Intraspinal anesthesia postoperative headache early warning method, device, equipment and medium

Abstract

The invention relates to the technical field of medical monitoring and analysis, in particular to an intra-spinal anesthesia postoperative headache early warning method, device and equipment and a medium, and the method comprises the steps: collecting target ONSD values and various target physiological sign data of a target patient in each time period of intra-spinal anesthesia; based on the target ONSD value of each time node and various target physiological sign data, adopting a prediction model to predict the PDPH risk of the target patient to obtain a prediction result; based on a prediction result, at the initial stage of intracranial pressure drop, corresponding measures are adopted to reduce occurrence of PDPH symptoms, a prediction model is adopted and based on target ONSD values and various target physiological sign data of the patient at all time nodes, so that PDPH risks of the target patient are predicted, and intervention is performed in advance at the initial stage of intracranial pressure drop through advanced early warning, so that the probability of occurrence of the intracranial pressure drop is reduced. The occurrence rate of serious PDPH is obviously reduced.

Description

Technical Field

[0001] The present invention relates to the field of medical monitoring and analysis technology, and in particular to a method, device, equipment and medium for early warning of headache after spinal anesthesia. Background Art

[0002] Traditional assessment methods for post-neuraxial postoperative headache (PDPH) have significant flaws. Specifically, they rely primarily on patient complaints and clinical observation after the onset of symptoms (e.g., supine relief after headache onset, fluid therapy, etc.), which are subject to a lag (intervention is not initiated until 24-48 hours after symptom onset). Existing technologies lack objective early warning indicators and are unable to intervene in the early stages of intracranial pressure drop caused by cerebrospinal fluid leakage, resulting in passive treatment and limited effectiveness.

[0003] Therefore, how to effectively predict the risk of headache after spinal anesthesia and intervene early is a technical problem that needs to be solved urgently. Summary of the Invention

[0004] In view of the above problems, the present invention provides a method, device, equipment and medium for early warning of headache after spinal anesthesia that overcomes the above problems or at least partially solves the above problems.

[0005] In a first aspect, the present invention provides a method for early warning of headache after spinal anesthesia surgery, comprising:

[0006] Collect target ONSD values ​​and various target physiological sign data of target patients at various time points during spinal anesthesia;

[0007] Based on the target ONSD value at each time point and the various target physiological sign data, a prediction model is used to predict the PDPH risk of the target patient to obtain a prediction result;

[0008] Based on the prediction results, appropriate measures are taken to reduce the occurrence of PDPH symptoms in the early stage of intracranial pressure reduction.

[0009] Preferably, the various time nodes include:

[0010] Before anesthesia, 30 minutes after puncture, and 1 hour, 2 hours, 6 hours, 12 hours, and 24 hours after spinal anesthesia;

[0011] The various target physiological sign data include blood pressure and heart rate.

[0012] Preferably, based on the target ONSD value at each time point and the various target physiological sign data, a prediction model is used to predict the PDPH risk of the target patient to obtain a prediction result, including:

[0013] Based on the target ONSD values ​​at each time point, the target ONSD change rate, target ONSD fluctuation amplitude, and target ONSD trend slope are calculated;

[0014] Based on the target ONSD change rate, target ONSD fluctuation amplitude, target ONSD trend slope and various target physiological sign data, a prediction model is used to predict the PDPH risk of the target patient to obtain a prediction result.

[0015] Preferably, before predicting the PDPH risk of the target patient using a prediction model based on the ONSD change rate, ONSD fluctuation amplitude, ONSD trend slope and various target physiological sign data, and obtaining the prediction result, the method further includes:

[0016] Collect historical ONSD values, various historical physiological sign data, and historical pain conditions of historical patients at various time points during spinal anesthesia;

[0017] Based on historical ONSD values, various historical physiological sign data, and historical pain conditions, an XGBoost model was used to train a prediction model for predicting the risk of PDPH after spinal anesthesia.

[0018] Preferably, based on the target ONSD change rate, target ONSD fluctuation amplitude, target ONSD trend slope, and various target physiological sign data, a prediction model is used to predict the PDPH risk of the target patient, and a prediction result is obtained, including:

[0019] The target ONSD change rate, target ONSD fluctuation amplitude, target ONSD trend slope, and various target physiological sign data are input into the prediction model as input data, and the prediction model outputs the PDPH risk prediction result of the target patient.

[0020] Preferably, after predicting the PDPH risk of the target patient based on the target ONSD value and the various target physiological sign data using a prediction model and obtaining a prediction result, the method further includes:

[0021] Collect the target patient's age, gender, and puncture needle model;

[0022] The prediction result is adjusted based on the age, gender and puncture needle model of the target patient.

[0023] Preferably, based on the prediction results, appropriate measures are taken to reduce the occurrence of PDPH symptoms in the early stage of intracranial pressure reduction, including:

[0024] When the predicted result is low risk, observation is recommended;

[0025] When the predicted result is medium risk, intravenous fluid replacement is recommended;

[0026] When the predicted outcome is high risk, epidural blood patch treatment is recommended.

[0027] In a second aspect, the present invention further provides a device for warning headache after spinal anesthesia, comprising:

[0028] An acquisition module is used to collect target ONSD values ​​and various target physiological sign data of target patients at various time points during spinal anesthesia;

[0029] A prediction module is configured to predict the PDPH risk of the target patient using a prediction model based on the target ONSD value at each time point and the various target physiological sign data to obtain a prediction result;

[0030] The measure module is used to adopt corresponding measures to reduce the occurrence of PDPH symptoms in the early stage of intracranial pressure reduction based on the prediction results.

[0031] In a third aspect, the present invention further provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in the first aspect when executing the program.

[0032] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which implements the method described in the first aspect when the program is executed by a processor.

[0033] One or more technical solutions in the embodiments of the present invention have at least the following technical effects or advantages:

[0034] The present invention provides a method for early warning of headache after spinal anesthesia, comprising: collecting target ONSD values ​​and various target physiological sign data of a target patient at various time points during spinal anesthesia; using a prediction model to predict the target patient's PDPH risk based on the target ONSD values ​​and various target physiological sign data at each time point, and obtaining a prediction result; based on the prediction result, in the early stage of intracranial pressure reduction, adopting corresponding measures to reduce the occurrence of PDPH symptoms, adopting the prediction model and based on the target ONSD values ​​and various target physiological sign data of the patient at each time point, thereby predicting the target patient's PDPH risk, and through advanced warning, intervening in advance in the early stage of intracranial pressure reduction, thereby significantly reducing the incidence of severe PDPH. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present invention. Throughout the drawings, the same reference figures denote the same components. In the drawings:

[0036] Figure 1 A schematic diagram showing the steps of a method for early warning of headache after spinal anesthesia in an embodiment of the present invention is shown;

[0037] Figure 2 It shows a schematic structural diagram of a device for warning headache after spinal anesthesia in an embodiment of the present invention;

[0038] Figure 3 A schematic structural diagram of a computer device for implementing a method for early warning of headache during spinal anesthesia according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0039] Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0040] Example 1:

[0041] The embodiment of the present invention provides a method for early warning of headache after spinal anesthesia. Figure 1 As shown, including:

[0042] S101, collecting target ONSD values ​​and various target physiological sign data of target patients at various times during spinal anesthesia;

[0043] S102, based on the target ONSD values ​​at each time point and various target physiological sign data, a prediction model is used to predict the PDPH risk of the target patient to obtain a prediction result;

[0044] S103. Based on the prediction results, appropriate measures are taken to reduce the occurrence of PDPH symptoms in the early stage of intracranial pressure reduction.

[0045] Neuraxial anesthesia involves injecting anesthetic drugs into the subarachnoid or epidural space of the spinal canal. This blocks the spinal nerve roots, producing anesthesia in the area innervated by those nerve roots. This procedure often causes headaches, typically due to leakage of cerebrospinal fluid, which reduces intracranial pressure. To effectively prevent these postoperative headaches, the technical solution of the present invention is employed.

[0046] In a specific embodiment, in S101, a high-frequency ultrasound probe (10-15 Hz) is used to contact the patient's eyes through a coupling agent to collect the target ONSD value of the target patient at various time points. The target ONSD value is specifically the diameter of the optic nerve sheath. An increase in the diameter is a sensitive indicator of increased intracranial pressure. Specifically, when intracranial pressure increases, cerebrospinal fluid pressure is transmitted to the optic nerve sheath through the subarachnoid space surrounding the optic nerve, causing it to expand.

[0047] Collect various target physiological sign data at corresponding time nodes, specifically blood pressure, heart rate, etc.

[0048] The various time nodes here include: before anesthesia, 30 minutes after puncture, 1 hour, 2 hours, 6 hours, 12 hours and 24 hours after spinal anesthesia.

[0049] These time points are all before the occurrence of postoperative symptoms, and can effectively record data from various periods before, during, and after surgery.

[0050] By collecting and recording various types of data at each time node, input data is provided for subsequent predictions.

[0051] Next, S102 is executed, including:

[0052] Based on the target ONSD values ​​at each time point, the target ONSD change rate, target ONSD fluctuation amplitude, and target ONSD trend slope are calculated;

[0053] Based on the target ONSD change rate, target ONSD fluctuation amplitude, target ONSD trend slope and various target physiological sign data, a prediction model is used to predict the PDPH risk of the target patient and obtain the prediction results.

[0054] In a specific embodiment, based on the target ONSD value at each time point, the target ONSD change rate, target ONSD fluctuation amplitude, and target ONSD trend slope are calculated to extract the characteristic value of the ONSD value. This characteristic value can reflect the changing pattern of the target ONSD value.

[0055] The change rate calculation formula is used in the calculation process.

[0056] Next, the training process of the prediction model:

[0057] Collect historical ONSD values, various historical physiological sign data, and historical pain conditions after spinal anesthesia at various time points of historical patients' spinal anesthesia;

[0058] Based on the historical ONSD values, various historical physiological sign data, and historical pain conditions at each time point, the XGBoost model was used to train a prediction model, which was used to predict the risk of PDPH after spinal anesthesia.

[0059] By collecting the historical ONSD values, various historical physiological sign data, and historical pain conditions after spinal anesthesia at various time points of historical patients' spinal anesthesia, and then calculating the historical ONSD change rate, historical ONSD fluctuation range, and historical ONSD trend slope based on the historical ONSD values ​​at each time point. Next, the historical ONSD change rate, historical ONSD fluctuation range, historical ONSD trend slope, various historical physiological sign data, and corresponding historical pain conditions are used as training data, wherein the corresponding historical pain conditions are used as output data, and the historical ONSD change rate, historical ONSD fluctuation range, historical ONSD trend slope, and various historical physiological sign data are used as input data, and then input into the XGBoost model for training, thereby obtaining a prediction model, which is used to predict the risk of PDPH after spinal anesthesia. Of course, the training process requires continuous adjustment of the parameters of the XCBoost model until convergence.

[0060] After obtaining the prediction model, specifically, the target ONSD change rate, target ONSD fluctuation amplitude, target ONSD trend slope and various target physiological sign data are input into the prediction model as input data, and the prediction model outputs the PDPH risk prediction result of the target patient to obtain the prediction result.

[0061] After getting the prediction results, it also includes:

[0062] The target patient's age, gender, and puncture needle model are collected; based on the target patient's age, gender, and puncture needle model, the prediction results are adjusted.

[0063] Adjustments are made based on specific influencing factors. For example, as age increases, the corresponding influencing factors increase, and the corresponding risk value needs to be adjusted higher. Influencing factors also differ between men and women. Needle sizes include 25G and 27G, and each type of needle has different influencing factors. Based on the influence of these influencing factors, the risk value obtained above is adjusted to obtain the final risk value.

[0064] If the corresponding risk value is less than or equal to 30%, it corresponds to a low risk level; if the risk value is between 30% and 70%, it corresponds to a medium risk level; if the risk value is greater than or equal to 70%, it corresponds to a high risk level.

[0065] The predicted result is a risk value percentage, which is divided into three categories based on the high, low, and medium thresholds: a high risk level, a medium risk level, and a low risk level. The device used in this invention uses an HMI interactive interface and a GUI to display the risk level, triggering a graded alarm. These alarms are displayed in red for a high risk level, orange for a medium risk level, and yellow for a low risk level.

[0066] Different intervention measures are taken according to different risk levels.

[0067] Therefore, when the predicted result is low risk, observation is recommended;

[0068] When the predicted result is medium risk, intravenous fluid replacement is recommended;

[0069] When the predicted outcome is high risk, epidural blood patch treatment is recommended.

[0070] Since the prediction results appear at the early stage of intracranial pressure reduction (compared to 4 to 6 hours before the onset of symptoms), early intervention, that is, the intervention window is advanced by 80%, can significantly reduce the incidence of severe PDPH. Clinical verification shows that it can be reduced by 42%.

[0071] Moreover, based on the model prediction method, the risk threshold is dynamically adjusted in combination with the patient's age, gender, puncture parameters, etc. to achieve personalized graded warning, reducing the false alarm rate by 35%.

[0072] Moreover, the high-frequency ultrasonic probe used has an operating error of less than or equal to 0.1mm, which improves the accuracy of detection. The image denoising algorithm is used for the detected images to eliminate more than 90% of motion artifacts.

[0073] This invention solves the industry pain points such as delayed prediction of PDPH risk, high misjudgment rate, and reliance on experience-based decision-making through non-invasive monitoring, predictive models, and clinical pathway optimization, providing a new paradigm for the management of anesthesia complications.

[0074] One or more technical solutions in the embodiments of the present invention have at least the following technical effects or advantages:

[0075] The present invention provides a method for early warning of headache after spinal anesthesia, comprising: collecting target ONSD values ​​and various target physiological sign data of a target patient at various time points during spinal anesthesia; using a prediction model to predict the target patient's PDPH risk based on the target ONSD values ​​and various target physiological sign data at each time point, and obtaining a prediction result; based on the prediction result, in the early stage of intracranial pressure reduction, adopting corresponding measures to reduce the occurrence of PDPH symptoms, adopting the prediction model and based on the target ONSD values ​​and various target physiological sign data of the patient at each time point, thereby predicting the target patient's PDPH risk, and through advanced warning, intervening in advance in the early stage of intracranial pressure reduction, thereby significantly reducing the incidence of severe PDPH.

[0076] Example 2:

[0077] Based on the same inventive concept, the embodiment of the present invention also provides a device for warning headache after spinal anesthesia. Figure 2 As shown, including:

[0078] The acquisition module 201 is used to acquire target ONSD values ​​and various target physiological sign data of the target patient at various time points during spinal anesthesia;

[0079] A prediction module 202 is configured to predict the PDPH risk of the target patient using a prediction model based on the target ONSD value and the various target physiological sign data to obtain a prediction result;

[0080] The measure module 203 is used to adopt corresponding measures to reduce the occurrence of PDPH symptoms based on the prediction results.

[0081] In an optional implementation, the various time nodes include:

[0082] Before anesthesia, 30 minutes after puncture, and 1 hour, 2 hours, 6 hours, 12 hours, and 24 hours after spinal anesthesia;

[0083] The various target physiological sign data include blood pressure and heart rate.

[0084] In an optional embodiment, the prediction module 202 is configured to:

[0085] Based on the target ONSD values ​​at each time point, the target ONSD change rate, target ONSD fluctuation amplitude, and target ONSD trend slope are calculated;

[0086] Based on the target ONSD change rate, target ONSD fluctuation amplitude, target ONSD trend slope and various target physiological sign data, a prediction model is used to predict the PDPH risk of the target patient to obtain a prediction result.

[0087] In an optional embodiment, the system further includes a prediction model training module for:

[0088] Collect historical ONSD values, various historical physiological sign data, and historical pain conditions of historical patients at various time points during spinal anesthesia;

[0089] Based on historical ONSD values, various historical physiological sign data, and historical pain conditions, an XGBoost model was used to train a prediction model for predicting the risk of PDPH after spinal anesthesia.

[0090] In an optional embodiment, the prediction module 202 is further configured to:

[0091] The target ONSD change rate, target ONSD fluctuation amplitude, target ONSD trend slope, and various target physiological sign data are input into the prediction model as input data, and the prediction model outputs the PDPH risk prediction result of the target patient.

[0092] In an optional embodiment, the adjustment module is configured to:

[0093] Collect the target patient's age, gender, and puncture needle model;

[0094] The prediction result is adjusted based on the age, gender and puncture needle model of the target patient.

[0095] In an optional implementation, the measure module 203 is configured to:

[0096] When the predicted result is low risk, observation is recommended;

[0097] When the predicted result is medium risk, intravenous fluid replacement is recommended;

[0098] When the predicted outcome is high risk, epidural blood patch treatment is recommended.

[0099] Example 3:

[0100] Based on the same inventive concept, an embodiment of the present invention provides a computer device, such as Figure 3 As shown, it includes a memory 304, a processor 302 and a computer program stored in the memory 304 and capable of running on the processor 302. When the processor 302 executes the program, the steps of the above-mentioned method for early warning of headache after spinal anesthesia are implemented.

[0101] Among them, Figure 3In the embodiment of the present invention, a bus architecture (represented by bus 300) is shown. Bus 300 may include any number of interconnected buses and bridges, and bus 300 links together various circuits including one or more processors represented by processor 302 and memory represented by memory 304. Bus 300 may also link together various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. Bus interface 306 provides an interface between bus 300 and receiver 301 and transmitter 303. Receiver 301 and transmitter 303 may be the same component, namely a transceiver, which provides a unit for communicating with various other devices over a transmission medium. Processor 302 is responsible for managing bus 300 and general processing, while memory 304 may be used to store data used by processor 302 when performing operations.

[0102] Example 4:

[0103] Based on the same inventive concept, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned method for early warning of headache after spinal anesthesia.

[0104] The algorithm and display provided herein are not inherently related to any particular computer, virtual system or other device. Various general-purpose systems can also be used together with the teachings based on this. According to the above description, it is obvious that the structure required for constructing this type of system. In addition, the present invention is not directed to any specific programming language. It should be understood that various programming languages ​​can be utilized to realize the content of the present invention described herein, and the above description of specific languages ​​is for the purpose of disclosing the best mode of the present invention.

[0105] In the description provided herein, numerous specific details are described. However, it is understood that embodiments of the present invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques are not shown in detail so as not to obscure the understanding of this description.

[0106] Similarly, it should be understood that in order to streamline the present invention and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the present invention, various features of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. However, this disclosed method should not be interpreted as reflecting an intention that the claimed invention requires more features than those explicitly recited in each embodiment. Rather, as reflected in each embodiment, inventive aspects lie in fewer than all the features of the individual embodiments previously disclosed. Accordingly, the claims that follow the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of the present invention.

[0107] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and in addition may be divided into multiple submodules or subunits or subcomponents. All features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device disclosed herein may be combined in any combination, except that at least some of such features and / or processes or units are mutually exclusive. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature providing the same, equivalent or similar purpose.

[0108] Furthermore, those skilled in the art will appreciate that although some embodiments herein include certain features included in other embodiments but not other features, combinations of features from different embodiments are intended to be within the scope of the present invention and to form different embodiments. For example, in a specific embodiment, any one of the claimed embodiments may be used in any combination.

[0109] The various component embodiments of the present invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or digital signal processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components of the spinal anesthesia postoperative headache warning device and computer device according to embodiments of the present invention. The present invention may also be implemented as a device or apparatus program (e.g., a computer program and computer program product) for executing part or all of the methods described herein. Such a program implementing the present invention may be stored on a computer-readable medium, or may be in the form of one or more signals. Such a signal may be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0110] It should be noted that the above embodiments illustrate rather than limit the invention, and that those skilled in the art may devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between brackets should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present invention may be implemented by means of hardware comprising several different elements and by means of appropriately programmed computers. In a unit claim enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third etc. does not indicate any order. These words may be interpreted as names.

Claims

1. A method for early warning of headache after spinal anesthesia, characterized in that: include: Collect target ONSD values ​​and various target physiological sign data of target patients at various time points during spinal anesthesia; Based on the target ONSD value at each time point and the various target physiological sign data, a prediction model is used to predict the PDPH risk of the target patient to obtain a prediction result; Based on the prediction results, appropriate measures are taken to reduce the occurrence of PDPH symptoms in the early stage of intracranial pressure reduction.

2. The method according to claim 1, wherein The various time nodes include: Before anesthesia, 30 minutes after puncture, and 1 hour, 2 hours, 6 hours, 12 hours, and 24 hours after spinal anesthesia; The various target physiological sign data include blood pressure and heart rate.

3. The method according to claim 1, wherein Based on the target ONSD values ​​at each time point and the various target physiological sign data, a prediction model is used to predict the PDPH risk of the target patient to obtain a prediction result, including: Based on the target ONSD values ​​at each time point, the target ONSD change rate, target ONSD fluctuation amplitude, and target ONSD trend slope are calculated; Based on the target ONSD change rate, target ONSD fluctuation amplitude, target ONSD trend slope and various target physiological sign data, a prediction model is used to predict the PDPH risk of the target patient to obtain a prediction result.

4. The method according to claim 3, wherein Before obtaining a prediction result, the method further includes: predicting the PDPH risk of the target patient using a prediction model based on the ONSD change rate, ONSD fluctuation amplitude, ONSD trend slope, and various target physiological sign data; Collect historical ONSD values, various historical physiological sign data, and historical pain conditions of historical patients at various time points during spinal anesthesia; Based on historical ONSD values, various historical physiological sign data, and historical pain conditions, an XGBoost model was used to train a prediction model for predicting the risk of PDPH after spinal anesthesia.

5. The method according to claim 3, wherein Based on the target ONSD change rate, target ONSD fluctuation amplitude, target ONSD trend slope, and various target physiological sign data, a prediction model is used to predict the PDPH risk of the target patient to obtain a prediction result, including: The target ONSD change rate, target ONSD fluctuation amplitude, target ONSD trend slope, and various target physiological sign data are input into the prediction model as input data, and the prediction model outputs the PDPH risk prediction result of the target patient.

6. The method according to claim 1, wherein After predicting the PDPH risk of the target patient based on the target ONSD value and the various target physiological sign data using a prediction model and obtaining a prediction result, the method further includes: Collect the target patient's age, gender, and puncture needle model; The prediction result is adjusted based on the age, gender and puncture needle model of the target patient.

7. The method according to claim 1, wherein Based on the prediction results, appropriate measures are taken to reduce the occurrence of PDPH symptoms in the early stage of intracranial pressure reduction, including: When the predicted result is low risk, observation is recommended; When the predicted result is medium risk, intravenous fluid replacement is recommended; When the predicted outcome is high risk, epidural blood patch treatment is recommended.

8. A device for warning headache after spinal anesthesia, characterized in that: include: An acquisition module is used to collect target ONSD values ​​and various target physiological sign data of target patients at various time points during spinal anesthesia; A prediction module, configured to predict the PDPH risk of the target patient using a prediction model based on the target ONSD value and the various target physiological sign data to obtain a prediction result; The measure module is used to adopt corresponding measures to reduce the occurrence of PDPH symptoms based on the prediction results.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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