Ultrasonic examination auxiliary decision-making system and method, electronic equipment and medium

Abstract

The invention provides an ultrasonic examination auxiliary decision making system and method, electronic equipment and a medium, and the system comprises a data obtaining module which is used for obtaining preoperative basic information and D-dimer values of a patient; wherein the preoperative basic information at least comprises the age of the patient and the medical history of the patient; the risk determination module is used for calculating a thrombus risk value based on the preoperative basic information and a Caprini risk assessment model, and determining a thrombus risk level of the patient based on a predetermined corresponding relationship between the thrombus risk value and the thrombus risk level; and the auxiliary decision-making module is used for determining ultrasonic examination on the patient based on the thrombus risk level of the patient and the D-dimer numerical value of the patient, and carrying out priority ranking on the patient based on the thrombus risk level when the ultrasonic examination is carried out. According to the invention, the unnecessary vein ultrasonic examination before the operation can be reduced, the hospitalization cost of a patient is reduced, and the use efficiency of medical resources is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of decision support, and in particular to an ultrasonic inspection decision support system, method, electronic equipment and medium. Background Art

[0002] Major orthopedic surgeries include total knee arthroplasty (TKA), total hip arthroplasty (THA), and hip fracture surgery (HFS). The incidence of deep vein thrombosis (DVT) is high in major orthopedic surgeries. Without thrombus prevention measures, the incidence of DVT is as high as 40% to 60%. When routine thrombus prevention is implemented, the incidence of symptomatic DVT within 0-35 days is 1.3% to 10%.

[0003] At present, the most commonly used auxiliary examination for DVT diagnosis is lower extremity venous ultrasound examination, which has high sensitivity and accuracy and is widely used clinically. However, for the sake of surgical safety, surgeons routinely perform lower extremity venous ultrasound examination on all patients undergoing major orthopedic surgery before surgery, which may result in a waste of medical resources and unnecessary expenses for patients. At the same time, for emergency surgery, patients also need to wait for venous ultrasound examination, which may delay the operation time; and for elderly patients with hip fractures, the long waiting time for emergency examination may lead to a decrease in patient satisfaction. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide an ultrasound examination decision-making support system, method, electronic device and medium to reduce unnecessary preoperative venous ultrasound examinations, reduce patients' hospitalization costs, and improve the efficiency of medical resource utilization.

[0005] In order to achieve the above purpose, the technical solution adopted by the embodiment of the present invention is as follows:

[0006] In a first aspect, an embodiment of the present invention provides an ultrasound examination decision support system, comprising: a data acquisition module, used to obtain a patient's preoperative basic information and D-dimer value; wherein the preoperative basic information includes at least the patient's age and the patient's medical history; a risk determination module, used to calculate a thrombosis risk value based on the preoperative basic information and the Caprini risk assessment model, and determine the patient's thrombosis risk level based on a predetermined correspondence between the thrombosis risk value and the thrombosis risk level; an auxiliary decision module, used to determine whether to perform an ultrasound examination on the patient based on the patient's thrombosis risk level and the patient's D-dimer value, and to prioritize the ultrasound examination of the patient based on the thrombosis risk level when performing the ultrasound examination.

[0007] In one embodiment, the thrombosis risk level includes at least: low risk, medium risk, high risk and extremely high risk; the auxiliary decision module is specifically used to: if the patient's thrombosis risk level is extremely high risk, determine that the patient undergoes an ultrasound examination; if the patient's thrombosis risk level is low risk, medium risk or high risk, determine whether the patient's D-dimer value is greater than a predetermined D-dimer critical value, and if so, determine that the patient undergoes an ultrasound examination.

[0008] In one embodiment, the auxiliary decision module is further used for: when the patient's thrombosis risk level is extremely high risk, if the patient's ultrasound examination result is negative, then the patient's probability prediction value of DVT is calculated based on the patient's D-dimer value and the patient's age, and it is determined whether the patient's probability prediction value of DVT is greater than a predetermined critical value of the probability of DVT in patients with extremely high risk; if so, it is determined that the patient will undergo another ultrasound examination after a preset time.

[0009] In one embodiment, the system also includes: a critical value determination module, which is used to: obtain sample data; wherein the sample data includes thrombosis risk levels, D-dimer values ​​and ultrasound examination results of multiple patients; for patients with low risk, medium risk and high risk, the Youden index of the sample data is calculated based on the D-dimer value of the patient, and the D-dimer value corresponding to the maximum value of the Youden index is determined as the D-dimer critical value; for patients with extremely high risk, the probability prediction value of the patient developing DVT in the sample data is calculated based on the D-dimer value of the patient and the patient's age, and the Youden index of the sample data is calculated, and the probability prediction value corresponding to the maximum value of the Youden index is determined as the probability critical value of the patient developing DVT at extremely high risk.

[0010] In one embodiment, the critical value determination module is specifically used to calculate the probability prediction value of DVT in extremely high risk patients according to the following formula:

[0011]

[0012] Among them, P represents the predicted probability of DVT in extremely high-risk patients, D-dimer represents the D-dimer value, and age represents the patient's age.

[0013] In one embodiment, the critical value determination module is also used to: obtain sample data; wherein the sample data includes thrombosis risk levels, D-dimer values ​​and ultrasound examination results of multiple patients; calculate the sensitivity and NPV value of the sample data; for patients with low risk and medium risk, determine the first preset value as the D-dimer critical value; for patients with high risk, calculate the Youden index of sample data with a sensitivity greater than a second preset value and an NPV value greater than the second preset value based on the patient's D-dimer value, and determine the D-dimer value corresponding to the maximum value of the Youden index as the D-dimer critical value; for patients with extremely high risk, calculate the probability prediction value of DVT in patients with sample data with a sensitivity of a third preset value based on the patient's D-dimer value and the patient's age, and calculate the Youden index of sample data with a sensitivity of the third preset value, and determine the probability prediction value corresponding to the maximum value of the Youden index as the probability critical value of DVT in patients with extremely high risk.

[0014] In one embodiment, the critical value determination module is also used to: obtain sample data; wherein the sample data includes thrombosis risk levels, D-dimer values ​​and ultrasound examination results of multiple patients; calculate the sensitivity of the sample data; for patients with low risk, medium risk and high risk, calculate the Youden index of the sample data with a sensitivity of a third preset value based on the patient's D-dimer value, and determine the D-dimer value corresponding to the maximum value of the Youden index as the D-dimer critical value; for patients with extremely high risk, calculate the probability prediction value of the patient's sample data with a sensitivity of the third preset value based on the patient's D-dimer value and the patient's age to develop DVT, and calculate the Youden index of the sample data with a sensitivity of the third preset value, and determine the probability prediction value corresponding to the maximum value of the Youden index as the probability critical value of the patient's extremely high risk of developing DVT.

[0015] In a second aspect, an embodiment of the present invention provides an ultrasound examination decision-making assistance method, comprising: obtaining a patient's preoperative basic information and D-dimer value; wherein the preoperative basic information includes at least the patient's age and the patient's medical history; calculating a thrombosis risk value based on the preoperative basic information and the Caprini risk assessment model, and determining the patient's thrombosis risk level based on a predetermined correspondence between the thrombosis risk value and the thrombosis risk level; determining to perform an ultrasound examination on the patient based on the patient's thrombosis risk level and the patient's D-dimer value, and prioritizing the ultrasound examination of the patient based on the thrombosis risk level when performing the ultrasound examination.

[0016] In a third aspect, an embodiment of the present invention provides an electronic device, including a processor and a memory, wherein the memory stores computer-executable instructions that can be executed by the processor, and the processor executes the computer-executable instructions to implement the steps of the method provided in the second aspect above.

[0017] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method provided in the second aspect are executed.

[0018] The embodiments of the present invention bring the following beneficial effects:

[0019] The above-mentioned ultrasound examination decision-making support system, method, electronic device and medium provided by the embodiment of the present invention include: a data acquisition module, which is used to obtain the patient's preoperative basic information and D-dimer value; a risk determination module, which is used to calculate the thrombosis risk value based on the preoperative basic information and the Caprini risk assessment model, and determine the patient's thrombosis risk level based on the correspondence between the predetermined thrombosis risk value and the thrombosis risk level; an auxiliary decision module, which is used to determine the patient to undergo ultrasound examination based on the patient's thrombosis risk level and D-dimer value, and to prioritize the ultrasound examination of the patient based on the thrombosis risk level when performing the ultrasound examination. The above-mentioned system can determine whether the patient needs to undergo ultrasound examination based on the risk level of thrombosis and the D-dimer value before the patient's surgery, and assist medical staff in prioritizing the ultrasound examination of the patient, thereby reducing unnecessary preoperative venous ultrasound examinations, reducing the patient's hospitalization costs, and improving the efficiency of medical resource utilization.

[0020] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0021] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0023] Figure 1 A schematic diagram of the structure of an ultrasonic inspection decision-making support system provided by an embodiment of the present invention;

[0024] Figure 2 An evaluation flow chart of an ultrasonic examination decision support system provided by an embodiment of the present invention;

[0025] Figure 3 A flowchart of an ultrasonic examination decision-making assistance method provided by an embodiment of the present invention;

[0026] Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] At present, there is no good solution to the problems of not being able to determine which groups of people need to undergo preoperative lower limb venous ultrasound examination and ultrasound doctors being unable to determine the priority of patients for ultrasound examination.

[0029] Based on this, an ultrasound examination decision-making support system, method, electronic device and medium provided by the embodiments of the present invention can reduce unnecessary preoperative venous ultrasound examinations, reduce patients' hospitalization costs, and improve the efficiency of medical resource utilization.

[0030] To facilitate understanding of this embodiment, firstly, an ultrasonic examination decision-making support system disclosed in an embodiment of the present invention is introduced in detail. Figure 1 The schematic structural diagram of an ultrasonic inspection decision support system shown in the figure illustrates that the system includes: a data acquisition module 101 , a risk determination module 102 and a decision support module 103 .

[0031] The data acquisition module 101 is used to obtain the patient's preoperative basic information and D-dimer value.

[0032] In one embodiment, the medical records of patients in the His system can be extracted, and the basic preoperative information and D-dimer values ​​of the patients can be obtained from the admission diagnosis section of the admission record. The basic preoperative information at least includes the patient's age and medical history, that is, the contents of each item of the Caprini risk assessment model, such as age, surgical history, varicose veins, inflammatory bowel disease history, heart disease, severe infection, lung disease, bed rest or limited activity (including removable leg braces), etc.

[0033] The risk determination module 102 is used to calculate the thrombosis risk value based on the preoperative basic information and the Caprini risk assessment model, and determine the patient's thrombosis risk level based on the predetermined correspondence between the thrombosis risk value and the thrombosis risk level.

[0034] In one embodiment, the thrombosis risk value may be calculated using the Caprini risk assessment model. The specific Caprini risk assessment model includes:

[0035] (1) Thrombotic risk factors (1 sub-item): age 41-60 years; planned minor surgery (operation time < 45 minutes); recent history of major surgery (operation time > 45 minutes; within 1 month); varicose veins; history of inflammatory bowel disease (including Crohn's disease or ulcerative colitis); leg swelling (edema); overweight or obesity (BMI > 25 kg / m 2 ); heart disease (acute myocardial infarction) (within 1 month); congestive heart failure (within 1 month); serious infection (including pneumonia, diverticulitis, bacterial infection of the bladder and lungs, and sepsis); lung disease (such as emphysema, chronic obstructive pulmonary disease, interstitial pneumonia, pulmonary nodules, pulmonary fibrosis, pulmonary hypertension, bronchiectasis); bed rest or limited activity, including removable leg braces (used for < 72 hours); other high-risk factors (1 point) (now or within 1 month): ① BMI> 40 kg / m 2 ; ② Smoking (within the last month); ③ Diabetes treated with insulin; ④ Chemotherapy; ⑤ Blood transfusion; ⑥ Operation time > 2h.

[0036] (2) Thrombotic risk factor 1 sub-item (for female patients): current use of birth control or hormone replacement therapy (HRT); pregnancy or childbirth within the last month (within 1 month); history of unexplained stillbirth, recurrent spontaneous abortion (≥3 times), premature birth with toxemia or growth restriction in infants. This includes any type of estrogen contraceptives, as well as estrogen-like drugs, including raloxifene, tamoxifen, anastrozole, and letrozole.

[0037] (3) Thrombotic risk factors (2 sub-items): age 61-74 years; previous or current malignancy (excluding skin cancer but including melanoma); planned major surgery (>45 minutes) (including laparoscopic surgery and arthroscopic surgery); plaster immobilization restricting leg movement (within the past month); central venous catheter (including PICC catheter and infusion port) (within the past month); bed rest (≥72 hours) (unable to walk 9 meters continuously).

[0038] (4) Thrombotic risk factors (3 sub-items): age ≥ 75 years; history of venous thromboembolism, including deep vein thrombosis (DVT) or pulmonary embolism (PE), which also includes a history of superficial vein thrombosis (SVT); family history of thrombosis; personal or family history of positive blood tests indicating an increased risk of thrombosis (such as hereditary or acquired thrombotic disease). Hereditary factors include: Factor V Leiden / activated protein C resistance, antithrombin III deficiency, protein C and S deficiency, dysfibrinogenemia, 20210A prothrombin mutation. Acquired factors: lupus anticoagulants, antiphospholipid antibodies, myeloproliferative diseases (including thrombocythaemia), plasminogen and plasmin activation disorders, heparin-induced thrombocytopenia, hyperviscosity syndrome, and homocystinemia.

[0039] (5) Thrombotic risk factors (5 sub-items): elective lower limb arthroplasty; hip, pelvic or lower limb fracture (within 1 month); multiple trauma (within 1 month); stroke (within 1 month); acute spinal cord injury (paralysis) (within 1 month).

[0040] In specific implementation, the thrombosis risk value can be obtained based on the patient's preoperative basic information using the above-mentioned Caprini risk assessment model, and then the thrombosis risk level is determined based on the correspondence between the predetermined thrombosis risk value and the thrombosis risk level. Among them, the Caprini risk (i.e., thrombosis risk level) is divided into: ① low risk level (0-2 points); ② medium risk level (3 points); ③ high risk level (4 points); ④ very high risk level (≥5 points).

[0041] The auxiliary decision module 103 is used to determine whether to perform ultrasound examination on the patient based on the thrombosis risk level and the patient's D-dimer value, and to prioritize the ultrasound examination of the patient based on the thrombosis risk level during the ultrasound examination.

[0042] In one embodiment, it is possible to determine whether the patient needs an ultrasound examination based on the patient's thrombosis risk level and the D-dimer value, and prioritize the patients for ultrasound examination. Specifically, for patients with extremely high risk, ultrasound examination can be given priority, and for patients with low risk, medium risk, and high risk, it is determined whether the patient needs an ultrasound examination based on the D-dimer value.

[0043] Furthermore, in this embodiment, for patients who need ultrasound examination, the priority of ultrasound examination can also be determined according to the risk level of thrombosis. Specifically, the priority of providing 24-hour bedside ultrasound examination is: extremely high risk, high risk; among them, 24-hour bedside ultrasound examination is provided by ultrasound doctors for critically ill patients or patients who are unable to get out of bed, and it needs to be completed within 24 hours; but for emergency patients, it needs to be completed within 15 to 30 minutes. The priority of providing routine ultrasound examination for hospitalized patients is: high risk, medium risk, and low risk; among them, routine ultrasound examination for hospitalized patients means that hospitalized patients need to queue up in the ultrasound examination room for ultrasound examination, and the general appointment time is 1-3 days.

[0044] The above-mentioned ultrasound examination decision-making support system provided by the embodiment of the present invention can determine whether the patient needs an ultrasound examination based on the risk level of possible thrombosis and the D-dimer value before the patient's surgery, and assist medical staff in prioritizing ultrasound examinations for patients, thereby reducing unnecessary preoperative venous ultrasound examinations, reducing patients' hospitalization costs, and improving the efficiency of medical resource utilization.

[0045] In one implementation, the auxiliary decision module 103 is specifically used to:

[0046] (1) If the patient's thrombotic risk level is extremely high, the patient is determined to undergo ultrasound examination.

[0047] In specific implementation, if the patient's thrombosis risk level is extremely high risk, it is determined that the patient needs an ultrasound examination, and after the ultrasound examination, it is determined whether the patient needs further examination based on the ultrasound examination results. Specifically, when the patient's thrombosis risk level is extremely high risk, if the patient's ultrasound examination result is negative, the patient's probability prediction value of DVT is calculated based on the patient's D-dimer value and the patient's age, and it is determined whether the patient's probability prediction value of DVT is greater than the predetermined critical value of the probability of DVT in patients with extremely high risk; if so, it is determined that the patient will undergo another ultrasound examination after a preset time. The preset time can be one week.

[0048] (2) If the patient's thrombosis risk level is low risk, medium risk, or high risk, determine whether the patient's D-dimer value is greater than a predetermined D-dimer critical value. If so, determine that the patient undergoes an ultrasound examination.

[0049] In specific implementation, if the patient's thrombosis risk level is low risk, medium risk or high risk, whether the patient needs to undergo ultrasound examination is determined based on the patient's D-dimer value; if the patient's D-dimer value is greater than the predetermined D-dimer critical value, the patient is subjected to ultrasound examination, otherwise no ultrasound examination is performed.

[0050] In one embodiment, the system further comprises: a critical value determination module, which is used to determine the D-dimer critical value and the probability critical value of DVT in patients with extremely high risk. In this embodiment, the critical value can be determined by including but not limited to the following methods:

[0051] Method 1:

[0052] First, sample data is obtained; wherein the sample data includes thrombosis risk levels, D-dimer values, and ultrasound examination results of multiple patients.

[0053] In a specific implementation, D-dimer values ​​and ultrasound examination results of multiple patients may be collected as sample data.

[0054] Then, for patients with low risk, medium risk, and high risk, the Youden index of the sample data is calculated based on the D-dimer value of the patient, and the D-dimer value corresponding to the maximum value of the Youden index is determined as the D-dimer critical value.

[0055] In specific implementation, for patients with low risk, medium risk and high risk, the D-dimer critical value can be determined by calculating the Youden index of the sample data. Among them, the Youden index, also known as the correct index, is used to evaluate the authenticity of the screening test. The Youden index is the sum of the sensitivity and specificity minus 1, which represents the total ability of the screening method to detect real patients and non-patients. The larger the index, the better the effect of the screening experiment and the greater the authenticity. Sensitivity refers to the proportion of people who are actually sick that the screening method can correctly judge as patients; specificity refers to the proportion of people who are actually free of disease that the screening method can correctly judge as non-patients.

[0056] Finally, for patients at extremely high risk, the predicted probability value of DVT in the sample data was calculated based on the patient's D-dimer value and age, and the Youden index of the sample data was calculated, and the predicted probability value corresponding to the maximum value of the Youden index was determined as the critical probability value of DVT in patients at extremely high risk.

[0057] In specific implementation, for patients with extremely high risk, D-dimer + age can be used to jointly determine the probability prediction value of the patient developing DVT, and the probability prediction value corresponding to the maximum value of the Youden index is used as the probability critical value of the patient with extremely high risk of developing DVT. Specifically, the probability prediction value of the patient with extremely high risk sample data on the occurrence of DVT is first calculated by combining the D-dimer value and the patient's age, and the probability prediction value of the point with the largest Youden index is determined as the probability critical value of the patient with extremely high risk of developing DVT.

[0058] In specific implementation, the predicted probability of DVT in patients at very high risk can be calculated according to the following formula:

[0059]

[0060] Among them, P represents the predicted probability of DVT in patients with extremely high risk, D-dimer represents the D-dimer value, and age represents the patient's age.

[0061] Method 2:

[0062] First, sample data is obtained; wherein the sample data includes thrombosis risk levels, D-dimer values, and ultrasound examination results of multiple patients.

[0063] Secondly, calculate the sensitivity and NPV value of the sample data.

[0064] Then, for patients with low risk and moderate risk, the first preset value is determined as the D-dimer critical value.

[0065] In specific implementation, for patients with low risk and medium risk, the first preset value can be selected as the D-dimer critical value. Among them, the first preset value can be 0.5ug / ml. Since 0.5ug / ml is the critical value for judging whether a patient has thrombosis, this critical value is often used in clinical applications. Therefore, the first preset value in the embodiment of the present invention can be 0.5ug / ml. That is, in this application, a D-dimer value > 0.5ug / ml can be used as a basis for judging whether to perform an ultrasound examination.

[0066] Next, for patients at high risk, the Youden index of sample data with a sensitivity greater than a second preset value and an NPV value greater than the second preset value is calculated based on the patient's D-dimer value, and the D-dimer value corresponding to the maximum value of the Youden index is determined as the D-dimer critical value.

[0067] In a specific implementation, the second preset value may be 90%. In this embodiment, for patients at high risk, the D-dimer critical value may be determined using sensitivity>90% and NPV>90%. Specifically, the Youden Index is calculated using sample data with sensitivity>90% and NPV>90%, and then the D-dimer value corresponding to the maximum value of the Youden Index is determined as the D-dimer critical value.

[0068] Finally, for patients at extremely high risk, the predicted probability value of DVT in the sample data with a sensitivity of the third preset value is calculated based on the patient's D-dimer value and the patient's age, and the Youden index of the sample data with a sensitivity of the third preset value is calculated, and the predicted probability value of DVT in the patient corresponding to the maximum value of the Youden index is determined as the critical probability value of DVT in patients at extremely high risk.

[0069] In specific implementation, the third preset value can be 100%. In this embodiment, for patients with extremely high risk, the probability prediction value of DVT in patients with sample data with 100% sensitivity can be calculated based on the patient's D-dimer value and the patient's age, and the Youden index of the sample data with 100% sensitivity can be calculated, and then the probability prediction value of DVT in patients corresponding to the maximum value of the Youden index is determined as the probability critical value of DVT in patients with extremely high risk. Specifically, the probability prediction value and Youden index of DVT in patients are calculated using sample data with 100% sensitivity, and the probability critical value of DVT in patients with extremely high risk is determined using the probability prediction value corresponding to the maximum value of the Youden index.

[0070] Method 3:

[0071] First, sample data is obtained; wherein the sample data includes thrombosis risk levels, D-dimer values, and ultrasound examination results of multiple patients.

[0072] Then, the sensitivity of the sample data is calculated.

[0073] Next, for patients with low risk, medium risk and high risk, the Youden index of the sample data with a sensitivity of the third preset value is calculated based on the patient's D-dimer value, and the D-dimer value corresponding to the maximum value of the Youden index is determined as the D-dimer critical value.

[0074] In specific implementation, for patients with low risk, medium risk and high risk, the D-dimer critical value can be determined using sample data with 100% sensitivity. Specifically, the Youden index is calculated using sample data with 100% sensitivity, and then the D-dimer value corresponding to the maximum value of the Youden index is determined as the D-dimer critical value.

[0075] Finally, for patients at extremely high risk, the probability prediction value of DVT in the patient's sample data with a sensitivity of the third preset value is calculated based on the patient's D-dimer value and the patient's age, and the Youden index of the sample data with a sensitivity of the third preset value is calculated, and the probability prediction value corresponding to the maximum value of the Youden index is determined as the probability critical value of DVT in patients at extremely high risk.

[0076] In specific implementation, for patients with extremely high risk, the probability prediction value of DVT in patients with sample data with 100% sensitivity can be calculated based on the patient's D-dimer value and the patient's age, and the Youden index of sample data with 100% sensitivity can be calculated, and then the probability prediction value corresponding to the maximum value of the Youden index is determined as the probability critical value of DVT in patients with extremely high risk. Specifically, the probability prediction value and Youden index of DVT in patients with extremely high risk are calculated using sample data with 100% sensitivity, and the probability prediction value corresponding to the maximum value of the Youden index is used as the probability critical value of DVT in patients with extremely high risk.

[0077] In addition, in this embodiment, the D-dimer critical value (low risk, medium risk, and high risk stratification) can be defined according to sensitivity and NPV; the probability prediction value of the combined indicator D-dimer + age (extremely high risk stratification) can be defined according to sensitivity and NPV to determine the probability critical value of DVT in patients with extremely high risk.

[0078] For ease of understanding, the present invention also provides an evaluation flow chart of an ultrasound examination decision support system, see Figure 2 As shown, firstly, Caprini risk stratification of patients is performed preoperatively, and then it is determined whether to perform ultrasound examination according to different thrombosis risk levels; for low risk, medium risk and high risk, judgment is made according to the D-dimer value. If the patient's D-dimer value is greater than the D-dimer critical value, ultrasound examination is performed; if the patient's D-dimer value is less than the D-dimer critical value, ultrasound examination is not performed; for extremely high risk, ultrasound examination is performed, and when the ultrasound examination result is negative, it is determined whether to further perform ultrasound review according to the D-dimer value. If the predicted probability value of DVT in the patient calculated based on the patient's D-dimer data and age is greater than the probability critical value, ultrasound review is performed one week later; if the predicted probability value of DVT in the patient calculated based on the patient's D-dimer value and age is less than the probability critical value, ultrasound review is no longer performed.

[0079] The above-mentioned ultrasound examination decision-making support system provided by the embodiment of the present invention can screen out the necessary population for preoperative venous ultrasound examination and sort the priority of venous ultrasound examination for patients, thereby reducing unnecessary preoperative venous ultrasound examinations and reducing patients' hospitalization costs; helping medical staff to identify high-risk groups who need ultrasound examinations and provide a basis for timing selection, thereby reducing the workload; helping ultrasound doctors to sort the priority of venous ultrasound examinations for patients (divided into emergency, priority, and general), which not only improves the work focus and efficiency of bedside ultrasound examination doctors, but also improves the efficiency of medical resource utilization.

[0080] For the ultrasonic inspection decision-making assistance system provided in the above-mentioned embodiment, the present invention also provides an ultrasonic inspection decision-making assistance method, see Figure 3 The flowchart of an ultrasound examination decision-making assistance method shown in FIG. 1 illustrates that the method mainly includes the following steps S301 to S303:

[0081] Step S301: Obtain the patient's preoperative basic information and D-dimer value; wherein the preoperative basic information at least includes the patient's age and medical history.

[0082] Step S302: Calculate the thrombosis risk value based on the preoperative basic information and the Caprini risk assessment model, and determine the patient's thrombosis risk level based on the predetermined correspondence between the thrombosis risk value and the thrombosis risk level.

[0083] Step S303: Determine whether to perform ultrasound examination on the patient based on the patient's thrombosis risk level and the patient's D-dimer value, and prioritize the ultrasound examination of the patient based on the thrombosis risk level during the ultrasound examination.

[0084] The above-mentioned ultrasound examination decision-making assistance method provided by the embodiment of the present invention can determine whether the patient needs an ultrasound examination based on the risk level of possible thrombosis and the D-dimer value before the patient's surgery, and assist medical staff in prioritizing ultrasound examinations for the patients, thereby reducing unnecessary preoperative venous ultrasound examinations, reducing the patient's hospitalization costs, and improving the efficiency of medical resource utilization.

[0085] It should be noted that the implementation principle and technical effects of the method provided in the embodiment of the present invention are the same as those of the aforementioned system embodiment. For the sake of brief description, for matters not mentioned in the method embodiment, reference can be made to the corresponding contents in the aforementioned system embodiment. The specific numerical values ​​provided in the implementation of the present invention are only exemplary and are not limited here.

[0086] An embodiment of the present invention further provides an electronic device. Specifically, the electronic device includes a processor and a storage device. The storage device stores a computer program, and when the computer program is executed by the processor, it executes the method described in any one of the above implementation methods.

[0087] Figure 4 A structural diagram of an electronic device provided in an embodiment of the present invention, the electronic device 100 includes: a processor 40, a memory 41, a bus 42 and a communication interface 43, wherein the processor 40, the communication interface 43 and the memory 41 are connected via the bus 42; the processor 40 is used to execute an executable module stored in the memory 41, such as a computer program.

[0088] The memory 41 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 43 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0089] The bus 42 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0090] Among them, the memory 41 is used to store programs, and the processor 40 executes the program after receiving the execution instruction. The method executed by the device for flow process definition disclosed in any of the above embodiments can be applied to the processor 40 or implemented by the processor 40.

[0091] The processor 40 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 40. The above processor 40 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in the embodiment of the present invention can be directly embodied as a hardware decoding processor to execute, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 41, and the processor 40 reads the information in the memory 41 and completes the steps of the above method in combination with its hardware.

[0092] The computer program product of the readable storage medium provided in the embodiment of the present invention includes a computer-readable storage medium storing program code, and the instructions included in the program code can be used to execute the method described in the previous method embodiment. The specific implementation can be referred to the previous method embodiment, which will not be repeated here.

[0093] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0094] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. An ultrasonic examination decision support system, characterized in that: include: A data acquisition module, used to obtain the patient's preoperative basic information and D-dimer value; wherein the preoperative basic information at least includes the patient's age and medical history; a risk determination module, configured to calculate a thrombosis risk value based on the preoperative basic information and the Caprini risk assessment model, and determine the thrombosis risk level of the patient based on a predetermined correspondence between the thrombosis risk value and the thrombosis risk level; The auxiliary decision-making module is used to determine whether to perform an ultrasound examination on the patient based on the patient's thrombosis risk level and the D-dimer value, and to prioritize the ultrasound examinations of the patients based on the thrombosis risk level when performing the ultrasound examinations.

2. The system according to claim 1, characterized in that The thrombosis risk levels include at least: low risk, medium risk, high risk and very high risk; The auxiliary decision module is specifically used for: If the thrombosis risk level is extremely high risk, determining that the patient undergoes an ultrasound examination; If the thrombosis risk level is low risk, medium risk or high risk, it is determined whether the patient's D-dimer value is greater than a predetermined D-dimer critical value. If so, it is determined that the patient should undergo an ultrasound examination.

3. The system according to claim 2, characterized in that The auxiliary decision module is further used for: When the patient's thrombosis risk level is extremely high risk, if the patient's ultrasound examination result is negative, the predicted probability value of the patient's DVT is calculated based on the patient's D-dimer value and the patient's age, and it is determined whether the predicted probability value of the patient's DVT is greater than a predetermined critical value of the probability of DVT in patients with extremely high risk; If yes, it is determined that the patient should undergo ultrasound examination again after a preset time.

4. The system according to claim 1, characterized in that Also includes: A critical value determination module is used to: Acquiring sample data; wherein the sample data includes thrombosis risk levels, D-dimer values, and ultrasound examination results of multiple patients; For patients with low risk, medium risk and high risk, the Youden index of the sample data is calculated based on the D-dimer value of the patient, and the D-dimer value corresponding to the maximum value of the Youden index is determined as the D-dimer critical value; For patients with extremely high risk, the probability prediction value of the patient developing DVT in the sample data is calculated based on the D-dimer value and the patient's age, and the Youden index of the sample data is calculated, and the probability prediction value corresponding to the maximum value of the Youden index is determined as the probability critical value of the patient with extremely high risk developing DVT.

5. The system according to claim 4, characterized in that The critical value determination module is specifically used for: The predicted probability of DVT in patients at very high risk is calculated according to the following formula: Among them, P represents the predicted probability of DVT in patients with extremely high risk, D-dimer represents the D-dimer value, and age represents the patient's age.

6. The system according to claim 4, characterized in that The critical value determination module is also used for: Acquiring sample data; wherein the sample data includes thrombosis risk levels, D-dimer values, and ultrasound examination results of multiple patients; Calculate the sensitivity and NPV value of the sample data; For patients with low risk and intermediate risk, the first preset value is determined as the D-dimer cutoff value; For patients at high risk, the Youden index of sample data with a sensitivity greater than a second preset value and an NPV value greater than the second preset value is calculated based on the D-dimer value of the patient, and the D-dimer value corresponding to the maximum value of the Youden index is determined as the D-dimer critical value; For patients with extremely high risk, the probability prediction value of DVT in the patient with sample data whose sensitivity is the third preset value is calculated based on the D-dimer value of the patient and the patient's age, and the Youden index of the sample data with sensitivity being the third preset value is calculated, and the probability prediction value corresponding to the maximum value of the Youden index is determined as the probability critical value of DVT in the patient with extremely high risk.

7. The system according to claim 4, characterized in that The critical value determination module is also used for: Acquiring sample data; wherein the sample data includes thrombosis risk levels, D-dimer values, and ultrasound examination results of multiple patients; Calculating the sensitivity of the sample data; For patients with low risk, medium risk and high risk, calculating the Youden index of sample data with a sensitivity of a third preset value based on the D-dimer value of the patient, and determining the D-dimer value corresponding to the maximum value of the Youden index as the D-dimer critical value; For patients with extremely high risk, the probability prediction value of DVT in the patient with sample data whose sensitivity is the third preset value is calculated based on the D-dimer value of the patient and the patient's age, and the Youden index of the sample data with sensitivity being the third preset value is calculated, and the probability prediction value corresponding to the maximum value of the Youden index is determined as the probability critical value of DVT in the patient with extremely high risk.

8. An ultrasound examination decision-making aid method, characterized in that: include: Obtaining the patient's preoperative basic information and D-dimer value; wherein the preoperative basic information at least includes the patient's age and medical history; Calculating a thrombosis risk value based on the preoperative basic information and the Caprini risk assessment model, and determining the patient's thrombosis risk level based on a predetermined correspondence between the thrombosis risk value and the thrombosis risk level; Based on the patient's thrombosis risk level and the patient's D-dimer value, it is determined that the patient should be examined with ultrasound, and when the ultrasound examination is performed, the patient's ultrasound examination priority is ranked based on the thrombosis risk level.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the steps of the method according to claim 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to claim 8 are performed.

Images