A real-time monitoring and early warning method and system for ventilator-related events

Abstract

A method and system for real-time analysis and early warning of ventilator-related events, comprising the following steps: Step 1: Acquire data; Step 2: Perform real-time analysis based on the ventilation data of the ventilator; Step 3: Determine the baseline window; Step 4: Perform independent early warning of ventilator-related events, and perform comprehensive early warning of the next step; Step 5: Set a composite reference area based on synchronized physiological parameter data, test data, and diagnosis and treatment data, and perform data encoding conversion; Step 6: Establish a judgment logic decision tree for composite data to make a comprehensive early warning of ventilator-related events. This application adopts a dual early warning method of independent early warning and comprehensive early warning using composite data to exclude primary diseases, accurately analyze and identify ventilator-related events during mechanical ventilation treatment, and minimize the risk of lung damage and concurrent infectious diseases in patients, thereby achieving socioeconomic effects such as improving medical quality and reducing medical expenses.

Description

Technical Field

[0001] The present application relates to a method and system for real-time monitoring and early warning of ventilator-related events. Background Art

[0002] Ventilators are essential life-support measures, but they can also be associated with adverse complications. Ventilator-associated pneumonia, sepsis, acute respiratory distress syndrome, pulmonary embolism, barotrauma, and pulmonary edema are among the complications that can occur in patients receiving mechanical ventilation. These complications can lead to prolonged mechanical ventilation and ICU stays, increased medical costs, and a higher risk of disability and death.

[0003] Safe and effective treatment and prevention strategies often require multidisciplinary collaboration and are crucial for reducing the adverse consequences of ventilator-associated events (AVEs), which include ventilator-associated conditions, infection-related ventilator-associated complications, and possible ventilator-associated pneumonia.

[0004] In actual clinical practice, ventilators themselves generate a significant amount of monitoring data, but existing data processing methods are limited in their effectiveness in predicting and resolving adverse complications, and these data are not fully utilized. Furthermore, complications during mechanical ventilation often coexist with, or even overshadow, the underlying illness, and the treatment approaches and strategies for both differ significantly, significantly increasing the workload for clinicians. Summary of the Invention

[0005] In order to solve the above problems, the present application proposes a real-time analysis and early warning method for ventilator-related events, including the following steps: Step 1: Acquire mechanical ventilation data, as well as synchronized physiological parameter data, test data, and medical data; Step 2: Perform real-time calculation, trend analysis, and event labeling based on the ventilator's ventilation data to obtain the oxygen consumption index labeling value; Step 3: Determine the baseline window through dynamic sliding window and time series analysis based on the oxygen consumption index labeling value; Step 4: After the baseline window is determined, monitor and analyze ventilator-related events. If the ventilator's ventilation data exceeds the preset oxygen consumption index benchmark area, an independent early warning of the ventilator-related event is performed, and a comprehensive early warning of the next step is performed; Step 5: Set a composite benchmark area based on the synchronized physiological parameter data, test data, and medical data, and perform data encoding conversion; Step 6: Establish a judgment logic decision tree for the composite data to make a comprehensive early warning of ventilator-related events. This application uses a dual warning method of independent warning and comprehensive warning based on composite data, excluding primary diseases, and only accurately analyzing and identifying ventilator-related events during mechanical ventilation treatment, thereby minimizing the risk of lung injury and concurrent infectious diseases in patients, thereby achieving socioeconomic effects such as improving medical quality and reducing medical expenses.

[0006] Preferably, the mechanical ventilation data includes waveform data of mechanical ventilation pressure, volume, and flow rate; the physiological parameter data includes body temperature data; the test data includes white blood cell test data; and the diagnosis and treatment data includes antibiotic medication records and infection records;

[0007] Preferably, the oxygen consumption index value refers to the daily minimum PEEP value and the daily minimum FiO2 value, which are obtained based on the ventilation data of the ventilator in the following manner:

[0008] S1: Real-time calculation: calculate the average positive end-expiratory pressure (PEEP) and average oxygen concentration (FiO2) for each respiratory cycle of the day;

[0009] S2: Trend analysis: Draw a daily trend chart consisting of the average positive end-expiratory pressure (PEEP) and average oxygen concentration (FiO2) values for all respiratory cycles in the day;

[0010] S3: Event marking: Based on the daily trend chart, the lowest value of the average positive end-expiratory pressure (PEEP) and the acceptable minimum fluctuation range are automatically marked. The time points falling within the range are counted and the continuous time length is calculated. The lowest value corresponding to the continuous duration greater than 1 hour is defined as the daily PEEP minimum value. The daily PEEP minimum value is marked as an event and recorded as Low_PEEP t ;

[0011] According to the daily trend chart, the lowest value of the average oxygen concentration FiO2 is automatically marked, the duration of the lowest value is calculated, and the lowest value corresponding to a continuous duration greater than 1 hour is defined as the daily lowest value of FiO2. The daily lowest value of FiO2 is marked as an event and recorded as

[0012] Preferably, the baseline window refers to the patient's stable or improved period at this time, and their physiological state is not affected by the primary disease during subsequent treatment. The baseline window is obtained as follows:

[0013] S1: Dynamic sliding window: Set a dynamic window based on the time length of the current day and the previous two days. Automatically obtain the daily minimum PEEP value from the oxygen consumption index value within the window, and form a time series of the daily minimum PEEP value, recorded as:

[0014] Low_PEEP=[Low_PEEP t-2 、Low_PEEP t-1 、Low_PEEP t ]

[0015] Automatically obtain the daily minimum value of FiO2 from the oxygen consumption index annotation value within the window, and form the daily minimum value time series of FiO2, which is recorded as:

[0016]

[0017] S2: Time series analysis: Calculate the dynamic change sequence of the daily minimum PEEP value time series within the current dynamic sliding window, recorded as:

[0018]

[0019] Calculate the dynamic change sequence of the FiO2 daily minimum value time series within the current dynamic sliding window, recorded as:

[0020]

[0021] S3: Baseline window determination: and If the dynamic change sequence of remains stable or shows a downward trend and the duration is greater than or equal to 2 days, it means that the patient is in a stable period or improvement period, and the dynamic sliding window at this time is defined as the baseline window; otherwise, jump to S1 to continue to execute the dynamic sliding window to determine the baseline window.

[0022] Preferably, the monitoring and analysis of ventilator-related events means: after determining the baseline window, continuing to monitor the subsequent daily oxygen consumption index marked values; the preset oxygen consumption index reference area includes the PEEP daily minimum value reference area and the FiO2 daily minimum value reference area; the ventilator independent event warning is performed according to the following steps:

[0023] S1: Determine the baseline window for the daily minimum PEEP value: After the baseline window is determined, the daily minimum PEEP value shows an upward trend starting from the mth day and meets the following requirements:

[0024] Low_PEEP m -Low_PEEP m-1 ≥3 cmH2O

[0025] Set Low_PEEP m -low_PEEP m-1 The interval segment was defined as the baseline area of the daily minimum PEEP value;

[0026] S2: Determine the baseline area for the daily minimum FiO2 value: After the baseline window is determined, the daily minimum FiO2 value shows an upward trend starting from the mth day and meets the following requirements:

[0027]

[0028] Will The interval segment was defined as the baseline area of the daily minimum value of FiO2;

[0029] S3: Continue to monitor the dynamic change sequence: set the mth day as the start time of PEEP index deterioration PEEP start , the dynamic change sequence of the daily minimum PEEP value after day m is recorded as:

[0030]

[0031] Day m is the time when the FiO2 index starts to deteriorate The dynamic change rate sequence of the daily minimum value of FiO2 after day m is recorded as:

[0032]

[0033] S4: Ventilator independent event warning: If Sequence or The sequence continued to rise, indicating that the patient's oxygen consumption index had worsened and there might be ventilator-associated lung injury. Therefore, a ventilator-independent event warning was issued on day m+1.

[0034] Preferably, the setting of the composite reference area refers to dividing the standard interval range according to the definitions and diagnostic standards of physiological parameter data, test data, and treatment data in the clinical guidelines, and performing data encoding conversion accordingly, specifically in the following manner:

[0035] Convert infection record information into code: PEEP start or If the patient develops clinical infection symptoms within two days before the time point, the infection information is recorded as 1; otherwise, it is recorded as 0;

[0036] Convert the antibiotic medication record information into code: obtain all antibiotics in PEEP start or The duration of use before the time point is met if the following conditions are met: the use of a certain antibiotic drug lasts for more than 4 days, but during PEEP start or If the patient was not given this drug before the time point, the antibiotic medication information Antibiotic is recorded as 1; if the patient used multiple antibiotics and met the above conditions, the antibiotic medication information Antibiotic is recorded as 2; otherwise, it is recorded as 0;

[0037] Convert the body temperature data into code: If PEEP start or If the lowest temperature is greater than 38°C or the highest temperature is less than 36°C in the two days before the time node, the temperature parameter information Temperature is recorded as 1; otherwise, if the PEEP start or If the lowest temperature is greater than 38°C or the highest temperature is less than 36°C two days after the time point, the temperature parameter information Temperature is recorded as 2; otherwise, it is recorded as 0;

[0038] Convert the white blood cell test data into code: If PEEP start or Two days before the time point, the lowest white blood cell count was greater than 12×10 9 / L or the highest white blood cell count is less than or equal to 4×10 9 / L, the white blood cell parameter information WBC is recorded as 1; otherwise, if the PEEP start or Two days after the time point, the lowest white blood cell count was greater than 12×10 9 / L or the highest white blood cell count is less than or equal to 4×10 9 / L, the white blood cell parameter information WBC is recorded as 2, otherwise it is recorded as 0.

[0039] Preferably, the establishment of a judgment logic decision tree for composite data and the making of a comprehensive warning of ventilator-related events are performed in the following manner:

[0040] S1: If Information is equal to 1, execute S2 logic judgment, otherwise terminate this judgment;

[0041] S2: If Antibiotic is equal to 1 or Antibiotic is equal to 2, execute S3 logic judgment, otherwise terminate this judgment;

[0042] S3: If Temperature is equal to 1 and WBC is equal to 1, it indicates that there may be a risk of concurrent infectious diseases, so start or Push relevant comprehensive warnings daily;

[0043] S4: If Temperature is equal to 2 and WBC is equal to 2, it indicates that there may be a risk of concurrent infectious diseases, so start or A relevant comprehensive warning will be issued in the next two days.

[0044] Preferably, the comprehensive warning needs to be started after the independent warning, and the independent warning needs to be started after the baseline window is determined. When the ventilator is put into use, the calculation of the oxygen consumption index mark value and the determination of the baseline window are started.

[0045] On the other hand, the present application discloses a real-time analysis and early warning system for ventilator-related events, comprising:

[0046] Data acquisition module: used to obtain mechanical ventilation data, as well as synchronized physiological parameter data, test data, and diagnosis and treatment data;

[0047] Data preprocessing module: used for real-time calculation, trend analysis and event annotation based on the ventilation data of the ventilator to obtain the oxygen consumption index annotation value;

[0048] Independent warning module: used to determine the baseline window based on the oxygen consumption index mark value through dynamic sliding window and time series analysis. After the baseline window is determined, ventilator-related events are monitored and analyzed. If the ventilator's ventilation data exceeds the preset oxygen consumption index baseline area, an independent warning of ventilator-related events is issued.

[0049] Comprehensive early warning module: used to set composite reference areas based on synchronized physiological parameter data, test data, and diagnosis and treatment data, and perform data encoding conversion; establish a judgment logic decision tree for composite data, and make comprehensive early warnings for ventilator-related events.

[0050] This application can bring the following beneficial effects: This application adopts a dual warning method of independent warning and comprehensive warning based on composite data, excludes primary diseases, and only accurately analyzes and identifies ventilator-related events during mechanical ventilation treatment, thereby minimizing the risk of lung injury and concurrent infectious diseases in patients, thereby achieving socioeconomic effects such as improving medical quality and reducing medical expenses. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0052] Figure 1 is a schematic diagram of Example 1;

[0053] Figure 2 This is a schematic diagram of Example 2. DETAILED DESCRIPTION

[0054] In order to clearly illustrate the technical features of this solution, this application is described in detail below through specific implementation methods.

[0055] In Example 1, Figure 1 As shown in the figure, the steps for monitoring and warning of ventilator-related events in patients with pulmonary edema are used as an example to illustrate the following steps:

[0056] S101: Acquire mechanical ventilation data, as well as synchronized physiological parameter data, test data, and diagnosis and treatment data;

[0057] The mechanical ventilation data includes waveform data of mechanical ventilation pressure, volume, and flow rate; physiological parameter data includes body temperature data; test data includes white blood cell test data; diagnosis and treatment data includes antibiotic medication records and infection records;

[0058] S102: Performing real-time calculation, trend analysis, and event annotation based on the ventilation data of the ventilator to obtain an oxygen consumption index annotation value;

[0059] The oxygen consumption index value refers to the daily minimum PEEP and FiO2 values, which are obtained based on the ventilation data of the ventilator as follows:

[0060] S1: Real-time calculation: calculate the average positive end-expiratory pressure (PEEP) and average oxygen concentration (FiO2) for each respiratory cycle of the day;

[0061] S2: Trend analysis: Draw a daily trend chart consisting of the average positive end-expiratory pressure (PEEP) and average oxygen concentration (FiO2) values for all respiratory cycles in the day;

[0062] S3: Event marking: Based on the daily trend chart, the lowest value of the average positive end-expiratory pressure (PEEP) and the acceptable minimum fluctuation range are automatically marked. The time points falling within the range are counted and the continuous time length is calculated. The lowest value corresponding to the continuous duration greater than 1 hour is defined as the daily PEEP minimum value. The daily PEEP minimum value is marked as an event and recorded as Low_PEEP t ;

[0063] According to the daily trend chart, the lowest value of the average oxygen concentration FiO2 is automatically marked, the duration of the lowest value is calculated, and the lowest value corresponding to a continuous duration greater than 1 hour is defined as the daily lowest value of FiO2. The daily lowest value of FiO2 is marked as an event and recorded as

[0064] S103: Determine a baseline window based on the oxygen consumption index labeled value through a dynamic sliding window and time series analysis method;

[0065] The baseline window refers to the period when the patient is in a stable or improved phase, and their physiological state is not affected by the primary disease during subsequent treatment;

[0066] The baseline window is obtained as follows:

[0067] S1: Dynamic sliding window: Set a dynamic window based on the time length of the current day and the previous two days. Automatically obtain the daily minimum PEEP value from the oxygen consumption index value within the window, and form a time series of the daily minimum PEEP value, recorded as:

[0068] Low_PEEP=[Low_PEEP t-2 、Low_PEEP t-1 、Low_PEEP t ]

[0069] Automatically obtain the daily minimum value of FiO2 from the oxygen consumption index annotation value within the window, and form the daily minimum value time series of FiO2, which is recorded as:

[0070]

[0071] S2: Time series analysis: Calculate the dynamic change sequence of the daily minimum PEEP value time series within the current dynamic sliding window, recorded as:

[0072]

[0073] Calculate the dynamic change sequence of the FiO2 daily minimum value time series within the current dynamic sliding window, recorded as:

[0074]

[0075] S3: Baseline window determination: and If the dynamic change sequence of remains stable or shows a downward trend and the duration is greater than or equal to 2 days, it means that the patient is in a stable period or improvement period, and the dynamic sliding window at this time is defined as the baseline window; otherwise, jump to S1 to continue to execute the dynamic sliding window to determine the baseline window.

[0076] S104: After the baseline window is determined, ventilator-related events are monitored and analyzed. If the ventilation data of the ventilator exceeds the preset oxygen consumption index baseline area, an independent warning of ventilator-related events is issued, and a comprehensive warning is carried out in the next step; the comprehensive warning needs to be started after the independent warning, and the independent warning needs to be started after the baseline window is determined. The calculation of the oxygen consumption index mark value and the determination of the baseline window are started when the ventilator is put into use.

[0077] After determining the baseline window, continue to monitor the daily oxygen consumption index annotation values. The preset oxygen consumption index reference area includes the daily minimum PEEP reference area and the daily minimum FiO2 reference area. The ventilator independent event warning is performed according to the following steps:

[0078] S1: Determine the baseline window for the daily minimum PEEP value: After the baseline window is determined, the daily minimum PEEP value shows an upward trend starting from the mth day and meets the following requirements:

[0079] Low_PEEP m -Low_PEEP m-1 ≥3 cmH2O

[0080] Set Low_PEEP m -Low_PEEP m-1 The interval segment was defined as the baseline area of the daily minimum PEEP value;

[0081] S2: Determine the baseline area for the daily minimum FiO2 value: After the baseline window is determined, the daily minimum FiO2 value shows an upward trend starting from the mth day and meets the following requirements:

[0082]

[0083] Will The interval segment was defined as the baseline area of the daily minimum value of FiO2;

[0084] S3: Continue to monitor the dynamic change sequence: set the mth day as the start time of PEEP index deterioration PEEP start , the dynamic change sequence of the daily minimum PEEP value after day m is recorded as:

[0085]

[0086] Day m is the time when the FiO2 index starts to deteriorate The dynamic change rate sequence of the daily minimum value of FiO2 after day m is recorded as:

[0087]

[0088] S4: Ventilator independent event warning: If Sequence or The sequence continued to rise, indicating that the patient's oxygen consumption index had worsened and there might be ventilator-associated lung injury. Therefore, a ventilator-independent event warning was issued on day m+1.

[0089] S105: setting a composite reference area based on the synchronized physiological parameter data, test data, and diagnosis and treatment data, and performing data encoding conversion;

[0090] The setting of composite reference areas refers to dividing the standard interval range according to the definitions and diagnostic standards of physiological parameter data, test data, and treatment data in clinical guidelines, and performing data encoding conversion accordingly. The specific method is as follows:

[0091] Convert infection record information into code: PEEP start or If the patient develops clinical infection symptoms within two days before the time point, the infection information is recorded as 1; otherwise, it is recorded as 0;

[0092] Convert the antibiotic medication record information into code: obtain all antibiotics in PEEP start or The duration of use before the time point is met if the following conditions are met: the use of a certain antibiotic drug lasts for more than 4 days, but during PEEP start or If the patient was not given this drug before the time point, the antibiotic medication information Antibiotic is recorded as 1; if the patient used multiple antibiotics and met the above conditions, the antibiotic medication information Antibiotic is recorded as 2; otherwise, it is recorded as 0;

[0093] Convert the body temperature data into code: If PEEP start or If the lowest body temperature is greater than 38°C or the highest body temperature is less than 36°C in the two days before the time node, the temperature parameter information Temperature is recorded as 1; otherwise, if the PEEP start or If the lowest temperature is greater than 38°C or the highest temperature is less than 36°C two days after the time point, the temperature parameter information Temperature is recorded as 2; otherwise, it is recorded as 0;

[0094] Convert the white blood cell test data into code: If PEEP start or Two days before the time point, the lowest white blood cell count was greater than 12×10 9 / L or the highest white blood cell count is less than or equal to 4×10 9 / L, the white blood cell parameter information WBC is recorded as 1; otherwise, if the PEEPstart or Two days after the time point, the lowest white blood cell count was greater than 12×10 9 / L or the highest white blood cell count is less than or equal to 4×10 9 / L, the white blood cell parameter information WBC is recorded as 2, otherwise it is recorded as 0.

[0095] S106: Establishing a judgment logic decision tree for composite data to make a comprehensive warning of ventilator-related events. Establishing a judgment logic decision tree for composite data to make a comprehensive warning of ventilator-related events is done as follows:

[0096] S1: If Information is equal to 1, execute S2 logic judgment, otherwise terminate this judgment;

[0097] S2: If Antibiotic is equal to 1 or Antibiotic is equal to 2, execute S3 logic judgment, otherwise terminate this judgment;

[0098] S3: If Temperature is equal to 1 and WBC is equal to 1, it indicates that there may be a risk of concurrent infectious diseases, so start or Push relevant comprehensive warnings daily;

[0099] S4: If Temperature is equal to 2 and WBC is equal to 2, it indicates that there may be a risk of concurrent infectious diseases, which may cause the patient to be infected with related diseases. Therefore, start or A relevant comprehensive warning will be issued in the next two days.

[0100] Example 2, as Figure 2 As shown, a real-time monitoring and early warning system for ventilator-related events includes:

[0101] Data acquisition module 201; used to acquire mechanical ventilation data, as well as synchronized physiological parameter data, test data, and diagnosis and treatment data;

[0102] Data preprocessing module 202; used to perform real-time calculation, trend analysis and event annotation based on the ventilation data of the ventilator to obtain the oxygen consumption index annotation value;

[0103] Independent warning module 203: used to determine a baseline window based on the oxygen consumption index mark value through dynamic sliding window and time series analysis; after the baseline window is determined, monitoring and analysis of ventilator-related events are performed. If the ventilator ventilation data exceeds the preset oxygen consumption index baseline range, an independent warning of ventilator-related events is issued;

[0104] Comprehensive warning module 204: used to set a composite reference area based on synchronized physiological parameter data, test data, and diagnosis and treatment data, and perform data encoding conversion; establish a judgment logic decision tree for composite data, and make a comprehensive warning of ventilator-related events.

[0105] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A method for real-time analysis and early warning of ventilator-related events, characterized by: The steps include: Step 1: Obtain mechanical ventilation data, as well as synchronized physiological parameter data, test data, and diagnosis and treatment data; Step 2: Perform real-time calculation, trend analysis, and event annotation based on the ventilator's ventilation data to obtain the oxygen consumption index annotation value; Step 3: Determine the baseline window based on the oxygen consumption index annotation value through dynamic sliding window and time series analysis; Step 4: After the baseline window is determined, monitor and analyze ventilator-related events. If the ventilator's ventilation data exceeds the preset oxygen consumption index baseline area, an independent warning of ventilator-related events will be issued, and a comprehensive warning for the next step will be carried out; Step 5: Set a composite reference area based on the synchronized physiological parameter data, test data, and diagnosis and treatment data, and perform data encoding conversion; Step 6: Establish a logical decision tree for composite data to generate comprehensive early warnings for ventilator-related events; The oxygen consumption index value refers to the daily minimum PEEP and FiO2 values, which are obtained based on the ventilation data of the ventilator as follows: S1: Real-time calculation: calculate the average positive end-expiratory pressure (PEEP) and average oxygen concentration (FiO2) for each respiratory cycle of the day; S2: Trend analysis: Draw a daily trend chart consisting of the average positive end-expiratory pressure (PEEP) and average oxygen concentration (FiO2) values for all respiratory cycles in the day; S3: Event marking: Based on the daily trend chart, the lowest value of the average positive end-expiratory pressure (PEEP) and the acceptable minimum fluctuation range are automatically marked. The time points falling within the range are counted and the continuous time length is calculated. The lowest value corresponding to the continuous duration greater than 1 hour is defined as the daily PEEP minimum value. The daily PEEP minimum value is marked as an event and recorded as Low_PEEP t ; According to the daily trend chart, the lowest value of the average oxygen concentration FiO2 is automatically marked, and the duration of the lowest value is calculated. The lowest value corresponding to a continuous duration greater than 1 hour is defined as the daily lowest value of FiO2. The daily lowest value of FiO2 is marked as an event and recorded as Low_FiO2; The baseline window refers to the period when the patient is in a stable or improved phase, and their physiological state is not affected by the primary disease during subsequent treatment; The baseline window is obtained as follows: S1: Dynamic sliding window: Set a dynamic window based on the time length of the current day and the previous two days. Automatically obtain the daily minimum PEEP value from the oxygen consumption index value within the window, and form a time series of the daily minimum PEEP value, recorded as: Low_PEEP=[Low_PEEP t-2 、Low_PEEP t-1 、Low_PEEP t ] Automatically obtain the daily minimum value of FiO2 from the oxygen consumption index annotation value within the window, and form the daily minimum value time series of FiO2, which is recorded as: S2: Time series analysis: Calculate the dynamic change sequence of the daily minimum PEEP value time series within the current dynamic sliding window, recorded as: Calculate the dynamic change sequence of the FiO2 daily minimum value time series within the current dynamic sliding window, recorded as: S3: Baseline window determination: and If the dynamic change sequence of remains stable or shows a downward trend and the duration is greater than or equal to 2 days, it means that the patient is in a stable period or improvement period, and the dynamic sliding window at this time is defined as the baseline window; otherwise, jump to S1 to continue to execute the dynamic sliding window to determine the baseline window.

2. The method for real-time analysis and early warning of ventilator-related events according to claim 1, characterized in that: The mechanical ventilation data includes waveform data of mechanical ventilation pressure, volume, and flow rate; the physiological parameter data includes body temperature data; the test data includes white blood cell test data; and the diagnosis and treatment data includes antibiotic medication records and infection records.

3. The method for real-time analysis and early warning of ventilator-related events according to claim 1, characterized in that: The monitoring and analysis of ventilator-related events indicates that: after determining the baseline window, the daily oxygen consumption index marked value is continuously monitored; the preset oxygen consumption index reference area includes the PEEP daily minimum value reference area and the FiO2 daily minimum value reference area; The ventilator independent event warning is carried out according to the following steps: S1: Determine the baseline window for the daily minimum PEEP value: After the baseline window is determined, the daily minimum PEEP value shows an upward trend starting from the mth day and meets the following requirements: Low_PEEP m -Low_PEEP m-1 ≥3cmH2O Set Low_PEEP m -Low_PEEP m-1 The interval segment was defined as the baseline area of the daily minimum PEEP value; S2: Determine the baseline area for the daily minimum FiO2 value: After the baseline window is determined, the daily minimum FiO2 value shows an upward trend starting from the mth day and meets the following requirements: Will The interval segment was defined as the baseline area of the daily minimum value of FiO2; S3: Continue to monitor the dynamic change sequence: set the mth day as the start time of PEEP index deterioration PEEP start , the dynamic change sequence of the daily minimum PEEP value after day m is recorded as: Day m is the time when the FiO2 index starts to deteriorate The dynamic change rate sequence of the daily minimum value of FiO2 after day m is recorded as: S4: Ventilator independent event warning: If Sequence or The sequence continued to rise, indicating that the patient's oxygen consumption index had worsened and there might be ventilator-associated lung injury. Therefore, a ventilator-independent event warning was issued on day m+1.

4. The method for real-time analysis and early warning of ventilator-related events according to claim 1, characterized in that: The setting of composite reference areas refers to dividing the standard interval range according to the definitions and diagnostic standards of physiological parameter data, test data, and treatment data in clinical guidelines, and performing data encoding conversion accordingly. The specific method is as follows: Convert infection record information into code: PEEP start or If the patient develops clinical infection symptoms within two days before the time point, the infection information is recorded as 1; otherwise, it is recorded as 0; Convert the antibiotic medication record information into code: obtain all antibiotics in PEEP start or The duration of use before the time point is met if the following conditions are met: the use of a certain antibiotic drug lasts for more than 4 days, but during PEEP start or If the patient was not given this drug before the time point, the antibiotic medication information Antibiotic is recorded as 1; if the patient used multiple antibiotics and met the above conditions, the antibiotic medication information Antibiotic is recorded as 2; otherwise, it is recorded as 0; Convert the body temperature data into code: If PEEP start or If the lowest temperature is greater than 38°C or the highest temperature is less than 36°C in the two days before the time node, the temperature parameter information Temperature is recorded as 1; otherwise, if the PEEP start or If the lowest temperature is greater than 38°C or the highest temperature is less than 36°C two days after the time point, the temperature parameter information Temperature is recorded as 2; otherwise, it is recorded as 0; Convert the white blood cell test data into code: If PEEP start or Two days before the time point, the lowest white blood cell count was greater than 12×10 9 / L or the highest white blood cell count is less than or equal to 4×10 9 / L, the white blood cell parameter information WBC is recorded as 1; otherwise, if the PEEP start or Two days after the time point, the lowest white blood cell count was greater than 12×10 9 / L or the highest white blood cell count is less than or equal to 4×10 9 / L, the white blood cell parameter information WBC is recorded as 2, otherwise it is recorded as 0.

5. The method for real-time analysis and early warning of ventilator-related events according to claim 1, characterized in that: The establishment of a judgment logic decision tree for composite data and the generation of a comprehensive warning for ventilator-related events are performed in the following manner: S1: If Information is equal to 1, execute S2 logic judgment, otherwise terminate this judgment; S2: If Antibiotic is equal to 1 or Antibiotic is equal to 2, execute S3 logic judgment, otherwise terminate this judgment; S3: If Temperature is equal to 1 and WBC is equal to 1, it indicates that there may be a risk of concurrent infectious diseases, so start or Push relevant comprehensive warnings daily; S4: If Temperature is equal to 2 and WBC is equal to 2, it indicates that there may be a risk of concurrent infectious diseases, so start or A relevant comprehensive warning will be issued in the next two days.

6. The method for real-time analysis and early warning of ventilator-related events according to claim 1, characterized in that: The comprehensive warning needs to be started after the independent warning, and the independent warning needs to be started after the baseline window is determined. When the ventilator is put into use, the oxygen consumption index mark value is calculated and the baseline window is determined.

7. A real-time analysis and early warning system for implementing the method for real-time analysis and early warning of ventilator-related events according to any one of claims 1 to 6, characterized in that: include: Data acquisition module; Used to obtain mechanical ventilation data, as well as synchronized physiological parameter data, test data, and diagnosis and treatment data; Data preprocessing module; Used to perform real-time calculation, trend analysis and event annotation based on ventilator ventilation data to obtain oxygen consumption index annotation values; Independent early warning module: used to determine the baseline window based on the oxygen consumption index value through dynamic sliding window and time series analysis; After the baseline window is determined, ventilator-related events are monitored and analyzed. If the ventilator's ventilation data exceeds the preset oxygen consumption index baseline area, an independent warning of ventilator-related events will be issued; Comprehensive early warning module: used to set composite reference areas based on synchronized physiological parameter data, test data, and diagnosis and treatment data, and perform data coding conversion; Establish a logical decision tree for composite data to make comprehensive early warnings for ventilator-related events.

Images