Respirator withdrawal decision-making method and system based on combination of diaphragm ultrasound and RSBI index

Abstract

The invention discloses a breathing machine withdrawal decision-making method and a breathing machine withdrawal decision-making system based on combination of diaphragm ultrasound and an RSBI index, and the method comprises the steps: firstly, evaluating the withdrawal success rate of successful removal of a breathing machine by a patient to be withdrawn based on a shallow fast breathing index, and evaluating the withdrawal success rate of successful removal of the breathing machine by the patient to be withdrawn based on a diaphragm ultrasound index; and then, fusing the machine withdrawal success rates corresponding to the two indexes, and determining a fused machine withdrawal success rate of successfully withdrawing the breathing machine of the patient to be withdrawn. Therefore, the rapid shallow breathing index and the diaphragm ultrasound index can be combined to comprehensively evaluate the success rate of machine withdrawal of the patient, and the accuracy of machine withdrawal decision of the neurological critical patient is improved, so that the most appropriate machine withdrawal opportunity of the breathing machine is found, and unnecessary mechanical ventilation time and bad prognosis related to machine withdrawal failure are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of instruments that influence a patient's respiratory system by a gas treatment method, and in particular to a ventilator weaning decision method and system based on diaphragm ultrasound combined with RSBI index. Background Art

[0002] Neurological critically ill patients are in serious condition. Due to damage to the nerve center, poor airway protection ability, spontaneous or iatrogenic factors affecting spontaneous breathing, reduced respiratory drive ability, and prone to central respiratory failure, they need long-term mechanical ventilation. Mechanical ventilation can optimize oxygenation, ensure patients' spontaneous breathing, and buy more treatment time for patients. However, long-term mechanical ventilation may cause complications such as ventilator-associated lung injury and ventilator-associated pneumonia. Moreover, patients are prone to ventilator dependence and even failure of weaning. Not only does it bring poor prognosis to patients and prolong hospitalization time, but it also increases the medical economic burden and occupies too many medical resources. Therefore, there is an urgent need for effective weaning assessment technology to find the appropriate weaning time, reduce unnecessary mechanical ventilation time and the adverse prognosis associated with weaning failure.

[0003] At present, most comprehensive ICUs usually use the shallow rapid breathing index (RSBI index) as a common indicator for the evaluation of mechanical ventilation in patients. However, for patients with neurological critical illness, the primary disease of the patients is different from that of patients in comprehensive ICUs, and their ventilator-assisted ventilation characteristics are also different. Moreover, the use of analgesics and sedatives and high fever will affect the accuracy of the shallow rapid breathing index in evaluating the decision to wean patients with neurological critical illness. In addition, neurological critical illness patients often have impaired consciousness, respiratory center dysfunction and peripheral neuropathy due to craniocerebral damage, which is prone to decreased respiratory drive and requires long-term mechanical ventilation. However, some studies have reported that the effect of weaning evaluated by the RSBI index will be reduced in patients with ventilator-assisted ventilation for more than 8 days. It can be seen that the accuracy of the single RSBI index in evaluating the timing of weaning in neurological critical illness patients is low. Summary of the invention

[0004] In view of the shortcomings of the existing technology, the present invention proposes a ventilator weaning decision method and system based on diaphragm ultrasound combined with RSBI index, which can improve the accuracy of weaning decision for critically ill neurological patients. The specific technical solution is as follows:

[0005] In a first aspect, a ventilator weaning decision method based on diaphragm ultrasound combined with RSBI index is provided. In a first implementable manner of the first aspect, the method includes:

[0006] To obtain the success rate of weaning from patients to be weaned based on the shallow rapid breathing index and the success rate of weaning based on the diaphragm ultrasound index;

[0007] The weaning success rate obtained by combining the shallow rapid breathing index assessment and the weaning success rate obtained by evaluating the diaphragm ultrasound index are used to determine the corresponding fusion weaning success rate for the patients to be weaned.

[0008] In combination with the first implementable manner of the first aspect, in a second implementable manner of the first aspect, obtaining the weaning success rate obtained by evaluating based on the shallow rapid breathing index and the weaning success rate obtained by evaluating based on the diaphragm ultrasound index includes:

[0009] Obtain the success rate of weaning from the ventilator corresponding to different index intervals of the shallow rapid breathing index, and the success rate of weaning from the ventilator corresponding to different index intervals of the diaphragm ultrasound index;

[0010] Real-time monitoring of the shallow rapid breathing index and diaphragm ultrasound index of patients waiting to be weaned;

[0011] The shallow rapid breathing index and diaphragm ultrasound index were matched with each index interval and indicator interval respectively, and the success rate of weaning of patients to be weaned was determined based on the shallow rapid breathing index and diaphragm ultrasound index.

[0012] In combination with the second implementable manner of the first aspect, in a third implementable manner of the first aspect, obtaining the weaning success rates corresponding to different index intervals includes:

[0013] Obtain multiple weaning cases based on shallow rapid breathing index, classify the weaning cases, and determine the index interval to which each weaning case belongs;

[0014] The number of cases in different index intervals was statistically analyzed to determine the corresponding success rate of weaning in each index interval.

[0015] In combination with the first implementable manner of the first aspect, in a fourth implementable manner of the first aspect, determining the combined weaning success rate corresponding to the patient to be weaned includes:

[0016] Assign weights corresponding to the shallow rapid breathing index and diaphragm ultrasound index of patients waiting for weaning;

[0017] The combined weaning success rate corresponding to the shallow rapid breathing index and diaphragm ultrasound index of the patient to be weaned is determined by combining the weaning success rate and its weight corresponding to the patient to be weaned.

[0018] In combination with the fourth implementable manner of the first aspect, in a fifth implementable manner of the first aspect, allocating weights corresponding to the shallow rapid breathing index and the diaphragm ultrasound index includes:

[0019] Obtain the confidence levels corresponding to different index intervals of the shallow rapid breathing index and the confidence levels corresponding to different index intervals of the diaphragm ultrasound index;

[0020] The weights corresponding to the shallow rapid breathing index and the diaphragm ultrasound index are allocated according to the corresponding confidence levels.

[0021] In a second aspect, a ventilator weaning decision system based on diaphragm ultrasound combined with RSBI index is provided. In a first achievable manner of the second aspect, the system includes:

[0022] An acquisition module configured to acquire a weaning success rate of a patient to be weaned obtained based on a shallow rapid breathing index assessment, and an weaning success rate obtained based on a diaphragm ultrasound index assessment;

[0023] The decision module is configured to determine the fusion weaning success rate of the patient to be weaned by combining the weaning success rate obtained by evaluating the shallow rapid breathing index and the weaning success rate obtained by evaluating the diaphragm ultrasound index.

[0024] In combination with the first implementable manner of the second aspect, in a second implementable manner of the second aspect, the acquisition module includes:

[0025] A data acquisition unit configured to acquire the weaning success rate corresponding to different index intervals of the shallow rapid breathing index and the weaning success rate corresponding to different index intervals of the diaphragm ultrasound index;

[0026] A human monitoring unit configured to monitor in real time the shallow rapid breathing index and diaphragm ultrasound index corresponding to the patient to be weaned;

[0027] The data matching unit is configured to match the shallow rapid breathing index and the diaphragm ultrasound index with each index interval and the index interval respectively, and determine the success rate of weaning of the patient to be weaned based on the shallow rapid breathing index and the diaphragm ultrasound index.

[0028] In combination with the second implementable manner of the second aspect, in a third implementable manner of the second aspect, the data acquisition unit includes:

[0029] A case collection unit is configured to obtain a plurality of weaning cases based on the shallow rapid breathing index, classify the weaning cases, and determine the index interval to which each weaning case belongs;

[0030] The statistical analysis unit is configured to perform statistical analysis on the number of cases in different index intervals, and determine the success rate of weaning corresponding to each index interval.

[0031] In combination with the first implementable manner of the second aspect, in a fourth implementable manner of the second aspect, the decision module includes:

[0032] An allocating unit configured to allocate weights corresponding to the shallow rapid breathing index and the diaphragm ultrasound index of the patient to be weaned;

[0033] The calculation unit is configured to determine the combined weaning success rate corresponding to the patient to be weaned by combining the shallow rapid breathing index and the weaning success rate corresponding to the diaphragm ultrasound index of the patient to be weaned and their weights.

[0034] In combination with the fourth implementable manner of the second aspect, in a fifth implementable manner of the second aspect, the allocating unit includes:

[0035] A confidence acquisition unit configured to acquire confidences corresponding to different index intervals of the shallow rapid breathing index and confidences corresponding to different index intervals of the diaphragm ultrasound index;

[0036] The weight allocation unit is configured to allocate weights corresponding to the shallow rapid breathing index and the diaphragm ultrasound index according to corresponding confidence levels.

[0037] Beneficial effect: The ventilator weaning decision method and system using diaphragm ultrasound combined with RSBI index of the present invention can improve the accuracy of weaning decision-making for critically ill neurological patients by evaluating the probability of successful weaning of the ventilator in the current patient by combining the weaning success rate obtained by evaluating the shallow rapid breathing index of the patient to be weaned and the weaning success rate obtained by evaluating the diaphragm ultrasound index, so as to find the most appropriate time to wean the ventilator and reduce unnecessary mechanical ventilation time and adverse prognosis related to weaning failure. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the specific implementation of the present invention, the following will briefly introduce the drawings required for use in the specific implementation. In all the drawings, each element or part is not necessarily drawn according to the actual scale.

[0039] Figure 1 A flowchart of a ventilator weaning decision method based on diaphragm ultrasound combined with RSBI index provided in one embodiment of the present invention;

[0040] Figure 2 A system block diagram of a ventilator weaning decision system based on diaphragm ultrasound combined with RSBI index provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0041] The following embodiments of the technical solution of the present invention are described in detail in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and are therefore only used as examples, and cannot be used to limit the protection scope of the present invention.

[0042] like Figure 1 The flowchart of the ventilator weaning decision method based on diaphragm ultrasound combined with RSBI index is shown in the figure. The decision method includes:

[0043] Step 1, obtaining the weaning success rate of the patient to be weaned based on the shallow rapid breathing index and the weaning success rate based on the diaphragm ultrasound index;

[0044] Step 2: Determine the fusion weaning success rate corresponding to the patient to be weaned by combining the weaning success rate obtained by shallow rapid breathing index assessment and the weaning success rate obtained by diaphragm ultrasound index assessment.

[0045] Specifically, first, the success rate of successful weaning of the ventilator in patients to be weaned can be evaluated based on the shallow rapid breathing index, and the success rate of successful weaning of the ventilator in patients to be weaned can be evaluated based on the diaphragm ultrasound index. Then, the weaning success rates corresponding to these two indicators can be fused to determine the fused weaning success rate of successful weaning of the ventilator in patients to be weaned. In this way, the shallow rapid breathing index and diaphragm ultrasound indicators can be combined to comprehensively evaluate the patient's weaning success rate, improve the accuracy of weaning decisions for critically ill neurological patients, find the most appropriate time to wean the ventilator, and reduce unnecessary mechanical ventilation time and adverse prognosis associated with weaning failure.

[0046] In this embodiment, optionally, in step 1, obtaining the weaning success rate obtained by evaluating the shallow rapid breathing index and the weaning success rate obtained by evaluating the diaphragm ultrasound index includes:

[0047] Obtain the success rate of weaning from the ventilator corresponding to different index intervals of the shallow rapid breathing index, and the success rate of weaning from the ventilator corresponding to different index intervals of the diaphragm ultrasound index;

[0048] Real-time monitoring of the shallow rapid breathing index and diaphragm ultrasound index of patients waiting to be weaned;

[0049] The shallow rapid breathing index and diaphragm ultrasound index were matched with each index interval and indicator interval respectively, and the success rate of weaning of patients to be weaned was determined based on the shallow rapid breathing index and diaphragm ultrasound index.

[0050] Specifically, first, by statistically analyzing previous weaning cases, the weaning success rates corresponding to different index intervals of the shallow rapid breathing index and the weaning success rates corresponding to different index intervals of the diaphragm ultrasound index can be obtained. For example, by statistically analyzing previous weaning cases, the weaning success rates corresponding to the two index intervals of RSBI ≤ 105 and RSBI > 105 can be obtained.

[0051] Then, the shallow rapid breathing index currently monitored by the patient to be weaned can be matched with the value ranges corresponding to the two index intervals, so as to determine the index interval to which the shallow rapid breathing index of the patient to be weaned belongs, and then obtain the probability of successful weaning corresponding to the shallow rapid breathing index of the patient to be weaned. Similarly, the same method can be used to obtain the probability of successful weaning corresponding to the diaphragm ultrasound index of the patient to be weaned.

[0052] It should be understood that the physiological characteristics of critically ill neurological patients may change at any time. If the weaning decision is not made in time, the patient may miss the best time for weaning. Interval matching can quickly determine the probability of successful weaning corresponding to the shallow rapid breathing index and diaphragm ultrasound index of the patient to be weaned, thereby improving the efficiency of weaning decision-making and avoiding missing the best time for weaning.

[0053] In this embodiment, optionally, obtaining the weaning success rates corresponding to different index intervals includes:

[0054] Obtain multiple weaning cases based on shallow rapid breathing index, classify the weaning cases, and determine the index interval to which each weaning case belongs;

[0055] The number of cases in different index intervals was statistically analyzed to determine the corresponding success rate of weaning in each index interval.

[0056] Specifically, by statistically analyzing previous weaning cases, different index intervals of the shallow rapid breathing index and the weaning success rates corresponding to different index intervals are obtained. Take the weaning success rate corresponding to each index interval as an example. First, multiple weaning cases in which weaning decisions are made only based on the shallow rapid breathing index can be collected. The shallow rapid breathing index in the collected weaning cases is matched with the two index intervals of RSBI≤105 and RSBI>105 to determine the index interval to which the weaning case belongs. Then, a statistical analysis of weaning cases within the two index intervals of RSBI≤105 and RSBI>105 can be performed to determine the number of successful weaning cases in these two index intervals. Combined with the total number of weaning cases in the index interval, the weaning success rate of each index interval can be calculated.

[0057] In this embodiment, optionally, determining the combined weaning success rate corresponding to the patient to be weaned includes:

[0058] Assign weights corresponding to the shallow rapid breathing index and diaphragm ultrasound index of patients waiting for weaning;

[0059] The combined weaning success rate corresponding to the shallow rapid breathing index and diaphragm ultrasound index of the patient to be weaned is determined by combining the weaning success rate and its weight corresponding to the patient to be weaned.

[0060] It should be understood that for critically ill neurological patients, the shallow rapid breathing index and diaphragm ultrasound index have different evaluation effects on ventilator weaning. Therefore, in order to improve the accuracy of weaning decisions for critically ill neurological patients, when calculating the combined weaning success rate of patients to be weaned, the weights corresponding to the shallow rapid breathing index and diaphragm ultrasound index of patients to be weaned can be set first. Then, based on the set weights, according to the weaning success rate corresponding to the shallow rapid breathing index and diaphragm ultrasound index of patients to be weaned, a weighted algorithm is used to calculate the combined weaning success rate corresponding to the patients to be weaned, thereby achieving the purpose of combining the shallow rapid breathing index and diaphragm ultrasound index to comprehensively evaluate the patient's weaning success rate.

[0061] In this embodiment, optionally, the weights corresponding to the shallow rapid breathing index and the diaphragm ultrasound index are allocated, including:

[0062] Obtain the confidence levels corresponding to different index intervals of the shallow rapid breathing index and the confidence levels corresponding to different index intervals of the diaphragm ultrasound index;

[0063] The weights corresponding to the shallow rapid breathing index and the diaphragm ultrasound index are allocated according to the corresponding confidence levels.

[0064] It should be understood that for critically ill neurological patients, different intervals of shallow rapid breathing index and diaphragm ultrasound index have different evaluation effects on ventilator weaning, resulting in different confidence levels in different intervals. In order to improve the accuracy of weaning decisions for critically ill neurological patients, the weights of shallow rapid breathing index and diaphragm ultrasound index can be assigned according to the confidence levels of the intervals to which the shallow rapid breathing index and diaphragm ultrasound index of the patients to be weaned belong.

[0065] Specifically, different index intervals of the shallow rapid breathing index and the confidence corresponding to different index intervals can be obtained by statistically analyzing previous weaning cases. Take the index interval as an example. First, collect a plurality of weaning cases in which weaning decisions are made based only on the shallow rapid breathing index. Match the shallow rapid breathing index in the collected weaning cases with the two index intervals of RSBI≤105 and RSBI>105 to determine the index interval to which the weaning case belongs. Then, statistical analysis is performed on the weaning cases within the two index intervals of RSBI≤105 and RSBI>105 to determine the number of successful weaning cases in these two index intervals. Combined with the total number of weaning cases in the index interval, the weaning success rate of each index interval can be calculated. Finally, according to the corresponding weaning success rate and the total number of weaning cases collected, the confidence of each index interval is calculated.

[0066] After obtaining the confidence corresponding to each index interval, the current shallow rapid breathing index of the patient to be weaned can be matched with the value range of each index interval, so as to determine the index interval to which the patient to be weaned currently belongs, and then determine the confidence corresponding to the current shallow rapid breathing index of the patient to be weaned. According to the confidence corresponding to the shallow rapid breathing index. Similarly, the confidence corresponding to the diaphragm ultrasound index can be determined by the same method. Finally, weights are assigned based on the confidence corresponding to the shallow rapid breathing index and the diaphragm ultrasound index. Among them, the higher the confidence, the greater the assigned weight.

[0067] like Figure 2 The ventilator weaning decision system based on diaphragm ultrasound combined with RSBI index is characterized by comprising:

[0068] An acquisition module configured to acquire a weaning success rate of a patient to be weaned obtained by evaluating the shallow rapid breathing index and a weaning success rate obtained by evaluating the diaphragm ultrasound index;

[0069] The decision module is configured to determine the fusion weaning success rate of the patient to be weaned by combining the weaning success rate obtained by evaluating the shallow rapid breathing index and the weaning success rate obtained by evaluating the diaphragm ultrasound index.

[0070] Specifically, the decision system includes an acquisition module and a decision module. Among them, the acquisition module can evaluate the success rate of successful weaning of the ventilator for patients to be weaned based on the shallow rapid breathing index, and evaluate the success rate of successful weaning of the ventilator for patients to be weaned based on the diaphragm ultrasound index. The decision module can integrate the weaning success rates corresponding to these two indicators to determine the integrated weaning success rate of successful weaning of the ventilator for patients to be weaned. In this way, the shallow rapid breathing index and the diaphragm ultrasound index can be combined to comprehensively evaluate the patient's weaning success rate, improve the accuracy of weaning decisions for critically ill neurological patients, find the most appropriate time to wean the ventilator, and reduce unnecessary mechanical ventilation time and adverse prognosis related to weaning failure.

[0071] In this embodiment, optionally, the acquisition module includes:

[0072] A data acquisition unit configured to acquire the weaning success rate corresponding to different index intervals of the shallow rapid breathing index and the weaning success rate corresponding to different index intervals of the diaphragm ultrasound index;

[0073] A human monitoring unit configured to monitor in real time the shallow rapid breathing index and diaphragm ultrasound index corresponding to the patient to be weaned;

[0074] The data matching unit is configured to match the shallow rapid breathing index and the diaphragm ultrasound index with each index interval and the index interval respectively, and determine the success rate of weaning of the patient to be weaned based on the shallow rapid breathing index and the diaphragm ultrasound index.

[0075] Specifically, the acquisition module includes a data acquisition unit, a human monitoring unit and a data matching unit. The data acquisition unit can obtain the weaning success rate corresponding to different index intervals of the shallow rapid breathing index and the weaning success rate corresponding to different index intervals of the diaphragm ultrasound index by statistically analyzing previous weaning cases.

[0076] The human body monitoring unit can monitor the shallow rapid breathing index and diaphragm ultrasound index of the patient to be weaned in real time. The data matching unit can match the shallow rapid breathing index currently monitored by the patient to be weaned with the value ranges corresponding to the two index intervals, thereby determining the index interval to which the shallow rapid breathing index of the patient to be weaned belongs, and then obtaining the probability of successful weaning corresponding to the shallow rapid breathing index of the patient to be weaned. Similarly, the probability of successful weaning corresponding to the diaphragm ultrasound index of the patient to be weaned can be obtained through data matching.

[0077] In this embodiment, optionally, the data acquisition unit includes:

[0078] A case collection unit is configured to obtain a plurality of weaning cases based on the shallow rapid breathing index, classify the weaning cases, and determine the index interval to which each weaning case belongs;

[0079] The statistical analysis unit is configured to perform statistical analysis on the number of cases in different index intervals, and determine the success rate of weaning corresponding to each index interval.

[0080] Specifically, the data acquisition unit includes a case collection unit and a statistical analysis unit. Taking the shallow rapid breathing index as an example, the case collection unit can collect multiple weaning cases in which weaning decisions are made based only on the shallow rapid breathing index. The shallow rapid breathing index in the collected weaning cases is matched with the two index intervals of RSBI≤105 and RSBI>105 to determine the index interval to which the weaning case belongs. The statistical analysis unit can perform statistical analysis on the successful weaning cases within the two index intervals of RSBI≤105 and RSBI>105, respectively, to determine the number of successful weaning cases in these two index intervals, and combine the total number of weaning cases in the index interval to calculate the weaning success rate of each index interval.

[0081] In this embodiment, optionally, the decision module includes:

[0082] An allocating unit configured to allocate weights corresponding to the shallow rapid breathing index and the diaphragm ultrasound index of the patient to be weaned;

[0083] The calculation unit is configured to determine the combined weaning success rate corresponding to the patient to be weaned by combining the shallow rapid breathing index and the weaning success rate corresponding to the diaphragm ultrasound index of the patient to be weaned and their weights.

[0084] Specifically, the decision module includes an allocation unit and a calculation unit. The allocation unit can set the weights corresponding to the shallow rapid breathing index and the diaphragm ultrasound index of the patient to be weaned. The calculation unit can calculate the combined weaning success rate corresponding to the patient to be weaned by a weighted algorithm based on the set weights and the weaning success rate corresponding to the shallow rapid breathing index and the diaphragm ultrasound index of the patient to be weaned, thereby achieving the purpose of combining the shallow rapid breathing index and the diaphragm ultrasound index to comprehensively evaluate the patient's weaning success rate.

[0085] In this embodiment, optionally, the allocation unit includes:

[0086] A confidence acquisition unit configured to acquire confidences corresponding to different index intervals of the shallow rapid breathing index and confidences corresponding to different index intervals of the diaphragm ultrasound index;

[0087] The weight allocation unit is configured to allocate weights corresponding to the shallow rapid breathing index and the diaphragm ultrasound index according to corresponding confidence levels.

[0088] Specifically, the allocation unit includes a confidence acquisition unit and a weight allocation unit. The confidence acquisition unit can perform statistical analysis on previous weaning cases to obtain different index intervals of the shallow rapid breathing index and the confidence corresponding to different index intervals. The weight allocation unit allocates weights based on the confidence corresponding to the shallow rapid breathing index and the diaphragm ultrasound index.

[0089] After assigning weights corresponding to the shallow rapid breathing index and the diaphragm ultrasound index, the combined weaning success rate of the patients to be weaned can be calculated using a weighted algorithm based on the set weights and the weaning success rate corresponding to the shallow rapid breathing index and the diaphragm ultrasound index of the patients to be weaned, thereby achieving the purpose of comprehensively evaluating the patient's weaning success rate by combining the shallow rapid breathing index and the diaphragm ultrasound index.

[0090] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A ventilator weaning decision method based on diaphragm ultrasound combined with RSBI index, characterized in that: include: To obtain the success rate of weaning from patients to be weaned based on the shallow rapid breathing index and the success rate of weaning based on the diaphragm ultrasound index; The weaning success rate obtained by combining the shallow rapid breathing index assessment and the weaning success rate obtained by evaluating the diaphragm ultrasound index are used to determine the corresponding fusion weaning success rate for the patients to be weaned.

2. The ventilator weaning decision method based on diaphragm ultrasound combined with RSBI index according to claim 1, characterized in that: Obtain the weaning success rate based on the shallow rapid breathing index and the weaning success rate based on the diaphragm ultrasound index, including: Obtain the success rate of weaning from the ventilator corresponding to different index intervals of the shallow rapid breathing index, and the success rate of weaning from the ventilator corresponding to different index intervals of the diaphragm ultrasound index; Real-time monitoring of the shallow rapid breathing index and diaphragm ultrasound index of patients waiting to be weaned; The shallow rapid breathing index and diaphragm ultrasound index were matched with each index interval and indicator interval respectively, and the success rate of weaning of patients to be weaned was determined based on the shallow rapid breathing index and diaphragm ultrasound index.

3. The ventilator weaning decision method based on diaphragm ultrasound combined with RSBI index according to claim 2, characterized in that: Obtain the corresponding weaning success rate for different index intervals, including: Obtain multiple weaning cases based on shallow rapid breathing index, classify the weaning cases, and determine the index interval to which each weaning case belongs; The number of cases in different index intervals was statistically analyzed to determine the corresponding success rate of weaning in each index interval.

4. The ventilator weaning decision method based on diaphragm ultrasound combined with RSBI index according to claim 1, characterized in that: Determine the combined weaning success rate for patients to be weaned, including: Assign weights corresponding to the shallow rapid breathing index and diaphragm ultrasound index of patients waiting for weaning; The combined weaning success rate corresponding to the shallow rapid breathing index and diaphragm ultrasound index of the patient to be weaned is determined by combining the weaning success rate and its weight corresponding to the patient to be weaned.

5. The ventilator weaning decision method based on diaphragm ultrasound combined with RSBI index according to claim 4, characterized in that: The weights corresponding to the shallow rapid breathing index and the diaphragm ultrasound index are allocated, including: Obtain the confidence levels corresponding to different index intervals of the shallow rapid breathing index and the confidence levels corresponding to different index intervals of the diaphragm ultrasound index; The weights corresponding to the shallow rapid breathing index and the diaphragm ultrasound index are allocated according to the corresponding confidence levels.

6. A ventilator weaning decision system based on diaphragm ultrasound combined with RSBI index, characterized in that: include: An acquisition module configured to acquire a weaning success rate of a patient to be weaned obtained by evaluating the shallow rapid breathing index and a weaning success rate obtained by evaluating the diaphragm ultrasound index; The decision module is configured to determine the fusion weaning success rate of the patient to be weaned by combining the weaning success rate obtained by evaluating the shallow rapid breathing index and the weaning success rate obtained by evaluating the diaphragm ultrasound index.

7. The ventilator weaning decision system based on diaphragm ultrasound combined with RSBI index according to claim 6, characterized in that: The acquisition module comprises: A data acquisition unit configured to acquire the weaning success rate corresponding to different index intervals of the shallow rapid breathing index and the weaning success rate corresponding to different index intervals of the diaphragm ultrasound index; A human monitoring unit configured to monitor in real time the shallow rapid breathing index and diaphragm ultrasound index corresponding to the patient to be weaned; The data matching unit is configured to match the shallow rapid breathing index and the diaphragm ultrasound index with each index interval and the index interval respectively, and determine the success rate of weaning of the patient to be weaned based on the shallow rapid breathing index and the diaphragm ultrasound index.

8. The ventilator weaning decision system based on diaphragm ultrasound combined with RSBI index according to claim 7, characterized in that: The data acquisition unit includes: A case collection unit is configured to obtain a plurality of weaning cases based on the shallow rapid breathing index, classify the weaning cases, and determine the index interval to which each weaning case belongs; The statistical analysis unit is configured to perform statistical analysis on the number of cases in different index intervals, and determine the success rate of weaning corresponding to each index interval.

9. The ventilator weaning decision system based on diaphragm ultrasound combined with RSBI index according to claim 6, characterized in that: The decision module comprises: An allocating unit configured to allocate weights corresponding to the shallow rapid breathing index and the diaphragm ultrasound index of the patient to be weaned; The calculation unit is configured to determine the combined weaning success rate corresponding to the patient to be weaned by combining the shallow rapid breathing index and the weaning success rate corresponding to the diaphragm ultrasound index of the patient to be weaned and their weights.

10. The ventilator weaning decision system based on diaphragm ultrasound combined with RSBI index according to claim 9, characterized in that: The distribution unit comprises: A confidence acquisition unit configured to acquire confidences corresponding to different index intervals of the shallow rapid breathing index and confidences corresponding to different index intervals of the diaphragm ultrasound index; The weight allocation unit is configured to allocate weights corresponding to the shallow rapid breathing index and the diaphragm ultrasound index according to corresponding confidence levels.

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