other respiration sensor(s) for measuring respiration information of patient P. In some embodiments, the respiration sensor is configured to communicate wirelessly with computer system 102. In some embodiments, the respiration sensor is implemented as a wearable device, i.e., device that can be attached or arranged on the skin of patient P.

[35] In some embodiments, one or more sensors l07a..l07n include a

electrocardiogram (ECG) sensor for measuring Electrocardiography (ECG) information of patient P. In some embodiments, the electrocardiogram (ECG) sensor is implemented as a wearable device, i.e., device that can be attached or arranged on the skin of patient P. In some embodiments, the wearable electrocardiogram (ECG) sensor is applied to patient’s wrist, allowing measurement of heart rate, along with the analysis of the PQRST complex. In some embodiments, the electrocardiogram (ECG) sensor is configured to communicate wirelessly with computer system 102. In some embodiments, the electrocardiogram (ECG) sensor may also be referred to as wearable EKG sensor. In some embodiments, the electrocardiogram (ECG) sensor is configured to record the electrical activity of the heart over a period of time

using electrodes placed on the patient’s skin. In some embodiments, the electrocardiogram (ECG) sensor includes an electrode operatively associated with patient P and to measure the ECG information of patient P, and a transmitter for sending the ECG information to computer system 102.

[36] In some embodiments, one or more sensors l07a..l07n include a blood pressure sensor for measuring blood pressure information of patient P. In some embodiments, the blood pressure sensor is implemented as a wearable device, i.e., device that can be attached or arranged on the skin of patient P. In some embodiments, the blood pressure sensor is configured to communicate wirelessly with computer system 102.

[37] In some embodiments, the blood pressure information of the patient, the

Electrocardiography (ECG) information of the patient and/or the heart rate information of the patient may together be referred to as cardiac information of the patient. In some embodiments, the heart rate sensor and the ECG sensor may be integrally formed as a single sensor.

[38] In some embodiments, the cardiac information and the respiration information may be obtained from a database 132 that is being updated in real-time by one or more sensors l06a..l06n; l 07a..l07n.

[39] In one scenario, one or more sensors l06a..l06n; l07a..l07n may provide the cardiac information and the respiration information to a computer system (e.g., comprising server 102) over a network (e.g., network 150) for processing. In another scenario, upon obtaining the cardiac information and the respiration information, the sensors may process the obtained cardiac information and the obtained respiration information, and provide processed cardiac information and processed respiration information to the computer system (e.g., comprising server 102) over a network (e.g., network 150).

[40] In some embodiments, referring to FIG. 2, system 100 includes the screen or patient support 202 that is equipped with a number of sensors l06a..l06n; l07a..l07n including 204, 206, 208, 210, 212, and 214 that are used to scan patient P, assess his condition severity and emergency risk, which is used in updating the triage list in the Emergency Department. In some embodiments, system 100 includes a carrier support or member 216 operatively connected to patient support 202. In some embodiments, a sound emitter 208 and a stethoscope 214 are attached to carrier support 216. [41] In some embodiments, patient P is placed or positioned on screen 202. In some embodiments, patient P reclines against screen 202 (i.e., in an oblique position angle) with hand palms placed against screen 202, arms away from the body and legs at a distance from one another.

[42] In some embodiments, screen 202 is made of transparent plastic material.

In some embodiments, screen 202 includes an IR/thermal camera 210 and an RGB camera 212 both positioned above screen 202. In some embodiments, an RGB camera 206 positioned below screen 202 such that patient P can be scanned from above and below (i.e., front and back). In some embodiments, screen 202 embeds a matrix of optical sensors in its mid-section where the patient palms are expected to be placed. In some embodiments, radar sensor 204 is embedded with screen 202. In some embodiments, radar sensor 204 is placed beneath screen 202. In some embodiments, the transmissive structure can be made of, including glass material, plastic material, etc. In some embodiments, radio and/or optical waves can travel easily through transmissive structure. In some embodiments,“transmissive” may be transparent in the context of light, but otherwise means a material through which information/data can pass through. In some embodiments, it can be opaque in the case of radio wave embodiments.

[43] In some embodiments, system 100 is configured to automatically determine the patient body contour. In some embodiments, system 100 is configured to screen patient P. In some embodiments, system 100 is configured to determine the cardiovascular emergency risk.

In some embodiments, system 100 is configured to assess the overall patient condition severity and emergency risk. In some embodiments, system 100 is configured to calculate projections regarding the patient expected deterioration rate in case no medical intervention is provided based on similar cases and patients. In some embodiments, system 100 is configured to advise optimal intervention timeframe and medical specialists needed for the case. In some

embodiments, system 100 is configured to update the triage list. In some embodiments, system 100 is configured to inform medical staff of a) patient current status and deterioration projection, b) optimal intervention time, and c) update to the triage list in the Emergency Department.

[44] In some embodiments, referring to FIG. 1 , system 100 includes 1) myocardial infarction risk assessment subsystem 1 12, 2) hypokalemia risk assessment subsystem 1 14, 3) hyperkalemia risk assessment subsystem 1 16, and 4) cardiovascular system analysis subsystem 1 18. In some embodiments, myocardial infarction risk assessment subsystem 1 12, hypokalemia risk assessment subsystem 1 14 and hyperkalemia risk assessment subsystem 1 16 are configured to monitor and determine the respiratory system status of patient P. In some embodiments, cardiovascular system analysis subsystem 1 18 is configured to monitor and determine the cardiovascular system status of patient P. In some embodiments, cardiovascular distress is determined based on: myocardial infarction risk assessment by myocardial infarction risk assessment subsystem 112, hyperkalemia risk assessment by hyperkalemia risk assessment subsystem 1 16, and hypokalemia risk assessment by hypokalemia risk assessment subsystem 1 14.

[45] In some embodiments, as will be clear from the discussion below, computer system 102 is further configured to: determine a myocardial infraction risk parameter for the patient using the blood pressure information of the patient obtained from the blood pressure sensor, the Electrocardiography (ECG) information of the patient obtained from the

Electrocardiography (ECG) sensor, the respiration information of the patient obtained from the respiration sensor and the heart rate information of the patient obtained from the heart rate sensor, determine a hyperkalemia risk parameter for the patient and a hypokalemia risk parameter for the patient using the Electrocardiography (ECG) information of the patient obtained from the Electrocardiography (ECG) sensor; and determine the cardiovascular risk parameter for the patient using the determined myocardial infraction risk parameter, the determined hyperkalemia risk parameter, and the determined hypokalemia risk parameter for the patient.

[46] In some embodiments, myocardial infarction risk assessment subsystem 1 12 is configured to detect the following symptoms to calculate a myocardial infarction risk parameter using a) non-typical values of heart rate and blood pressure, b) ECG characteristic patterns and c) dyspnea. In some embodiments, the heart rate information, the blood pressure, the ECG characteristic patterns of the patient are obtained from the heart rate sensor, the blood pressure and the ECG sensor, respectively. In some embodiments, the dyspnea information is obtained from the respiration sensor, respectively.

[47] In some embodiments, myocardial infarction risk assessment subsystem 1 12 is configured to analyze the heart rate information and the blood pressure information of patient P (obtained from the heart rate sensor and blood pressure sensor, respectively) to determine a risk of an impending myocardial infraction. For example, myocardial infarction risk assessment subsystem 112 is configured to determine a risk of an impending myocardial infraction using 1) whether the obtained heart rate information is above a predetermined threshold, 2) whether the obtained heart rate information is below a predetermined threshold, 3) whether the obtained blood pressure information is above a predetermined threshold, or 4) whether the obtained blood pressure information is below a predetermined threshold.

[48] In some embodiments, non-typical values of heart rate and blood pressure are configured to increase the risk of an impending myocardial infarction. In some embodiments, elevated heart rate values increases the risk of an impending myocardial infarction. In some embodiments, depressed heart rate values increases the risk of an impending myocardial infarction. In some embodiments, the heart rate is generally measured by the number of contractions of the heart per minute (beats per minute, bpm). In some embodiments, elevated blood pressure values increases the risk of an impending myocardial infarction. In some embodiments, depressed blood pressure values increases the risk of an impending myocardial infarction. In some embodiments, the blood pressure is generally measured in millimeters of mercury (mm Hg).

[49] In some embodiments, the heart rate is generally in the range of between 60 and 70 beats per minute. In some embodiments, the heat rate is generally elevated when the heart rate is more than 100 beats per minute. In some embodiments, the heart rate is generally depressed when the heart rate is less than 60 beats per minute.

[50] In some embodiments, the blood pressure is generally in the range of

between 90/60 and 120/80. In some embodiments, the blood pressure is generally elevated when the blood pressure is more than 120/80. In some embodiments, the blood pressure is generally elevated when the blood pressure is more than 140/90. In some embodiments, the blood pressure is generally depressed when the blood pressure is less than 90/60.

[51] In some embodiments, myocardial infarction risk assessment subsystem 1 12 is configured to analyze the ECG characteristic patterns of patient P (obtained from the ECG sensor) to determine a risk of an impending myocardial infraction. For example, myocardial infarction risk assessment subsystem 1 12 is configured to determine a risk of an impending myocardial infraction by detecting the presence of the pathological Q waves, and ST elevation or ST depression.

[52] FIG. 3 shows ECG patterns characteristic of myocardial infarction. In some embodiments, ECG characteristic patterns of myocardial infarction include the following a) ST elevation or ST depression (as shown in FIG. 3) and b) pathological Q waves. In general, normal rhythm produces four entities - a P wave, a QRS complex, a T wave, and a U wave - each having a fairly unique pattern. For example, the P wave represents atrial depolarization, the QRS complex represents ventricular depolarization, the T wave represents ventricular repolarization, and the Li wave represents papillary muscle repolarization.

[53] In some embodiments, the pathological Q waves characteristics typically are

exhibited due to a previous myocardial infarction. This is relevant to assessing the current risk as according to WHO:“Survivors of MI are at increased risk of recurrent infarctions and have an annual death rate of 5% - six times that in people of the same age who do not have coronary heart disease.” See

http://www.who.int/cardiovascular_diseases/priorities/secondary 3revention/country/en/indexl . html

[54] In some embodiments, the pathological Q waves characteristics include 1) any Q-wave in ECG leads V2-V3 greater than or equal to (>) 0.02 seconds or QS complex in ECG leads V2 and V3, 2) Q-wave greater than or equal to (>) 0.03 seconds and greater than (>) 0.1 mV deep or QS complex in ECG leads I, II, aVL, aVF, or in ECG leads V— V6 in any two ECG leads of a contiguous lead grouping (in ECG leads I, aVL,V6; V4-V6; II, III, and aVF), and c) R-wave greater than or equal to (>) 0.04 seconds in ECG leads V1-V2 and R/S > 1 with a concordant positive T-wave in the absence of a conduction defect.

[55] In some embodiments, myocardial infarction risk assessment subsystem 1 12

is configured to analyze the respiration information of patient P (obtained from the respiration sensor) to determine a risk of an impending myocardial infraction. For example, myocardial infarction risk assessment subsystem 1 12 is configured to determine a risk of an impending myocardial infraction by detecting the symptoms of dyspnea. In some embodiments, myocardial infarction risk assessment subsystem 112 is configured to determine dyspnea onset and dyspnea progression by monitoring 1) the mean inhale -exhale duration, 2) mean respiration rate and/or 3) mean respiration amplitude.

[56] Systematic management of patients suffering high-risk symptoms is essential in emergency medical services. Patients suspected of myocardial infarction presenting with dyspnea have significantly higher short- and long-term mortality than patients with chest pain irrespective of a confirmed myocardial infarction diagnosis. See

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC475 l637/

[57] Dyspnea (or shortness of breath) more typically arises as part of

the constellation of symptoms in an acute coronary syndrome or myocardial infarction. See https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5247680/

[58] In some embodiments, dyspnea symptoms are detected using radar sensor 204. In some embodiments, radar sensor 204 is embedded in screen 202. In some embodiments, system 100 is configured to determine respiration sinusoid signal from the data or information obtained from radar sensor 204. In some embodiments, system 100 is configured to detect respiration distress by scanning the respiration sinusoid signal for dyspnea characteristics. In some embodiments, dyspnea characteristics may include low signal amplitude, increased respiration rate, or both.

[59] In some embodiments, dyspnea is characterized by shallow and rapid breathing.

In some embodiments, shallow breathing is reflected in the respiration signal in the significantly decreased inhale-exhale amplitude compared to normal breathing (as shown in FIG. 4). FIG. 4 shows a graphical representation of the normal respiration signal (at rest) and the respiration signal (at rest) with dyspnea (or shortness of breath). The signal amplitudes of the normal respiration signal and the dyspnea respiration signal (i.e., both signals measured at rest) are shown on the left hand side Y-axis of the graph in FIG. 4 and the time of the normal respiration signal and the dyspnea respiration signal (i.e., both signals measured at rest) are on the X-axis of the graph FIG. 4. The wave with shorter signal amplitude in FIG. 4 represents the dyspnea respiration signal, while the wave with taller signal amplitude in FIG. 4 represents the normal respiration signal. In some embodiments, rapid breathing is reflected in the respiration signal in the high respiration rate (i.e., number of inhale-exhale cycles/min) compared to the normal breathing. Normal respiration rate is typically within 10-18 inhale-exhale cycles/min.

Respiration rates at rest (i.e., without emotional or physical exertion) that are higher than 18 cycles/min are outside healthy bounds. A high respiration rate also implies low inhale-exhale duration thereby implying that dyspnea is characterized by a respiratory signal in which the mean inhale-exhale duration is significantly lower than in normal breathing.

[60] In some embodiments, dyspnea onset and progression monitoring is

performed by monitoring 1) the mean inhale-exhale duration, 2) mean respiration rate and/or 3) mean respiration amplitude.

[61] In some embodiments, myocardial infarction risk assessment subsystem 1 12

is configured to determine myocardial infarction risk score or parameter. In some embodiments, the determined myocardial infarction risk score or parameter is elevated when establishing if the ECG wave exhibits ST elevations. In some embodiments, the myocardial infarction risk parameter is further amplified by the detection of pathological Q waves, dyspnea and finally abnormal heart rate and blood pressure values as below in Equation (1):

MyocardiallnfarctionRiskScore = ST_Elevated ® PathologicalQ Waves ® Dyspnea ®

HeartRateDifftoNorm ® BloodPressureDifftoNorm Equation (1)

[62] In some embodiments, myocardial infarction risk assessment subsystem 1 12

is configured to determine myocardial infarction risk score or parameter using Equation (2) below.

MyocardiallnfarctionRiskScore = w 1 *ST_Elcvatcd + w2 * Pathol ogi cal Q Waves + w3* Dyspnea + w4* HeartRateDifftoNorm + w5* BloodPressureDifftoNorm Equation (2)

[63] In some embodiments, wl , w2, w3, w4 and w5 in Equation (2) are weights that are leamed/determined from and characteristic to the group of patients similar to the patient at hand.

[64] In some embodiments, hyperkalemia risk assessment subsystem 116 is configured to monitor and determine the respiratory system status of the patient. In some embodiments, hyperkalemia risk assessment subsystem 1 16 is configured to determine the patient hyperkalemia status by automatically analyzing the ECG information obtained from the ECG sensor. In some embodiments, the hyperkalemia status of the patient includes onset/mild hyperkalemia, moderate hyperkalemia, or severe hyperkalemia.

[65] In some embodiments, hyperkalemia risk assessment subsystem 1 16 is configured to analyze the ECG information obtained from the ECG sensor to determine whether the ECG signal morphology has changed from its normal characteristics (at normokalemia), towards exhibiting features characteristic of hyperkalemia.

[66] In some embodiments, once characteristic anomalous features are determined, hyperkalemia risk assessment subsystem 1 16 is configured to classify the ECG signal pattern to identify the hyperkalemia progression status. The normal ECG signal morphology in comparison with ECG signal features characteristic of the various hyperkalemia phases are presented below.

[67] FIG. 5 shows an example of ECG signal at normokalemia (e.g., for

a healthy individual having normal levels of potassium in blood). The illustration in FIG. 5 shows the lesser P and T waves and pronounced QRS peak, with associated intervals and segments in-between (PR, ST and QT). In contrast, medical studies ( See“Electrocardiographic manifestations of hyperkalemia,” by Mattu, Amal, William J. Brady, and David A. Robinson in The American journal of emergency medicine 18.6 (2000): 721-729;

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3627796/;“ABC of clinical electrocardiography: Conditions not primarily affecting the heart,” by Slovis C, Jenkins R., in BMJ 2002 Jun l;324(7349): l320-3. DOI: http://dx.doi.Org/l0. l l36/bmj.324.7349.1320 Erratum in: BMJ 2002 Aug 3; 325(7358):259. BMJ 2007 May 26;334(7603). DOI: http://dx.doi.

org/lO.l l36/bmj.39219.615243.AD;“Recognising signs of danger: EKG changes resulting from an abnormal serum potassium concentration,” by Webster A, Brady W, Morris F., in Emerg Med J 2002 Jan 19;19(1):74-7. DOI: http://dx.doi. org/lO.l l36/emj.19.1.74;“Electrocardiographic manifestations: electrolyte abnormalities,” by Diercks DB, Shumaik GM, Harrigan RA, Brady WJ, Chan TC., in J Emerg Med 2004 Aug;27(2): 153-60. DOI: http://dx.doi.

org/ 10.1016/j .jemermed.2004.04.006; and

http://www.bpac.org.nz/BT/20l l/September/imbalance.aspx) show that the ECG signal morphology changes in a specific way according to the various phases of hyperkalemia severity. In that sense in the article ( See http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3627796/).

“typical ECG in hyperkalemia progress from tall,“peaked” T waves and a shortened QT interval, to lengthening PR interval and loss of P waves, and then to widening of the QRS complex culminating in a“sine wave” morphology and death if not treated” ( See FIG. 6).

[68] FIG. 7 illustrates this progression, while FIG. 8 shows that ECG final sinusoidal wave at severe stage of hyperkalemia as described in http://epomedicine.com/emergency- medicine/ecg-changes-hyperkalemia/ FIG. 7 shows ECG QRS pattern at mild, moderate and severe hyperkalemia as depicted in http://epomedicine.com/emergency-medicine/ecg-changes- hyperkalemia/ In http://epomedicine.com/emergency-medicine/ecg-changes-hyperkalemia/. the various phases of hyperkalemia are described with corresponding changes in ECG.

[69] In some embodiments, mild hyperkalemia is detected at potassium levels, K in the range between 5.5 and 6.0 milliequivalents of solute per liter of solution (mEq/L), where rapid repolarization causes peaked T waves. In some embodiments, mild hyperkalemia is detected at potassium levels, K in the range between 6.0 and 6.5 mEq/L, where decrease in conduction causes prolonged PR and QT intervals.

[70] In some embodiments, moderate hyperkalemia is detected at potassium levels, K in the range between 6.5 and 7.0 mEq/L, where P waves are diminished and ST segment may be depressed. In some embodiments, moderate hyperkalemia is detected at potassium levels, K in the range between 7.0 and 8.0 mEq/L, where P waves disappear, QRS widens.

[71] In some embodiments, severe hyperkalemia is detected potassium levels, K

in the range between 8.0 and 10.0 mEq/L, where QRS merges with T wave to produce classic sine wave (QRS-T fusion - a sinusoidal waveform). In some embodiments, severe hyperkalemia is detected at potassium levels, K in the range between 10.0 and 12.0 mEq/L, where ventricular fibrillation and diastolic arrest occur.

[72] FIG. 8 shows ECG sinusoidal pattern at final stage hyperkalemia as depicted in https://researchportal.port.ac.Uk/portal/files/5878l24/thesis_for_Binding_final_copy_.3.pdf

[73] Table in FIG. 9 summarizes the ECG signal features characteristic of each

hyperkalemia phase. All ECG features detection pre -require automatic detection of the PQRST wave. For example, for mild hyperkalemia progression, the ECG features are identified as“Fl,” “F2” and“F3,” when the corresponding characteristic ECG features include“T waves peaked,” “PR interval prolonged,” and“QT interval prolonged.” For moderate hyperkalemia progression, the ECG features are identified as“F4,”“F5” and“F6,” when the corresponding characteristic ECG features include“P waves diminishing trend until disappearing,”“ST segment depressed,” and“QRS widening trend.” For severe hyperkalemia progression, the ECG feature is identified as“F7,” when the corresponding characteristic ECG features include“QRS-T fusion producing a sinusoidal waveform.”

[74] In some embodiments, an automatic detection of ECG features characteristic of hyperkalemia progression using hyperkalemia risk assessment subsystem 116, include the following procedures. The first procedure is an automatic detection of the PQRST wave - done by applying real time signal processing peak detection techniques to identify P, Q, R, S, T points, as well as h, h, h, U (as depicted in FIG. 5). This allows identifying their corresponding amplitudes and time stamps throughout the ECG signal. At the next procedure, P, Q, R, S, T points amplitudes (Amp) and time stamps (TS) are stored in associated vectors, including, P Amp, P_TS, Q Amp, Q_TS, R Amp, R TS, S Amp, S TS, T Amp, T_TS, li_Amp, l2_Amp, b_Amp, l₄_Amp, and li_TS, b_TS, b_TS, b_TS.

[75] In some embodiments, for mild hyperkalemia progression, the ECG feature

identified as“Fl ,” and the corresponding characteristic ECG features include“T waves peaked,” the automatic detection of ECG features characteristic of hyperkalemia progression using hyperkalemia risk assessment subsystem 116 includes the procedures of trend detection within the T Amp vector in order to determine increasing amplitude of the T wave (peaking). In some embodiments, hyperkalemia risk assessment subsystem 116 is configured to confirm the presence of Fl when 1) significant increasing trend is established in T Amp and 2) there are instances of T Amp >= R Amp (i.e., within the same PQRST wave).

[76] In some embodiments, for mild hyperkalemia progression, the ECG feature

identified as“F2,” and the corresponding characteristic ECG features include“PR interval prolonged,” the automatic detection of ECG features characteristic of hyperkalemia progression using hyperkalemia risk assessment subsystem 1 16 includes the procedures of 1) calculating the PR interval within each PQRST wave based on the corresponding P_TS and R TS values; and b) trend detection over the PR intervals length. In some embodiments, hyperkalemia risk assessment subsystem 116 is configured to confirm the presence of F2 when a significantly increasing trend of values within the PR intervals is determined.

[77] In some embodiments, for mild hyperkalemia progression, the ECG feature

identified as“F3,” and the corresponding characteristic ECG features include“QT interval prolonged,” the automatic detection of ECG features characteristic of hyperkalemia progression using hyperkalemia risk assessment subsystem 1 16 includes the procedures of 1) calculating the QT interval length within each PQRST wave based on the corresponding Q_TS and T_TS values and b) trend detection over the QT intervals length. In some embodiments, hyperkalemia risk assessment subsystem 116 is configured to confirm the presence of F3 when a significantly increasing trend of values within the QT intervals length is determined.

[78] In some embodiments, for moderate hyperkalemia progression, the ECG

feature identified as“F4,” and the corresponding characteristic ECG features include“P waves diminishing trend until disappearing,” the automatic detection of ECG features characteristic of hyperkalemia progression using hyperkalemia risk assessment subsystem 116 includes the procedures of trend detection within the P Amp vector in order to determine decreasing amplitude of the P wave. In some embodiments, hyperkalemia risk assessment subsystem 116 is configured to confirm the presence of F4 when 1) significant decreasing trend is established in the P Amp vector and 2) P Amp values approach 0 flat values within e.

[79] In some embodiments, for moderate hyperkalemia progression, the ECG

feature identified as“F5,” and the corresponding characteristic ECG features include“ST segment depressed,” the automatic detection of ECG features characteristic of hyperkalemia progression using hyperkalemia risk assessment subsystem 116 includes the procedures of 1) determining ST segment depression trend and 2) determining ST segment length decreasing trend.

[80] In some embodiments, the procedure of determining ST segment depression trend includes calculating the ST segment orientation by calculating the difference D Amp = U Amp - b_Amp. In normal ST segments, D Amp is close to 0 (i.e., ST segment is close to horizontal).

In contrast, in depressed ST segments the value of this difference is significantly higher. In some embodiments, the procedure of determining ST segment depression trend also includes trend detection over D Amp values over time to determine ST segment depression trend.

[81] In some embodiments, the procedure of determining ST segment length

decreasing trend includes 1) calculating the ST segment length within each PQRST wave based on the corresponding D_TS = h_TS and l₄_TS values, and 2) trend detection over D_TS values over time to determine ST segment length decrease.

[82] In some embodiments, hyperkalemia risk assessment subsystem 116 is

configured to confirm the presence of F5 when at least one of the trends above is established.

[83] In some embodiments, for moderate hyperkalemia progression, the ECG feature identified as“F6,” and the corresponding characteristic ECG features include“QRS widening trend,” the automatic detection of ECG features characteristic of hyperkalemia progression using hyperkalemia risk assessment subsystem 116 includes the procedures of 1) calculating the length of each QRS complex based on the corresponding Q_TS and S TS values; and 2) trend detection over the QRS complex lengths. In some embodiments, hyperkalemia risk assessment subsystem 116 is configured to confirm the presence of F6 when a significantly increasing trend of values within the QRS complex length is determined.

[84] In some embodiments, for severe hyperkalemia progression, the ECG feature identified as“F7,” and the corresponding characteristic ECG features include“QRS-T fusion producing a sinusoidal waveform,” the automatic detection of ECG features characteristic of hyperkalemia progression using hyperkalemia risk assessment subsystem 1 16. In some embodiments, hyperkalemia risk assessment subsystem 1 16 is configured to confirm the presence of F7 when 1) Fl - F6 are detected and 2) sinusoid detected by applying pattern recognition techniques.

[85] In some embodiments, the automatic assessment of hyperkalemia risk score

includes calculation of hyperkalemia risk score based on the detection of features above resulting in three levels of risk: mild hyperkalemia, moderate hyperkalemia, and severe hyperkalemia. In some embodiments, hyperkalemia risk assessment subsystem 1 16 is configured to confirm the onset of mild hyperkalemia when features Fl, F2, F3 are detected over a defined period of time (epoch). In some embodiments, hyperkalemia risk assessment subsystem 1 16 is configured to confirm the onset of moderate hyperkalemia when features F4, F5, F6 are detected over a defined period of time (epoch). In some embodiments, hyperkalemia risk assessment subsystem

116 is configured to confirm the onset of severe hyperkalemia when feature F7 are detected over a defined period of time (epoch).

[86] In some embodiments, hypokalemia risk assessment subsystem 114 is configured to monitor and determine the respiratory system status of the patient. In some embodiments, hypokalemia risk assessment subsystem 114 is configured to determine the patient hypokalemia status (i.e., onset/mild, moderate, severe) by automatically analyzing the ECG input signal to determine whether the ECG signal morphology has changed from its normal characteristics (i.e., at normokalemia), towards exhibiting features characteristic of hypokalemia. In some embodiments, once characteristic anomalous features are determined, hypokalemia risk assessment subsystem 114 is configured to classify the signal pattern to identify the hypokalemia progression status.

[87] The normal ECG signal morphology in comparison with ECG signal features characteristic of the various hypokalemia phases are presented below. FIG. 10 illustrates an example of ECG signal at normokalemia (i.e., healthy individual, normal levels of potassium in blood). The illustration shows the lesser P and T waves and pronounced QRS peak, with associated intervals and segments in-between (PR, ST and QT). FIG. 10 also illustrates a comparison between normal ECG wave at normokalemia (i.e., healthy individual, normal levels of potassium in blood) and ECG wave changes at hypokalemia. [88] In contrast, medical studies show that the ECG signal morphology changes in specific way according to the various phases of hypokalemia severity. In that sense Diercks et al. ( See“Electrocardiographic manifestations: electrolyte abnormalities,” by Diercks DB, Shumaik GM, Harrigan RA, Brady WJ, Chan TC. in J Emerg Med 2004 Aug;27(2):l53-60.

DOI: http://dx.doi. org/l0.l0l6/j.jemermed.2004.04.006) Ofound that hypokalemia causes first a decrease in the T wave amplitude, followed by ST segment depression and actual T wave inversions in correspondence to a further decrease in potassium level. Moreover, PR interval increases and P wave amplitude can increase as well. Severe hypokalemia manifests a prominent Li wave, a positive deflection after the T-wave.

[89] The classification for hypokalemia based on potassium, K level was proposed by the medical study“Prevalence of severe hypokalaemia in patients with traumatic brain injury. Injury,” by Wu X, Lu X, Lu X, Yu J, Sun Y, Du Z, Wu X, Mao Y, Zhou L, Wu S, Hu J. in January 2015; 46(l):35-4l . doi: l0.l0l6/j.injury.20l4.08.002. Epub 2014 Aug 10. According to this classification, when the potassium levels are between 3.0 mmoEL (a molar concentration, measured in millimoles per litre) and 3.5 mmoEL, hypokalemia is classified as mild

hypokalemia. When the potassium levels are between 2.5 mmol/L and 3.0 mmol/L,

hypokalemia is classified as moderate hypokalemia. When the potassium levels are less than 2.5 mmol/L, hypokalemia is classified as severe hypokalemia.

[90] In some embodiments, the above-noted medical studies and the medical study “Electrolyte disorders and arrythmogenesis,” by El-Sherif N, Turitto G. in Cardiol J

2011 ; 18(3):233-45 lead to the following features characterization: 1) moderate hypokalemia shows decrease in amplitude and broadening of T waves, ST segment depression and increase of El wave amplitude; and 2) severe hypokalemia shows an increase of QRS duration (without a concomitant change in the QRS configuration), increase in P wave amplitude and duration and a prolongation of P-R interval.

[91] Table in FIG. 11 summarizes the ECG signal features characteristic of each

hypokalemia phase. In some embodiments, for moderate hypokalemia progression, the ECG feature is identified as“Fl,”“F2”“F3,” and“F4,” when the corresponding characteristic ECG features include“decrease of T waves amplitude,”“broadening of T waves,”“ST segment depression,” and“U waves amplitude increase.” For severe hypokalemia progression, the ECG feature is identified as“F5,”“F6,”“F7,” and“F8,” when the corresponding characteristic ECG features include“QRS duration increase,”“P waves amplitude increase,”“P waves duration increase,” and“PR interval prolongation.”

[92] Automatic detection of ECG features characteristic of the hypokalemia

progression are discussed here. In some embodiments, automatic detection of the PQRST(U) wave is performed by applying real time signal processing peak detection techniques to identify P, Q, R, S, T, and (U) points, as well as lo, li, h, h, U, and h. This will allow identifying their corresponding amplitudes and time stamps throughout the ECG signal. The procedure of detecting the ECG signal features identifying hypokalemia progression is similar as for hyperkalemia above and, therefore, will not be described in great detail here.

[93] In some embodiments, the automatic assessment of hypokalemia risk score

includes calculation of hypokalemia risk score based on the detection of features above resulting in two levels of risk: moderate hypokalemia and severe hypokalemia. In some embodiments, hypokalemia risk assessment subsystem 114 is configured to confirm the onset of moderate hypokalemia when features Fl, F2, F3, and F4 are detected over a defined period of time (epoch). In some embodiments, hypokalemia risk assessment subsystem 114 is configured to confirm the onset of severe hypokalemia when features F5, F6, F7, and F8 are detected over a defined period of time (epoch).

[94] In some embodiments, cardiovascular system analysis subsystem 118 is

configured to monitor and determine the cardiovascular system status of the patient. In some embodiments, cardiovascular system analysis subsystem 118 is configured to determine the cardiovascular distress as described below.

[95] In some embodiments, cardiovascular system analysis subsystem 118 is

configured to determine cardiovascular risk score using the wearable ECG sensor and blood pressure sensors applied to patient wrist, allowing measurement of heart rate, along with the analysis of the PQRST complex. In some embodiments, cardiovascular system analysis subsystem 1 18 is configured to determine cardiovascular distress based on: 1) myocardial infarction risk assessment by myocardial infarction risk assessment subsystem 112; 2) hyperkalemia risk assessment by hyperkalemia risk assessment subsystem 1 16; and 3) hypokalemia risk assessment by hypokalemia risk assessment subsystem 114. In some embodiments, the cardiovascular risk score is a vector comprising the three components above.

[96] In some embodiments, based on the values of each of the three component risk scores (i.e., myocardial infarction risk score, hypokalemia risk assessment score, hyperkalemia risk assessment score), system 100 (including cardiovascular system analysis subsystem 118) is configured to 1) assesses the overall patient condition severity and emergency risk, 2) calculate projections regarding the patient expected deterioration rate in case no intervention is provided based on similar cases and patients; 3) advice optimal intervention timeframe and medical specialists needed for the case; 4) update the triage list; 4) inform medical staff of a) patient current status and deterioration projection, b) optimal intervention time, c) update to triage list.

[97] Referring to FIG. 12, a method 700 for assessing cardiovascular risk of patients in an emergency department is provided. Method 700 is implemented by computer system 102 comprising one or more physical processors executing computer program instructions that, when executed, perform method 700. In some embodiments, method 700 comprises: obtaining, from one or more optical or radar sensors l06a..l06n embedded in a transparent structure of a patient carrier, respiration information of the patient via light or radio waves traveling through at least a portion of the transparent structure of the patient carrier, the patient carrier being configured to support a patient to lay over the transparent structure of the patient carrier, at procedure 702; obtaining, from one or more additional sensors l07a..l07n, cardiac information of the patient, the cardiac information of the patient including blood pressure information of the patient, Electrocardiography (ECG) information of the patient and/or heart rate information of the patient at procedure 704; and determining, using computer system 102, a cardiovascular risk parameter for the patient based on the cardiac information and the respiration information of the patient obtained from one or more sensors l06a..l06n; l07a..l07n, the cardiovascular risk parameter for the patient indicating that the patient requires medical intervention within a specified time period at procedure 706.

[98] In some embodiments, computer system 102 is further configured to determine projected deterioration rate information for the patient in case no medical intervention is provided within the specified time period using previously determined cardiovascular risk parameters of similar patients. In some embodiments, computer system 102 is further configured to calculate projections regarding the patient expected deterioration rate in case no medical intervention is provided based on similar cases and patients.

[99] In some embodiments, computer system 102 is configured to notify a clinician of the determined cardiovascular risk parameter for the patient, the specified time period for the medical intervention, and the determined projected deterioration rate information for the patient. In some embodiments, when the determined cardiovascular risk parameter indicates a patient needs medical attention within the specified time period, computer system 102 is configured to generate audio and/or visuals alerts and/or messages notifying clinicians thereof It is contemplated that such a message can be provided to the clinicians via the communication network 150. In some embodiments, computer system 102 is also configured to notify only (and all) medical specialists needed for the case. In some embodiments, computer system 102 is configured to notify a clinician of 1) optimal intervention timeframe, and 2) the overall patient condition severity and emergency risk.

[100] In some embodiments, computer system 102 is configured to update the triage list in real time. In some embodiments, computer system 102 is configured to dynamically update the triage list. In some embodiments, computer system 102 is configured to update the triage list to include 1) optimal intervention timeframe, and 2) the overall patient condition severity and emergency risk. In some embodiments, computer system 102 is configured to enable the medical personnel or clinician to prioritize patients that are most critical.

[101] In some embodiments, the various computers and subsystems illustrated in FIG. 1 may comprise one or more computing devices that are programmed to perform the functions described herein. The computing devices may include one or more electronic storages

(e.g., database 132, or other electronic storages), one or more physical processors programmed with one or more computer program instructions, and/or other components. The computing devices may include communication lines or ports to enable the exchange of information with a network (e.g., network 150) or other computing platforms via wired or wireless techniques (e.g., Ethernet, fiber optics, coaxial cable, WiFi, Bluetooth, near field communication, or other communication technologies). The computing devices may include a plurality of hardware, software, and/or firmware components operating together to provide the functionality attributed herein to the servers. For example, the computing devices may be implemented by a cloud of computing platforms operating together as the computing devices.

[102] The electronic storages may comprise non-transitory storage media that

electronically stores information or data. The electronic storage media of the electronic storages may include one or both of system storage that is provided integrally (e.g., substantially non removable) with the servers or removable storage that is removably connectable to the servers via, for example, a port (e.g., a USB port, a firewire port, etc.) or a drive (e.g., a disk drive, etc.). The electronic storages may include one or more of optically readable storage media (e.g., optical disks, etc.), magnetically readable storage media (e.g., magnetic tape, magnetic hard drive, floppy drive, etc.), electrical charge-based storage media (e.g., EEPROM, RAM, etc.), solid-state storage media (e.g., flash drive, etc.), and/or other electronically readable storage media. The electronic storages may include one or more virtual storage resources (e.g., cloud storage, a virtual private network, and/or other virtual storage resources). The electronic storages may store software algorithms, information determined by the processors, information received from the servers, information received from client computing platforms, or other information that enables the servers to function as described herein.

[103] The processors may be programmed to provide information processing

capabilities in the system 100. As such, the processors may include one or more of a digital processor, an analog processor, or a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and/or other mechanisms for electronically processing information. In some embodiments, the processors may include a plurality of processing units. These processing units may be physically located within the same device, or the processors may represent processing functionality of a plurality of devices operating in coordination. The processors may be programmed to execute computer program instructions to perform functions described herein of subsystems 112, 114, 1 16, 118 or other subsystems. The processors may be programmed to execute computer program instructions by software; hardware; firmware; some combination of software, hardware, or firmware; and/or other mechanisms for configuring processing capabilities on the processors.

[104] It should be appreciated that the description of the functionality provided by the subsystems 112, 1 14, 116, or 118 described herein is for illustrative purposes, and is not intended to be limiting, as subsystems 1 12, 114, 116, or 118 may provide more or less functionality than is described. As another example, additional subsystems may be programmed to perform some or all of the functionality attributed herein to subsystems 112, 114, 1 16, or 118.

[105] In some embodiments, system 100 may also include a communication interface that is configured to send the determined cardiovascular risk assessment through an appropriate wireless communication method (e.g., Wi-Fi, Bluetooth, internet, etc.) to necessary medical or clinical personnel or systems for further processing. In some embodiments, system 100 may include a recursive tuning subsystem that is configured to recursively tune its intelligent decision making subsystem using available data or information to provide better overall cardiovascular risk assessment. In some embodiments, intelligent decision making subsystem, communication interface and recursive tuning subsystem may be part of computer system (comprising server 102).

[106] In some embodiments, a subsystem of system 100 is configured to continuously obtain subsequent patient cardiac information and/or cardiovascular risk parameter. That is, the subsystem may continuously obtain subsequent patient cardiac information and/or cardiovascular risk parameter. As an example, the subsequent information may comprise additional information corresponding to a subsequent time (after a time corresponding to information that was used to determine the cardiovascular risk parameter). The subsequent information may be utilized to further update or modify the cardiovascular risk parameter (e.g., new information may be used to dynamically update or modify the cardiovascular risk parameter), etc. For example, the subsequent information may also be configured to provide further input to determine the cardiovascular risk parameter. In some embodiments, a subsystem of system 100 may be configured to determine the cardiovascular risk parameter in accordance with a recursively refined profile (e.g., refined through recursive application of profile refinement algorithms) based on previously collected or subsequent patient health/cardiac information.

[107] In some embodiments, intelligent decision making subsystem may be a machine learning algorithm or method that is used for combining different data sources and intelligent decision making. In some embodiments, the machine learning algorithm of intelligent decision making subsystem may include time -varying algorithm that may model the changes of parameters and their relationship over time. For example, in some embodiments, the machine learning algorithm of intelligent decision making subsystem may include Hidden Markov models or Dynamic Bayesian networks. In some embodiments, the machine learning algorithm of intelligent decision making subsystem may include non-time varying models. For example, the machine learning algorithm of intelligent decision making subsystem may include a classifier such as support vector machines or Naive Bayes. In some embodiments, the machine learning algorithm of intelligent decision making subsystem may include modelling previous operations and learning from data or information. There are several sources of information that are used as inputs to intelligent decision making subsystem so as to get more certainty in cardiovascular risk assessment score. Intelligent decision making subsystem combines inputs from these several sources.

[108] The present patent application is used in the healthcare domain.

[109] In the claims, any reference signs placed between parentheses shall not be

construed as limiting the claim. The word“comprising” or“including” does not exclude the presence of elements or steps other than those listed in a claim. In a device claim enumerating several means, several of these means may be embodied by one and the same item of hardware. The word“a” or“an” preceding an element does not exclude the presence of a plurality of such elements. In any device claim enumerating several means, several of these means may be embodied by one and the same item of hardware. The mere fact that certain elements are recited in mutually different dependent claims does not indicate that these elements cannot be used in combination.

[110] Although the present patent application has been described in detail for the

purpose of illustration based on what is currently considered to be the most practical and preferred embodiments, it is to be understood that such detail is solely for that purpose and that the present patent application is not limited to the disclosed embodiments, but, on the contrary, is intended to cover modifications and equivalent arrangements that are within the spirit and scope of the appended claims. For example, it is to be understood that the present patent application contemplates that, to the extent possible, one or more features of any embodiment can be combined with one or more features of any other embodiment.

Claims

What is Claimed is:

1. A system ( 100) for assessing cardiovascular risk of patients in an emergency department, the system comprising:

a patient carrier (202) comprising a transparent structure, the patient carrier being configured to support a patient to lay over the transparent structure of the patient carrier;

one or more optical or radar sensors (l06a..l06n) being configured to measure respiration information of the patient via light or radio waves traveling through at least a portion of the transparent structure of the patient carrier;

one or more additional sensors (l07a..l07n) configured to measure cardiac information of the patient, the cardiac information of the patient comprising blood pressure information of the patient, Electrocardiography (ECG) information of the patient, and/or heart rate information of the patient; and

a computer system (102) comprising one or more physical processors programmed with computer program instructions that, when executed, cause the computer system to:

determine a cardiovascular risk parameter for the patient based on the cardiac information and the respiration information of the patient obtained from the one or more sensors, the cardiovascular risk parameter for the patient indicating that the patient requires medical intervention within a specified time period.

2. The system of claim 1, wherein the one or more optical or radar sensors comprises one or more radar sensors (i) embedded in the transparent portion of the patient carrier and (ii) configured to measure the respiration information of the patient via radio waves traveling through at least a portion of the transparent structure of the patient carrier.

3. The system of claim 1 , wherein the one or more optical or radar sensors comprises one or more optical sensors (i) embedded in the transparent portion of the patient carrier and (ii) configured to measure the respiration information of the patient via light traveling through at least a portion of the transparent structure of the patient carrier.

4. The system of claim 2, wherein the computer system is further configured to: determine a myocardial infraction risk parameter for the patient using the blood pressure information of the patient obtained from a blood pressure sensor, the Electrocardiography (ECG) information of the patient obtained from an Electrocardiography (ECG) sensor, the respiration information of the patient obtained from a respiration sensor and the heart rate information of the patient obtained from a heart rate sensor, the one or more optical or radar sensor comprises the respiration sensor, and the one or more additional sensors comprises the blood pressure sensor, the Electrocardiography (ECG) sensor and the heart rate sensor;

determine a hyperkalemia risk parameter for the patient and a hypokalemia risk parameter for the patient using the Electrocardiography (ECG) information of the patient obtained from the Electrocardiography (ECG) sensor; and

determine the cardiovascular risk parameter for the patient using the determined myocardial infraction risk parameter, the determined hyperkalemia risk parameter, and the determined hypokalemia risk parameter for the patient.

5. The system of claim 1 , wherein the computer system is further configured to determine projected deterioration rate information for the patient in case no medical intervention is provided within the specified time period using previously determined cardiovascular risk parameters of similar patients.

6. The system of claim 5, wherein the computer system is further configured to notify a clinician of the determined cardiovascular risk parameter for the patient, the specified time period for the medical intervention, and the determined projected deterioration rate information for the patient.

7. A method (700) for assessing cardiovascular risk of patients in an emergency department, the method being implemented by a computer system (102) comprising one or more physical processors executing computer program instructions that, when executed, perform the method, the method comprising:

obtaining, from one or more optical or radar sensors (l 06a.. l06n) disposed in a transparent structure of a patient carrier (202), respiration information of the patient via light or radio waves traveling through at least a portion of the transparent structure of the patient carrier, the patient carrier being configured to support a patient to lay over the transparent structure of the patient carrier;

obtaining, from one or more additional sensors (l07a..l07n), cardiac information of the patient, the cardiac information of the patient comprising blood pressure information of the patient, Electrocardiography (ECG) information of the patient and/or heart rate information of the patient; and

determining, using the computer system, a cardiovascular risk parameter for the patient based on the cardiac information and the respiration information of the patient obtained from the one or more sensors, the cardiovascular risk parameter for the patient indicating that the patient requires medical intervention within a specified time period.

8. The method of claim 7, wherein the optical or radar sensors comprises one or more radar sensors (i) embedded in the transparent portion of the patient carrier and (ii) configured to measure the respiration information of the patient via radio waves traveling through at least a portion of the transparent structure of the patient carrier..

9. The method of claim 7, wherein the one or more optical or radar sensors comprises one or more optical sensors (i) embedded in the transparent portion of the patient carrier and (ii) configured to measure the respiration information of the patient via light traveling through at least a portion of the transparent structure of the patient carrier.

10. The method of claim 7, further comprising:

determining, using the computer system, a myocardial infraction risk parameter for the patient using the blood pressure information of the patient obtained from a blood pressure sensor, the Electrocardiography (ECG) information of the patient obtained from an Electrocardiography (ECG) sensor, the respiration information of the patient obtained from a respiration sensor and the heart rate information of the patient obtained from a heart rate sensor, the one or more optical or radar sensor comprises the respiration sensor, and the one or more additional sensors comprises the blood pressure sensor, the Electrocardiography (ECG) sensor and the heart rate sensor; determining, using the computer system, a hyperkalemia risk parameter for the patient and a hypokalemia risk parameter for the patient using the Electrocardiography (ECG) information of the patient obtained from the Electrocardiography (ECG) sensor; and

determining, using the computer system, the cardiovascular risk parameter for the patient using the determined myocardial infraction risk parameter, the determined hyperkalemia risk parameter, and the determined hypokalemia risk parameter for the patient.

11. The method of claim 7, further comprising determining, using the computer system, projected deterioration rate information for the patient in case no medical intervention is provided within the specified time period using previously determined cardiovascular risk parameters of similar patients.

12. The method of claim 11 , further comprising notifying, using the computer system, a clinician of the determined cardiovascular risk parameter for the patient, the specified time period for the medical intervention, and the determined projected deterioration rate information for the patient.

13. A system (100) for assessing cardiovascular risk of patients in an emergency department, the system comprising:

a means (102) for executing machine-readable instructions with at least one processor, wherein the machine-readable instructions comprising:

obtaining, from one or more additional sensors (l07a..l07n), cardiac information of the patient, the cardiac information of the patient comprising blood pressure information of the patient, Electrocardiography (ECG) information of the patient and/or heart rate information of the patient; and determining a cardiovascular risk parameter for the patient based on the cardiac information and the respiration information of the patient obtained from the one or more sensors, the cardiovascular risk parameter for the patient indicating that the patient requires medical intervention within a specified time period.

14. The system of claim 13, wherein the one or more optical or radar sensors comprises one or more radar sensors (i) embedded in the transparent portion of the patient carrier and (ii) configured to measure the respiration information of the patient via radio waves traveling through at least a portion of the transparent structure of the patient carrier.

15. The system of claim 13, wherein the one or more optical or radar sensors comprises one or more optical sensors (i) embedded in the transparent portion of the patient carrier and (ii) configured to measure the respiration information of the patient via light traveling through at least a portion of the transparent structure of the patient carrier.

16. The system of claim 13, wherein the machine -readable instructions further comprising:

determining a myocardial infraction risk parameter for the patient using the blood pressure information of the patient obtained from a blood pressure sensor, the

Electrocardiography (ECG) information of the patient obtained from an Electrocardiography (ECG) sensor, the respiration information of the patient obtained from a respiration sensor and the heart rate information of the patient obtained from a heart rate sensor, the one or more optical or radar sensor comprises the respiration sensor, and the one or more additional sensors comprises the blood pressure sensor, the Electrocardiography (ECG) sensor and the heart rate sensor;

determining a hyperkalemia risk parameter for the patient and a hypokalemia risk parameter for the patient using the Electrocardiography (ECG) information of the patient obtained from the Electrocardiography (ECG) sensor; and

determining the cardiovascular risk parameter for the patient using the determined myocardial infraction risk parameter, the determined hyperkalemia risk parameter, and the determined hypokalemia risk parameter for the patient.

17. The system of claim 13, wherein the machine -readable instructions further comprising: determining projected deterioration rate information for the patient in case no medical intervention is provided within the specified time period using previously determined cardiovascular risk parameters of similar patients.

18. The system of claim 17, wherein the machine -readable instructions further comprising: notifying a clinician of the determined cardiovascular risk parameter for the patient, the specified time period for the medical intervention, and the determined projected deterioration rate information for the patient.