Pleural effusion estimation system, pleural effusion estimation device, intrathoracic estimation device, pleural effusion estimation method and program

Abstract

To provide a pleural effusion estimation system, etc. capable of measuring impedance in the thoracic cavity with time in a non-invasive manner.SOLUTION: A pleural effusion estimation system for estimating presence or absence of pleural effusion accumulated in an object or a degree of accumulation of pleural effusion includes: an electrode unit for coming in contact percutaneously with the chest of the object; an impedance measuring unit for applying AC current to the electrode unit and measuring impedance; and a pleural effusion estimation unit for setting an intersection point of a transverse section of a living body including a sternum xiphisternum and an armpit median line to a sticking reference position of the electrode unit, and estimating presence or absence of pleural effusion or a degree of accumulation of pleural effusion using an impedance measurement value measured by the impedance measuring unit.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a pleural effusion estimation system, a pleural effusion estimation device, an intrathoracic estimation device, a pleural effusion estimation method, and a program.

Background Art

[0002] When pneumonia or heart failure develops, water accumulates in various proportions inside and outside the lungs within the thoracic cavity. In the case of pneumonia, inflammation occurs in the lungs, causing water to leak from blood vessels. In the case of heart failure, it is due to a decrease in the cardiac output, resulting in water leakage in the lungs located upstream of the heart.

[0003] For the diagnosis of water accumulation in the thoracic cavity, generally, CT images or X-ray images are used. FIG. 17 shows X-ray images (a) at the time of pleural effusion and (b) when the pleural effusion has disappeared.

[0004] In addition, in the case of patients implanted with a cardiac pacemaker, a chest implantable device that monitors the amount of lung water by measuring the resistance value (thoracic impedance) between the lead wire and the body of the implanted pacemaker and measures the state of heart failure over time is known (Non-Patent Documents 1 and 2). The chest implantable device utilizes the correlation that the impedance decreases when heart failure develops and water accumulates in the lungs as shown in FIG. 17.

Prior Art Documents

Non-Patent Documents

[0005]

Non-Patent Document 1

Non-Patent Document 2

[0006] However, chest implantable devices are implanted in the chest and, due to their high invasiveness, impose a significant burden on patients. Nevertheless, it is known that, for example, their sensitivity for heart failure remains at only 60-70% even under controlled conditions.

[0007] Furthermore, diagnosis using CT or X-ray images requires large equipment, which is only available in medical facilities, making a visit to a medical institution essential.

[0008] Furthermore, a problem with severe respiratory infections, including COVID-19, is the risk of infection to those around the infected person even while they are traveling to a testing site. Therefore, the inventors of this invention believed there was a need for a device that could easily detect signs of symptom exacerbation anywhere.

[0009] Therefore, the present invention aims to provide a pleural effusion estimation system, etc., that enables continuous and non-invasive measurement of intrathoracic impedance. [Means for solving the problem]

[0010] The first aspect of the present invention is a pleural effusion estimation system for estimating the presence or absence or degree of pleural effusion in a subject, comprising: an electrode portion that percutaneously contacts the chest of the subject; an impedance measuring portion that applies an alternating current to the electrode portion to measure impedance; and a pleural effusion estimation unit that sets the intersection of a cross-section of the living body including the xiphoid process of the sternum and the midline of the axilla as the reference position for attaching the electrode portion, and estimates the presence or absence or degree of pleural effusion using the impedance measurement value measured by the impedance measuring portion.

[0011] A second aspect of the present invention is the pleural effusion estimation system of the first aspect, wherein the electrode section comprises a first voltage electrode plate and a second voltage electrode plate, which are electrode plates connected to a voltmeter, and a first current electrode plate and a second current electrode plate, which are electrode plates connected to an ammeter.

[0012] A third aspect of the present invention is a pleural effusion estimation system according to the first aspect, further comprising: a storage unit that stores the impedance measurement value of the subject and the presence or absence of pleural effusion or the degree of accumulation of pleural effusion in the subject; and a model generation unit that uses the impedance measurement value and the presence or absence of pleural effusion or the degree of accumulation stored in the storage unit as training data, and generates an estimation model by machine learning in which the input is the impedance measurement value and the output is the presence or absence of pleural effusion or the degree of accumulation of pleural effusion at the time of measurement of the impedance measurement value, wherein the pleural effusion estimation unit estimates the presence or absence of pleural effusion or the degree of accumulation of pleural effusion in the subject from the impedance measurement value measured by the impedance measurement unit, using the estimation model generated by the model generation unit.

[0013] A fourth aspect of the present invention is a pleural effusion estimation system according to any of the first to third aspects, wherein the pleural effusion estimation unit estimates the presence or absence or degree of accumulation of pleural effusion based on the chest circumference or body width of the subject.

[0014] A fifth aspect of the present invention is a pleural effusion estimation system according to the fourth aspect, further comprising: a storage unit that stores the chest circumference, the square of the chest circumference, the body width, or the impedance index which is the quotient obtained by dividing the body width square by the impedance measurement value of the subject, and the presence or absence of pleural effusion or the degree of accumulation of pleural effusion in the subject; and a model generation unit that uses the impedance index and the presence or absence of pleural effusion or the degree of accumulation stored in the storage unit as training data, and generates an estimation model by machine learning in which the input is the impedance index and the output is the presence or absence of pleural effusion or the degree of accumulation of pleural effusion at the time of measurement of the impedance measurement value, wherein the pleural effusion estimation unit estimates the presence or absence of pleural effusion or the degree of accumulation of pleural effusion in the subject from the impedance index using the impedance measurement value measured by the impedance measurement unit, using the estimation model generated by the model generation unit.

[0015] A sixth aspect of the present invention is a pleural effusion estimation system according to any of the third to fifth aspects, wherein the model generation unit further uses estimated oxygen saturation, which is obtained by estimating a virtual value of oxygen saturation without oxygen administration using the partial pressure of oxygen under oxygen administration, as training data, and the estimation model further uses the estimated oxygen saturation as input.

[0016] The seventh aspect of the present invention is a pleural effusion estimation system according to any of the third to sixth aspects, wherein the model generation unit further uses pulse rate as training data, and the estimation model further uses pulse rate as input.

[0017] The eighth aspect of the present invention is a pleural effusion estimation system according to any of the first to seventh aspects, wherein the frequency applied in the impedance measuring unit is 2 to 500 kHz.

[0018] The ninth aspect of the present invention is the pleural effusion estimation system of the eighth aspect, wherein the impedance measurement value or impedance index is the limit value when the frequency approaches 0 or infinity, which is estimated from the measurement value using a Cole-Cole plot.

[0019] The tenth aspect of the present invention is a pleural effusion estimation device for estimating the presence or absence or degree of pleural effusion stored in a subject, comprising: an impedance measuring unit that applies an alternating current to an electrode portion that is in percutaneous contact with the subject, with the intersection of a cross-section of a living body including the xiphoid process of the sternum and the midline of the axilla set as the attachment reference position, and measures the impedance; and a pleural effusion estimation unit that estimates the presence or absence or degree of pleural effusion based on the impedance measurement value measured by the impedance measuring unit.

[0020] The eleventh aspect of the present invention is an intrathoracic estimation device for estimating the state of the intrathoracic cavity of a subject, wherein, in addition to the impedance measurement values ​​of the tenth aspect, the device also uses body weight, phase angle, impedance index, CRP value, extracellular fluid volume, body surface area, aspartate aminotransferase value, estimated oxygen saturation, hematocrit value, Na value, blood pressure, or heart rate, blood oxygen saturation, white blood cell count, γGTP value, Cre value, or K value to estimate the state of the intrathoracic cavity.

[0021] A twelfth aspect of the present invention is a pleural effusion estimation method using a pleural effusion estimation system for estimating the presence or absence or degree of pleural effusion stored in a subject, wherein the pleural effusion estimation system comprises an electrode portion that makes percutaneous contact with the chest of the subject, an impedance measurement portion that applies an alternating current to the electrode portion to measure impedance, a pleural effusion estimation portion that estimates the presence or absence or degree of pleural effusion using the impedance measurement value measured by the impedance measurement portion, and a control unit that controls the impedance measurement portion and the pleural effusion estimation portion, and the pleural effusion estimation method includes an electrode contact step of setting the intersection of the cross-section of the living body including the xiphoid process of the sternum of the subject and the midline of the axilla as the attachment reference position and making percutaneous contact with the electrode portion, an impedance measurement step in which the control unit causes the impedance measurement portion to apply an alternating current to the electrode portion to measure impedance, and a pleural effusion estimation step in which the control unit causes the pleural effusion estimation portion to estimate the presence or absence or degree of pleural effusion using the impedance measurement value measured in the impedance measurement step.

[0022] A 13th aspect of the present invention is the pleural effusion estimation method of the 12th aspect, wherein the pleural effusion estimation system includes, as the electrode part, a first voltage electrode plate and a second voltage electrode plate which are electrode plates connected to a voltmeter, and a first current electrode plate and a second current electrode plate which are electrode plates connected to an ammeter. In the electrode contact step, the first voltage electrode plate and the first current electrode plate are brought into contact with symmetric positions along the cross section on one side of the body side of the living body, sandwiching the intersection point. The second voltage electrode plate and the second current electrode plate are brought into contact with symmetric positions along the cross section on the opposite side of the one side of the body side of the living body, sandwiching the intersection point.

[0023] A 14th aspect of the present invention is the pleural effusion estimation method of the 13th aspect, wherein the first voltage electrode plate and the first current electrode plate are brought into contact with the same side with respect to the coronal plane of the living body, and the second voltage electrode plate and the second current electrode plate are brought into contact with the opposite side to the first voltage electrode plate with respect to the coronal plane. In the impedance measurement step, a voltage is measured between the first voltage electrode plate and the second voltage electrode plate, and a current is measured between the first current electrode plate and the second current electrode plate.

[0024] A 15th aspect of the present invention is a program for causing a computer to function as the control unit of the 12th aspect.

[0025] A 16th aspect of the present invention is a chest impedance measurement method for measuring an impedance measurement value of a subject's chest, including an electrode contact step of setting an intersection point between a cross section of the living body including the xiphoid process of the subject and the midaxillary line as an attachment reference position and bringing an electrode part into percutaneous contact, and an impedance measurement step of applying an alternating current to the electrode part and measuring the impedance.

[0026] A seventeenth aspect of the present invention is an intrathoracic estimation method using the pleural effusion estimation method of the twelfth aspect, which, in addition to the pleural effusion estimation step of the twelfth aspect, uses body weight, phase angle, impedance index, CRP value, extracellular fluid volume, body surface area, aspartate aminotransferase value, estimated oxygen saturation, hematocrit value, Na value, blood pressure, or heart rate, blood oxygen saturation, white blood cell count, γGTP value, Cre value, or K value to estimate the intrathoracic state. The intrathoracic estimation method includes an intrathoracic estimation step.

Advantages of the Invention

[0027] According to each aspect of the present invention, it is possible to perform non-invasive measurement of the impedance within the subject's chest over time and estimate the presence and / or degree of pleural effusion. In particular, in the past, due to the great differences in the human body and the like that were the objects of measurement, stable data could not be obtained. However, according to each aspect of the present invention, it is possible to obtain stable data as the impedance measurement value of the chest for the first time by specifying the measurement location.

[0028] In addition, compared with the CT apparatus or the X-ray apparatus that have conventionally been used for diagnosing pleural effusion, the pleural effusion estimation system of the present invention can be miniaturized and lightened. Therefore, the degree of freedom in the installation location is increased, and it can be installed not only in hospitals but also, for example, in patients' homes and elderly care facilities. Moreover, it can be measured by attaching the electrode part to a specified location. Therefore, it is possible to easily and non-invasively grasp the severity and signs of exacerbation anywhere.

[0029] In addition, it is possible to continuously and remotely grasp the conditions of pneumonia and heart failure in patients at risk of infection to the surroundings, such as those with respiratory infections, even online.

[0030] Furthermore, different from image diagnosis, it is possible to continuously evaluate the conditions within the lungs. Therefore, it is easy for not only medical staff but also non-medical staff such as the patient's family members and the staff of elderly care facilities to understand the situation. As a result, it becomes easy to grasp the need for medical treatment and attention in patients' homes and elderly care facilities.

[0031] Furthermore, according to the second or thirteenth aspect of the present invention, the impedance value can be measured with even higher accuracy by measuring using the four-terminal method.

[0032] Furthermore, according to the fourteenth aspect of the present invention, the current and voltage are measured by sandwiching the central part of the living body between two electrode plates, making it possible to measure the impedance of the thoracic cavity more reliably.

[0033] According to a third aspect of the present invention, it becomes easier to estimate the presence and / or degree of pleural effusion.

[0034] According to the fourth and fifth aspects of the present invention, by reflecting the body size of the subject, it becomes possible to estimate the presence or absence of pleural effusion and / or the degree of accumulation with even greater accuracy.

[0035] According to the eighth and ninth aspects of the present invention, it becomes possible to estimate the presence and / or degree of pleural effusion with even greater accuracy. Studies on respiration and circulatory dynamics using transthoracic impedance measurements have been conducted for a long time. However, factors determining impedance include not only intrathoracic fluid volume but also skin, subcutaneous tissue, fat volume, and lung parenchymal tissue, making clinical application difficult in terms of accuracy, and a sufficiently established measurement method has not existed. By measuring at the measurement frequency proposed by the inventor, the influence of intrathoracic fluid volume on the factors determining impedance becomes more easily reflected, making it possible to estimate the presence and / or degree of pleural effusion with even greater accuracy. [Brief explanation of the drawing]

[0036] [Figure 1] This is a block diagram showing an overview of the pleural effusion estimation system 1 according to Example 1. [Figure 2] This figure shows an overview of the estimation model used by the pleural effusion estimation system 1 in Example 1. [Figure 3] This figure shows the relationship between the presence or absence of pleural effusion and impedance measurements. [Figure 4]This figure shows the results of logistic regression analysis of the impedance measurements in Figure 3 using the pleural effusion estimation system according to Example 1. [Figure 5] This figure shows an overview of the estimation model used by the pleural effusion estimation system according to Example 2. [Figure 6] This diagram shows the relationship between the presence or absence of pleural effusion and the impedance index. [Figure 7] This figure shows the results of logistic regression analysis of the impedance index in Figure 6 using the pleural effusion estimation system according to Example 2. [Figure 8] This figure shows the relationship between the presence or absence of pleural effusion and another impedance index. [Figure 9] This figure shows the results of logistic regression analysis of the impedance index in Figure 8 using the pleural effusion estimation system according to Example 3. [Figure 10] This diagram illustrates a conversion table used to calculate estimated oxygen saturation. [Figure 11] This figure compares ROC curves using oxygen saturation and estimated oxygen saturation. [Figure 12] This diagram shows the relationship between the degree of pleural effusion (effusion in the chest cavity), divided into three stages, and the impedance index. [Figure 13] This figure shows the results of logistic regression analysis of the impedance index in Figure 12 using the pleural effusion estimation system according to Example 7. [Figure 14] This figure shows the results of the receiver operating characteristics (ROC) analysis performed in Example 7. [Figure 15] This figure shows the SHAP values ​​for each indicator. [Figure 16] This diagram shows an example where a total of four electrodes are attached, one voltage electrode plate and one current electrode plate, to each of the left and right reference attachment positions. [Figure 17] This figure shows (a) X-ray images when pleural effusion is present and (b) when the pleural effusion has disappeared, along with impedance measurement results using a conventional implantable device. [Modes for carrying out the invention]

[0037] Embodiments of the present invention will be described in detail below with reference to the drawings. However, the embodiments of the present invention are not limited to those described below. [Examples]

[0038] Figure 1 is a block diagram illustrating the overview of the pleural effusion estimation system 1 according to Example 1. The pleural effusion estimation system 1 comprises a pleural effusion estimation device 2 and an electrode unit 3. The pleural effusion estimation device 2 comprises an impedance measurement unit 5, a pleural effusion estimation unit 7, a control unit 9, a storage unit 11, a communication unit 13, and a model generation unit 15.

[0039] The electrode unit 3 is applied transcutaneously to the chest of the subject to apply an electric current. In this embodiment, multiple electrode pads with an adhesive surface of 30 mm x 40 mm that can be attached to the chest of the subject were used. The impedance measurement unit 5 measures the impedance by applying an alternating current between the electrodes.

[0040] The pleural effusion estimation unit 7 estimates the presence or absence of pleural effusion or the degree of accumulation using the impedance measurement value measured by the impedance measurement unit. Specifically, it estimates the presence or absence of pleural effusion or the degree of accumulation from the impedance measurement value measured by the impedance measurement unit 5 using the estimation model generated by the model generation unit 15.

[0041] The control unit 9 controls the impedance measurement unit 5 to apply alternating current of a predetermined frequency from the electrode unit 3. The control unit 9 also controls the pleural effusion estimation unit 7 to estimate the presence or absence of pleural effusion or the degree of accumulation based on the measurement results from the impedance measurement unit 5. The storage unit 11 stores the measurement results from the impedance measurement unit 5, the diagnostic results of the presence or absence of pleural effusion or the degree of accumulation by other methods, the estimation model generated by the model generation unit 15, and / or the estimation results from the pleural effusion estimation unit 7.

[0042] The communication unit 13 communicates the measurement results from the impedance measurement unit 5, the estimation results from the pleural effusion estimation unit 7, and / or the measurement results stored in the memory unit 9.

[0043] The model generation unit 15 generates an estimation model that estimates the presence or absence of pleural effusion and the degree of pleural effusion accumulation by analyzing statistical data or by machine learning based on training data.

[0044] Figure 2 shows an overview of the estimation model by the pleural effusion estimation system 1 according to Example 1. The model generation unit 15 uses the impedance measurement values ​​and the presence or absence or degree of pleural effusion stored in the memory unit 11 as training data, and generates an estimation model by machine learning in which the input is the impedance measurement value and the output is the presence or absence or degree of pleural effusion at the time of measurement of the impedance measurement value. The degree of effusion used as training data is, for example, a classification into three stages based on the results of imaging diagnosis: "no pleural effusion," "mild (pleural effusion is stored in less than 1 / 3 of the pleural cavity)," and "moderate or severe (pleural effusion is more than 1 / 3 of the pleural cavity)."

[0045] Figure 3 shows the impedance measurement results using a pleural effusion estimation system. The study targeted heart failure patients with fluid accumulation in the pleural cavity, and measured pleural impedance before and after disease improvement.

[0046] The specific measurement method is described below. First, after maintaining a supine position for at least 15 minutes, the intersection of the cross-section of the living body including the xiphoid process of the sternum and the midline of the axilla was set as the reference position for electrode placement. After wiping the epidermis with an alcohol wipe, a rectangular electrode pad measuring 30 mm x 40 mm was attached. Then, an alternating current was applied in the range of 2 to 500 kHz to measure impedance. The "impedance measurement value" below is the limit value as the frequency approaches 0, estimated from the measurement value using a Cole-Cole plot.

[0047] Patients with heart failure exhibit fluid accumulation in the pleural cavity and lungs, as seen on chest X-rays and computed tomography (CT). Therefore, intrapleural impedance was first measured when pleural effusion occurred, and then repeated once heart failure improved and intrapleural fluid accumulation subsided.

[0048] The obtained impedance measurements showed a significant difference between the time of pleural effusion and the time of pleural effusion resolution. The mean value was 46.1 (95% confidence interval 42.05-50.12) when pleural effusion was resolved, and 30.2 (95% confidence interval 26.82-33.55) when pleural effusion was present. These results indicate that impedance measurements are useful for analyzing the intrapleural cavity in patients with heart failure.

[0049] As shown in Figure 3, the p-value is sufficiently small, indicating that there is a significant difference in impedance measurements depending on the presence or absence of pleural effusion.

[0050] Figure 4 shows the results of the logistic regression analysis of the impedance measurements in Figure 3.

[0051] Figure 4 shows a curve graph illustrating the boundary where the presence or absence of pleural effusion is predicted from impedance measurements. The horizontal axis represents the impedance measurement (Ω), and the vertical axis represents the probability p of having pleural effusion, obtained by fitting a regression equation derived from the data. Specifically, with the dependent variable being logit(p), the regression variables being C0 and C1, and the independent variable being the impedance measurement, the equation is expressed as logitZ = Logit(p) = C0 + C1 × impedance measurement. The logistic curve is given by p = 1 / (1 + e (-Z) This is represented by the curve graph. If the value of p exceeds 0.5, it is determined that there is "pleural effusion present," and if it is less than 0.5, it is determined that there is "pleural effusion absent." This curve graph and the results of actually checking for the presence or absence of pleural effusion are shown in Table 1 as a confusion matrix.

[0052] [Table 1]

[0053] In a study of 100 subjects, 27 were predicted to have no pleural effusion and actually did not; 9 were predicted to have no pleural effusion and actually did; 14 were predicted to have pleural effusion and actually did not; and 50 were predicted to have pleural effusion and actually did. Therefore, the sensitivity was 50 / (50+9)=84.7%. The overall prediction accuracy was (27+50) / 100=77%.

[0054] Generally, considering that the sensitivity of chest implantable devices for diagnosing heart failure is only 60-70% even under controlled conditions, this method can be considered sufficiently accurate and practical for simple and non-invasive diagnosis outside of a medical setting. [Examples]

[0055] The pleural effusion estimation system according to Example 2 has the same basic configuration as that of Example 1.

[0056] The memory unit of the pleural effusion estimation system according to Example 2 stores the measurement results from the impedance measurement unit 5, the impedance index which is the quotient obtained by dividing the square of the subject's chest circumference by the measured impedance value, the results of a diagnosis of the presence or absence or degree of pleural effusion by other methods, the estimation model generated by the model generation unit, and / or the estimation results from the pleural effusion estimation unit.

[0057] Figure 5 shows an overview of the estimation model by the pleural effusion estimation system according to Example 2. The model generation unit uses the impedance index stored in the memory unit and the presence or absence or degree of pleural effusion obtained from the image diagnostic results as training data, and generates an estimation model by machine learning in which the input is the impedance index and the output is the presence or absence of pleural effusion at the time of measurement of the impedance measurement value. Here, the impedance index is a value calculated as an index value using the subject's chest circumference or body width and the impedance measurement value.

[0058] The pleural effusion estimation unit uses an estimation model generated by the model generation unit to estimate the presence or degree of pleural effusion from an impedance index using impedance measurements taken by the impedance measurement unit 5. The "impedance index" used below is the limit value obtained by approaching zero frequency, estimated from the measured values ​​using a Cole-Cole plot.

[0059] Furthermore, by using physical findings, blood test results, past impedance measurements, body surface area, and / or imaging diagnostic results such as thoracic ratio, presence or absence of infiltrates, and extent of infiltrates as training data, in addition to impedance index and the presence or absence of pleural effusion or the degree of accumulation, it becomes possible to estimate the presence or absence of pleural effusion or the degree of accumulation with even greater accuracy. Physical findings may include oxygen saturation, pulse rate, and blood pressure.

[0060] Figure 6 shows the relationship between the presence or absence of pleural effusion and the impedance index. The study targeted heart failure patients with fluid retention in the pleural cavity. Impedance was measured before and after improvement of the disease, and the impedance index was calculated as the quotient obtained by dividing the square of the subject's chest circumference or body width by the measured impedance value.

[0061] The specific measurement method is the same as in Example 1.

[0062] The obtained impedance index showed a significant difference between the time of pleural effusion and the time of pleural effusion resolution. The mean value was 34.6 (95% confidence interval 29.17-40.13) when pleural effusion was resolved, and 54.7 (95% confidence interval 50.14-59.28) when pleural effusion was present. These results indicate that the impedance index is useful for analyzing the intrapleural cavity in patients with heart failure.

[0063] Figure 7 shows the results of a logistic regression analysis of the impedance measurements in Figure 6 using the pleural effusion estimation system. This curve graph and the results of actually confirming the presence or absence of pleural effusion are shown as a confusion matrix in Table 2.

[0064] [Table 2]

[0065] In a study of 100 subjects, 28 were predicted to have no pleural effusion and actually did not; 13 were predicted to have no pleural effusion and actually did; 13 were predicted to have pleural effusion and actually did not; and 46 were predicted to have pleural effusion and actually did. Therefore, the sensitivity was 46 / (13+46)=77.9%. The overall prediction accuracy was (28+46) / 100=74%.

[0066] Even analysis using impedance indices can be considered sufficiently accurate and practical as a method for making simple judgments outside of medical settings.

[0067] In Example 2, by reflecting the differences in body size among the subjects, it becomes possible to appropriately estimate the presence or absence of pleural effusion for an even wider range of subjects than in Example 1. [Examples]

[0068] The pleural effusion estimation system according to this embodiment is basically configured the same as the pleural effusion estimation system according to Embodiment 2. However, in Embodiment 3, the quotient obtained by dividing the width of the subject by the measured impedance was used as the impedance index. The measurement method is the same as in Embodiments 1 and 2.

[0069] Figure 8 shows the relationship between the presence or absence of pleural effusion and another impedance index. The obtained impedance index showed a significant difference between the time of pleural effusion accumulation and the time of pleural effusion resolution. The mean value was 1.02 (95% confidence interval 0.956-1.086) when pleural effusion resolution occurred, and 0.576 (95% confidence interval 0.519-0.633) when pleural effusion accumulation occurred. From these results, it was found that the impedance index in this embodiment is also useful for analyzing the intrathoracic cavity in patients with heart failure.

[0070] Figure 9 shows the results of a logistic regression analysis of the impedance measurements in Figure 8 using the pleural effusion estimation system. This curve graph and the results of actually confirming the presence or absence of pleural effusion are shown as a confusion matrix in Table 3.

[0071] [Table 3]

[0072] In a study of 195 subjects, 94 were predicted to have no pleural effusion and actually did not have it, 20 were predicted to have no pleural effusion and actually did, 16 were predicted to have pleural effusion and actually did not, and 65 were predicted to have pleural effusion and actually did. Therefore, the sensitivity was 65 / (65+20)=76.5%. The overall prediction accuracy was (65+94) / 195=81.5%.

[0073] The analysis using the impedance index in this embodiment can be said to be sufficiently accurate and practical as a method for making simple judgments outside of medical settings. [Examples]

[0074] In this embodiment, the model generation unit generated an estimation model that took oxygen saturation as input in addition to impedance measurement values ​​or impedance index.

[0075] However, instead of using a general oxygen saturation level, the "estimated oxygen saturation level" proposed by the inventor was calculated and used as input, as follows.

[0076] Table 4 shows a portion of the oxygen saturation-to-oxygen partial pressure conversion table and guidelines for inhaled oxygen concentration relative to oxygen flow rate. First, the measured oxygen saturation of a subject receiving oxygen via a nasal cannula, etc., is converted to the oxygen partial pressure at that time. At this time, refer to the oxygen saturation-to-oxygen partial pressure conversion table (I in Table 4) as an example in Table 4. Let the obtained oxygen partial pressure value be A. Next, refer to III in Table 4 to identify the corresponding inhaled oxygen concentration B from the administered oxygen flow rate. Subsequently, calculate the estimated oxygen partial pressure being administered to the patient as C = A × 0.21 / B. Here, 0.21 is the oxygen concentration in the atmosphere. Finally, obtain the estimated oxygen saturation corresponding to the calculated estimated oxygen partial pressure C by referring to the oxygen saturation-to-oxygen partial pressure conversion table (II in Table 4). As exemplified in Table 4, II in Table 4 is obtained by inverse conversion of I in Table 4. Figure 10 shows a table that covers a wider range. Figure 10 is an example of the conversion table used when calculating estimated oxygen saturation. The obtained estimated oxygen saturation corresponds to the estimated oxygen saturation value assuming no oxygen administration.

[0077] The pleural effusion estimation system according to the present invention may also obtain an estimated oxygen saturation conversion unit that obtains estimated oxygen saturation from oxygen saturation using the above procedure.

[0078] [Table 4]

[0079] Let's consider a specific example. Suppose the oxygen saturation (SpO2) is 96% and oxygen is being administered via a nasal cannula at a rate of 2.0 L / min. Referring to Table 4, section I, we can see that the corresponding oxygen partial pressure is 82 Torr (A). Next, since the inhaled oxygen flow rate is 2.0 L / min, referring to Table 4, section III, we can see that the inhaled oxygen concentration is 28% (B). Therefore, we calculate C = 82 × 0.21 / 0.28 = 61.5. Finally, referring to Table 4, section II, we obtain an estimated oxygen saturation of 91%.

[0080] The following describes a comparison of the estimation accuracy when using the generally known oxygen saturation (SpO2) and the estimated oxygen saturation (eSpO2) proposed by the present inventors.

[0081] Generally, the oxygen saturation cutoff value used to determine the quality of respiratory status is 90%. Table 5 shows the results when this value is applied even under oxygen administration to distinguish between normal (no pleural effusion) and abnormal (with pleural effusion).

[0082] [Table 5]

[0083] As shown in Table 5, when using oxygen saturation, only 2 out of 184 abnormalities were detected. It is unlikely that using the same value under oxygen administration would be effective. Furthermore, data with such a small number of abnormalities cannot be used for machine learning.

[0084] Next, we will describe the results using the estimated oxygen saturation proposed by the inventors as an indicator. The cutoff value used to distinguish between normal (no pleural effusion) and abnormal (with pleural effusion) was 90%. The results are shown in Table 6.

[0085] [Table 6]

[0086] As shown in Table 6, when using estimated oxygen saturation, abnormalities were detected in 51 individuals, of which 41 were correct. This shows that a significantly larger number of abnormalities can be detected compared to when using oxygen saturation alone. By using the estimated oxygen saturation proposed by the inventors, it becomes possible to estimate oxygen saturation while taking into account the presence of oxygen administration. Furthermore, when using estimated oxygen saturation as an indicator, it is also possible to secure training data for machine learning.

[0087] Note that while a cutoff value of 90% was used above, Table 7 shows the number of correct answers and accuracy rate when different cutoff values ​​are used.

[0088] [Table 7]

[0089] As shown in Table 7, using estimated oxygen saturation as an indicator increased the number of correct answers for all cutoff values. The accuracy rate also increased by approximately 8-10%. This indicates that using estimated oxygen saturation as an indicator, rather than general oxygen saturation, allows for a more accurate estimation of the presence or absence of pleural effusion.

[0090] Furthermore, Table 7 shows that the number of correct answers and the accuracy rate increase or decrease depending on the cutoff value. Therefore, it is possible to improve the accuracy of machine learning models by setting an appropriate cutoff value.

[0091] Furthermore, both were evaluated using ROC curves based on logistic regression analysis. Figure 11 compares the ROC curves using (a) oxygen saturation and (b) estimated oxygen saturation.

[0092] Referring to Figure 11, the AUC value when using oxygen saturation was only 0.59. On the other hand, the AUC value when using estimated oxygen saturation was a good 0.74.

[0093] The inventors have found that using the estimated oxygen saturation value proposed by the inventors allows for more accurate estimation of the model compared to using the conventional oxygen saturation value. [Examples]

[0094] In this embodiment, the model generation unit generated an estimation model that took pulse rate as input, in addition to impedance measurement values ​​or impedance index and estimated oxygen saturation.

[0095] The presence or accumulation of pleural effusion reduces blood oxygen saturation, which tends to cause an abnormally high pulse rate. Therefore, by including pulse rate as an input, it is possible to further improve the estimation accuracy of the estimation model.

[0096] In this example, prediction was performed using multiple logistic regression analysis with impedance measurements or impedance index, estimated oxygen saturation, and pulse rate. Specifically, the logistic regression equation used in this example is Logit(p) = C0 + C1 × (impedance index) + C2 × (estimated oxygen saturation [%]) + C3 × (pulse rate [bpm]). [Examples]

[0097] In this example, prediction was performed using multiple logistic regression analysis with impedance measurement values ​​or impedance index, estimated oxygen saturation, pulse rate, and blood test data (white blood cell count, Na value, CRP value). Specifically, the logistic regression equation used in this example was Logit(p) = C0 + C1 × R (impedance) + C2 × CRP value + C3 × pulse rate - C4 × estimated oxygen saturation - C5 × white blood cell count + C6 × Na value.

[0098] The method used in this embodiment and the results of actually confirming the presence or absence of pleural effusion are shown in Table 5 as a confusion matrix.

[0099] [Table 8]

[0100] In a study of 181 subjects, 97 were predicted to have no pleural effusion and actually did not; 11 were predicted to have no pleural effusion and actually did; 10 were predicted to have pleural effusion and actually did not; and 63 were predicted to have pleural effusion and actually did. Therefore, the sensitivity was 63 / (11+63)=85.1%. The overall prediction accuracy was (97+63) / 181=88.3%. [Examples]

[0101] Furthermore, in this embodiment, we will describe the analysis results of dividing the degree of pleural effusion into three stages: "no signs," "mild," and "moderate or severe." In this embodiment, the impedance index used was the quotient obtained by dividing the subject's body width by the measured impedance value.

[0102] Figure 12 shows the relationship between the degree of pleural effusion, divided into three stages, and the impedance index. The obtained impedance index showed significant differences in each of the three stages: the mean was 0.576 (95% confidence interval 0.521-0.631) for "no signs of pleural effusion," 0.890 (95% confidence interval 0.793-0.987) for "mild," and 1.117 (95% confidence interval 1.034-1.199) for "moderate or severe."

[0103] Furthermore, referring to Figures 13 and 14, we demonstrate that the analysis of this embodiment is useful for analyzing the degree of pleural effusion. Figure 13 shows the results of the logistic regression analysis of the impedance index in Figure 12 using the pleural effusion estimation system according to this embodiment. Figure 14 shows the results of the receiver operating characteristic (ROC) analysis using the analysis of this embodiment.

[0104] Referring to Figure 13, it is shown that the three stages of "no symptoms," "mild," and "moderate or severe" are clearly separated. Also, referring to Figure 14, the AUC (Area Under the Curve) of the patient response curves showed high values ​​of 0.885 and 0.848 for the curves corresponding to "mild" and "moderate or severe," respectively.

[0105] The results above demonstrate that the impedance index analysis in this embodiment is also useful for analyzing the degree of pleural effusion.

[0106] In the above embodiment, the impedance index was calculated as the quotient obtained by dividing the square of the chest circumference by the measured impedance, or by dividing the body width by the measured impedance. However, other definitions may be used for the impedance index. For example, it may be the quotient obtained by dividing the square of the body width by the measured impedance, or by dividing the chest circumference by the measured impedance.

[0107] Furthermore, the model generation unit may also generate an estimation model that takes body weight and changes in body weight as input. As a result of pleural effusion, body weight tends to increase by about 3-4 kg. Therefore, by also using body weight and changes in body weight as input, it becomes possible to further improve the estimation accuracy of the estimation model.

[0108] Furthermore, the model generation unit may also generate an estimation model that takes K values, γGTP values, and Cre values ​​from the blood test data as input.

[0109] Furthermore, the model generation unit may generate an estimation model that also takes items such as the subject's age as input, in addition to the impedance measurement values ​​and other items described in this embodiment.

[0110] Furthermore, as the "impedance measurement value" or "impedance index," the limit value as the frequency approaches infinity, estimated from the measurement value using a Cole-Cole plot, may also be used.

[0111] Furthermore, the effectiveness of each indicator is shown using SHAP values. Figure 15 shows the SHAP values ​​for each indicator.

[0112] Figure 15 shows the SHAP values ​​when (a) logistic regression analysis is performed only, and (b) machine learning is performed, using multiple indicators.

[0113] Referring to Figure 15, it is immediately apparent that the importance of indicators can change when machine learning is performed. In Figure 15, WIPer corresponds to weight change. PhA50.0[degrees] corresponds to the phase angle at 50[kHz]. II8_R0[cm2 / ohm] corresponds to the impedance index obtained by dividing the square of the body width by the estimated value of the impedance measurement at frequency 0[kHz]. CRPS corresponds to the value binarized with a specific CRP value as the reference value. r30.0[ohm] corresponds to the impedance value at frequency 30[kHz]. Cre[mg / dL] corresponds to the Cre value. PhA10.0[degrees] corresponds to the phase angle at frequency 10[kHz]. Kurasumi_ECW / BSA corresponds to the quotient obtained by dividing the volume of extracellular fluid by the body surface area measured by the method proposed by Kurasumi et al. PhAfc[degrees] corresponds to the phase angle at the critical frequency. ASTS corresponds to a value obtained by binarizing a specific aspartate aminotransferase value as the reference value. eSpO2S corresponds to a value obtained by binarizing a specific estimated oxygen saturation value as the reference value. HctS corresponds to a value obtained by binarizing a specific hematocrit value as the reference value. NaS corresponds to a value obtained by binarizing a specific Na value as the reference value. SBPS corresponds to a value obtained by binarizing a specific systolic blood pressure value as the reference value. HRS corresponds to a value obtained by binarizing a specific heart rate as the reference value.

[0114] Here, the critical frequency is the current frequency at which the capacitance of the cell membrane is at its maximum, and can be said to be the frequency that best reflects the state of the cell membrane. In a normal cell, the capacitance of the cell membrane is large, and electricity can be stored, so the phase angle, which is the difference between voltage and current, is large. In contrast, when a cell is damaged, the capacitance of the cell membrane decreases, and it can no longer store electricity. Therefore, the phase angle, which is the difference between voltage and current, becomes small. Thus, by looking at the phase angle at the critical frequency, it is possible to objectively evaluate and compare the state of the cell membrane.

[0115] The accuracy when using only logistic regression analysis was approximately 84.7%. In contrast, the accuracy improved to approximately 88% when machine learning was used.

[0116] Furthermore, by considering blood pressure and other factors along with the presence or degree of pleural effusion, it becomes easier to determine symptoms such as heart failure and pneumonia. The present invention may also be considered as a symptom determination system further comprising a symptom determination unit for determining these symptoms.

[0117] For example, elderly people tend to accumulate pleural effusion following pneumonia. Furthermore, in cases of heart failure, pleural effusion tends to accumulate even more than in cases of pneumonia. Therefore, if pleural effusion is present or estimated to be above a certain level, the condition estimation unit may estimate that the patient has pneumonia or heart failure. Also, if an even larger amount of pleural effusion is estimated to be present, the condition estimation unit may estimate that the patient has heart failure.

[0118] Furthermore, in heart failure, blood pressure may drop abnormally due to a decrease in heart function. As a result, pleural effusion may accumulate. In some cases, it may be caused by extremely high blood pressure. The symptom assessment unit may determine that there is a high probability of heart failure if pleural effusion is present or exceeds a certain level, and blood pressure is outside the normal range and shows an abnormal value.

[0119] There is a strong possibility that new respiratory infections, like COVID-19, will continue to emerge. Therefore, the ability to evaluate intrapulmonary changes over time and in a non-invasive manner will become increasingly important.

[0120] In this embodiment, one electrode plate was attached to each of the left and right reference attachment positions. However, as shown in Figure 16, the electrode unit may have two electrode plates connected to a voltage system (voltage electrode plates) and two electrode plates connected to an ammeter (current electrode plates), and a total of four plates, two on each side, may be placed in contact with a living body such as a human body near the left and right reference attachment positions. Furthermore, the two voltage electrode plates may be positioned symmetrically with respect to the sagittal plane of the living body, including the xiphoid process of the sternum, and the two current electrode plates may also be positioned symmetrically with respect to the sagittal plane. In this case, since voltage and current can be measured using the four-terminal method, it becomes possible to measure impedance with high accuracy. [Examples]

[0121] In the embodiments described above, the electrode plate was attached to a reference position at the intersection of the cross-section of the living body including the xiphoid process of the chest and the midline of the axilla, and the electrode plate was brought into contact with that position or a position in the vicinity. However, in this embodiment, the electrode plate is brought into contact with a position other than these, such as the leg or arm, which is easy to measure even while wearing clothes.

[0122] Since impedance measurements obtained when electrode plates are in contact with the chest are thought to be related to impedance measurements obtained when electrode plates are in contact with other parts of the body, such as the legs or arms, it is expected that domain-adaptive transfer learning may be possible. [Examples]

[0123] The following describes a lifestyle behavior recognition system that enables the non-invasive estimation of physical changes at an early stage based on the daily activities of the elderly and other individuals, leading to early treatment. This system is intended for use in homes and elderly care facilities. Furthermore, by using it in conjunction with the pleural effusion estimation system described in the above example, it will be possible to promote comprehensive initiatives throughout the community.

[0124] The lifestyle behavior recognition system comprises a sensor unit and a server. The sensor unit includes a human presence sensor, an environmental sensor, a door sensor, and an acceleration sensor. The server uses a machine learning model to estimate behavior based on the collected data and builds a model that predicts changes in the subject's overall condition early on based on their behavior. The human presence sensor detects the presence and movement of people and transmits the acquired data wirelessly to the server. The environmental sensor measures temperature, humidity, illuminance, and noise and transmits the acquired data wirelessly to the server.

[0125] By linking a pleural effusion estimation system with a lifestyle behavior recognition system, it becomes possible to estimate the deterioration of a patient's condition at a very early stage based on behavioral changes before symptoms such as heart failure appear. Furthermore, by informing family members and facility staff of this information, efforts toward healthy longevity throughout the community can be promoted.

[0126] For example, if the lifestyle behavior recognition system detects an abnormality, the abnormality is automatically notified to the relevant parties who have given prior consent, and the pleural effusion estimation system is activated.

[0127] Here, the lifestyle behavior recognition system is evaluated by the information recipient, and then further evaluated and improved by comparing the results of the estimation model with objective diagnoses at the hospital (chest X-rays, CT images, ultrasound examinations, and blood tests). Even if the subject does not visit a medical institution, evaluation and improvement may be carried out by using ultrasound diagnostic equipment to diagnose the presence or absence of pleural effusion and differential diagnoses.

[0128] When the early anomaly detection system, which links the two systems described above, constructs an estimation model, it may use data collected by the sensor unit of the lifestyle behavior recognition system as input, in addition to impedance measurements, blood tests, and physical findings. [Explanation of symbols]

[0129] 1; Pleural effusion estimation system, 2; Pleural effusion estimation device, 3; Electrode unit, 5; Impedance measurement unit, 7; Pleural effusion estimation unit, 9; Control unit, 11; Memory unit, 13; Communication unit, 15; Model generation unit

Claims

1. A pleural effusion estimation system that estimates the presence or degree of pleural effusion in a subject, An electrode portion that makes transcutaneous contact with the chest of the subject, An impedance measuring unit that applies an alternating current to the electrode portion to measure the impedance, The system includes a pleural effusion estimation unit that estimates the presence or absence or degree of pleural effusion using impedance measurements taken by the impedance measurement unit, with the intersection of the cross-section of the living body including the xiphoid process of the sternum and the midline of the axilla being set as the reference position for attaching the electrode unit. The electrode portion is, A first voltage electrode plate and a second voltage electrode plate, which are electrode plates connected to a voltmeter, A pleural effusion estimation system comprising a first current electrode plate and a second current electrode plate, which are electrode plates connected to an ammeter.

2. A storage unit that stores the impedance measurement value of the subject and the presence or absence of pleural effusion or the degree of accumulation in the subject, The system further comprises a model generation unit that uses the impedance measurement values ​​and the presence or absence or degree of pleural effusion stored in the memory unit as training data, and generates an estimation model by machine learning in which the impedance measurement value is the input and the presence or absence or degree of pleural effusion at the time of measurement of the impedance measurement value is the output, The pleural effusion estimation system according to claim 1, wherein the pleural effusion estimation unit estimates the presence or absence or degree of accumulation of pleural effusion in the target from the impedance measurement value measured by the impedance measurement unit, using the estimation model generated by the model generation unit.

3. A pleural effusion estimation system for estimating the presence or absence or degree of pleural effusion in a subject, An electrode portion that makes transcutaneous contact with the chest of the subject, An impedance measuring unit that applies an alternating current to the electrode portion to measure the impedance, The system includes a pleural effusion estimation unit that estimates the presence or absence or degree of pleural effusion using impedance measurements taken by the impedance measurement unit, with the intersection of the cross-section of the living body including the xiphoid process of the sternum and the midline of the axilla being set as the reference position for attaching the electrode unit. The pleural effusion estimation unit is a pleural effusion estimation system that estimates the presence or absence of pleural effusion or the degree of accumulation based on the chest circumference or body width of the subject.

4. A storage unit that stores the chest circumference of the subject, the square of the chest circumference, the body width, or the impedance index which is the quotient obtained by dividing the square of the body width by the impedance measurement value, and the presence or absence of pleural effusion or the degree of accumulation of pleural effusion in the subject, The system further comprises a model generation unit that uses the impedance index and the presence or absence or degree of pleural effusion stored in the memory unit as training data, and generates an estimation model by machine learning in which the input is the impedance index and the output is the presence or absence or degree of pleural effusion at the time of measurement of the impedance measurement value. The pleural effusion estimation system according to claim 3, wherein the pleural effusion estimation unit estimates the presence or absence or degree of accumulation of pleural effusion in the target from the impedance index using the impedance measurement value measured by the impedance measurement unit, using the estimation model generated by the model generation unit.

5. A pleural effusion estimation system for estimating the presence or absence or degree of pleural effusion in a subject, An electrode portion that makes transcutaneous contact with the chest of the subject, An impedance measuring unit that applies an alternating current to the electrode portion to measure the impedance, A pleural effusion estimation unit that estimates the presence or degree of pleural effusion using impedance measurements taken by the impedance measurement unit, with the intersection of the cross-section of the living body including the xiphoid process of the sternum and the midline of the axilla set as the reference position for attaching the electrode unit, A storage unit that stores the impedance measurement value of the subject and the presence or absence of pleural effusion or the degree of accumulation in the subject, The system includes a model generation unit that uses the impedance measurement values ​​and the presence or absence or degree of pleural effusion stored in the memory unit as training data, and generates an estimation model by machine learning in which the impedance measurement value is the input and the presence or absence or degree of pleural effusion at the time of measurement of the impedance measurement value is the output. The pleural effusion estimation unit uses the estimation model generated by the model generation unit to estimate the presence or absence of pleural effusion or the degree of accumulation of pleural effusion in the target from the impedance measurement value measured by the impedance measurement unit. The model generation unit further uses estimated oxygen saturation, which is calculated using the partial pressure of oxygen under oxygen administration to estimate a hypothetical oxygen saturation value assuming no oxygen administration, as training data. The estimation model is a pleural effusion estimation system that also takes the estimated oxygen saturation as input.

6. A pleural effusion estimation system for estimating the presence or absence or degree of pleural effusion in a subject, An electrode portion that makes transcutaneous contact with the chest of the subject, An impedance measuring unit that applies an alternating current to the electrode portion to measure the impedance, A pleural effusion estimation unit that estimates the presence or degree of pleural effusion using impedance measurements taken by the impedance measurement unit, with the intersection of the cross-section of the living body including the xiphoid process of the sternum and the midline of the axilla set as the reference position for attaching the electrode unit, A storage unit that stores the impedance measurement value of the subject and the presence or absence of pleural effusion or the degree of accumulation in the subject, The system includes a model generation unit that uses the impedance measurement values ​​and the presence or absence or degree of pleural effusion stored in the memory unit as training data, and generates an estimation model by machine learning in which the impedance measurement value is the input and the presence or absence or degree of pleural effusion at the time of measurement of the impedance measurement value is the output. The pleural effusion estimation unit uses the estimation model generated by the model generation unit to estimate the presence or absence of pleural effusion or the degree of accumulation of pleural effusion in the target from the impedance measurement value measured by the impedance measurement unit. The aforementioned model generation unit also uses pulse rate as training data. The aforementioned estimation model is a pleural effusion estimation system that also takes pulse rate as input.

7. The pleural effusion estimation system according to claim 1, wherein the frequency applied in the impedance measuring unit is 2 to 500 kHz.

8. The pleural effusion estimation system according to claim 7, wherein the impedance measurement value used is the limit value when the frequency approaches 0 or infinity, which is estimated from the measurement value using a Cole-Cole plot.

9. A pleural effusion estimation device that estimates the presence or degree of pleural effusion in a subject, An impedance measuring unit that applies an alternating current to an electrode portion that makes percutaneous contact with the target, setting the intersection of the cross-section of the living body including the xiphoid process of the sternum and the midline of the axilla as the attachment reference position, and measures the impedance. The system includes a pleural effusion estimation unit that estimates the presence or absence of pleural effusion or the degree of accumulation based on the impedance measurement value measured by the impedance measurement unit, The pleural effusion estimation unit is a pleural effusion estimation device that estimates the presence or absence of pleural effusion or the degree of accumulation based on the chest circumference or body width of the subject.

10. A thoracic estimation device that estimates the state of the thoracic cavity of a subject, Intrathoracic estimation device that estimates the state of the intrathoracic cavity using, in addition to the impedance measurement value described in claim 9, body weight, phase angle, impedance index, CRP value, extracellular fluid volume, body surface area, aspartate aminotransferase value, estimated oxygen saturation, hematocrit value, Na value, blood pressure, or heart rate, blood oxygen saturation, white blood cell count, γGTP value, Cre value, or K value.

11. A method for estimating pleural effusion using a pleural effusion estimation system that estimates the presence or degree of pleural effusion in a subject, The aforementioned pleural effusion estimation system is As an electrode portion that comes into transcutaneous contact with the chest of the subject, A first voltage electrode plate and a second voltage electrode plate, which are electrode plates connected to a voltmeter, It has a first current electrode plate and a second current electrode plate, which are electrode plates connected to an ammeter. An impedance measuring unit that applies an alternating current to the electrode portion to measure the impedance, A pleural effusion estimation unit that estimates the presence or absence or degree of accumulation of pleural effusion using the impedance measurement value measured by the impedance measurement unit, The system includes a control unit that controls the impedance measurement unit and the pleural effusion estimation unit. The electrode contact step involves setting the intersection of the cross-section of the living body including the xiphoid process of the sternum and the midline of the axilla as the reference position for application, and then making percutaneous contact with the electrode portion. The control unit performs an impedance measurement step in which it causes the impedance measurement unit to apply an alternating current to the electrode unit and measure the impedance, The control unit includes a pleural effusion estimation step in which it causes the pleural effusion estimation unit to estimate the presence or absence of pleural effusion or the degree of accumulation using the impedance measurement value measured in the impedance measurement step, In the electrode contact step, The first voltage electrode plate and the first current electrode plate are brought into contact with one side of the living organism at a position symmetrical along the cross-section, with the intersection in between. A method for estimating pleural effusion, wherein the second voltage electrode plate and the second current electrode plate are brought into contact with the body side of the living organism at positions symmetrical along the cross-section, on the opposite side of the intersection.

12. The first voltage electrode plate and the first current electrode plate are brought into contact with the same side of the coronal surface of the living organism. The second voltage electrode plate and the second current electrode plate are brought into contact with the coronal surface on the side opposite to the first voltage electrode plate. In the impedance measurement step, A voltage is measured between the first voltage electrode plate and the second voltage electrode plate. The method for estimating pleural effusion according to claim 11, wherein a current is measured between the first current electrode plate and the second current electrode plate.

13. A method for estimating pleural effusion using a pleural effusion estimation system that estimates the presence or degree of pleural effusion in a subject, The aforementioned pleural effusion estimation system is An electrode portion that makes transcutaneous contact with the chest of the subject, An impedance measuring unit that applies an alternating current to the electrode portion to measure the impedance, A pleural effusion estimation unit that estimates the presence or absence or degree of accumulation of pleural effusion using the impedance measurement value measured by the impedance measurement unit, The system includes a control unit that controls the impedance measurement unit and the pleural effusion estimation unit. The electrode contact step involves setting the intersection of the cross-section of the living body including the xiphoid process of the sternum and the midline of the axilla as the reference position for application, and then making percutaneous contact with the electrode portion. The control unit performs an impedance measurement step in which it causes the impedance measurement unit to apply an alternating current to the electrode unit and measure the impedance, A pleural effusion estimation method comprising: a pleural effusion estimation step in which the control unit causes the pleural effusion estimation unit to estimate the presence or absence of pleural effusion or the degree of accumulation using the impedance measurement value measured in the impedance measurement step and the chest circumference or body width of the subject.

14. A program that causes a computer to function as a control unit according to any one of claims 11 to 13.

15. A method for measuring chest impedance, which measures the impedance measurement value of the target chest, The electrode contact step involves setting the intersection of the cross-section of the living body including the xiphoid process of the sternum and the midline of the axilla as the reference position for application, and then making percutaneous contact with the electrode portion. The process includes an impedance measurement step of applying an alternating current to the electrode portion and measuring the impedance, In the electrode contact step, using a first voltage electrode plate and a second voltage electrode plate, which are electrode plates connected to a voltmeter, and a first current electrode plate and a second current electrode plate, which are electrode plates connected to an ammeter, The first voltage electrode plate and the first current electrode plate are brought into contact with one side of the living organism at a position symmetrical along the cross-section, with the intersection in between. A method for measuring chest impedance, wherein the second voltage electrode plate and the second current electrode plate are brought into contact with the body side of the living organism at positions symmetrical along the cross-section, on the opposite side of the intersection.

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