Methods, devices, equipment and media for managing dynamic hemodynamic parameters

Abstract

This disclosure relates to a method, apparatus, device, and medium for managing dynamic hemodynamic parameters. The method includes: monitoring hemodynamic parameters; wherein the hemodynamic parameters include at least: patient age, maximum pleural fluid level, cardiac index, maximum rate of increase of cardiac index, and maximum rate of increase of myocardial contractility; inputting the hemodynamic parameters into a preset decision tree to output mechanical ventilation time; wherein the decision tree is used to represent the decision relationship between various hemodynamic parameters and mechanical ventilation time; and sending the hemodynamic parameters and mechanical ventilation time to a terminal for display. This disclosure can significantly improve the efficiency of clinical decision-making.

Description

Technical Field

[0001] This disclosure relates to the field of data management technology, and in particular to a method, device, equipment and medium for managing dynamic hemodynamic parameters. Background Technology

[0002] Traditional monitoring techniques for congenital heart disease each have their own limitations. For example, invasive monitoring is difficult to use routinely due to its traumatic and high-risk nature; echocardiography assesses cardiac function only through static indicators (such as left ventricular ejection fraction and ventricular size) and cannot achieve continuous monitoring; biomarkers such as N-terminal pro-B-type natriuretic peptide have detection lags and cannot provide real-time guidance for clinical decision-making.

[0003] In terms of empirical assessment, scoring systems such as the Ross functional classification are highly subjective and lack quantitative standards. While related technologies such as the ICON (Index of Contractility) non-invasive cardiac output monitor can monitor hemodynamic parameters, they are limited to the data acquisition level and have neither established predictive models nor provided intelligent decision support, thus limiting their clinical application value.

[0004] Therefore, the main shortcomings of the relevant technologies are reflected in four aspects: First, the intermittent nature of monitoring methods makes it impossible to capture the dynamic changes in cardiac function, which may delay the best treatment time; second, traditional prediction models have low prediction accuracy because they rely on subjective judgment and do not integrate dynamic parameters; third, the raw data output by existing ICON devices lacks automated analysis functions and quantitative decision thresholds, making it difficult to directly guide clinical practice; and finally, the monitoring, assessment and intervention links are isolated from each other, failing to form a closed-loop management system, which increases the complexity of clinical operations. Summary of the Invention

[0005] To address the aforementioned technical problems, this disclosure provides a method, apparatus, device, and medium for managing dynamic hemodynamic parameters.

[0006] According to one aspect of this disclosure, a method for managing dynamic hemodynamic parameters is provided, the method comprising:

[0007] Monitor hemodynamic parameters; wherein the hemodynamic parameters include at least: patient age, maximum pleural fluid level, cardiac index, maximum rate of increase of cardiac index, and maximum rate of increase of myocardial contractility;

[0008] The hemodynamic parameters are input into a preset decision tree to output the mechanical ventilation time; wherein, the decision tree is used to represent the decision relationship between each of the hemodynamic parameters and the mechanical ventilation time;

[0009] The hemodynamic parameters and the mechanical ventilation time are sent to the terminal for display.

[0010] According to another aspect of this disclosure, a dynamic hemodynamic parameter management device is also provided, the device comprising:

[0011] The monitoring module is used to monitor hemodynamic parameters; wherein the hemodynamic parameters include at least: patient age, maximum pleural fluid level, cardiac index, maximum rate of increase of cardiac index, and maximum rate of increase of myocardial contractility;

[0012] A decision module is used to input the hemodynamic parameters into a preset decision tree to output the mechanical ventilation time; wherein, the decision tree is used to represent the decision relationship between each of the hemodynamic parameters and the mechanical ventilation time;

[0013] The display module is used to send the hemodynamic parameters and the mechanical ventilation time to the terminal for display.

[0014] According to another aspect of this disclosure, an electronic device is also provided, the electronic device comprising:

[0015] processor;

[0016] Memory used to store the processor's executable instructions;

[0017] The processor is configured to read the executable instructions from the memory and execute the instructions to implement the above-described dynamic hemodynamic parameter management method.

[0018] According to another aspect of this disclosure, a computer-readable storage medium is also provided, the storage medium storing a computer program for performing the above-described dynamic hemodynamic parameter management method.

[0019] The technical solution provided in this disclosure has the following advantages compared with the prior art:

[0020] The technical solution provided in this disclosure includes: monitoring hemodynamic parameters; wherein the hemodynamic parameters include at least: patient age, maximum pleural fluid level, cardiac index, maximum rate of increase of cardiac index, and maximum rate of increase of myocardial contractility; inputting the hemodynamic parameters into a preset decision tree to output mechanical ventilation time; wherein the decision tree is used to represent the decision relationship between various hemodynamic parameters and mechanical ventilation time; and sending the hemodynamic parameters and mechanical ventilation time to a terminal for display.

[0021] This technical solution constructs a fully automated system that monitors hemodynamic parameters, predicts mechanical ventilation time based on decision trees, and displays various data, which can significantly improve the efficiency of clinical decision-making. Attached Figure Description

[0022] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0023] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart of the dynamic hemodynamic parameter management method described in the embodiments of this disclosure;

[0025] Figure 2 This is a schematic diagram of the system architecture described in the embodiments of this disclosure;

[0026] Figure 3 This is a schematic diagram of the decision tree described in an embodiment of this disclosure;

[0027] Figure 4 This is a schematic diagram of the structure of the dynamic hemodynamic parameter management device described in the embodiments of this disclosure;

[0028] Figure 5 This is a schematic diagram of the structure of the electronic device described in an embodiment of this disclosure. Detailed Implementation

[0029] To better understand the above-mentioned objectives, features, and advantages of this disclosure, the solutions disclosed herein will be further described below. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0030] Numerous specific details are set forth in the following description in order to provide a full understanding of this disclosure, but this disclosure may also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only some, and not all, of the embodiments of this disclosure.

[0031] Currently, the main shortcomings of technologies related to perioperative management of congenital heart disease in infants and young children lie in four aspects: First, the intermittent nature of monitoring methods makes it impossible to capture dynamic changes in cardiac function, which may delay the optimal treatment time; second, traditional prediction models have low accuracy due to their reliance on subjective judgment and lack of integration of dynamic parameters such as ICON rate of change (ICONR), cardiac index rate of change (CIR), and maximum thoracic fluid content (TFCmax); third, the raw data output by existing ICON devices lacks automated analysis functions and quantitative decision thresholds, making it difficult to directly guide clinical practice; and finally, the monitoring, assessment, and intervention processes are isolated from each other, failing to form a closed-loop management system and increasing the complexity of clinical operations.

[0032] To improve at least one of the aforementioned systemic defects, this disclosure provides a method, apparatus, device, and medium for managing dynamic hemodynamic parameters.

[0033] This disclosure can be applied, for example, to the perioperative management of congenital heart disease in infants and young children, providing an intelligent and dynamic decision support system. Specifically, this disclosure aims to solve the following key technical problems: 1) Overcoming the limitations of traditional intermittent monitoring by continuously and dynamically collecting hemodynamic parameters such as ICNR, CIR, and TFCmax to track the trend of cardiac function changes in real time; 2) Compensating for the subjectivity and bias of existing assessment methods by constructing a decision tree to significantly improve the accuracy of predicting mechanical ventilation time and complication risks; 3) Eliminating gaps in clinical decision support by developing an automated decision module with clear quantitative thresholds (such as ICNR ≥ 25%); 4) Integrating existing discrete monitoring-assessment-intervention links to form a closed-loop management system based on real-time data-driven approaches.

[0034] Figure 1 This is a flowchart illustrating a dynamic hemodynamic parameter management method provided in an embodiment of this disclosure. This method can be executed by a dynamic hemodynamic parameter management device, which can be implemented using software and / or hardware. Figure 1 As shown, the dynamic hemodynamic parameter management method may include the following steps:

[0035] S102, monitor hemodynamic parameters; among which, the hemodynamic parameters include at least: patient age, TFCmax, CI, CIR, and ICONR;

[0036] S104, input the hemodynamic parameters into the preset decision tree to output the duration of mechanical ventilation (DMV); wherein, the decision tree is used to represent the influence relationship between various hemodynamic parameters and the duration of mechanical ventilation (DMV);

[0037] S106 sends hemodynamic parameters and mechanical ventilation time to the terminal for display.

[0038] To better understand the solution, each of the above steps will be described in detail below.

[0039] Regarding step S102, this embodiment provides a method for monitoring hemodynamic parameters, including the following:

[0040] The steps for monitoring baseline parameters include: collecting baseline parameters from patients at multiple time windows using a pre-set monitoring device; among which, baseline parameters include, but are not limited to: baseline, final and maximum values ​​of cardiac index (CI), baseline, final and maximum values ​​of myocardial contractility (ICON), thoracic fluid content (TFC), and systolic time ratio (STR).

[0041] Specifically, refer to Figure 2 The system architecture shown includes: a monitoring device layer, a data transmission layer, a server layer, and a terminal layer.

[0042] At the monitoring equipment level, there are devices such as non-invasive cardiac function monitors and electrocardiogram monitors. These devices collect baseline parameters from patients at multiple time windows.

[0043] The aforementioned time window can be multiple time periods divided based on a period of time in the early stages of a patient's hospitalization, such as: the baseline period, the adjustment period, and the final value period. For example, the first day of admission can be used as the baseline period, the period from the day after admission to the day before surgery can be used as the adjustment period, and the day before surgery can be used as the final value period.

[0044] During the baseline period, baseline values ​​of the cardiac index (CI) and myocardial contractility (ICON) are monitored using monitoring equipment, denoted as Baseline CI and Baseline ICON, respectively. It is understood that during the baseline period, the monitoring equipment can continuously collect a large number of cardiac index values. To combat data fluctuations, this embodiment can use median filtering to select the median as the baseline value of the cardiac index, improving data accuracy. Similarly, the median of the myocardial contractility (ICON) is selected as the baseline value.

[0045] During the adjustment period, real-time values ​​of the cardiac index (CI), myocardial contractility (ICON), and pleural fluid level (TFC) are continuously collected using monitoring equipment. Taking the cardiac index (CI) as an example, a large number of real-time CI values ​​will be collected throughout the adjustment period, and the maximum value needs to be determined. To prevent transient interference, this embodiment can use a sliding window peak detection algorithm to select the peak value from all real-time CI values ​​during the adjustment period to obtain the maximum value of the cardiac index (denoted as CImax). Similarly, the maximum values ​​of myocardial contractility (denoted as ICONmax) and pleural fluid level (denoted as TFCmax) can be obtained.

[0046] During the final value period, the final values ​​of the cardiac index (CI), myocardial contractility (ICON), pleural fluid level (TFC), and systolic time ratio (STR) are collected using monitoring equipment. Similar to the baseline period, in order to resist data fluctuations during the final value period, the median of the cardiac index (CI), myocardial contractility (ICON), pleural fluid level (TFC), and systolic time ratio (STR) can be selected as the final values ​​of these three values, thereby improving data accuracy.

[0047] In the above embodiments, median filtering can effectively reduce the influence of motion artifacts and equipment noise, and enhance anti-interference characteristics; by adopting the sliding window peak monitoring algorithm, subjective judgment bias can be eliminated, the objectivity of various hemodynamic parameters can be improved, and an objective quantitative evaluation system can be established based on this.

[0048] After the monitoring equipment layer collects the aforementioned basic parameters, it can send these parameters to the server layer via the data transmission layer. Specifically, the monitoring equipment layer can output basic parameters such as the baseline, final, and maximum values ​​of the cardiac index (CI), the baseline, final, and maximum values ​​of myocardial contractility (ICON), the pleural fluid level (TFC), and the systolic time ratio (STR) to the data transmission layer in real time according to a preset transmission frequency (e.g., 0.5Hz).

[0049] The data transmission layer may include a medical-grade gateway that supports both Wi-Fi and Bluetooth dual-mode transmission. The data transmission layer converts the received basic parameters into HL7 FHIR format, then encrypts the converted parameters using a preset encryption protocol (such as AES-256+HTTPS) (e.g., AES-256), and finally uploads the encrypted parameters to the server layer via HTTPS.

[0050] The server layer may include at least one edge computing node, which is used to perform the following steps to calculate dynamic parameters; the edge computing node may also be used to cache various data within a preset time period (such as the most recent 24 hours) and store historical cases, etc.

[0051] At the server layer, the steps for calculating dynamic parameters include: determining the maximum rate of increase (CIR) of the cardiac index (CI) based on the maximum value and baseline value of CI; and determining the maximum rate of increase (ICONR) of myocardial contractility (ICON) based on the maximum value and baseline value of ICON.

[0052] Specifically, the maximum rate of increase in cardiac index (CIR) and the maximum rate of increase in myocardial contractility (ICONR) represent the degree of improvement in cardiac function after preoperative adjustments. They are calculated as follows:

[0053] Referring to the following formula (1), the maximum increase rate (CIR) of the cardiac index (CI) is determined based on the percentage difference between the maximum value and the baseline value of CI.

[0054] CIR =(CImax- Baseline CI) / Baseline CI * 100% (1)

[0055] Referring to the following formula (2), the maximum increase rate of myocardial contractility ICON, ICOR, is determined based on the percentage difference between the maximum and baseline values ​​of myocardial contractility ICON:

[0056] ICONR =(ICONmax-Baseline ICON) / Baselinex100% (2)

[0057] At the server layer, dynamic parameters such as the maximum rate of increase in cardiac index (CIR) and the maximum rate of increase in myocardial contractility (ICONR) can be calculated using Python. During the calculation process, a denominator protection mechanism is added for the maximum rate of increase in cardiac index (CIR) and the maximum rate of increase in myocardial contractility (ICONR) to prevent division by zero errors.

[0058] For monitoring hemodynamic parameters, this embodiment also includes the step of collecting auxiliary parameters, namely: collecting the patient's auxiliary parameters; wherein, the auxiliary parameters include: patient age, N-terminal pro-Brain natriuretic peptide (NT-proBNP) and vital signs displayed by the electrocardiogram monitor.

[0059] In the above embodiments for monitoring hemodynamic parameters, the monitoring equipment can continuously and uninterruptedly collect multiple hemodynamic parameters such as cardiac index (CI), myocardial contractility (ICON), pleural fluid level (TFC), systolic time ratio (STR), maximum rate of increase of cardiac index (CIR), maximum rate of increase of myocardial contractility (ICONR), and maximum value of pleural fluid level (TFCmax). This significantly improves the dynamic tracking capability of the parameters, breaks through the limitations of existing single-parameter monitoring, and realizes real-time tracking of changes in cardiac function.

[0060] In step S104, hemodynamic parameters are input into a pre-defined decision tree to output the mechanical ventilation time; this decision tree is pre-built and can be directly applied here. The method for constructing the decision tree will be described in subsequent embodiments.

[0061] In this embodiment, the implementation process of step S104 will be described first, referring to... Figure 3 It can include the following:

[0062] (1) Input the hemodynamic parameters into the preset decision tree.

[0063] (2) Determine whether the patient’s age is less than the preset first age through the decision tree; the first age is, for example, 6 months in the infant scenario, or more specifically, 6.115 months after accurate calculation.

[0064] (3) If the patient’s age is not less than the first age, the predicted mechanical ventilation time is not greater than the first time.

[0065] In practical applications, the mechanical ventilation duration (DMV) is generally divided into two groups based on 12 hours. Duration within 12 hours is considered the normal DMV group, while duration exceeding 12 hours is considered the delayed DMV group. Therefore, in this embodiment, the first time is set to 12 hours.

[0066] Among the factors considered in clinical decision-making, age is the most important predictor; children over 6 months of age have normal postoperative DMV; children under 3 months of age have prolonged DMV; for patients aged 3 to 6 months, the duration of mechanical ventilation is predicted by combining parameters such as the maximum increase rate of myocardial contractility (ICONR), the maximum pleural fluid level (TFCmax), and the cardiac index (CI).

[0067] In this embodiment, if the patient's age is determined to be no less than the first age (i.e., the patient's age ≥ 6.115), the predicted mechanical ventilation time is no more than the first time (DMV ≤ 12h).

[0068] (4) If the patient's age is less than the first age and the patient's age is determined to be less than the preset second age, then the predicted mechanical ventilation time is greater than the first time. The second age is, for example, 3 months in an infant scenario, or more specifically, 3.185 months after accurate calculation.

[0069] In this embodiment, when the patient's age is determined to be less than the second age (i.e., the patient's age < 3.185), the predicted mechanical ventilation time is greater than the first time (DMV > 12h).

[0070] (5) When the patient’s age is not less than the second age and less than the first age (i.e., between 3 and 6 months of age), determine whether the maximum value of the pleural fluid level TFCmax is less than the preset first threshold (e.g., 43).

[0071] (6) If the maximum value of the pleural fluid level TFCmax is not less than the preset first threshold (i.e., TFCmax≥43), determine whether the maximum increase rate of the cardiac index CIR is less than the preset second threshold (e.g., 0.399).

[0072] (7) If it is less than (i.e., CIR < 0.399), then the predicted mechanical ventilation time is greater than the first time (DMV > 12h).

[0073] (8) If it is not less than (i.e., CIR≥0.399), the predicted mechanical ventilation time is not greater than the first time (DMV≤12h).

[0074] (9) When the maximum value of the pleural fluid level TFCmax is less than the preset first threshold (i.e., TFCmax < 43), determine whether the maximum increase rate of myocardial contractility ICNR is less than the preset third threshold (e.g., 25%).

[0075] Specifically, practical research has found that the maximum increase rate of myocardial contractility (ICONR) has the strongest correlation with cardiac function classification and the highest diagnostic value for assessing the severity of cardiac function impairment. Furthermore, multivariate analysis has determined that whether the maximum increase rate of myocardial contractility (ICONR) reaches 25% is an independent factor affecting cardiac function assessment. The reasons may include: firstly, ICON reflects myocardial contractility and is less affected by hemodynamic parameters such as cardiac rhythm, volume overload, and individual characteristics such as body surface area compared to hemodynamic parameters like CI; secondly, as the effects of treatments such as cardiotonics, sedation, diuretics, and vasodilators become apparent, the child's heart rate slows down, pulmonary blood flow is relatively reduced, pulmonary interstitial edema is alleviated, the cardiothoracic ratio decreases, and the excessive volume overload caused by excessive circulating blood volume is improved. Therefore, cardiac output decreases slightly, but myocardial compliance and contractility further improve.

[0076] Based on this, this embodiment uses the maximum increase rate of myocardial contractility (ICONR) as a judgment condition in the decision tree to determine whether the condition is satisfied (ICONR≥25%).

[0077] (10) If the maximum increase rate of myocardial contractility ICNR is not less than the third threshold (i.e., ICNR≥25%), the predicted mechanical ventilation time is not greater than the first time (DMV≤12h).

[0078] (11) If the maximum increase rate of myocardial contractility ICNR is less than the third threshold (i.e., ICNR < 25%), then determine whether the cardiac index CI is less than the preset fourth threshold (e.g., 4.1 L / min / m2).

[0079] (12) If the cardiac index CI is less than the fourth threshold (i.e. CI < 4.1), the predicted mechanical ventilation time is greater than the first time (DMV > 12h).

[0080] (13) If CI is not less than the fourth threshold (i.e. CI≥4.1), the predicted mechanical ventilation time is not greater than the first time (DMV≤12h).

[0081] Based on the above steps, the implementation of predicting mechanical ventilation time using decision trees shows that patient age is the most important predictor. Patients older than 6 months have normal postoperative mechanical ventilation time (DMV ≤ 12h), while patients younger than 3 months have normal prolonged postoperative mechanical ventilation time (DMV > 12h).

[0082] For patients aged 3–6 months: (a) If TFCmax < 43, ICONR ≥ 25% or CI ≥ 4.1 L / min / m2, the postoperative mechanical ventilation time is normal (DMV ≤ 12 h); otherwise, the postoperative mechanical ventilation time is prolonged (DMV > 12 h). (b) If TFCmax ≥ 43, CIR ≥ 0.399, the postoperative mechanical ventilation time is normal (DMV ≤ 12 h); if CIR < 0.399, the postoperative mechanical ventilation time is prolonged (DMV > 12 h).

[0083] This embodiment uses a decision tree to make a composite decision on the duration of mechanical ventilation after surgery, based on several important hemodynamic parameters that affect the duration of mechanical ventilation, such as patient age, maximum intracavitary fluid level (TFCmax), maximum increase rate of cardiac index (CIR), maximum increase rate of myocardial contractility (ICONR), and cardiac index (CI). This improves the comprehensiveness and accuracy of the prediction.

[0084] Regarding step S106, in one embodiment, the hemodynamic parameters and mechanical ventilation time are sent to the terminal for display, as shown below.

[0085] Determine if the mechanical ventilation time is greater than the preset first time; if not, send the hemodynamic parameters and mechanical ventilation time to the terminal for display.

[0086] If the value is greater than the threshold, a risk of complications is identified, and an alarm is generated. Hemodynamic parameters, mechanical ventilation time, and alarm information are then sent to the terminal for display.

[0087] Mechanical ventilation is extremely important after cardiac surgery, especially for infants and young children. Premature extubation directly affects respiratory function, increases the probability of reintubation, and can even lead to respiratory and circulatory failure and death. Delayed extubation increases the incidence of pulmonary complications and prolongs hospital stay, both of which negatively impact surgical prognosis. Early identification of the incidence of complications in high-risk patients with prolonged mechanical ventilation can improve outcomes. Therefore, this embodiment determines the risk of complications and generates an alarm when the mechanical ventilation time exceeds a preset first time.

[0088] Specifically, this embodiment allows the display of the aforementioned hemodynamic parameters, mechanical ventilation time, and alarm information at the terminal layer. The terminal layer may include a clinical terminal layer and a mobile terminal layer.

[0089] The clinical terminal layer can include a physician workstation terminal, which can display hemodynamic parameters, mechanical ventilation time, alarm information, etc.; and can also display dynamic trend graphs, such as a trend graph of a specific hemodynamic parameter (e.g., cardiac index CI) changing over time. This terminal also supports multi-parameter overlay comparison, for example, overlaying and comparing monitoring results of the same hemodynamic parameter in different time windows.

[0090] The mobile terminal layer can include mobile workstations, such as mobile phones and computers. These mobile terminal terminals are highly mobile, allowing healthcare professionals and hospitals to access various data anytime, anywhere. Similar to the clinical terminal layer, mobile terminal terminals can display hemodynamic parameters, mechanical ventilation time, alarm information, and other data.

[0091] In the embodiments from steps S102 to S106 above, a fully automated system for monitoring, decision-making, and display was constructed. In this process, an alarm process was also added, which can significantly improve the efficiency of clinical decision-making.

[0092] based on Figure 2 The system architecture shown includes a monitoring device layer, a data transmission layer, a server layer, and a terminal layer. Based on a modular architecture and standardized data interfaces, the system supports rapid integration with various types of medical information systems and can improve system scalability.

[0093] Regarding the decision tree in the above embodiments, a decision tree construction process is provided herein. This embodiment includes:

[0094] First, obtain multiple hemodynamic parameters related to cardiac function.

[0095] Specifically, based on the hemodynamic parameters of Electronic Cardiometry (EC), which are highly correlated with cardiac function, this embodiment can use multiple hemodynamic parameters such as patient age, weight, height, cardiac function classification, baseline and maximum increase rate of NT-proBNp, CI and ICON, and maximum TFC as principal components for subsequent principal component analysis.

[0096] Secondly, principal component analysis was used to determine the correlation between each hemodynamic parameter and mechanical ventilation time; then, principal component parameters were determined from the hemodynamic parameters based on the correlation; among them, the principal component parameters included: patient age, maximum pleural fluid level (TFCmax), cardiac index (CI) and its maximum increase rate (CIR), and maximum increase rate (ICONR) of myocardial contractility (ICON).

[0097] Specifically, in the principal component analysis algorithm, mechanical ventilation time is a major clinical factor affecting postoperative recovery, so it is included as an analysis term. Through principal component analysis, combining hemodynamic parameters (principal components) with mechanical ventilation time (analytical term), the loading coefficients between the principal components and the analytical term are determined. The loading coefficients represent the degree of relationship between the principal components and the analytical term; the larger the absolute value of the loading coefficient, the stronger the correlation between hemodynamic parameters and mechanical ventilation time. This allows for the effective extraction of principal component parameters, including: patient age, maximum pleural fluid level (TFCmax), cardiac index (CI) and its maximum rate of increase (CIR), and the maximum rate of increase of myocardial contractility (ICON) (ICONR).

[0098] Finally, a decision path is constructed between the principal component indices and the mechanical ventilation time, generating a decision tree. This embodiment can construct a decision path between the principal component indices and the mechanical ventilation time based on the impact of a large amount of real historical data, and generate a decision tree.

[0099] The decision tree constructed through the above embodiments can comprehensively consider multiple hemodynamic parameters and effectively extract the principal component parameters with the highest correlation, i.e., the greatest impact on mechanical ventilation time, thereby improving the objectivity, rationality, and accuracy of the decision tree. These principal component parameters are the hemodynamic parameters input into the decision tree during practical applications.

[0100] In summary, the dynamic hemodynamic parameter management method provided in this embodiment includes: monitoring hemodynamic parameters; wherein the hemodynamic parameters include at least: patient age, maximum pleural fluid level, cardiac index, maximum rate of increase of cardiac index, and maximum rate of increase of myocardial contractility; inputting the hemodynamic parameters into a preset decision tree to output mechanical ventilation time; wherein the decision tree is used to represent the decision relationship between each of the hemodynamic parameters and the mechanical ventilation time; and sending the hemodynamic parameters and the mechanical ventilation time to a terminal for display.

[0101] This technical solution constructs a fully automated system that monitors hemodynamic parameters, predicts mechanical ventilation time based on decision trees, and displays various data, significantly improving the efficiency of clinical decision-making. It represents a leap from experience-based medicine to data-driven decision-making, providing a safer, more accurate, and efficient solution for the perioperative management of patients (especially infants) with congenital heart disease.

[0102] In some application scenarios, this technical solution can also be extended in the following ways: In terms of application, it can enhance the evaluation dimensions by integrating ultrasound and biochemical indicators through multimodal data fusion, develop mobile monitoring terminals to achieve home monitoring, or link with surgical robots to achieve intelligent perfusion control.

[0103] Figure 4 This is a schematic diagram of a dynamic hemodynamic parameter management device provided in an embodiment of the present disclosure. This device can be used to implement a dynamic hemodynamic parameter management method. Figure 4 As shown, the dynamic hemodynamic parameter management device may include the following modules:

[0104] The monitoring module 210 is used to monitor hemodynamic parameters; wherein the hemodynamic parameters include at least: patient age, maximum pleural fluid level, cardiac index, maximum rate of increase of cardiac index, and maximum rate of increase of myocardial contractility;

[0105] The decision module 220 is used to input the hemodynamic parameters into a preset decision tree to output the mechanical ventilation time; wherein, the decision tree is used to represent the decision relationship between each of the hemodynamic parameters and the mechanical ventilation time;

[0106] Display module 230 is used to send the hemodynamic parameters and the mechanical ventilation time to the terminal for display.

[0107] In one embodiment, the monitoring module 210 is further configured to:

[0108] Using pre-set monitoring equipment, baseline parameters of patients are collected at multiple time windows; these baseline parameters include: baseline, final and maximum values ​​of cardiac index, baseline, final and maximum values ​​of myocardial contractility, pleural fluid level, and contraction time ratio.

[0109] The maximum rate of increase in the cardiac index is determined based on the maximum value and the baseline value of the cardiac index.

[0110] The maximum increase rate of myocardial contractility is determined based on the maximum value and the baseline value of myocardial contractility.

[0111] Collect auxiliary parameters of the patient; wherein, the auxiliary parameters include: patient age, NT-proBNP and vital signs displayed by the electrocardiogram monitor.

[0112] In one embodiment, the decision module 220 is further configured to:

[0113] The hemodynamic parameters are input into a preset decision tree;

[0114] The decision tree is used to determine whether the patient's age is less than a preset first age.

[0115] If the patient's age is not less than the first age, then the predicted mechanical ventilation time is not greater than the first time.

[0116] If the patient's age is less than the first age, and it is determined that the patient's age is less than a preset second age, then the predicted mechanical ventilation time is greater than the first time.

[0117] In one embodiment, the decision module 220 is further configured to:

[0118] If the patient's age is not less than the second age and less than the first age, determine whether the maximum value of the pleural fluid level is less than a preset first threshold.

[0119] If the maximum value of the pleural fluid level is not less than a preset first threshold, determine whether the maximum increase rate of the cardiac index is less than a preset second threshold.

[0120] If it is less than, then the predicted mechanical ventilation time is greater than the first time;

[0121] If it is not less than, then the predicted mechanical ventilation time is not greater than the first time.

[0122] In one embodiment, the decision module 220 is further configured to:

[0123] If the patient's age is not less than the second age and less than the first age, determine whether the maximum value of the pleural fluid level is less than a preset first threshold.

[0124] If the maximum value of the pleural fluid level is less than a preset first threshold, determine whether the maximum increase rate of the myocardial contractility is less than a preset third threshold.

[0125] If it is not less than the third threshold, the predicted mechanical ventilation time is not greater than the first time.

[0126] If it is less than the third threshold, then determine whether the cardiac index is less than the preset fourth threshold;

[0127] If the cardiac index is less than the fourth threshold, the predicted mechanical ventilation time is greater than the first time.

[0128] If the cardiac index is not less than the fourth threshold, the predicted mechanical ventilation time is not greater than the first time.

[0129] In one embodiment, the display module 230 is further configured to:

[0130] Determine whether the mechanical ventilation time is greater than a preset first time;

[0131] If the values ​​are not greater than the specified values, the hemodynamic parameters and the mechanical ventilation time are sent to the terminal for display.

[0132] If the value is greater than the threshold, a risk of complications is identified, and an alarm message is generated.

[0133] The hemodynamic parameters, the mechanical ventilation time, and the alarm information are sent to the terminal for display.

[0134] In one embodiment, the dynamic hemodynamic parameter management device further includes a decision tree construction module, which is used for:

[0135] Obtain multiple hemodynamic parameters related to cardiac function;

[0136] Principal component analysis was used to determine the correlation between each hemodynamic parameter and the mechanical ventilation time.

[0137] Principal component indices are determined from the hemodynamic parameters based on the correlation; wherein the principal component indices include: the patient's age, the maximum value of the pleural fluid level, the cardiac index and its maximum increase rate, and the maximum increase rate of the myocardial contractility;

[0138] Construct the decision path between the principal component index and the mechanical ventilation time to generate the decision tree.

[0139] The device provided in this embodiment has the same implementation principle and technical effect as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the aforementioned method embodiment.

[0140] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure. Figure 5 As shown, the electronic device 300 includes one or more processors 301 and memory 302.

[0141] The processor 301 may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 300 to perform desired functions.

[0142] The memory 302 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 301 may execute the program instructions to implement the dynamic hemodynamic parameter management method of the embodiments of this disclosure described above and / or other desired functions. Various contents such as input signals, signal components, and noise components may also be stored in the computer-readable storage medium.

[0143] In one example, the electronic device 300 may also include an input device 303 and an output device 304, which are interconnected via a bus system and / or other forms of connection mechanism (not shown).

[0144] In addition, the input device 303 may also include, for example, a keyboard, a mouse, etc.

[0145] The output device 304 can output various information to the outside, including determined distance information, direction information, etc. The output device 304 may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.

[0146] Of course, for the sake of simplicity, Figure 5 Only some of the components of the electronic device 300 relevant to this disclosure are shown, omitting components such as buses, input / output interfaces, etc. In addition, the electronic device 300 may include any other suitable components depending on the specific application.

[0147] Furthermore, this embodiment also provides a computer-readable storage medium storing a computer program for executing the above-described dynamic hemodynamic parameter management method.

[0148] The present disclosure provides a computer program product for a dynamic hemodynamic parameter management method, device, electronic device, and medium, including a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the preceding method embodiments. For specific implementation details, please refer to the method embodiments, which will not be repeated here.

[0149] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0150] The above description is merely a specific embodiment of this disclosure, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not to be limited to the embodiments described herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method of dynamic blood flow hemodynamic parameter management, characterized by, The method comprises: monitoring hemodynamic parameters; wherein the hemodynamic parameters at least include: patient age, chest fluid level maximum, cardiac index, maximum rate of increase of cardiac index, maximum rate of increase of myocardial contractility; inputting the hemodynamic parameters into a preset decision tree to output mechanical ventilation time; wherein the decision tree is used to represent the decision relationship between each of the hemodynamic parameters and the mechanical ventilation time; sending the hemodynamic parameters and the mechanical ventilation time to a terminal for display; The monitoring of the hemodynamic parameters comprises: acquiring basic parameters of the patient in multiple time windows through a preset monitoring device; wherein the basic parameters include: basic value, final value and maximum value of cardiac index, basic value, final value and maximum value of myocardial contractility, chest fluid level, and systolic time ratio; the time windows include: baseline period, adjustment period and final value period; the first day of admission is taken as the baseline period, the day after admission to the day before surgery is taken as the adjustment period, and the day before surgery is taken as the final value period; determining the maximum rate of increase of cardiac index according to the percentage between the difference between the maximum value and the basic value of the cardiac index and the basic value of the cardiac index; determining the maximum rate of increase of myocardial contractility according to the percentage between the difference between the maximum value and the basic value of the myocardial contractility and the basic value of the myocardial contractility; wherein the maximum rate of increase of cardiac index and the maximum rate of increase of myocardial contractility are used to represent the degree of improvement of heart function of the patient after preoperative adjustment; acquiring auxiliary parameters of the patient; wherein the auxiliary parameters include: patient age, NT-proBNP, and vital signs displayed by an electrocardiograph; The sending of the hemodynamic parameters and the mechanical ventilation time to the terminal for display comprises: determining whether the mechanical ventilation time is greater than a preset first time; if not, sending the hemodynamic parameters and the mechanical ventilation time to the terminal for display; if greater, determining that there is a complication risk and generating an alarm information; sending the hemodynamic parameters, the mechanical ventilation time and the alarm information to the terminal for display; The construction process of the decision tree comprises: obtaining multiple hemodynamic indicators related to heart function; wherein according to the electronic heart force measurement method hemodynamic indicators with high correlation with heart function, multiple hemodynamic indicators are taken as principal components for subsequent principal component analysis, wherein the hemodynamic indicators include: patient age, weight, height, heart function classification, NT-proBNp, CI and ICON, and their respective basic values and maximum increase rates, TFC maximum value; determining the correlation between each of the hemodynamic indicators and the mechanical ventilation time through principal component analysis method; determining a principal component index from the hemodynamic indexes according to the correlation; wherein the principal component index comprises the patient age, the maximum thoracic fluid level, the cardiac index and its maximum increasing rate, and the maximum increasing rate of myocardial contractility; wherein, by principal component analysis, the load coefficients between the principal components and the analysis items are determined by combining the hemodynamic indexes and the mechanical ventilation time; the load coefficient represents the relationship degree between the principal component and the analysis item, the greater the absolute value of the load coefficient, the stronger the correlation between the hemodynamic indexes and the mechanical ventilation time, and thus the principal component index is extracted; constructing a decision path between the principal component index and the mechanical ventilation time to generate the decision tree.

2. The method of claim 1, wherein, The method comprises: inputting the hemodynamic parameters into the preset decision tree; determining whether the patient age is less than a preset first age by the decision tree; if the patient age is not less than the first age, predicting the mechanical ventilation time to be not greater than a first time; if the patient age is less than the first age and it is determined that the patient age is less than a preset second age, predicting the mechanical ventilation time to be greater than the first time.

3. The method of claim 2, wherein, The method comprises: in the case that the patient age is not less than the second age and less than the first age, determining whether the maximum thoracic fluid level is less than a preset first threshold value; in the case that the maximum thoracic fluid level is not less than the preset first threshold value, determining whether the maximum increasing rate of the cardiac index is less than a preset second threshold value; if yes, predicting the mechanical ventilation time to be greater than the first time; if no, predicting the mechanical ventilation time to be not greater than the first time.

4. The method of claim 2, wherein, The method comprises: in the case that the patient age is not less than the second age and less than the first age, determining whether the maximum thoracic fluid level is less than a preset first threshold value; in the case that the maximum thoracic fluid level is less than the preset first threshold value, determining whether the maximum increasing rate of myocardial contractility is less than a preset third threshold value; if not less than the third threshold value, predicting the mechanical ventilation time to be not greater than the first time; if less than the third threshold value, determining whether the cardiac index is less than a preset fourth threshold value; if the cardiac index is less than the fourth threshold value, predicting the mechanical ventilation time to be greater than the first time; if the cardiac index is not less than the fourth threshold value, predicting the mechanical ventilation time to be not greater than the first time.

5. A dynamic hemodynamic parameter management apparatus, characterized by, The device comprises: a monitoring module for monitoring hemodynamic parameters; wherein the hemodynamic parameters at least comprise the patient age, the maximum thoracic fluid level, the cardiac index, the maximum increasing rate of the cardiac index, and the maximum increasing rate of myocardial contractility; a decision module, configured to input the hemodynamic parameters into a preset decision tree to output a mechanical ventilation time; wherein the decision tree is used to represent a decision relationship between each of the hemodynamic parameters and the mechanical ventilation time; a display module, configured to send the hemodynamic parameters and the mechanical ventilation time to a terminal for display; the monitoring module is further configured to: collect, by a preset monitoring device, basic parameters of the patient in multiple time windows; wherein the basic parameters include: basic values, final values and maximum values of cardiac index, basic values, final values and maximum values of myocardial contractility, chest fluid level, and systolic time ratio; the time windows include: baseline period, adjustment period, and final value period; the first day of admission is taken as the baseline period, the day after admission to the day before surgery is taken as the adjustment period, and the day before surgery is taken as the final value period; determine the maximum increase rate of cardiac index according to the percentage between the difference between the maximum value and the basic value of the cardiac index and the basic value of the cardiac index; determine the maximum increase rate of myocardial contractility according to the percentage between the difference between the maximum value and the basic value of the myocardial contractility and the basic value of the myocardial contractility; wherein the maximum increase rate of cardiac index and the maximum increase rate of myocardial contractility are used to represent the degree of improvement of heart function of the patient after preoperative adjustment; collect auxiliary parameters of the patient; wherein the auxiliary parameters include: patient age, NT-proBNP, and vital signs displayed by an electrocardiograph; the display module is further configured to: determine whether the mechanical ventilation time is greater than a preset first time; if not, send the hemodynamic parameters and the mechanical ventilation time to the terminal for display; if greater, determine that there is a complication risk, and generate an alarm information; send the hemodynamic parameters, the mechanical ventilation time, and the alarm information to the terminal for display; the dynamic hemodynamic parameter management device further includes a decision tree construction module, configured to: obtain multiple hemodynamic indexes related to heart function; wherein according to the electronic heart force measurement method hemodynamic indexes with high correlation with heart function, multiple hemodynamic indexes are taken as principal components for subsequent principal component analysis, wherein the hemodynamic indexes include: patient age, body weight, height, heart function classification, NT-proBNp, CI, and ICON, respectively, basic values and maximum increase rates, and TFC maximum values; determine the correlation between each of the hemodynamic indexes and the mechanical ventilation time by principal component analysis method; determining a principal component index from the hemodynamic indexes according to the correlation; wherein the principal component index comprises: the patient age, the maximum chest fluid level, the cardiac index and its maximum increasing rate, the maximum increasing rate of myocardial contractility; wherein by principal component analysis, the load coefficient between the principal component and the analysis item is determined by combining the hemodynamic indexes and the mechanical ventilation time; the load coefficient represents the relationship degree between the principal component and the analysis item, the greater the absolute value of the load coefficient, the stronger the correlation between the hemodynamic indexes and the mechanical ventilation time, thereby extracting the principal component index; constructing a decision path between the principal component index and the mechanical ventilation time to generate the decision tree.

6. An electronic device, comprising: The electronic device comprises: a processor; a memory for storing executable instructions of the processor; the processor is configured to read the executable instructions from the memory and execute the instructions to implement the method of any one of claims 1-4.

7. A computer readable storage medium characterized in that, The computer readable storage medium stores instructions, when the instructions run on a terminal device, cause the terminal device to implement the method of any one of claims 1-4.

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