JP6219590B2 - Diagnostic device and medical system - Google Patents

Description

  The present invention relates to a method and apparatus for diagnosing chronic heart failure.

  In the last few decades, the treatment of chronic heart failure (CHF) has made significant progress, but the mortality rate still remains at a high level of 20-30%. Therefore, there is a need for a new means for predicting the deterioration of CHF patients' illness and subsequent death with high sensitivity and early.

  Although enhanced sympathetic nerve activity is known to worsen the pathology of CHF patients, it is difficult to use it as a predictive index because it is not easy to measure sympathetic nerve activity. In order to enhance respiratory instability represented by periodic breathing by enhancing sympathetic nerve activity, the state of breathing is expected to be a means to predict sympathetic nerve activity. In fact, chain-Stokes breathing, which is one of periodic breathing, is frequently observed during the day and night of CHF patients, and sympathetic nerve excitement (Non-patent Document 1) and chemical sensitivity enhancement (Non-patent Document 2). 4), known to be related to the efficiency of gas exchange, circulatory delay between lung and chemoreceptor (Non-patent document 5), pulmonary congestion (Non-patent document 6). It is considered to be involved (Non-Patent Documents 7 and 8).

  Brack et al. Used a 24-hour monitoring device to examine the respiratory pattern of a heart failure patient in a 24-hour cycle and found that heart failure was better when chain-Stokes breathing was observed during the day than during night-time Chain-Stokes breathing. Reported to be severe. However, at present, no quantitative evaluation of daytime chain-Stokes respiration has been made (Non-Patent Document 9).

  The present inventor has already shown a new index called RSI (Respiration Stability Index) in Patent Document 1. RSI extracts a respiratory cycle band from a respiratory waveform, calculates an average value of the respiratory frequency, calculates a standard deviation of the respiratory frequency, and calculates the reciprocal of this standard deviation. RSI can evaluate the quality of sleep, and it has been found that there is a difference in RSI during sleep between a person with poor sleep quality such as a patient with chronic heart failure and a healthy person.

International Publication No. 2011/019091

Harada D, Joho S, Hirai T, Asanoi H, Inoue H. Short term effect of adaptive servo-ventilation on muscle sympathetic nerve activity in patients with heart failure.Auton Nuerosci 2011; 161: 95-102 Francis DP, Willson K, Davies LC, Coats AJS, Piepoli M. Quantitative general theory for periodic breathing in chronic heart failure and its clinical implication. Circulation 2000; 102: 2214-2221 Manisty CH, Willson K, Wensel R, Whinnett ZI, Davies JE, Oldfield WLG, Mayet J, Francis DP.Development of respiratory control instability in heart failure: a novel approach to dessect the pathophysiological mechniasms.J Physiol2006; 577: 387-401 Ponikowski P, Anker SD, Chua TP, Francis D, Banasiak W, Pool-Wilson A, Coats AJS, Piepoli M. Oscillatory breathing pattern during wakefulness in patients with chronic heart failure.Clinical implications and role of augmented peripheral chemosennsitivity.Circulation 1999; 100: 2418-2424. Khoo MCK, Kronauer RE, Strohl KP, Slutsky AS.Factors inducing periodic breathing in humans: a general model.J Appl Physiiol: Respirat Environ Exercise Physiol 1982; 53: 644-659 Roberts AM, Bhattacharya J, Schultz HD, Coleridge HM, Coleridge JC. Stimulation of pulmonary vagal afferent C-fibers by lung edema in dogs.Circ Res 1986; 58: 512-522. Corra U, Pistono M, mezzani a, BraghiroliA, Giordano A, Lanfranchi P, BosiminiE, Gnemmi M, Giannuzzi P. Sleep and exertinal breathing in chronic heart failure: prognostic importance and interdependence. Circulation 2006; 113: 44-50. Sun XG, Hansen JE, Beshai JF, Wasserman K. Oscillatory breathing and exercise gas exchange abnormalities prognosticate early mortality and morbidity in heart failure.J Am Coll Cardiol 2010; 55: 1814-1823. Brack T, Thuer I, Clarenbach CF, Senn O, Noll G, Russi EW, Bloch KE.Daytime Cheyne-Stokes respiration in ambulatory patients with severe congestive heart failure is associated with increased mortality.Chest 2007; 132: 1463-1471

Currently, the NYHA (New York Heart Association) cardiac function classification is widely used as an index indicating the severity of heart failure disease.
This NYHA classification classifies the pathological state of cardiac function for a patient into the following four stages based on the subjective symptoms of physical activity.
Class I
1. Those with heart disease but without physical activity restrictions
2. Normal labor does not cause fatigue, palpitation, dyspnea, or angina
Class II
1. Those with mild restrictions on physical activity
2. Asymptomatic at rest and light work
Class III
1. Highly restricted physical activity
2. Asymptomatic at rest, but with a mild or subordinary effort
Class IV
1. Any physical activity is painful
2. There is cardiac dysfunction or angina symptom even at rest and is enhanced by exertion

  Since this NYHA cardiac function classification is intended to determine the severity stage based on the qualitative and subjective matters of subjective symptoms of physical activity, heart failure is difficult due to the lack of quantitative and objective accuracy. At each stage of patient diagnosis, determination of treatment method, confirmation of treatment effect, and prognosis observation, it was not sufficient as an index.

  In addition, new indicators such as pulmonary artery pressure and blood components have been found to evaluate the severity and prognosis of heart failure, but all of them are invasive and impose a burden on patients. On the other hand, conventional physiological data (blood pressure, weight, etc.) that is non-invasive and less burdensome for the patient can be used to assess the severity of heart failure in the patient with high accuracy, detect deterioration early, or It cannot be predicted with high accuracy.

  In such a situation, the inventor assumes that daytime respiratory instability is also a comprehensive sign of abnormalities in autonomic nerves, hemodynamics, and ventilation associated with exacerbation of heart failure. We thought that there was a possibility of warning of deterioration and prognosis. To date, however, there has been no simple and quantitative method for quantitatively measuring not only periodic breaths, but also irregular fast, shallow, or unstable breaths that have no fixed period.

An object of the present invention is to provide an apparatus or method for objectively diagnosing the severity of a heart failure patient.
Another object of the present invention is to provide an apparatus or method for diagnosing a sign of serious risk in a patient with heart failure, that is, predicting prognosis and detecting deterioration early.

  The inventor can use this new method for quantitatively measuring respiratory instability, the respiratory stability index (RSI), as a tool for assessing the severity of patients with heart failure and use it to assess the prognosis in this fatal disease. I found.

  The RSI index is a quantitative evaluation indicating the instability of breathing motion, and the fact that the RSI index is less than 20 (1 / Hz), for example, less than 20 (1 / Hz) is a serious pathological condition. It becomes an index for predicting that It has been found that the stability of breathing motion taught by the RSI index can detect heart failure patients (particularly chronic heart failure) having unstable breathing patterns and can be utilized as a new treatment option.

That is, this invention is a diagnostic apparatus and a medical system as described in each following item.
(1) Prediction of prognosis of heart failure, heart failure comprising calculation means for calculating an index value indicating instability of a respiratory cycle or respiratory frequency, and means for displaying or outputting the calculated result A diagnostic device used for at least one of early detection of deterioration and quantitative evaluation of the severity of heart failure.
(2) The display or output is
(A) when the value of the obtained index is within a predetermined numerical zone;
(B) In cases other than (A),
The diagnostic device according to (1), wherein the diagnostic device is displayed or output in a different manner.
(3) The display or output is
(A) When the obtained value of the index is within a predetermined numerical zone,
(B) If the value of the index tends to approach the predetermined numerical zone, and (C) if it is not any of (A) and (B),
The diagnostic device according to (1), wherein the diagnostic device is displayed or output in a different manner.
(4) The diagnostic apparatus according to any one of (1) to (3), wherein the value of the index is generated as an RSI that is a reciprocal of a standard deviation of a respiratory frequency of a target patient.
(5) The diagnostic device according to (4), wherein the predetermined numerical zone is less than 20 (1 / Hz).
(6) A medical system comprising: the diagnostic apparatus according to any one of (1) to (5); and a receiving unit that receives the output index value via a communication path.
(7) The medical system according to (6), wherein the diagnostic device is in a location remote from the receiving unit.
(8) calculation means for calculating an index value indicating instability of the respiratory cycle or respiratory frequency;
(A) Predictive means for predicting the prognosis of heart failure using the value of the index,
(A) Early detection means for worsening heart failure using the value of the index, and
(C) Evaluation means for quantitatively evaluating the severity of heart failure using the value of the index,
At least one of the means, and
A diagnostic apparatus comprising: means for displaying or outputting at least one of the predicted result, the detected result, and the evaluated result.
(9) The prediction means predicts that the prognosis of the target patient becomes serious when the value of the index is within a predetermined numerical zone.
The detection means detects early deterioration of heart failure of the target patient when the value of the index is within a predetermined numerical zone; and
(8) The evaluation means comprises at least one of the configurations in which the target patient evaluates that the heart failure is serious when the value of the index is within a predetermined numerical zone. Diagnostic equipment.
(10) At least one of a case where seriousness is predicted by the prediction means, a case where deterioration is detected by the early detection means, and a case where seriousness is evaluated by the evaluation means The diagnostic apparatus according to (9), wherein an alarm is generated only by the output or display means.
(11) The diagnostic apparatus according to any one of (8) to (10), wherein the value of the index is generated as an inverse number of a standard deviation of a respiratory frequency of the target patient.
(12) The diagnostic device according to (11), wherein the predetermined numerical zone is less than 20 (1 / Hz).
(13) When the value of the index tends to approach the predetermined numerical zone, display or output for alerting is performed by display or output means, (8) to (8) The diagnostic apparatus according to any one of 12).
(14) A medical system comprising: the diagnostic device according to any one of (8) to (13); and a reception unit that receives a signal output from the diagnostic device via a communication path.
(15) The medical system according to (14), wherein the diagnostic device is located remotely from the receiving unit.

  According to the present invention, the prognosis of heart failure patients can be diagnosed. In particular, since a patient at a serious risk can be diagnosed, it is possible to cope with it in a medical institution before falling into a serious situation.

It is a figure which shows an example of the evaluation system in this invention. It is a schematic diagram which shows the principle at the time of measuring with the evaluation system in this invention. It is a figure which shows the example of a measurement waveform using the evaluation system in this invention. It is a wave form diagram which shows the time transition of the some index | exponent created using the evaluation system in this invention. It is a schematic diagram for demonstrating the principle which calculates a fluctuation index using the evaluation system in this invention. It is a schematic diagram which shows the principle which calculates the standard deviation of a respiratory cycle using the evaluation system in this invention. FIG. 6 shows Kaplan-Meier survival curves for all reasons in patients with RSI of 20 (1 / Hz) or higher and patients of less than 20 (1 / Hz). It is a figure which shows the Kaplan-Meier survival curve of the thing by cardiovascular origin in the patient whose RSI is 20 (1 / Hz) or more and the patient who is less than 20 (1 / Hz).

  The present invention provides a quantitative measure of daytime respiratory stability detected using the RSI index, i.e., various types of respiratory instability, which is a measure of the severity of patients with transitional heart failure (particularly chronic heart failure). They have found that they are factors that can clearly and independently predict the risk of deterioration.

The RSI in the present invention is a quantitative index indicating the stability of respiration, and is a new index that can quantitatively display the variation in the respiratory cycle. RSI extracts a respiratory cycle band from a respiratory waveform, calculates a standard deviation of a frequency of 5% or more of the maximum value of the respiratory frequency component, and calculates it as the reciprocal of the standard deviation.
RSI = 1 / A
(A = standard deviation of the frequency of 5% or more of the maximum value of the respiratory frequency component)

By utilizing the RSI index, it is possible to detect early signs of exacerbation of heart failure in response to changes in pulmonary artery pressure that it can be measured non-invasively, is easy to analyze.
RSI shows that it is a strong predictor of exacerbation of heart failure patients, even in the absence of periodic breathing or Chain Stokes breathing. The evaluation method using the RSI index is not only quantitative, but also can be performed quickly and continuously, and the cost is low. Further, this method is not intermittent or invasive, and it is one of the advantages that the physical burden on the subject is small.

  When the RSI value is 20 (1 / Hz) or less, the state of heart failure has deteriorated (sympathetic nerve activity has increased, pulmonary congestion has worsened, symptoms have become stronger, etc.) It was found that when it was 90 (1 / Hz) or more, it was in a stable state close to a deep sleep state. Therefore, in the present invention, it is possible to provide a diagnostic apparatus or diagnostic method that uses the numerical value to predict the prognosis of heart failure or quantitatively evaluate the severity of heart failure. For example, when the average of several days (2 to 5 days) is 20 or less, it can be determined that the condition of heart failure has deteriorated regardless of changes in other indices and symptoms. Further, the index indicating the stability of respiration is not limited to RSI, and any index indicating the instability of the respiratory cycle or the respiratory frequency can be used as a diagnostic device or a diagnostic method.

  As for the value of RSI, if it is 20 (1 / Hz) or more (or larger than 20 (1 / Hz)) with 20 (1 / Hz) as a boundary, the prognosis or the evaluation of heart failure severity is good. If it is less than (1 / Hz) (or 20 (1 / Hz) or less), it can be judged that the prognosis or the evaluation of the severity of heart failure is poor. In addition, since it is important that the numerical value in the present invention is handled based on the numerical value (for example, 20), whether or not the numerical value is included is not particularly limited by the description such as “more than” or “less than”. . It is most preferable that the value of RSI is 20 (1 / Hz), but it is a value of plus / minus 10 (1 / Hz), preferably a number of plus / minus 7 (1 / Hz), more preferably plus. A numerical value of minus 5 (1 / Hz), more preferably a numerical value of plus or minus 3 (1 / Hz), can also be used to determine the prognosis or the degree of severity.

  Heart failure (especially chronic heart failure) in the present invention is a general term (syndrome) of terminal images of heart diseases such as myocardial infarction or cardiomyopathy, and guidelines for treatment of chronic heart failure prepared by the Japanese Circulation Society (2010 revised edition) , JCS2010).

  Hereinafter, the heart failure disease prognostic evaluation system using the RSI index and the RSI index will be described in detail.

[Configuration of heart failure disease prognosis evaluation system]
A heart failure disease prognostic evaluation system based on a respiratory waveform (hereinafter also referred to as “this system” or “this evaluation system”), which is an optimal configuration according to an embodiment of the present invention, will be described below with reference to the drawings.
The heart failure disease prognostic evaluation system of the present embodiment generates and outputs waveform information based on the respiratory waveform of the subject, and the medical doctor performs diagnosis including heart failure disease prognosis evaluation or heart failure severity evaluation based on the waveform information. The main purpose is to do.

As shown in the configuration diagram of FIG. 1, the evaluation system 1 includes a respiratory waveform recorder 2 and a respiratory waveform analyzer 3.
The respiratory waveform recorder 2 is a device that can record a respiratory waveform and may be portable, and is lent to a subject from a medical institution, and the subject continuously records and holds the recorded waveform after returning home. Then, it may be transferred to a medical institution, or the evaluation system may be installed in the medical institution to diagnose the patient as outpatient treatment.

  The respiratory waveform recorder 2 may be any device that can record a respiratory waveform. For example, a biological information monitor “Morpheus (registered trademark) R set” (manufacturing and sales Teijin Pharma, medical equipment approval number 21300BZY00123000, classification management medical equipment, The specific maintenance management medical device) employs a pressure sensor (nasal cannula) for detecting airflow and snoring, and may be used to detect apnea, hypopnea, and snoring in detail.

  Of course, the recording of the respiratory waveform may be performed in a medical institution, and the recorded waveform data is recorded and transported on a recording medium such as a flash memory, a magnetic disk, or an optical disk, or transmitted via a communication path. Of course, it may be delivered to the device to be analyzed. Examples of the communication path include an Internet communication network, a dedicated communication line, a dial-up telephone line, and the like, regardless of wired or wireless.

  In order to realize the above function, the respiratory waveform recorder 2 as an example includes a respiratory airflow sensor 2-1, a respiratory waveform detection amplification unit 2-2, and an A / D conversion unit that are attached to the skin surface near the nasal cavity of the subject. 2-3, a memory unit 2-4 for recording and holding a respiration waveform as a digital signal, and an output terminal 2-5 for outputting digital respiration waveform data from the memory unit 2-4 to the outside.

  The respiratory airflow sensor 2-1 is attached to the vicinity of the subject's nasal cavity, and for example, by detecting and measuring the temperature of the respiratory airflow and the temperature of other outside air, the presence or absence of the airflow due to the subject's breathing. It is a measuring device that uses the principle of a thermal sensor to measure strength.

  As a configuration for measuring the respiratory airflow of the subject, in addition to the above thermal sensor, a resistance change method caused by deformation due to the respiratory airflow of the strip-shaped member, a thing using rotation by the airflow of the windmill structure, etc. Any device that can detect the presence and intensity of respiratory airflow can be used.

  Furthermore, instead of directly measuring the respiratory airflow, measure the tension at which the band wrapped around the subject's chest or abdomen is stretched by breathing, or provide a pressure sensor on the mat placed under the subject, etc. Thus, the subject's breathing effort or breathing movement (ventilation movement) may be measured and recorded.

  These various respiration sensors are attached to a predetermined part of the patient in order to detect the patient's respiratory airflow or the patient's respiratory effort or breathing movement (ventilation movement). It should be instructed to patients from institutions. However, compared with an electrode sensor for measuring an electrocardiogram, the tolerance of the position, direction, etc. for attaching the respiration sensor is large. Therefore, in order to obtain the correct measurement value, the electrocardiogram must be electrode-attached by a medical practitioner in the medical institution, while the respiratory sensor is attached by the patient or the patient's family according to the instruction of the medical institution. Correspondence becomes possible.

  Furthermore, in recent years, instead of detecting respiratory action by wearing some sensing means on the subject as described above, the subject's body movement is analyzed by irradiating the subject from a position away from electromagnetic waves and analyzing the reflected wave, Many non-contact type respiration sensors that detect respiration motion have been proposed.

  The respiratory waveform analysis device 3 constituting the evaluation system 1 in the present invention may be any device that can analyze respiratory data. For example, a personal computer system including a display screen and a printer, and a computer program that is installed in the computer and operates, and installed in a medical institution or the like, a respiratory waveform record from which a respiratory waveform has been acquired from a subject The total 2 is connected, the respiratory waveform data is transferred, and the calculation using the respiratory waveform data is executed according to the procedure described later. Furthermore, these respiratory waveforms, the temporal (time-dependent) transition of waveforms that are the result of computation based on the respiratory waveforms, and the RSI values are displayed on the display screen in time series, or printed by a printer. It is possible that a medical person who observes the screen display or the print result can predict and / or evaluate the prognosis of a patient with chronic heart failure or evaluate the severity.

  The respiratory waveform analysis device 3 may be any device that can analyze the respiratory waveform and calculate the RSI in the present invention as described above. For example, the input 3-1 for capturing the respiratory waveform digital data from the outside, The memory unit 3-2 that temporarily records and holds the data, the recorded data is read out, the analysis unit 3-3 that performs the calculation operation described later using the data, and the calculation output from the analysis unit 3-3 A display unit 3-4 for displaying the time series data as a result on the display screen, a printer unit 3-5 for printing the time series data output in the same manner, and a data sending terminal 3-6 for sending the data of the calculation result to the outside Can be provided.

[Operation of respiratory waveform analyzer]
Next, the operation of the respiration waveform calculation performed by the respiration waveform analyzer 3 which is a characteristic configuration of the system 1 will be described.

  The RSI index value that is generated and output as the final result of this system is the index value that is measured at a fixed time every day when it is generated and output (including display) so as to be able to grasp the change (trend) from moment to moment during the measurement time. In order to be able to grasp long-term trends such as daily changes, various forms such as those that are similarly generated and output can be considered.

  Here, a system that generates and outputs a change (trend) from moment to moment during measurement time, which is the basis of various aspects, will be described. Long-term trends can be generated and output (including display) so that RSI index values can be measured and recorded periodically, such as daily or weekly, in the system described here, and their transitions can be grasped. That's fine.

  The analysis unit 3-3 included in the respiratory waveform analysis device 3 may be anything that can calculate the RSI from the respiratory waveform. For example, a plurality of Fouriers at the time that is the starting point of each Fourier window period obtained by executing Fast Fourier Transform (FFT) by shifting the time by 5 seconds for a Fourier window period of 5 minutes from the input respiratory waveform. For example, a frequency region of 0.11 to 0.5 Hz (corresponding to a breathing frequency band) is extracted from the spectrum, and a waveform that changes with time at the above-described shift interval of 50 seconds is generated and output. More specifically, in FIG. 2 schematically showing the waveform analyzed or generated by the system 1 for each stage, an unprocessed respiratory waveform including various frequency components is as shown in FIG. On the other hand, the analysis unit 3-3 sets a window time tFFT from the starting point 2a of this waveform, specifically a conversion window 2b1 of, for example, 5 minutes, and performs fast Fourier transform on the waveform included in this section. Perform transformation (FFT). The window time tFFT is not limited to 5 minutes, and may be various ranges such as 30 seconds to 30 minutes, for example, as long as the transition of the target frequency band power during the measurement period of the subject can be observed. As a result of the execution, a Fourier spectrum 2c1 of the waveform in this section is generated.

Next, the analysis unit 3-3 sets the Fourier transform window 2b2 having the same window time tFFT from the position shifted from the waveform start point 2a, specifically, the time forward direction by, for example, 50 seconds, and performs fast Fourier transform. The transformation is performed again, resulting in a Fourier spectrum 2c2 in this interval.
Similar to the window time, the shift time ts is not limited to 50 seconds, but may be any range from 2 seconds to 5 minutes, for example, as long as the transition of the target frequency band power during the sleep period of the subject can be observed. .

  In the same manner, the Fourier transform window is shifted by an integer multiple of the time ts, the fast Fourier transform is performed on each Fourier transform window, and a Fourier spectrum is generated. Continue until the end point 2d of the waveform is reached. In the actual calculation operation, the respiratory waveform is measured over a predetermined measurement period of the subject, for example, 8 hours, the waveform start point 2a corresponds to the start time of the measurement period, and the end point 2d corresponds to the end time of the measurement period. To do.

Next, the analysis unit 3-3 has a frequency of 0.11 to 0.5 Hz (corresponding to a respiration frequency band) among the frequencies included in each Fourier spectrum for all the plurality of Fourier spectra obtained by the above operation. Is a waveform in which the power is extracted and plotted at the time of the starting point of each Fourier window, that is, how the power of the specific extraction frequency band described above changes according to the time transition during measurement. A power transition waveform (hereinafter also referred to as a specific frequency waveform) 2e in a specific frequency region is obtained.
In addition, all the frequency domains shown above are examples, and can be appropriately changed in implementing the present invention, and the present invention is not limited to the above.

This time transition waveform of the specific frequency power is a waveform showing a transition in which the respiratory frequency component changes with time, for example, for 8 hours from the respiratory waveform measurement start time to the measurement end time.
Therefore, a medical person who wants to diagnose the state of the subject during the measurement period can observe the transition of the specific frequency waveform that has become visible after screen display or printing.
In addition, the physiological data required for the observation is sufficient for one channel of the respiratory waveform, and there is no troublesome problem of contacting many electrodes without peeling like an electrocardiogram. There is no sensor part and measurement is relatively easy.

[Respiration waveform data analysis process]
Hereinafter, a process of generating a band extraction waveform and each calculation waveform necessary for generating the RSI index value from the original measured respiratory waveform will be described together with exemplary waveform data. In addition, the following numerical value is only an illustration and the implementation changed suitably is possible.

(A) Original respiration sensor output waveform Org Resp (Fig. 3 (a))
The horizontal axis is the time from the start of measurement, and the unit is (time). The vertical axis represents the magnitude of the measured power. Also in the following (b) and (c), the horizontal axis and the vertical axis are the same unit. )
The sampling frequency of the original respiration sensor output waveform is 16 Hz.

(B) Original measured respiratory waveform averaged four times Resp4, Res (FIG. 3 (b))
In order to suppress sudden noise accompanying sampling, the past four data are averaged, and this 4 Hz waveform is used as an original waveform for subsequent band extraction and data processing.
That is, it corresponds to the unprocessed breathing waveform in FIG.

(C) Respiratory motion period waveform mean long power (FIG. 3 (c))
From the original measured respiratory waveform Resp averaged four times, a 0.11-0.5 Hz component, which is a high frequency region corresponding to the respiratory frequency band, is extracted, and a 0.08 Hz band before and after the maximum power cycle is extracted. Average power. If the time transition of this waveform is followed up, the transition of the magnitude of the subject's breathing motion can be known.
This breathing motion cycle waveform mean long power corresponds to the power transition waveform 2e in the specific frequency region shown in FIG.

(G) Respiration cycle variation index var (FIG. 4 (g))
Next, the respiratory cycle variation index var for viewing the transition of the subject's respiratory cycle variation will be described with reference to FIG.
FIG. 5 schematically shows the respiratory motion period waveform mean long power described above. According to the above definition, the band is 0.11 to 0.50 Hz as shown. In FIG. 5, the horizontal axis represents frequency and the vertical axis represents power.

  Here, the peak frequency seen in the mean length power, that is, the central frequency of the respiratory cycle is defined as HF (high frequency), and regions defined with a width of 0.08 Hz on both sides of the HF are defined as the central band region (B ). A region lower than the central band region is defined as a left side band region (A), and a region higher than the central band region is defined as a right side band region (C).

  Here, if the subject's respiratory cycle varies greatly, the left sideband region (A) is obtained by frequency-integrating the entire spectral power, that is, the flow areas of A, B, and C in the spectrum diagram of FIG. ) And the right sideband region (C) spectral power, i.e., the quotient divided by the frequency integral of the A and C regions should increase. This value is called a breathing cycle variation index (var), and the transition of the actual measurement value is shown in FIG.

(H) Standard deviation of respiratory cycle RespHzSD (FIG. 4 (h))
Next, two indices selected from an approach different from the above-described breathing cycle variation index var for viewing the transition of the subject's breathing cycle variation will be described.

  Using the system described above, for example, the respiratory action cycle waveform mean lang power described above is extracted as a respiratory cycle band from the respiratory waveform obtained by measurement, and first, the average value (X bar) of the respiratory frequency is extracted. ) And the standard deviation (SD) of the respiratory frequency using a known statistical method, the magnitude of the fluctuation of the respiratory cycle can be known. Furthermore, by taking the reciprocal of this standard deviation (SD), the stability of the respiratory cycle can be shown. Instead of using the average value (X bar) of the respiration frequency, another index such as the respiration cycle peak frequency (HF) described above may be used.

Here, the reciprocal of the standard deviation of the measured respiratory frequency is referred to as RSI (Respiration Stability Index). If this RSI is graphed so that the time transition in the measurement period can be understood, it can be easily determined by observation by a medical practitioner, or it can be automatically determined by the diagnostic device from its regularity.
The meaning of observing the above RSI will be qualitatively explained from another viewpoint.

  In FIG. 6 schematically showing the frequency spectrum of the respiratory motion period waveform mean long power, for example, the frequency spectrum after extraction of the respiratory frequency, the subject's breathing is in a state where the respiratory motion is stable with little frequency displacement. As shown in the graph 10a, the frequency spectrum is considered to have a narrow envelope width around the frequency average value fxbar-s and a small standard deviation expressed as fSDs.

On the other hand, when the fluctuation of the respiration frequency is further increased, the envelope figure is widened, and the standard deviation fSD-r in this state is also a larger value.
The above numerical values such as the Fourier window period are merely examples, and can be carried out with other values as appropriate. The above method of calculating the RSI by the reciprocal of the standard deviation also shows the regularity of the respiratory cycle. Of course, it is possible to use an index obtained by the above-mentioned calculation method, and these are also a part of the present invention.

In addition, as shown in FIG. 6, SD, RSI, etc. are calculated using only data up to 95% from the peak of the respiratory frequency graph, and the lower 5% data is discarded to suppress the influence of noise. Also good.
In carrying out the present invention, various embodiments can be considered in addition to the system configuration described above.

[Other Embodiments 1 to Application to Telemedicine]
When measuring the respiratory waveform at a place other than the medical institution, such as the target patient's home, the respiratory waveform is measured and recorded with a portable respiratory waveform measuring device and then transported to the medical institution via a communication path. Implementation in a telemedicine system that transmits directly to the analyzer, a configuration that displays the transition of each frequency component in the respiratory waveform, and the like are also possible.

  Further, at home where the target patient lives, a respiratory waveform acquisition means, an RSI index value generation means, and a transmission means for transmitting the RSI index value via a communication path are placed, and a remote reception means communicates. By receiving the RSI index value via the road and performing display, data output, etc., it is possible for medical personnel, caregivers, families, etc. to remotely confirm the pathological condition of the patient's heart failure.

[Other Embodiments 2-Generation of RSI Index Not According to Frequency Analysis Method]
In the evaluation system of the embodiment described above, the respiratory waveform data measured and generated by the respiratory sensor is Fourier-analyzed to generate an RSI index indicating the stability of the respiratory cycle.
However, the generation of the RSI index is not necessarily limited to this method. For example, a method of counting when the magnitude of the respiratory airflow exceeds a certain threshold, or a continuous peak by detecting the peak value of the respiratory waveform. The respiratory cycle may be obtained from the time interval of the values, and the RSI index may be generated by the method described above from the measured respiratory cycle or the variance of the respiratory frequency or the standard deviation. Is of course good.

[Aspect for displaying or outputting analysis results]
In each embodiment described below, various analysis operations using the RSI index values described above, such as prediction of prognosis of heart failure disease, quantitative evaluation of current heart failure disease severity, etc. are performed. The analysis operation at the time of being performed may be performed by the analysis unit 3-3 of the respiratory waveform analysis device 3, or an analysis unit (not shown) provided separately from the respiratory waveform analysis device 3, such as a personal computer, A configuration in which a control program for performing these analysis operations, that is, application software is installed in a terminal such as a mobile phone such as a smartphone or a mobile device such as a tablet may be used.

  The prediction results and evaluation results, which are the results of the analysis in the present invention, are displayed as text information such as “Alert! Condition is getting worse”, “Caution! Condition is getting worse”, etc. It can be displayed as a graphic or animation, or a color change such as red suitable for warning, yellow suitable for alerting, or blue suitable for notifying that there is no problem. May be similarly performed on the display unit 3-4 of the respiratory waveform analysis apparatus 3, or a terminal such as a personal computer or a display unit (not shown) of a smartphone described above may be used.

In addition, it is conceivable that the analysis result in a warning or caution state is uttered as a sound from a speaker unit (not shown). It is also possible to transmit to other devices via, for example.
The display, generation, and output of analysis results according to these various aspects are collectively referred to as display or output in the following description, but these operations include simple monitor screen display and data transmission from the output terminal. Various embodiments are included, but not limited to.

Next, the use of these analysis results, in other words, various aspects of the user who uses the analysis results will be described.
Since the RSI index value itself is physiological data that can be used medically, it is mainly assumed that a medical practitioner grasps this index value and uses it for diagnosis. In addition, the results of various analyzes described below using RSI index values, such as prediction results of prognosis of patients with heart failure and quantitative evaluation results of current severity, are also considered to be used for diagnosis by medical personnel. It is done. However, the present inventor has found that the RSI index value is an index value that represents the prognosis and severity of heart failure in a simple and easy-to-measure manner that measures the variation in respiratory cycle, as will be described later. Thus, the patient can manage his / her disease using the RSI index value itself or the trend of change.

  That is, the analysis result based on the RSI index value generated by the analysis unit 3-3 of the respiratory waveform analysis device 3 is received via the communication path, or directly by the patient visually recognizing the display unit 3-4. For example, if a warning is displayed, a medical instructor will instruct the medical institution to immediately visit the medical institution, or a patient who has visually confirmed the warning display will refrain from exercising or drinking, etc. And can be used for patient care in nursing homes.

  The analysis means and display means using the RSI index value may be realized as a mobile phone such as a smartphone carried by the patient, a small dedicated terminal placed on the bedside, and further, the respiratory waveform recording described above The total 2 may be provided with an analysis function and a display function, and display and output these so that self-management of heart failure patients can be realized.

  In addition, the analysis result, the intermediate result, or the RSI index value itself is sent to a remote centralized monitoring center via a communication path, and the condition of a plurality of patients in the area is centrally monitored to increase the medical economic effect. . This is easy to use because the RSI index value is one-dimensional and has a relatively small amount of data.

  As described above, various aspects can be considered in the implementation of the present invention, and a substantial part of these aspects is the characteristic of the RSI index value (relatively small data amount, relatively low frequency region data). Therefore, it is possible to obtain a configuration that cannot be easily realized with other physiological data. These various implementation possibilities are common to the following embodiments, and are not mentioned in each description in order to avoid duplication.

[Other Embodiments 3-Apparatus for automatically predicting prognosis of chronic heart failure and apparatus for automatically evaluating severity of heart failure]
Since there is a correlation between the prognosis of heart failure disease (especially chronic heart failure) or the severity of heart failure and the RSI index value, the system generates the RSI index value or its trend (trend, transition) It is possible to automatically predict the prognosis of target heart failure patients, early detection of worsening heart failure, and current severity of heart failure.

  For example, when the RSI index value is less than 20 (1 / Hz), it is possible to predict or evaluate that the prognosis is poor or the current medical condition is bad (that is, serious). A system configured to display or output the result by some method to a medical person, a family member, a caregiver or the like is possible. The display or output of the result includes display by characters, color, or sound so that the patient, medical staff, etc. can understand. Moreover, it is good also as an aspect which displays or outputs as a warning only when the result is always displayed or outputted, or only when the prognosis is poor or serious such as less than 20.

  When issuing an alarm, simply issue an alarm “no problem” if the RSI index value is 20 (1 / Hz) or more, and “may be serious” if it is less than 20 (1 / Hz). In addition to the configuration that emits, the system judges the change trend of the RSI index value. For example, until now, a stable patient with an RSI value of 80 (1 / Hz) or more enters a downward trend, and eventually “Warning” (for example, yellow signal) when there is a possibility of entering a zone below 20 (1 / Hz), in other words, when an index value tends to approach a zone below 20 In combination with safety areas (eg, green light) and less than 20 (1 / Hz) (eg, red light) that are expected to be serious, preventive management prior to seriousness is possible. System (zone system) To be the case.

The blue signal, the yellow signal, and the red signal exemplified above may be displayed or output as a character string that can be read by the user instead of being identified by color, and the background color of the display screen of the device is blue, It may be changed to yellow or red so that the user will notice. As another aspect, a dedicated lamp or a light emitting diode may be made to emit light so as to be noticed. Note that blue, yellow, and red are merely examples, and the color is not limited as long as the user knows the difference.
In addition, this zone system can be combined with the telemedicine already described so that a medical staff, a caregiver, or a family member remote from the target patient can watch over the target patient.

[Aspect using numerical zone of indicator]
In the case where the RSI index is used, for example, a configuration in which an alarm is issued when the RSI index is less than 30 (1 / Hz) is possible. In this case, the possibility of seriousness is high 20 (1 In addition to the numerical zone of less than (/ Hz), an alarm in the sense of alerting also occurs when entering less than 30 (1 / Hz), which is an intermediate state of less than 20 (1 / Hz).

In addition, for example, a configuration in which an alarm is issued when the RSI index is less than 15 (1 / Hz) is conceivable, and the purpose is to predict the prognosis. In addition to less than 20 (1 / Hz), 15 ( If it is less than 1 / Hz), the possibility of seriousness becomes higher.
As described above, the condition of the patient can be measured more finely if it is an aspect in which several RSI index value categories are provided and an alarm is issued.

[Example 1]
RSI index (hereinafter simply referred to as RSI) calculated under resting condition during the day and prediction of exacerbation of heart failure in 87 patients with chronic heart failure registered in the study reported from July 1996 to January 2006 We compared other indicators, known as factors, and examined their association with prognosis.
As a result of univariate analysis, the indices that are considered to be significantly related to death regardless of cause are age, NYHA class, physical activity ability index (exercise tolerance index), RSI, left ventricular ejection fraction (LVEF), left The end-systolic diameter (LVDs), the frequency and cumulative intensity of sympathetic nerve bursts, and BNP.

Furthermore, when the cause of death was narrowed down to cardiovascular disease, the NYHA class, physical activity index, systolic blood pressure, RSI, sympathetic burst frequency / cumulative intensity, and BNP were significantly related. It was. Furthermore, there was a trend related to age, LVEF, and LVDs.
In the univariate analysis, when the average value of RSI and sympathetic burst cumulative intensity increased by 10%, the risk of death (hazard ratio) increased by 15.4% and 13.5%, respectively. The risk was calculated to increase by 27.8, 23.8%.

As a result of performing ROC analysis on death due to all causes regarding RSI, it was found that the cutoff value of RSI at which both sensitivity and specificity are high is around 20 (1 / Hz).
Therefore, RSI was stratified by 20 (1 / Hz) or more and less than 20 (1 / Hz), and the mortality was compared and analyzed. As a result, the mortality due to all causes in the group with RSI of 20 (1 / Hz) or more and the group with less than 20 (1 / Hz) is 19.6% vs. 36.6% (p <0.05), and mortality due to cardiovascular disease is 6.5% vs. 50% respectively. The death rate was 31.7% (p <0.01), and the mortality rate was significantly higher in the RSI group of less than 20 (1 / Hz) (Table 1).

  Patients with an RSI of less than 20 (1 / Hz) were characterized by dilated left ventricle, increased sympathetic nerve activity, and high prescription rate of angiotensin converting enzyme inhibitor or angiotensin II receptor antagonist . As shown by the Kaplan-Meier survival curve in FIG. 7, the mortality after 5 and 10 years in patients with RSI less than 20 (1 / Hz) is 32.2% and 41.9%, respectively, and the RSI is It was significantly higher (p <0.05) than that of patients with 20 (1 / Hz) or higher (2.4% and 22.2%, respectively). Even in cases of death due to cardiovascular disease, the incidences at 5 and 10 years are 29.6% and 32.2% in patients with RSI less than 20 (1 / Hz), respectively, and the RSI is 20 It was significantly higher (p <0.01) compared to that above (1 / Hz) (2.4% and 8.5%, respectively).

  Furthermore, as a result of performing multivariate analysis on death of all causes using six variables (RSI, age, physical activity ability, LVEF, sympathetic burst cumulative intensity, BNP), RSI is an independent predictor, In particular, RSI (hazard ratio [HR] 0.95, 95% CI 0.91 to 0.99, p <0.05), and sympathetic burst cumulative intensity (HR 1.10, 95% CI 1.01 to 1.20, p <0.05) was highly relevant. (Table 2).

Similarly, multivariate regression analysis was performed on death due to cardiovascular disease using four covariates (RSI, physical activity ability, sympathetic burst cumulative intensity, BNP).
As a result, RSI and burst cumulative intensity were independent predictors of death from cardiovascular disease and were highly related.

[Example 2]
In severe cases of chronic heart failure, unstable breathing patterns such as shallow and rapid breathing and periodic breathing are frequently observed, but the effect of cardiac rehabilitation (cardiac rehabilitation) on such respiratory abnormalities has not been clarified. Therefore, it was examined whether RSI can be used to determine the effects of cardiac rehabilitation in patients with chronic heart failure. That is, RSI before and after cardiac rehabilitation was obtained for 25 patients with chronic heart failure (NYHAI + II: 16 people, III + IV: 9 people).

  As a result, RSI before heart rehabilitation was significantly lower and respiratory stability was lower in severe heart failure cases (NYHAIII + IV) than in mild to moderate cases (NYHAI + II) (14 ± 4 vs. 24 ± 17 p < 0.05). The effect of cardiac rehabilitation on RSI depends on RSI before cardiac rehabilitation. In cases where pre-cardiac rehearsal RSI is less than 20 (1 / Hz) and breathing is extremely unstable, post-cardiac rehabilitation RSI significantly increases. Was large (front: 12 ± 3, rear: 22 ± 12, p <0.01). On the other hand, no significant change in RSI due to cardiac rehabilitation was observed in cases where RSI before cardiac rehabilitation was 20 (1 / Hz) or higher and respiration was relatively stable.

As shown in Examples 1 and 2, when the target patients were divided into two groups with the RSI being 20 (1 / Hz), a significant difference was found in the prognosis (comparison of mortality in Example 1). There was also a significant difference in the evaluation of severity (comparison of NYHA classification in Example 2).
From this result, it can be seen that there is a common threshold having generality using RSI as an index value that does not depend on individual patients or cases as a branch point of prognosis or severity evaluation.

  Based on the above findings, for example, in a general treatment guideline, an area where the prediction of prognosis using RSI index value or severity evaluation is “bad” is less than 20 (1 / Hz) (or less) On the other hand, each individual medical practitioner is provided with a smaller partial area included in the area indicated by the guideline (RSI is less than 20 (1 / Hz)) based on his / her medical judgment, for example, When the RSI is 15 (1 / Hz) or less, it is possible to perform a special treatment or to issue a special alarm when using the evaluation system.

  Similarly, each medical practitioner provides a larger area including an area indicated by the guideline (RSI is less than 20 (1 / Hz)) based on his / her medical judgment. For example, the RSI gradually increases from a normal value. When the medical staff takes measures to pay attention to the patient's condition when it falls to 25 (1 / Hz) or lower, or when using an evaluation system, the RSI is 25 (1 / Hz) or lower Can always issue an alarm.

In addition, RSI has an excellent feature not found in conventional diagnostic indicators that it can detect signs of aggravation or worsening of heart failure with high sensitivity specificity.
This is because the RSI is generated by “the reciprocal of the standard deviation of the respiration frequency component frequency”. Therefore, in a large numerical range, for example, 50 to 90 (1 / Hz), the fluctuation range of the RSI with respect to the change in the respiration frequency variation is It is considered that the change in symptoms is not clear in cases that are large and mild in the NYHA classification of these areas.

However, in cases where the RSI is 20 (1 / Hz) or in a smaller area, that is, cases classified as severe, the change in RSI shows a sharper and clearer change in severity, so the treatment for heart failure has been changed. Deterioration that must be performed, particularly deterioration that requires hospitalization, can be detected early with high sensitivity using RSI.
In other words, the implementation of the present invention provides a solution to today's theme of how to track symptom changes in heart failure diseases, thereby early detection and early intervention.

1 Evaluation system 2 Respiratory waveform recorder 3 Respiratory waveform analyzer

Claims (9)

Predicting prognosis of heart failure, early stage of heart failure worsening, characterized by comprising a calculating means for calculating an index value indicating instability of respiratory cycle or respiratory frequency, and means for displaying or outputting the calculated result A diagnostic device used for detection and / or quantitative assessment of the severity of heart failure ,
The value of the indicator is generated as an RSI that is the reciprocal of the standard deviation of the respiratory frequency of the subject patient;
The display or output is:
(A) when the value of the obtained index is within a predetermined numerical zone;
(B) In cases other than (A),
Are displayed or output in different ways,
The diagnostic device, wherein the predetermined numerical zone is less than 20 (1 / Hz). The display or output is:
(A) When the obtained value of the index is within a predetermined numerical zone,
(B) the value of the index tends to approach the predetermined numerical zone; and
(C) If neither (A) nor (B)
The diagnostic device according to claim 1, wherein the diagnostic device is displayed or output in a different manner. The medical system which has a diagnostic apparatus of any one of Claim 1 or 2, and the receiving means which receives the value of the said output parameter | index via a communication channel. The medical system according to claim 3, wherein the diagnostic device is located remotely from the receiving unit. Calculating means for calculating an index value indicating instability of a respiratory cycle or a respiratory frequency;
(A) Predictive means for predicting the prognosis of heart failure using the value of the index,
(A) detection means for early detection of deterioration of heart failure using the value of the index; and
(C) at least one of evaluation means for quantitatively evaluating the severity of heart failure using the value of the index;
A means for displaying or outputting at least one of the predicted result, the detected result, and the evaluated result, comprising:
The predicting means predicts that the prognosis of the target patient becomes serious when the value of the index is within a predetermined numerical zone;
The detection means detects early heart failure exacerbation of the target patient when the value of the index is within a predetermined numerical zone; and
The evaluation means comprises at least one of the following configurations in which the target patient evaluates that the heart failure is serious when the value of the index is within a predetermined numerical zone,
The value of the indicator is generated as an RSI that is the reciprocal of the standard deviation of the respiratory frequency of the subject patient;
The diagnostic device characterized in that the predetermined numerical zone is less than 20 (1 / Hz). When it is predicted that the prediction means will become serious, when the detection means detects a deterioration, and when the evaluation means evaluates that it is serious, at least one of the cases 6. The diagnostic apparatus according to claim 5, wherein an alarm is generated only by the output or display means. The display or output for alerting is performed by display or output means when the value of the index tends to approach the predetermined numerical zone. The diagnostic device according to item 1. A medical system comprising: the diagnostic device according to any one of claims 5 to 7; and a receiving unit that receives a signal output from the diagnostic device via a communication path. The medical system according to claim 8, wherein the diagnostic device is located remotely from the receiving unit.