Method and device for predicting cardiac troponin levels after non-cardiac surgery

Abstract

Provided according to exemplary embodiments of the present invention is a method for predicting a cardiac troponin level after noncardiac surgery, which is implemented by at least one processor. The method for predicting cardiac troponin level after noncardiac surgery may include the steps of: acquiring preoperative and intraoperative information of a subject undergoing noncardiac surgery; and determining the cardiac troponin level of the subject after noncardiac surgery on the basis of the acquired preoperative and intraoperative information of the subject undergoing noncardiac surgery.

Description

Method and device for predicting cardiac troponin levels after noncardiac surgery

[0001] The present invention relates to a method and device for predicting cardiac troponin levels after non-cardiac surgery. More specifically, the present invention relates to a method and device for predicting cardiac troponin levels after non-cardiac surgery in a subject based on information about the subject.

[0002] It is estimated that more than 300 million people worldwide undergo surgery each year. It has been reported that approximately 85% of major surgeries are non-cardiac surgeries.

[0003] Meanwhile, previous research has reported that myocardial injury after noncardiac surgery (MINS) may increase the patient's mortality rate.

[0004] The diagnostic criterion for MINS is an increase in cardiac troponin (cTn) levels within 30 days after noncardiac surgery. Specifically, patients with perioperative myocardial injury (PMI) and elevated cTn levels after noncardiac surgery have a higher risk of death than those without elevated cTn levels.

[0005] MINS is usually asymptomatic and is caused by an imbalance between myocardial oxygen supply and demand. MINS typically does not present with symptoms such as chest pain, chest tightness, or discomfort. Therefore, diagnosing MINS is challenging without routine monitoring of cTn levels. Recent guidelines recommend measuring cTn levels 48 hours after surgery, as MINS typically occurs within three days of surgery and is asymptomatic. However, testing cTn levels in all patients undergoing non-cardiac surgery is unrealistic due to cost and technical accessibility.

[0006] EP 3495822 A1 (prior document 1), US 2020-0118679 A1 (prior document 2), and KR 10-2019-0000681 A (prior document 3) disclose methods for predicting cardiovascular disease using cTn levels. However, these documents require direct testing of the patient's cTn levels, as is the case in the past. In other words, these documents do not disclose methods for predicting cTn levels themselves.

[0007] Considering that the incidence of MINS is approximately 20%, and a significant number of patients may develop myocardial infarction, cardiac arrest, or even death; and that it is practically difficult to test cTn levels after non-cardiac surgery in all patients due to cost and technical accessibility; development of a prediction model that can predict cTn levels is necessary.

[0008] One object of the present invention is to provide a method and device for predicting the cardiac troponin (cTn) level of a subject after non-cardiac surgery.

[0009] A method for predicting a cardiac troponin level after non-cardiac surgery, executed by at least one processor according to exemplary embodiments of the present invention, may include the steps of: obtaining information about a subject before non-cardiac surgery and information about a subject during non-cardiac surgery; and determining a cardiac troponin level of the subject after non-cardiac surgery based on the information about the subject before non-cardiac surgery and the information about the subject during non-cardiac surgery.

[0010] In one embodiment, the cardiac troponin level may include at least one of a cardiac troponin I level and a cardiac troponin T level.

[0011] In one embodiment, the information of the subject prior to the non-cardiac surgery may include at least one of: basic information of the subject; diagnostic information of the subject prior to the non-cardiac surgery; and information related to the non-cardiac surgery.

[0012] In one embodiment, the basic information of the subject may include at least one of age, gender, height, and weight.

[0013] In one embodiment, the diagnostic information of the subject prior to the non-cardiac surgery may include at least one of the presence or absence of diabetes, the presence or absence of hypertension, the presence or absence of dyslipidemia, the presence or absence of coronary artery disease, the presence or absence of peripheral artery disease, the presence or absence of cerebrovascular disease, the presence or absence of kidney disease, the presence or absence of heart failure, the presence or absence of atrial fibrillation, the presence or absence of aortic valve stenosis, the presence or absence of chronic obstructive pulmonary disease, the presence or absence of dialysis, the presence or absence of anemia, the blood hemoglobin level, the blood creatinine level, and the renal glomerular filtration rate.

[0014] In one embodiment, the non-cardiac surgery related information may include at least one of the type of non-cardiac surgery, whether the non-cardiac surgery is an emergency surgery, and whether the non-cardiac surgery includes vascular surgery.

[0015] In one embodiment, the information during the non-cardiac surgery may include at least one of: the operation time of the non-cardiac surgery; information related to blood transfusion during the non-cardiac surgery; information related to hypotension during the non-cardiac surgery; and information related to heart rate during the non-cardiac surgery.

[0016] In one embodiment, the information related to blood transfusion during the non-cardiac surgery may include at least one of whether a blood transfusion is performed during the non-cardiac surgery and the amount of blood transfusion during the non-cardiac surgery.

[0017] In one embodiment, the heart rate related information during the non-cardiac surgery may include tachycardia related information during the non-cardiac surgery.

[0018] In one embodiment, the information related to hypotension during the non-cardiac surgery may include at least one of: information related to the number of occurrences of hypotension during the non-cardiac surgery; information related to the duration of hypotension occurring during the non-cardiac surgery; and information related to the severity of hypotension occurring during the non-cardiac surgery.

[0019] In one embodiment, the information related to the number of occurrences of hypotension during the non-cardiac surgery may include the total number of hypotension events that occurred during the non-cardiac surgery.

[0020] In one embodiment, the information related to the duration of hypotension occurring during the non-cardiac surgery may include at least one of: the total duration of hypotension occurring during the non-cardiac surgery; and the average duration of hypotension occurring during the non-cardiac surgery.

[0021] In one embodiment, the severity of hypotension occurring during non-cardiac surgery may include at least one of: an average of mean arterial pressure (MAP) during the total occurrence period of hypotension events during the non-cardiac surgery; an area of ​​an area under a hypotension reference value line among areas between a hypotension reference value line and a MAP curve in a MAP (y-axis)-time (x-axis) graph acquired during the non-cardiac surgery; a time-weighted average of MAP during the non-cardiac surgery; and a total number of occurrences of events in which MAP is below a severe hypotension threshold value during the non-cardiac surgery. The severe hypotension threshold value may be a value satisfying 40 mmHg or more and 60 mmHg or less.

[0022] In one embodiment, the step of determining the cardiac troponin level after the non-cardiac surgery may include using an artificial intelligence model trained to predict the cardiac troponin level after the non-cardiac surgery of the subject based on information about the subject before the non-cardiac surgery and information about the subject during the non-cardiac surgery.

[0023] In one embodiment, the artificial intelligence model may include a machine learning or artificial neural network-based regression model.

[0024] In one embodiment, the method for predicting cardiac troponin after non-cardiac surgery described above may further include the steps of: when the post-non-cardiac surgery cardiac troponin level of the subject is defined as the first cardiac troponin level, and when the first cardiac troponin level of the subject is less than a predetermined value, obtaining post-non-cardiac surgery information of the subject; and determining a second cardiac troponin level of the subject based on the first cardiac troponin level of the subject and the post-non-cardiac surgery information.

[0025] In one embodiment, the post-cardiac surgery information may include post-cardiac surgery questionnaire information for the subject.

[0026] In one embodiment, the questionnaire information may include the subject's response data to a plurality of questionnaires.

[0027] A device for predicting cardiac troponin levels after non-cardiac surgery according to exemplary embodiments of the present invention may include a communication unit; a memory; and at least one processor connected to the communication unit and the memory.

[0028] The at least one processor may be controlled to obtain information about the subject before non-cardiac surgery and information about the subject during non-cardiac surgery, and determine a cardiac troponin level of the subject after non-cardiac surgery based on the information about the subject before non-cardiac surgery and the information about the subject during non-cardiac surgery.

[0029] According to exemplary embodiments of the present invention, an application program stored in a recording medium may be provided to execute the above-described method when operated by at least one processor.

[0030] According to exemplary embodiments of the present invention, a method and device can be provided that can more accurately predict cardiac troponin (cTn) levels in a subject after non-cardiac surgery.

[0031] For example, predicted cTn levels can be used to predict whether a subject will develop myocardial injury after noncardiac surgery (MINS). For example, additional testing (e.g., cTn testing) can be recommended or ordered for patients whose predicted cTn levels exceed a threshold. This allows for selective and efficient care for patients undergoing noncardiac surgery, and can help prevent the development of diseases and deaths associated with MINS.

[0032] FIG. 1 illustrates the structure of a system for predicting cardiac troponin levels after non-cardiac surgery in a subject according to one embodiment of the present invention.

[0033] Figure 2 illustrates the structure of a device according to one embodiment of the present invention.

[0034] FIG. 3 illustrates a methodology for predicting cardiac troponin levels after non-cardiac surgery in a subject according to one embodiment of the present invention.

[0035] FIG. 4 illustrates an operational flowchart for predicting cardiac troponin levels after non-cardiac surgery in a subject according to one embodiment of the present invention.

[0036] FIG. 5 illustrates an operational flowchart for predicting cardiac troponin levels after non-cardiac surgery in a subject according to another embodiment of the present invention.

[0037] The advantages and features of the present invention, and the methods for achieving them, will become clearer with reference to the embodiments described in detail below, along with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below and may be implemented in various different forms.

[0038] To clearly explain embodiments of the present invention, portions irrelevant to the description may be omitted. Furthermore, when describing embodiments of the present invention, if a detailed description of a related known configuration or function is deemed to obscure the gist or description of the present invention, a detailed description thereof may be omitted.

[0039] In describing components in this specification, terms such as "first," "second," etc. may be used. The aforementioned terms are intended to distinguish one component from another for convenience of description, and unless otherwise specified, the nature, order, etc. of the components are not limited by the aforementioned terms.

[0040] In each of the steps mentioned in this specification, unless the context clearly dictates a specific order, the steps may be performed in a different order than stated. That is, the steps may be performed in the same order as stated, may be performed substantially simultaneously, or may be performed in the opposite order.

[0041] In this specification, "and / or" may mean each of the listed components and any combination of two or more of the listed components. For example, "A, B and / or C" may be used with the same meaning as "at least one of A, B, and C."

[0042] According to exemplary embodiments of the present invention, methods, devices, and services for predicting cardiac troponin (cTn) levels in a subject after non-cardiac surgery may be provided. Hereinafter, for convenience of explanation, "cardiac troponin levels" may be abbreviated as "cTn levels."

[0043] FIG. 1 illustrates the structure of a system for predicting cardiac troponin levels after non-cardiac surgery in a subject according to one embodiment of the present invention.

[0044] Referring to FIG. 1, a system for predicting cTn levels after non-cardiac surgery may include a user device (101) and a server device (102).

[0045] The user device (101) may be a device used by a user to check and manage information of a subject (e.g., personal information, information before non-cardiac surgery, information during non-cardiac surgery, etc.). The user device (101) may connect to a server device (102) via a network and interact with the server device (102). For example, the user device (101) may provide information of a subject for predicting cTn levels after non-cardiac surgery to the server device (102) and receive a prediction result for cTn levels after non-cardiac surgery.

[0046] For example, the user device (101) may be a device of various forms, such as an electronic device such as a smartphone, a laptop, a desktop computer, or a tablet PC.

[0047] The server device (102) may be a device of a service provider that provides a service for predicting cTn levels after non-cardiac surgery. The server device (102) may transmit and receive information about subjects, etc., to and from the user device (101) via a network. For example, the server device (102) may store information about multiple subjects, a model for predicting cTn levels after non-cardiac surgery, etc., and may provide a function for predicting cTn levels after non-cardiac surgery in the form of a platform.

[0048] Although FIG. 1 illustrates only one user device (101), multiple devices including the user device (101) can access the server (102) and utilize the cTn level prediction service after non-cardiac surgery. However, the scope of accessible subject information may vary depending on the authority granted to the user device (101).

[0049] The relationship between the server device (102) and the user device (101) as shown in FIG. 1 can be established when the cTn level prediction service after non-cardiac surgery is provided in the form of a platform. According to another embodiment of the present invention, the cTn level prediction service after non-cardiac surgery may be provided in the form of a non-network-based program rather than a platform. In this case, unlike as shown in FIG. 1, the cTn level prediction service after non-cardiac surgery may be provided by a program or application installed on the user device (101) without the server device (102). Furthermore, the server device (102) may be the entity providing the program or application.

[0050] FIG. 2 illustrates the structure of a device according to one embodiment of the present invention. FIG. 2 illustrates an example of the structure of a user device (101) or a server device (102) of FIG. 1. In this specification, the user device (10) and the server device (102) may be collectively referred to as a "device."

[0051] Referring to FIG. 2, the device may include a control unit (201), a communication unit (202), and a storage unit (203).

[0052] The control unit (201) can control the overall functions and operations of the device. The control unit (201) can control components of the device, for example, the communication unit (202), the storage unit (203), and at least one other component. That is, the control unit (201) can provide information or data necessary for the operation of the components of the device, and perform operations based on information or data generated or managed by the components. For example, the control unit (201) can include at least one processor, at least one circuit, etc. For example, the at least one processor can include at least one of a central processing unit (CPU), a graphics processing unit (GPU), and a neural network processing unit (NPU). The control unit (201) can perform necessary controls so that the device operates according to various embodiments described herein. For example, the control unit (201) can control the operation of the device by executing software, programs, or commands stored in the storage unit (203).

[0053] The communication unit (202) can perform functions for transmitting and receiving signals with other devices. The communication unit (202) performs wired or wireless communication and can process signals according to the control of the control unit (201). For example, the communication unit (202) may include an RF circuit, an antenna, etc. for wireless communication, or a connection terminal, a modem, a driver module, etc. for wired communication. For example, the communication unit (202) may support at least one of various communication protocols, such as cellular communication such as LTE and 5G, short-range wireless communication such as WiFi and Bluetooth, and short-range wired communication such as Ethernet.

[0054] The storage unit (203) can store data used in the device, software for the operation of the device, programs, and commands. In addition, the storage unit (203) can provide stored data under the control of the control unit (201). In addition, the storage unit (203) can store applications, drivers, etc. to be driven by the control unit (201). For example, the storage unit (203) can include RAM such as DRAM, SRAM, etc.; ROM; EEPROM; HDD; SSD; flash storage means, etc.

[0055] Although not shown in FIG. 2, the device may further include at least one of a power supply, a display device, an input device, and an output device, depending on the type of device. For example, if the device is a user device (101), the device may further include a display device (e.g., a display), an input device (e.g., a touch panel, a button), and a power supply (e.g., a battery, a power supply).

[0056] FIG. 3 illustrates a methodology for predicting cTn levels after non-cardiac surgery according to one embodiment of the present invention.

[0057] Referring to FIG. 3, the prediction of cTn levels after non-cardiac surgery according to one embodiment of the present invention can be performed based on acquired information about a subject (i.e., a patient). The server device (102) acquires data about the subject from various sources, including the user device (101), and analyzes information contained in the acquired data to predict the cTn levels of the subject after non-cardiac surgery. For example, the server device (102) can predict the cTn levels of the subject after non-cardiac surgery.

[0058] For example, the 'cTn level' can include cTnI (i.e., cardiac troponin I) level, non-cardiac cTnT (i.e., cardiac troponin T) level, etc. As an example, the 'cTn' level can be the cTnI level. The cTnI level has a high specificity as an indicator of myocardial damage. Accordingly, predicting the cTnI level after non-cardiac surgery can be more advantageous in predicting myocardial injury after non-cardiac surgery (MINS).

[0059] For example, 'cTn level after non-cardiac surgery' can mean cTn level at any time point within 30 days, 14 days, 10 days, 5 days, or 3 days from the end of non-cardiac surgery. For example, 'cTn level after non-cardiac surgery' can mean cTn level at any time point 1 hour or 2 hours after the end of non-cardiac surgery. For example, 'cTn level after non-cardiac surgery' can mean cTn level at 12 hours, 24 hours, 48 ​​hours, 60 hours, or 72 hours after the end of non-cardiac surgery.

[0060] According to exemplary embodiments of the present invention, the subject's information used to predict cTn levels after non-cardiac surgery may include information about the subject before and during the non-cardiac surgery. That is, the server device (102) may obtain information about the subject before and during the non-cardiac surgery, and predict the subject's cTn levels after the non-cardiac surgery based on the obtained information. By using the information before and during the non-cardiac surgery together, the error rate in predicting cTn levels after the non-cardiac surgery can be significantly reduced.

[0061] For example, information about a subject prior to non-cardiac surgery may refer to information about the subject that is known even before the non-cardiac surgery, and information during non-cardiac surgery may refer to information about the subject that is known only during the non-cardiac surgery (e.g., phenomena occurring to the subject during the non-cardiac surgery). Specific examples of information before non-cardiac surgery and information during non-cardiac surgery are described below.

[0062] For example, the predicted cTn level after noncardiac surgery in a subject can be used as an indicator of whether the subject will have myocardial injury after noncardiac surgery (MINS). For example, the predicted cTn level after noncardiac surgery in a subject can be used as data to determine medical interventions (additional testing, such as cTn level testing; clinical treatment direction, etc.) for subjects who have undergone noncardiac surgery.

[0063] FIG. 4 illustrates an operational flowchart for predicting cTn levels after non-cardiac surgery according to one embodiment of the present invention. FIG. 4 shows operations performed by a server device (102). However, as described above, the cTn level prediction service after non-cardiac surgery may also be provided by a program or application installed on a user device (101) without a server device (102). In this case, the server device (102) described below may refer to the user device (101).

[0064] Referring to FIG. 4, the server device (102) can obtain information about a subject (i.e., a patient) before and during non-cardiac surgery (S401).

[0065] For example, information about a subject before and during non-cardiac surgery may be received by a server device (102) via a network. For example, information about a subject before and during non-cardiac surgery may be received from a user device (101), a third-party device used during the subject's non-cardiac surgery, etc.

[0066] In one embodiment, the subject's pre-cardiac surgery information may include at least one of the subject's basic information, the subject's pre-cardiac surgery diagnostic information, and the subject's non-cardiac surgery related information.

[0067] In some embodiments, the basic information of the subject may include at least one, at least two, or at least three of age, gender, height, and weight.

[0068] In some embodiments, the subject's pre-operative diagnostic information may include at least one, at least three, at least five, at least seven, or at least ten of the following: presence of diabetes, presence of hypertension, presence of dyslipidemia, presence of coronary artery disease, presence of peripheral artery disease, presence of cerebrovascular disease, presence of kidney disease (e.g., renal failure, etc.), presence of heart failure, presence of atrial fibrillation, presence of aortic stenosis, presence of chronic obstructive pulmonary disease, presence of dialysis patient, presence of anemia, blood hemoglobin level, blood creatinine level, and renal glomerular filtration rate.

[0069] We briefly describe the correlation between the aforementioned preoperative diagnostic information for MINS and cTn levels after non-cardiac surgery. As described above, since an increase in cTn levels is observed in the development of MINS, information deemed related to MINS may be associated with an increase in cTn levels. For example, i) type 2 diabetes mellitus is associated with insulin resistance, and insulin resistance can lead to cardiovascular disease, etc. Therefore, diabetes may be associated with MINS and cTn levels after non-cardiac surgery. ii) Elevated LDL cholesterol can lead to atherosclerotic plaques and premature cardiovascular disease, etc. Therefore, dyslipidemia may be associated with MINS and cTn levels after non-cardiac surgery. iii) Decreased renal function may increase the risk of MINS. Therefore, kidney disease, renal failure, glomerular filtration rate, dialysis, and serum creatinine levels may be associated with MINS and cTn levels after non-cardiac surgery. iv) Chronic obstructive pulmonary disease (COPD) can induce oxygenated hemoglobin desaturation and sympathetic stimulation during and / or after non-cardiac surgery, leading to an imbalance in myocardial oxygen supply and demand (i.e., an increased risk of MINS). Therefore, COPD may be associated with MINS and cTn levels after non-cardiac surgery. v) Anemia can cause an imbalance in myocardial oxygen supply and demand. Therefore, anemia, blood hemoglobin levels, etc. may be risk factors for MINS and may be associated with cTn levels after non-cardiac surgery. The correlation between other preoperative diagnostic information and MINS and cTn levels after non-cardiac surgery is omitted.

[0070] In some embodiments, the non-cardiac surgery related information may include at least one or at least two of the type of the non-cardiac surgery, whether the non-cardiac surgery is an emergency surgery, and whether the non-cardiac surgery includes vascular surgery.

[0071] The correlation between the aforementioned non-cardiac surgery-related information and cTn levels after MINS and non-cardiac surgery is briefly described. As described above, an increase in cTn levels is observed when MINS occurs, so information deemed to be related to MINS may be associated with an increase in cTn levels. Perioperative cardiac risk may be related to the type of non-cardiac surgery, and in particular, vascular surgery has a higher risk of thrombosis and occlusion due to vascular damage than other surgeries. Therefore, the inclusion of vascular surgery may be more closely related to MINS and cTn levels after non-cardiac surgery. In the case of emergency surgery (e.g., surgery due to an acute condition), the risk of MINS may increase, and therefore, the decision to perform emergency surgery may be related to MINS and cTn levels after non-cardiac surgery. According to exemplary embodiments of the present invention, by predicting cTn levels after non-cardiac surgery using non-cardiac surgery-related information, the prediction error rate for cTn levels after non-cardiac surgery can be further reduced.

[0072] In one embodiment, information regarding the subject's non-cardiac surgery may include at least one, at least two, or at least three of the following: surgery time; information regarding blood transfusion during non-cardiac surgery; information regarding hypotension during non-cardiac surgery; and information regarding heart rate during non-cardiac surgery. In this case, the prediction error rate for cTn levels after non-cardiac surgery may be reduced.

[0073] In some embodiments, information related to blood transfusion during non-cardiac surgery may include at least one of whether a blood transfusion is performed during non-cardiac surgery and the amount of blood transfusion during non-cardiac surgery.

[0074] In some embodiments, information related to hypotension during non-cardiac surgery may include at least one of: information related to the number of occurrences of hypotension during non-cardiac surgery; information related to the duration of hypotension occurring during non-cardiac surgery; and information related to the severity of hypotension occurring during non-cardiac surgery.

[0075] For example, when a subject's mean arterial pressure (MAP) falls below the hypotension threshold (H) and then rises above the hypotension threshold (P1), it can be defined as one occurrence of hypotension. For example, the hypotension threshold (H) can satisfy 60 mmHg≤H≤70 mmHg. As an example, the hypotension threshold (H) can be 65 mmHg.

[0076] In one embodiment, information regarding the number of occurrences of hypotension during non-cardiac surgery may include a total number of hypotensive events that occurred during non-cardiac surgery.

[0077] For example, a 'hypotension event' may be defined as an event in which a subject's MAP remains below the hypotension threshold (H) for a predetermined period of time (e.g., 1 minute, 30 seconds, 10 seconds, 5 seconds, 3 seconds, 1 second, etc.). As an example, a 'hypotension event' may be defined as an event in which a subject's MAP remains below 65 mmHg for more than 1 minute.

[0078] In one embodiment, information regarding the duration of hypotension occurring during non-cardiac surgery may include at least one of: total duration of hypotensive events occurring during non-cardiac surgery; and average duration of each hypotensive event occurring during non-cardiac surgery. For example, the average duration of hypotensive events may be calculated as an arithmetic mean by dividing the total duration of hypotensive events by the total number of hypotensive events.

[0079] In one embodiment, the severity of hypotension occurring during non-cardiac surgery may include (a) the mean of MAP over the total duration of the hypotensive event during the non-cardiac surgery (e.g., the mean of a function (y=f(x)) where MAP is defined as y and time as x, over the duration of the hypotensive event).

[0080] In one embodiment, the severity of hypotension occurring during non-cardiac surgery may include (b) the area between the hypotension threshold line (H) and the MAP curve, among the area under the hypotension threshold line, in the MAP (y-axis)-time (x-axis) graph obtained during the non-cardiac surgery.

[0081] In one embodiment, the severity of hypotension occurring during non-cardiac surgery may include (c) the time-weighted average MAP during non-cardiac surgery (TWA-MAP).

[0082] In one embodiment, the severity of hypotension occurring during non-cardiac surgery may include (d) the total number of events with MAP under severe hypotensive threshold (SH) during non-cardiac surgery. For example, SH may satisfy 40 mmHg≤SH<60 mmHg, preferably 42 mmHg≤SH≤58 mmHg, and more preferably 45 mmHg≤H≤55 mmHg. As an example, SH=50 mmHg.

[0083] The severity of hypotension occurring during non-cardiac surgery according to one embodiment of the present invention may include at least one, at least two, or at least three of the above-mentioned (a), (b), (c), and (d).

[0084] In a specific embodiment, information related to hypotension during non-cardiac surgery may include: a total number of occurrences of hypotensive events during non-cardiac surgery (wherein a 'hypotensive event' is defined as a mean arterial pressure (MAP) of less than 65 mmHg lasting for more than 1 minute); a total duration of hypotensive events occurring during non-cardiac surgery; an average duration of hypotensive events occurring during non-cardiac surgery; an average value of MAP during the hypotensive event occurrence period during non-cardiac surgery; an area of ​​an area between the MAP=65 mmHg line and the MAP curve in an area below the MAP=65 mmHg line in a MAP (y-axis, mmHg)-time (x-axis, minute) graph acquired during non-cardiac surgery; a TWA-MAP during non-cardiac surgery; and a total number of occurrences of events in which MAP is less than 50 mmHg during non-cardiac surgery. In this case, the prediction error rate for cTnI levels after non-cardiac surgery can be further reduced, and excellent R2 score values ​​can be achieved.

[0085] In some embodiments, information regarding the subject's heart rate during non-cardiac surgery may include information regarding tachycardia during non-cardiac surgery. For example, information regarding tachycardia during non-cardiac surgery may include at least one of: whether tachycardia occurred during non-cardiac surgery; information regarding the number of occurrences of tachycardia during non-cardiac surgery; and information regarding the duration of tachycardia occurring during non-cardiac surgery.

[0086] In one embodiment, the information related to tachycardia during non-cardiac surgery may include at least one, at least two, or at least three of: whether tachycardia occurred during non-cardiac surgery; the total number of tachycardia events that occurred during non-cardiac surgery; the total duration of tachycardia events that occurred during non-cardiac surgery; and the average duration of tachycardia events that occurred during non-cardiac surgery.

[0087] We briefly describe the correlation between the aforementioned intraoperative data and cTn levels during MINS and after non-cardiac surgery. As previously described, increased cTn levels are observed during MINS, so data considered to be related to MINS may be associated with increased cTn levels. i) As the duration of surgery increases, the anesthesia duration may also increase, increasing the likelihood of sympathetic hyperactivity, hypotension, tachycardia, and hypothermia. This may increase the likelihood of developing cardiac diseases in the subject. Accordingly, the duration of surgery may be associated with MINS and cTn levels after non-cardiac surgery. ii) Major bleeding may be an independent risk factor for myocardial infarction. Therefore, the amount of intraoperative blood transfusion may be associated with the amount of blood loss, and thus the presence and amount of intraoperative blood transfusion may be associated with MINS and cTn levels after non-cardiac surgery. iii) Even brief episodes of intraoperative hypotension are believed to affect myocardial injury, acute kidney injury, and mortality. In particular, the severity and duration of hypotension during non-cardiac surgery are believed to be critical factors in myocardial damage. Therefore, hypotension-related information may be associated with MINS and cTn levels after non-cardiac surgery. iv) Tachycardia is thought to increase myocardial oxygen demand and decrease diastolic coronary perfusion time, potentially affecting myocardial damage. Therefore, tachycardia-related information may be associated with MINS and cTn levels after non-cardiac surgery.

[0088] According to exemplary embodiments of the present invention, when predicting cTn levels after non-cardiac surgery by using information prior to and during a subject's non-cardiac surgery, the prediction error rate for cTn levels after non-cardiac surgery can be reduced. Furthermore, when predicting cTn levels after non-cardiac surgery by using information prior to the subject's non-cardiac surgery and information related to hypotension during the aforementioned non-cardiac surgery, the prediction error rate for cTn levels after non-cardiac surgery can be further reduced.

[0089] In particular, when predicting cTn levels after non-cardiac surgery by utilizing all of the information related to hypotension during the aforementioned non-cardiac surgery (i.e., utilizing all of the information related to the number of occurrences of hypotension during non-cardiac surgery, the information related to the duration of hypotension, and the severity of hypotension), the prediction error rate for cTn levels after non-cardiac surgery can be particularly reduced, and an excellent R2 score value can be implemented.

[0090] The server device (102) can determine the cTn level of the subject after non-cardiac surgery based on the information of the subject before and during the non-cardiac surgery (S402).

[0091] For example, the predicted cTn level after non-cardiac surgery in a subject can be used as an indicator to infer whether the subject will develop MINS. For example, the predicted cTn level after non-cardiac surgery in a subject can be used as data to determine medical interventions (e.g., additional testing; e.g., cTn level testing) for subjects who have undergone non-cardiac surgery.

[0092] For example, the server device (102) can transmit the predicted cTn level of the subject after non-cardiac surgery to the user device (101), a third party device, etc. For example, a hospital, a testing institution, etc. can decide on medical measures for the subject based on the predicted cTn level of the subject after non-cardiac surgery.

[0093] In one embodiment, the server device (102) may preprocess the acquired information about the subject before and during non-cardiac surgery. For example, the preprocessing may include vectorization, standardization, normalization, etc.

[0094] In one embodiment, in step S402, an artificial intelligence model trained to predict cTn levels after non-cardiac surgery of a subject may be used based on information before and during the non-cardiac surgery of the subject.

[0095] That is, in step S402, an artificial intelligence model that has been learned and trained using a learning data set that uses information before and during non-cardiac surgery as input data and cTn levels after non-cardiac surgery as output data (i.e., labels) can be used.

[0096] In some embodiments, the AI ​​model may use undersampling or oversampling (e.g., Synthetic Minority Over-Sampling Technique; SMOTE, etc.) during learning and training to address imbalances in the training data.

[0097] In some embodiments, the artificial intelligence model may include a machine learning or artificial neural network-based predictive model (e.g., a regression model, etc.). For example, the artificial neural network may include an input layer and an output layer, and one or more hidden layers (e.g., a deep neural network, DNN).

[0098] In some embodiments, the machine learning or artificial neural network-based predictive model may include at least one of a linear regression model, a ridge regression model, a lasso regression model, a polynomial regression model, and a multi-layer perceptron (MLP).

[0099] FIG. 5 illustrates a flowchart of operations for predicting myocardial injury (MINS) after non-cardiac surgery according to another embodiment of the present invention. FIG. 5 may be operations performed by a server device (102). However, as described above, the MINS prediction service may be provided by a program or application installed on a user device (101) without a server device (102), in which case the server device (102) described below may refer to the user device (101). Any content overlapping with the content related to FIG. 4 described above may be omitted.

[0100] Referring to FIG. 5, the server device (102) can obtain information about the subject before and during non-cardiac surgery (S501).

[0101] The server device (102) can determine the cTn level of the subject after non-cardiac surgery based on the subject's pre- and post-cardiac surgery information (S502). For S501 and S502, the contents of S401 and S402 described above can be applied substantially identically, and thus a detailed description thereof will be omitted.

[0102] The server device (102) can determine whether the cTn level after non-cardiac surgery is above a predetermined value (i.e., a threshold value) (S503). For example, the predetermined value may be a reference value for the occurrence of MIINS.

[0103] For example, if the cTn level is higher than a predetermined value, the server device (102) can transmit the cTn level after non-cardiac surgery to the user device (101), a third party device, etc. For example, a hospital, a testing institution, etc. can take follow-up medical measures for a subject whose cTn level is higher than a predetermined value after non-cardiac surgery.

[0104] If the cTn level is below a predetermined value after non-cardiac surgery, the server device (102) can obtain information about the subject after non-cardiac surgery (S504). For example, the information about the subject after non-cardiac surgery may be information obtained from the subject within one week, five days, three days, sixty hours, forty-eight hours, thirty-six hours, or twenty-four hours after the non-cardiac surgery. Information about tests known in the art to directly determine cTn levels may be excluded from the information about the subject after non-cardiac surgery.

[0105] For example, information about a subject following non-cardiac surgery may include information about the subject's post-surgical questionnaire. For example, the post-surgical questionnaire may include the subject's response data to multiple questionnaires. For example, the multiple questionnaires may include preset questionnaires and corresponding multiple-choice response items. For example, the questionnaire may include questions related to MINS symptoms that the subject may experience.

[0106] The server device (102) can additionally determine a second cTn level based on the cTn level (hereinafter, the first cTn level) of the subject after non-cardiac surgery determined in S502 and the information after non-cardiac surgery (S505). The server device (102) can transmit the second cTn level to the user device (101), a third device, etc. Hospitals, testing institutions, etc. can determine follow-up medical measures for the subject based on the second cTn level.

[0107] For example, the server device (102) may utilize an artificial intelligence model trained to predict a second cTn level based on the subject's first cTn level and post-cardiac surgery information. That is, the server device (102) may utilize an artificial intelligence model trained and learned using a learning data set that uses the first cTn level and post-cardiac surgery information as input data and the cTn level as output data (i.e., a label). For example, the artificial intelligence model may include a machine learning or artificial neural network-based model (e.g., a regression model).

[0108] According to one embodiment of the present invention, for a subject whose first cTn level is determined to be above a predetermined value, the first cTn level can be relied upon to promptly provide care for the subject by taking MINS-related follow-up medical measures. Furthermore, for a subject whose first cTn level is determined to be below a predetermined value, a second cTn level can be additionally determined, thereby enhancing care for the subject.

[0109] References to "one embodiment" of the principles of the present invention and various variations of this expression in this specification mean that a particular feature, structure, characteristic, etc., is included in at least one embodiment of the principles of the present invention in connection with that embodiment. Accordingly, the expression "in one embodiment" and any other variations disclosed throughout this specification are not necessarily all referring to the same embodiment.

[0110] The methods according to the various embodiments of the present invention described above may be implemented as a computer program or mobile application to be executed by combining a computer as hardware and stored on a medium. Alternatively, the steps of the methods or algorithms described in connection with the embodiments of the present invention may be implemented directly in hardware, implemented as a software module executed by hardware, or implemented by a combination thereof. The software module may reside in a random access memory (RAM), a read only memory (ROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), a flash memory, a hard disk, a removable disk, a CD-ROM, or any other form of computer-readable recording medium well known in the art to which the present invention pertains. In addition, the algorithm may be produced in the form of an installation file and provided in the form of an online download, and for this purpose, may be stored on a server accessible through an online software market.

[0111] All embodiments and conditional examples disclosed in this specification are intended to help those skilled in the art understand the principles and concepts of the present invention. Those skilled in the art will appreciate that the present invention can be implemented in modified forms without departing from the essential characteristics thereof. Therefore, the disclosed embodiments should be considered in an illustrative rather than a restrictive sense. The scope of the present invention is set forth in the claims, not the foregoing description, and all differences within the scope equivalent thereto should be construed as being included in the present invention.

Claims

1. A method for predicting cardiac troponin levels after non-cardiac surgery, the method comprising: A step of obtaining information about the subject before and during non-cardiac surgery; and A method for predicting cardiac troponin levels after non-cardiac surgery, comprising the step of determining a cardiac troponin level of the subject after non-cardiac surgery based on information before the non-cardiac surgery of the subject and information during the non-cardiac surgery of the subject.

2. In claim 1, A method for predicting cardiac troponin levels after non-cardiac surgery, wherein the cardiac troponin levels include at least one of cardiac troponin I levels and cardiac troponin T levels.

3. In claim 1, A method for predicting cardiac troponin levels after non-cardiac surgery, wherein the information of the subject before the non-cardiac surgery includes at least one of: basic information of the subject; diagnostic information of the subject before the non-cardiac surgery; and information related to the non-cardiac surgery.

4. In claim 3, The basic information of the above subject includes at least one of age, gender, height, and weight. The above-mentioned diagnosis information of the subject prior to the above-mentioned non-cardiac surgery includes at least one of the following: presence of diabetes, presence of hypertension, presence of dyslipidemia, presence of coronary artery disease, presence of peripheral artery disease, presence of cerebrovascular disease, presence of kidney disease, presence of heart failure, presence of atrial fibrillation, presence of aortic valve stenosis, presence of chronic obstructive pulmonary disease, presence of dialysis patient, presence of anemia, blood hemoglobin level, blood creatinine level, and renal glomerular filtration rate. A method for predicting cardiac troponin levels after non-cardiac surgery, wherein the non-cardiac surgery-related information includes at least one of the type of the non-cardiac surgery, whether the non-cardiac surgery is an emergency surgery, and whether the non-cardiac surgery includes vascular surgery.

5. In claim 1, A method for predicting cardiac troponin levels after non-cardiac surgery, wherein the information during the non-cardiac surgery includes at least one of: the operation time of the non-cardiac surgery; information related to blood transfusion during the non-cardiac surgery; information related to hypotension during the non-cardiac surgery; and information related to heart rate during the non-cardiac surgery.

6. In claim 5, The information related to blood transfusion during the above non-cardiac surgery includes at least one of whether blood transfusion is performed during the above non-cardiac surgery and the amount of blood transfusion during the above non-cardiac surgery. A method for predicting cardiac troponin levels after non-cardiac surgery, wherein the information related to heart rate during the non-cardiac surgery includes information related to tachycardia during the non-cardiac surgery.

7. In claim 5, A method for predicting cardiac troponin levels after non-cardiac surgery, wherein the information related to hypotension during the non-cardiac surgery comprises at least one of: information related to the number of occurrences of hypotension during the non-cardiac surgery; information related to the duration of hypotension that occurred during the non-cardiac surgery; and information related to the severity of hypotension that occurred during the non-cardiac surgery.

8. In claim 7, Information on the occurrence of hypotension during the above non-cardiac surgery includes the total number of hypotension events that occurred during the above non-cardiac surgery, A method for predicting cardiac troponin levels after non-cardiac surgery, wherein information related to the duration of hypotension that occurred during the non-cardiac surgery includes at least one of: total duration of hypotension that occurred during the non-cardiac surgery; and average duration of hypotension that occurred during the non-cardiac surgery.

9. In claim 7, The severity of hypotension occurring during non-cardiac surgery includes at least one of: an average of mean arterial pressure (MAP) during the total occurrence period of hypotension events during said non-cardiac surgery; an area of ​​an area between a hypotension reference value line and a MAP curve in a MAP (y-axis)-time (x-axis) graph acquired during said non-cardiac surgery that exists under a hypotension reference value line; a time-weighted average of MAP during said non-cardiac surgery; and a total number of occurrences of events in which MAP is below a severe hypotension threshold value during said non-cardiac surgery. A method for predicting cardiac troponin levels after non-cardiac surgery, wherein the severe hypotension threshold values ​​are values ​​equal to or greater than 40 mmHg and less than 60 mmHg.

10. In claim 1, A method for predicting cardiac troponin levels after non-cardiac surgery, wherein the step of determining the cardiac troponin level after the non-cardiac surgery comprises using an artificial intelligence model learned to predict the cardiac troponin level of the subject after the non-cardiac surgery based on information before the non-cardiac surgery of the subject and information during the non-cardiac surgery.

11. In claim 10, The above artificial intelligence model is a method for predicting cardiac troponin levels after non-cardiac surgery, including a regression model based on machine learning or artificial neural networks.

12. In claim 1, When the cardiac troponin level of the above subject after the above non-cardiac surgery is defined as the first cardiac troponin level, if the first cardiac troponin level of the above subject is below the specified value, A step of obtaining information after the non-cardiac surgery of the subject; and A method for predicting cardiac troponin levels after non-cardiac surgery, further comprising the step of determining a second cardiac troponin level of the subject based on the first cardiac troponin level of the subject and the post-non-cardiac surgery information.

13. In claim 12, The above post-cardiac surgery information includes post-cardiac surgery questionnaire information for the subject, A method for predicting cardiac troponin levels after non-cardiac surgery, wherein the above questionnaire information includes the subject's response data to multiple questionnaires.

14. A device for predicting cardiac troponin levels after non-cardiac surgery, Department of Communications; memory; and comprising at least one processor connected to the communication unit and the memory; A device for predicting cardiac troponin levels after non-cardiac surgery, wherein said at least one processor obtains information about the subject before the non-cardiac surgery and information about the subject during the non-cardiac surgery, and controls the determination of a cardiac troponin level of the subject after the non-cardiac surgery based on the information about the subject before the non-cardiac surgery and the information about the subject during the non-cardiac surgery.

15. An application program stored on a recording medium that executes the method according to claim 1 when operated by at least one processor.

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