Early warning method for preventing tracheal intubation accident

Abstract

The invention relates to the technical field of medical early warning, in particular to an early warning method for preventing tracheal intubation accidents, and the method comprises the steps: carrying out the real-time monitoring and collection of airway state data and intubation state data through a data collection port, and outputting the collected data to a data analysis port; the data analysis port identifies and extracts the collected threat data, prejudges a threat point in the airway, and outputs a first threat value; and changing the flow rate of oxygen input into the trachea cannula, screening hidden threat points in the airway, outputting a second threat value, calculating a residual threat value according to the first threat value and the second threat value, and transmitting the residual threat value to the data feedback port.

Description

Background Art

[0002] In clinical practice, endotracheal intubation is a crucial measure during cardiopulmonary resuscitation and the rescue of critically ill patients with respiratory dysfunction. It involves inserting a tube into the trachea through the oral or nasal cavity, transglottis, and connecting it to a ventilator to replace spontaneous breathing. It can promptly remove secretions or foreign matter from the trachea, prevent foreign matter from entering the airway, maintain airway patency, and allow for effective artificial or mechanical ventilation to prevent hypoxia and carbon dioxide retention in the patient. However, medical safety in endotracheal intubation cannot be consistently maintained, and the following high-risk factors currently exist:

[0003] Most patients have head injuries, resulting in impaired consciousness. Some patients may bite down on the endotracheal tube. In the case of agitation, the bite pad may shift, and the patient may bite the endotracheal tube, resulting in poor advanced airway ventilation. If the cause is not promptly identified, it may easily lead to suffocation of the patient.

[0004] In some critically ill patients, respiratory secretions are not cleared promptly due to factors such as the absence of a cough reflex and insufficient airway humidification, and the ventilator alarm is not triggered. After the nurse suctions the sputum, sputum re-accumulates in the respiratory tract and even forms sputum crusts, causing blockage of the endotracheal tube and further suffocation of the patient.

[0005] Because the tracheal tube is fixed with tape + laces, the tape can easily become loose due to the patient's oral secretions. In addition, the tracheal tube may be displaced due to the patient turning over and shifting. If the tracheal tube scale is not checked during the shift handover, it will also pose a threat to the patient's safety.

[0006] Therefore, it is necessary to propose an early warning method for preventing tracheal intubation accidents to solve the problems in the background technology. Summary of the Invention

[0007] The main purpose of the present invention is to provide an early warning method for preventing tracheal intubation accidents, which effectively solves the above-mentioned problems mentioned in the background technology.

[0008] The technical solutions of the present invention are as follows:

[0009] A method for preventing endotracheal intubation accidents is proposed, comprising the following steps:

[0010] S1, the data acquisition port monitors and collects airway status data and intubation status data in real time, and outputs the collected data to the data analysis port;

[0011] S2. The data analysis port identifies and extracts the collected threat data, predicts the threat points in the airway, and outputs a first threat value;

[0012] S3. Change the oxygen flow rate input into the endotracheal tube, screen hidden threat points in the airway, output a second threat value, calculate the residual threat value based on the first threat value and the second threat value, and transmit it to the data feedback port.

[0013] A further improvement of the present invention is that the specific contents of the airway status data and intubation status data in S1 are as follows: the airway status data includes airway internal pressure data F, airway humidity data S, airway secretion data W, and airway gas flow rate data V; the intubation status data includes intubation bite pressure data P, intubation deformation data X, intubation gas flow rate data v, intubation depth data, and intubation angle data.

[0014] A further improvement of the present invention is that the specific content of identifying and extracting the collected threat data in S2 is: comparing the collected data (F, S, W, V, v) with the pre-set element thresholds (f1, s1-s2, w1, V1, v1) to obtain the threat data set that is not within the threshold range. Extract threat data collection The threat data and its corresponding elements in the set

[0015] A further improvement of the present invention is that the specific content of the prejudgment of the threat point in the airway in S2 is: extracting the threat data set The threat data and its corresponding elements in the set Simultaneously extract threat data and threat location sets Aggregate threat data gather The values ​​in are imported into the Mahalanobis distance calculation formula and output in sequence Zhongyu The data with the highest correlation are sorted in descending order, and the correlation threshold is set to extract Zhongyu The data position with a correlation greater than the correlation threshold is regarded as the threat point position in the airway.

[0016] A further improvement of the present invention is that the step S2 further includes calculating the first threat value, and the specific calculation formula is:

[0017]

[0018] Among them, I1 is the first threat value, α1, α2, α3, α4, and α5 are the weight coefficients of airway pressure data, airway humidity data, airway secretion data, airway gas flow rate data, and intubation gas flow rate data, respectively, and α1+α2+α3+α4+α5=1, and sn is the value closest to S among s1-s2.

[0019] A further improvement of the present invention is that S3 includes the following specific steps:

[0020] S31. Change the oxygen flow rate input into the endotracheal tube to reduce the flow rate by one tenth. Perform secondary collection of the airway pressure data, airway humidity data, airway secretion data, airway gas flow rate data, and intubation gas flow rate data at the threat point in the airway to obtain (F2, S2, W2, V2, v2). Compare these with the pre-set thresholds of each element (f1, s1-s2, w1, V1, v1) to obtain a set of threat data that is not within the threshold range. Substitute the second threat value into the formula:

[0021]

[0022] Among them, I2 is the second threat value, sm is the value closest to S2 among s1-s2;

[0023] S32. Calculate the residual threat value of the threat point in the airway based on I1 and I2. The specific calculation formula is:

[0024]

[0025] in, is the residual threat coefficient of the threat point in the airway, and Hz is the current exploration frequency of the medical staff;

[0026] S33. The calculated residual threat value of the threat point position in the airway is transmitted to the data feedback port to provide an early warning to medical staff.

[0027] The technical effects of the present invention are as follows:

[0028] A new early warning method for preventing endotracheal intubation accidents has been developed. This method uses a data acquisition port to monitor and collect airway and intubation status data in real time, outputting the collected data to a data analysis port. The data analysis port then identifies and extracts the collected threat data, predicts threat points within the airway, and outputs a first threat value. The oxygen flow rate input to the endotracheal tube is then varied to screen for hidden threat points within the airway, outputting a second threat value. Based on the first and second threat values, a residual threat value is calculated and transmitted to a data feedback port. This early warning method effectively mitigates the medical care risks associated with unconscious patient clenching and the accumulation of secretions in the airway. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0030] Figure 1The figure is a flow chart of an early warning method for preventing tracheal intubation accidents according to the present invention. DETAILED DESCRIPTION

[0031] This invention aims to provide an early warning method to prevent endotracheal intubation accidents. This method uses a data acquisition port to monitor and collect airway and intubation status data in real time, outputting the collected data to a data analysis port. The data analysis port then identifies and extracts the collected threat data, predicts threat points within the airway, and outputs a first threat value. The oxygen flow rate input to the endotracheal tube is varied to screen for hidden threat points within the airway, outputting a second threat value. Based on the first and second threat values, a residual threat value is calculated and transmitted to a data feedback port. This early warning method effectively addresses the medical care risks posed by unconscious patient biting and the accumulation of secretions in the airway.

[0032] Example 1:

[0033] This embodiment proposes an early warning method for preventing tracheal intubation accidents, such as Figure 1 As shown, the following specific steps are included:

[0034] S1, the data acquisition port monitors and collects airway status data and intubation status data in real time, and outputs the collected data to the data analysis port;

[0035] S2. The data analysis port identifies and extracts the collected threat data, predicts the threat points in the airway, and outputs a first threat value;

[0036] S3. Change the oxygen flow rate input into the endotracheal tube, screen hidden threat points in the airway, output a second threat value, calculate the residual threat value based on the first threat value and the second threat value, and transmit it to the data feedback port.

[0037] In this embodiment, the specific contents of the airway status data and intubation status data in S1 are: the airway status data includes airway internal pressure data F, airway humidity data S, airway secretion data W, and airway gas flow rate data V; the intubation status data includes intubation bite pressure data P, intubation deformation data X, intubation gas flow rate data v, intubation depth data, and intubation angle data.

[0038] In this embodiment, the specific content of identifying and extracting the collected threat data in S2 is: comparing the collected data (F, S, W, V, v) with the pre-set element thresholds (f1, s1-s2, w1, V1, v1) to obtain the threat data set that is not within the threshold range. Extract threat data collection The threat data and its corresponding elements in the set

[0039] In this embodiment, the specific content of the prejudgment of the threat point in the airway in S2 is: extracting the threat data set The threat data and its corresponding elements in the set Simultaneously extract threat data and threat location sets Aggregate threat data gather The values ​​in are imported into the Mahalanobis distance calculation formula and output in sequence Zhongyu The data with the highest correlation are sorted in descending order, and the correlation threshold is set to extract Zhongyu The data position with a correlation greater than the correlation threshold is regarded as the threat point position in the airway.

[0040] In this embodiment, the step S2 further includes calculating a first threat value, and the specific calculation formula is:

[0041]

[0042] Among them, I1 is the first threat value, α1, α2, α3, α4, and α5 are the weight coefficients of airway pressure data, airway humidity data, airway secretion data, airway gas flow rate data, and intubation gas flow rate data, respectively, and α1+α2+α3+α4+α5=1, and sn is the value closest to S among s1-s2.

[0043] In this embodiment, S3 includes the following specific steps:

[0044] S31. Change the oxygen flow rate input into the endotracheal tube to reduce the flow rate by one tenth. Perform secondary collection of the airway pressure data, airway humidity data, airway secretion data, airway gas flow rate data, and intubation gas flow rate data at the threat point in the airway to obtain (F2, S2, W2, V2, v2). Compare these with the pre-set thresholds of each element (f1, s1-s2, w1, V1, v1) to obtain a set of threat data that is not within the threshold range. Substitute the second threat value into the formula:

[0045]

[0046] Among them, I2 is the second threat value, sm is the value closest to S2 among s1-s2;

[0047] S32. Calculate the residual threat value of the threat point in the airway based on I1 and I2. The specific calculation formula is:

[0048]

[0049] in, is the residual threat coefficient of the threat point in the airway, and Hz is the current exploration frequency of the medical staff;

[0050] S33. The calculated residual threat value of the threat point position in the airway is transmitted to the data feedback port to provide an early warning to medical staff.

[0051] This embodiment uses multiple steps to monitor airway and intubation status data in real time, combining algorithms such as the Mahalanobis distance to accurately predict threat points. It adjusts oxygen flow rates to screen for hidden threat points, and provides medical staff with early warnings based on the calculated threat value. This innovative method not only identifies overt threats but also uncovers potential airway hazards by dynamically adjusting system parameters, significantly improving medical safety.

[0052] Example 2:

[0053] This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; the processor executes the above-mentioned early warning method for preventing tracheal intubation accidents by calling the computer program stored in the memory.

[0054] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) and one or more memories, wherein the memories store at least one computer program, which is loaded and executed by the processor to implement an early warning method for preventing accidental tracheal intubation provided by the above method embodiment. The electronic device may also include other components for implementing the functions of the device. For example, the electronic device may also have components such as a wired or wireless network interface and an input / output interface for data input and output. This embodiment will not be described in detail here.

[0055] Those skilled in the art will appreciate that the present invention may be implemented as a system, method, or computer program product. Therefore, the present disclosure may be implemented in the following forms: entirely in hardware, entirely in software (including firmware, resident software, microcode, etc.), or in a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." Furthermore, in some embodiments, the present invention may be implemented in the form of a computer program product embodied in one or more computer-readable media containing computer-readable program code.

[0056] Any combination of one or more computer-readable media may be employed. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more conductors, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0057] The present invention is described with reference to flowcharts and block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process or block in the flowcharts and block diagrams, as well as combinations of processes and blocks in the flowcharts or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts or block diagrams. Figure 1 A process or multiple processes and boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0058] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 A process or multiple processes and boxes Figure 1 A step that specifies a function in one or more boxes.

[0059] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which are all protected by the present invention.

Claims

1. A warning method for preventing endotracheal intubation accidents, characterized in that: The specific steps include: S1, the data acquisition port monitors and collects airway status data and intubation status data in real time, and outputs the collected data to the data analysis port; S2, the data analysis port identifies and extracts the collected threat data, and predicts the threat points in the airway, and outputs a first threat value; S3. Change the oxygen flow rate input into the endotracheal tube, screen hidden threat points in the airway, output a second threat value, calculate the residual threat value based on the first threat value and the second threat value, and transmit it to the data feedback port.

2. The early warning method for preventing endotracheal intubation accidents according to claim 1, characterized in that: The specific contents of the airway status data and intubation status data in S1 are as follows: the airway status data includes airway internal pressure data F, airway humidity data S, airway secretion data W, and airway gas flow rate data V; the intubation status data includes intubation bite pressure data P, intubation deformation data X, intubation gas flow rate data v, intubation depth data, and intubation angle data.

3. The early warning method for preventing endotracheal intubation accidents according to claim 2, characterized in that: The specific content of identifying and extracting the collected threat data in S2 is: comparing the collected data (F, S, W, V, v) with the preset element thresholds (f1, s1-s2, w1, V1, v1) to obtain a set of threat data that is not within the threshold range. Extract threat data collection The threat data and its corresponding elements in constitute a set 4. The early warning method for preventing endotracheal intubation accidents according to claim 3, characterized in that: The specific content of predicting the threat points in the airway in S2 is: extracting the threat data set The threat data and its corresponding elements in constitute a set Extract threat data and threat location at the same time Aggregate threat data gather The values ​​in are imported into the Mahalanobis distance calculation formula and output in sequence Zhongyu The data with the highest correlation are located in descending order, and the correlation threshold is set to extract Zhongyu The data position with a correlation greater than the correlation threshold is regarded as the threat point position in the airway.

5. The early warning method for preventing endotracheal intubation accidents according to claim 4, characterized in that: The step S2 also includes calculating the first threat value, and the specific calculation formula is: Among them, I1 is the first threat value, α1, α2, α3, α4, and α5 are the weight coefficients of airway pressure data, airway humidity data, airway secretion data, airway gas flow rate data, and intubation gas flow rate data, respectively, and α1+α2+α3+α4+α5=1, sn is the value closest to S among s1-s2.

6. The early warning method for preventing endotracheal intubation accidents according to claim 5, characterized in that: The S3 includes the following specific steps: S31. Change the oxygen flow rate into the endotracheal tube to reduce the flow rate by one tenth, collect the airway pressure data, airway humidity data, airway secretion data, airway gas flow rate data, and intubation gas flow rate data at the threat point in the airway for a second time (F2, S2, W2, V2, v2), and compare them with the pre-set element thresholds (f1, s1-s2, w1, V1, v1) to obtain the threat data set that is not within the threshold range. Substitute it into the calculation of the second threat value, the calculation formula is: Among them, I2 is the second threat value, sm is the value closest to S2 among s1-s2; S32, calculating the residual threat value of the threat point position in the airway according to I1 and I2, the specific calculation formula is: in, is the residual threat coefficient of the threat point in the airway, Hz is the current exploration frequency of the medical staff; S33. The calculated residual threat value of the threat point position in the airway is transmitted to the data feedback port to give an early warning to the medical staff.

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