Death risk prediction model for severe burn and hypoxic hepatitis, construction method and storage medium

Abstract

The invention relates to a death risk prediction model for severe burn and hypoxic hepatitis, a construction method and a storage medium. The construction method of the death risk prediction model comprises the following steps: collecting clinical feature data of patients with severe burns and hypoxic hepatitis; using a regression model to analyze and screen the training set clinical feature data with the p value less than 0.05 and whether death exists, and screening out serious burn and hypoxic hepatitis and death-related clinical feature data; and constructing a death risk prediction model based on the screened clinical feature data. The invention also provides a death risk prediction model constructed by the construction method. The invention further provides a storage medium. According to the method, risk factors causing death of patients suffering from severe burns and hypoxic hepatitis are identified based on clinical basic data and disease related indexes of the patients, so that the death probability of the patients is conveniently, quickly and accurately predicted in advance, and doctors are helped to take corresponding prevention and treatment measures for the patients.

Description

Technical Field

[0001] The present invention relates to the field of medical diagnosis technology, and in particular to a death risk prediction model for severe burns combined with hypoxic hepatitis, a construction method and a storage medium. Background Art

[0002] Burns are a very common type of trauma in daily life, which poses a great threat to human life and health. Burns may not only cause long-term physical and mental problems, but also a large number of patients lose their precious lives due to burns every year around the world. The classification of burns mainly includes the depth of the burn and the area of ​​the burn. The depth of burns ranges from first to fourth degree, while the burn area is divided into mild and severe. Mild burns are characterized by a burn area less than 10% of the total body surface area (TBSA), mainly manifested as surface burns. In general, patients with a total surface area burn of >20% or full-thickness burns of >10% are defined as severe burns.

[0003] Although the prognosis of patients with severe burns has been greatly improved with the advancement of medical technology, more than 300,000 people still die from severe burns worldwide. Patients with severe burns often develop sepsis with (multiple organ dysfunction syndrome) MODS. In fact, MODS is the main cause of death within 24 hours of severe burn injury. The liver is the most important organ for biochemical synthesis and decomposition. Liver damage after burns has been confirmed. Liver necrosis and liver dysfunction after liver burns were reported in the late 1930s. Subsequently, many studies have shown that liver dysfunction, liver enlargement, and fatty infiltration are common in burn patients and are related to the total body surface area (TBSA) burned.

[0004] Hypoxic hepatitis (HH) is a complication of the underlying disease and is an acute liver injury that occurs in patients with heart failure, respiratory failure, septic or toxic shock, and other underlying diseases, characterized by a rapid and transient increase in AST or ALT. HH has a high mortality rate and a poor prognosis in the ICU. Hepatic ischemia (due to reduced hepatic blood flow and venous congestion caused by right heart failure) and arterial hypoxemia (due to reduced blood oxygen content) are the two main mechanisms leading to HH. Previous studies have shown that the mortality rate of patients with HH during or shortly after hospitalization is more than 50%. However, the prevalence, prognosis, and risk factors for the occurrence and mortality of HH in patients with burns are unclear.

[0005] Currently, patients with severe burns are often treated based on their existing clinical symptoms, which can easily miss the best treatment opportunity to prevent death after the onset of hypoxic hepatitis. Although the treatment of burn patients has improved significantly in recent decades, severe burns are still fatal. Among them, the mortality rate of hypoxic hepatitis in patients with severe burns is still at a high level. Existing technologies cannot predict whether patients with severe burns and hypoxic hepatitis will die in the future, resulting in the inability to prevent and treat them in a timely manner. Therefore, timely and accurate identification of risk factors for death is crucial to assessing patients' clinical prognosis and guiding individualized treatment. Summary of the invention

[0006] In view of this, the purpose of the present invention is to provide a death risk prediction model, construction method and storage medium for severe burns combined with hypoxic hepatitis, so as to identify the risk factors leading to death of patients with severe burns and hypoxic hepatitis based on the patient's basic clinical data and disease-related indicators, and use these factors to establish a fully verified death risk prediction model to predict the patient's probability of death in advance, conveniently, quickly and accurately, thereby helping doctors take corresponding preventive and treatment measures for patients.

[0007] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0008] A method for constructing a mortality risk prediction model for severe burns combined with hypoxic hepatitis comprises the following steps:

[0009] S1. Data collection and preprocessing: The clinical characteristic data of patients with severe burns and hypoxic hepatitis who died and those who survived were collected and preprocessed;

[0010] S2. Feature data screening: The preprocessed clinical feature data were divided into a training set and a validation set. The clinical feature data of the training set with a p-value less than 0.05 and whether the patient died were analyzed and screened using the LASSO regression model to screen out clinical feature data related to death in severe burns combined with hypoxic hepatitis.

[0011] S3. Model construction: Based on the screened clinical characteristic data related to death, a death risk prediction model for severe burns combined with hypoxic hepatitis was constructed.

[0012] According to the above technical means, the practicality and pertinence of the model are ensured by taking actual clinical data as the basis. Through data collection and preprocessing, the data quality can be improved and the impact of noise and outliers on the prediction accuracy of the model can be reduced. The use of the LASSO regression model to screen out clinical features significantly associated with death helps to improve the predictive ability and interpretability of the model. Dividing the data into training sets and validation sets helps to evaluate the generalization ability of the model and reduce the risk of overfitting. The model constructed based on relevant clinical features can provide a scientific basis for clinical decision-making and help improve the treatment effect and survival rate of patients with severe burns and hypoxic hepatitis.

[0013] Among them, hypoxic hepatitis (HH) is defined as a syndrome characterized by a rapid and transient increase in alanine aminotransferase (ALT) or aspartate aminotransferase (AST) to more than 10 times the upper limit of normal in the context of cardiac, circulatory, or respiratory failure. It is characterized by a primary pattern of hepatocellular injury without evidence of cholestasis.

[0014] Preferably, the clinical characteristic data include gender, age, cause of burns, past medical history, smoking history, drinking history, total burn area, third-degree burn area, BMI, burn site, number of liver injuries before HH, length of admission, length of ICU stay, mortality, AST admission value, ALT admission value, GGT admission value, AST maximum value, ALT maximum value, GGT maximum value, ALP, direct bilirubin, indirect bilirubin, LDH, albumin, prealbumin, fibrinogen, blood creatinine admission value and blood creatinine maximum value.

[0015] Preferably, the clinical characteristic data related to death of severe burns combined with hypoxic hepatitis include the patient's age, the patient's total burn area, the patient's third-degree burn area, the patient's admission blood creatinine value and the patient's maximum blood creatinine value.

[0016] Preferably, the mortality risk prediction model is a nomogram.

[0017] Preferably, in the nomogram:

[0018] The first column is the score scale, ranging from 0 to 100; the second column is the patient's admission blood creatinine value, ranging from 50 to 600; the third column is the patient's maximum blood creatinine value, ranging from 50 to 650; the fourth column is the patient's age, ranging from 20 to 80; the fifth column is the patient's total burn area, ranging from 20 to 100; the sixth column is the third-degree burn area, ranging from 0 to 70; the seventh column is the total score, ranging from 0 to 130; the eighth column is the probability of death risk, ranging from 0.01 to 0.99.

[0019] Preferably, the death risk prediction model also includes a death risk value formula:

[0020]

[0021] In formula (I), X 1 represents the patient's admission serum creatinine value; X 2 Indicates the patient's maximum serum creatinine value; X 3 represents the patient's age; X 4 Indicates the total burn area of ​​the patient; X 5 represents the third-degree burn area of ​​the patient; P represents the mortality rate of severe burns complicated with hypoxic hepatitis.

[0022] Preferably, the maximum AUROC value of the nomogram model is 0.945.

[0023] Preferably, the p-value is obtained by a t-test or a chi-square test.

[0024] Preferably, in S1, the clinical characteristic data is randomly divided into a 70% training set and a 30% test set; the model is trained by the training data set to obtain a death risk prediction model;

[0025] The death risk prediction model was validated using the test set.

[0026] The present invention also provides a mortality risk prediction model for severe burns combined with hypoxic hepatitis constructed by the construction method of the present invention.

[0027] Preferably, the mortality risk prediction model for severe burns complicated with hypoxic hepatitis includes:

[0028] The data collection and preprocessing module is used to collect clinical characteristic data of patients who died and those who survived from severe burns complicated with hypoxic hepatitis, and to preprocess the clinical characteristic data;

[0029] The feature data screening module is used to divide the preprocessed clinical feature data into a training set and a validation set, and use the LASSO regression model to analyze and screen the clinical feature data of the training set with a p value less than 0.05 and whether the patient died, and screen out the clinical feature data related to severe burns combined with hypoxic hepatitis and death;

[0030] The prediction model construction module constructs a death risk prediction model for severe burns combined with hypoxic hepatitis based on the screened clinical feature data related to death.

[0031] The present invention also 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 construction method described in the present invention are implemented.

[0032] Beneficial effects of the present invention:

[0033] The method for constructing a death risk prediction model for severe burns combined with hypoxic hepatitis of the present invention ensures that the basic data for constructing the prediction model is comprehensive and representative by collecting clinical characteristic data of patients with severe burns combined with hypoxic hepatitis; in the data preprocessing stage, the collected clinical characteristic data are cleaned and standardized to improve the data quality and provide accurate and reliable data support for subsequent analysis; the LASSO regression model is used for feature screening, which can effectively process high-dimensional data and screen out clinical characteristics with a high correlation with death, thereby improving the prediction accuracy of the model; the model is constructed based on the clinical characteristic data, which can more comprehensively and comprehensively reflect the severity of the patient's condition and the risk of death; the death risk prediction model constructed based on the screened indicators can provide an accurate and practical tool for clinicians, realize result visualization, help evaluate the patient's risk of death, and thus formulate a more personalized treatment plan, and has promotion and application value in the field of medical diagnostic technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 A flowchart of the method for constructing a mortality risk prediction model for severe burns complicated with hypoxic hepatitis;

[0035] Figure 2 A nomogram model for predicting the risk of death in severe burn patients with hypoxic hepatitis;

[0036] Figure 3 The training set subject cohort operating characteristic curve for the logistic regression model;

[0037] Figure 4 Receiver operating characteristic curve for the test set cohort of the logistic regression model;

[0038] Figure 5 This is the result diagram of the decision curve analysis of the training set cohort of the nomogram model;

[0039] Figure 6 This is the result diagram of the test set cohort decision curve analysis of the nomogram model;

[0040] Figure 7 This is a graph showing the results of the calibration curve analysis of the training set cohort of the nomogram model;

[0041] Figure 8 This is a graph showing the calibration curve analysis results for the test set cohort of the nomogram model;

[0042] Fig. 9 is a frequency distribution histogram. DETAILED DESCRIPTION

[0043] The following will describe the embodiments of the present invention with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention, not for limiting the scope of protection of the present invention.

[0044] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0045] The present invention intends to disclose a death risk prediction model, construction method and storage medium for severe burns combined with hypoxic hepatitis, so as to identify the risk factors leading to death of patients with severe burns and hypoxic hepatitis based on the patients' basic clinical data and disease-related indicators, and use these factors to establish a fully verified death risk prediction model to predict the patient's probability of death in advance, conveniently, quickly and accurately, thereby helping doctors take corresponding preventive and treatment measures for patients.

[0046] Among them, Figure 1 As shown in the figure, the method for constructing a mortality risk prediction model for severe burns combined with hypoxic hepatitis includes the following steps:

[0047] S1. Data collection: The clinical characteristic data of patients with severe burns and hypoxic hepatitis who died and those who survived were collected, and the clinical characteristic data were preprocessed to remove patients with incomplete clinical characteristic data;

[0048] S2. Feature data screening: The preprocessed clinical feature data were divided into a training set and a validation set. The clinical feature data of the training set with a p value of less than 0.05 in the t-test or chi-square test and whether the patient died were analyzed and screened using the LASSO regression model to screen out clinical feature data related to death in patients with severe burns combined with hypoxic hepatitis;

[0049] S3. Model construction: Based on the screened clinical characteristic data related to death, a death risk prediction model for severe burns combined with hypoxic hepatitis was constructed.

[0050] The practicality and pertinence of the model are ensured by basing it on actual clinical data. Data collection and preprocessing can improve data quality and reduce the impact of noise and outliers on the accuracy of model prediction. The use of the LASSO regression model to screen out clinical features significantly associated with death helps improve the predictive ability and interpretability of the model. Dividing the data into training sets and validation sets helps evaluate the generalization ability of the model and reduce the risk of overfitting. The model constructed based on relevant clinical features can provide a scientific basis for clinical decision-making and help improve the treatment effect and survival rate of patients with severe burns and hypoxic hepatitis.

[0051] Among them, hypoxic hepatitis (HH) is defined as a syndrome characterized by a rapid and transient increase in alanine aminotransferase (ALT) or aspartate aminotransferase (AST) to more than 10 times the upper limit of normal in the context of cardiac, circulatory, or respiratory failure. It is characterized by a primary pattern of hepatocellular injury without evidence of cholestasis.

[0052] In some embodiments, the clinical characteristic data include gender, age, cause of burns, past medical history, smoking history, drinking history, burn area, third-degree burn area, BMI, burn site, number of liver injuries before HH, length of admission, length of ICU stay, mortality, AST admission value, ALT admission value, GGT admission value, blood creatinine admission value, AST maximum value, ALT maximum value, GGT maximum value, blood creatinine maximum value, ALP, direct bilirubin, indirect bilirubin, LDH, albumin, prealbumin and fibrinogen.

[0053] In some embodiments, the screened clinical characteristics associated with death in severe burns with hypoxic hepatitis include the patient's age, the patient's total burn area, the patient's third-degree burn area, the patient's admission blood creatinine value, and the patient's maximum blood creatinine value.

[0054] In some embodiments, the mortality risk prediction model is a nomogram model.

[0055] Exemplarily, in S1, data collection and preprocessing include:

[0056] Data of 742 patients who were consecutively admitted to the Burn Department of Southwest Hospital from January 2017 to December 2022 were collected and preprocessed to remove the data of 88 patients with missing information and 81 patients with TBSA ≤ 20%, resulting in 573 patients with severe burns. Among them, 51 patients developed hypoxic hepatitis (HH). Among the HH patients, 19 died of hypoxic hepatitis after surgery, with a mortality rate of 37.25%. Multivariate logistic regression was used to screen factors affecting mortality. Among them, the mortality model included variables with p values ​​less than 0.1 into the univariate logistic regression, and variables with p values ​​less than 0.05 in the univariate logistic regression were included in the multivariate model. R Studio 2024.09.0 software was used for LASSO regression analysis, nomogram construction and evaluation.

[0057] Among them, during the pretreatment process, the exclusion criteria were: patients with outpatient or severely incomplete medical records; patients without liver function tests in the BICU; patients with BICU ≤ 48 hours of hospitalization; patients admitted more than 7 days after burns; patients with abnormal ALT / AST levels before BICU admission.

[0058] Exemplarily, S2, feature data screening, includes:

[0059] Patients with hypoxic hepatitis (HH) were further divided into two groups (survival group (N = 19, 37.3%) and non-survival group (N = 32). After analysis, it was found that the age of patients in the non-survival group was significantly older [54 (43, 70) vs. 44 (31.3, 49), p = 0.007]. The total burn surface area in the non-survival group was significantly higher [55 (37, 81) vs. 44 (31.3, 49), p = 0.014], and the third-degree burn surface area was higher [38 (22, 48) vs. 21 (13.3, 30), p = 0.008]. Compared with the survival group, the burn index in the non-survival group was also higher [46 (31.5, 62.5) vs. 30.3 (20.6, 42.4), p = 0.0100].

[0060] The analysis results showed that the survival group experienced more electrical burns after HH (21.1% vs. 53.1%, p = 0.024). The survival group was more likely to have a history of drinking (21.1% vs. 53.1%, p = 0.024) (see Table 1).

[0061] Table 1. Clinical characteristics of patients with severe burns complicated by hypoxic hepatitis (* represents the use of Fisher test, other categorical variables use chi-square test, continuous variables: deep 2 degrees, hospital stay, ICU stay use non-parametric test, other continuous variables use t test)

[0062]

[0063]

[0064] Compared with the survival group, the death group had higher TBSA [45 (30, 61) vs. 35 (25, 52), p = 0.006], higher III burn area [27 (14, 40) vs. 8 (0, 22), p < 0.001] and higher burn index [36 (24, 46) vs. 18.75 (9.5, 31.63), p < 0.001]. Liver function tests were normal on admission, and there was no significant difference between the death and survival groups. However, the death group had a higher mean AST [392.86 (104.83, 1267.25) vs. 112.33 (82.97, 181.45) p = 0.01], higher mean direct bilirubin [2.80 (2.29, 16.90) vs. 2.07 (1.14, 2.96), p = 0.01], higher mean indirect bilirubin [14.59 (11.70, 31.64) vs. 10.63 (8.02, 13.96), p = 0.006], and higher mean total bilirubin [23.89 (17.91, 57.70) vs. 14.90 (12.21, 22.93), p < 0.001]. The renal function of the death group was also worse, including significantly higher serum creatinine value on admission [121.66 (80.02, 174.3) vs. 77.43 (65.34, 97.65), p = 0.003], higher maximum serum creatinine [183.61 (61.32), vs. 55.54 (50.05, 67.30), p < 0.001], and higher mean serum creatinine [85.4 (67.73, 131.21), vs. 66.01 (54.78, 80.96), p < 0.001]. Specifically, the mean [52.04 (38.48, 76.05) vs. 91.85 (61.85, 141.51)] and maximum [101 (55.7, 205) vs. 243.4 (138.18, 404.65), p = 0.003] GGT values ​​in the death group were significantly lower than those in the survival group. The maximum ALP value was also the same [242.77 ± 170.17 vs. 264.43 ± 95.09, p = 0.001]. In addition, the maximum total bilirubin [58.53 (28.01, 112.67) vs. 35.40 (21.27, 51.97), p = 0.013] was significantly increased in patients who died of HH after severe burns (see Table 2).

[0065] Table 2. Clinical characteristics of patients with severe burns and hypoxic hepatitis. Laboratory test indicators (admission value, mean value, extreme value of albumin-globulin ratio, albumin, extreme value of fibrinogen and ALP in the mean value) were in accordance with normal distribution using t

[0066] test, and the rest used nonparametric tests)

[0067]

[0068]

[0069] Clinical treatment and prognostic data of HH patients (see Table 3) These data, including the use of liver protection drugs, hormones and antibiotics. The non-survival group patients used dexamethasone (21.1% vs. 68.8%, p < 0.001) (0 vs. 46.9%, p < 0.001) less than the survival group. To explore the difference in the type of urinary penicillin use between the two groups, we further classified and examined the urinary penicillin. There was a significant difference in the use of piperacillin (0 vs. 34.4%, p = 0.04).

[0070] Table 3. Clinical outcomes and treatment of patients with severe burns complicated by hypoxic hepatitis (* represents the use of Fisher test, and other categorical variables use chi-square test)

[0071]

[0072]

[0073] The above indicators with significant differences (p<0.05) (age, total burn area, third-degree burn area, BurnIndex, electrical burns, drinking history, admission serum creatinine value, AST average, GGT average, direct bilirubin average, indirect bilirubin average, total bilirubin average, serum creatinine average, GGT maximum, ALP maximum, total bilirubin maximum, serum creatinine maximum, whether dexamethasone was used, whether piperacillin was used) were selected for further screening by univariate logistic regression. Logistic regression analysis was used to screen 12 potential risk factors for death due to HH (see Table 4). The results showed that age (OR=1.087, p=0.020) and TBSA (OR=1.057, p=0.037) had a significant effect on the mortality rate after HH surgery (see Table 5).

[0074] Table 4. Univariate logistic regression analysis of risk factors for death in HH patients

[0075]

[0076]

[0077] Table 5. Multivariate logistic regression analysis of risk factors for death in HH patients

[0078]

[0079] LASSO regression analysis showed that these indicators were positively correlated with the occurrence of death.

[0080] Exemplarily, S3, model construction, includes: based on the above-screened clinical characteristic data related to death and the clinical characteristic data related to death (all case data are retrieved from the Burn biological database of Southwest Hospital), construct a nomogram for predicting mortality, such as Figure 2 shown.

[0081] Among them, in the nomogram model:

[0082] The first column is the score scale, ranging from 0 to 100; the second column is the patient's admission blood creatinine value, ranging from 50 to 600; the third column is the patient's maximum blood creatinine value, ranging from 50 to 650; the fourth column is the patient's age, ranging from 20 to 80; the fifth column is the patient's total burn area, ranging from 20 to 100; the sixth column is the patient's third-degree burn area, ranging from 0 to 70; the seventh column is the total score, ranging from 0 to 130; the eighth column is the probability of death risk, ranging from 0.01 to 0.99.

[0083] The maximum AUROC value of the nomogram model was 0.945 (95% CI 0.786-1.000), indicating a good fit.

[0084] In some embodiments, the death risk prediction model further includes a death risk value formula:

[0085]

[0086] In formula (I), X 1 represents the patient's admission serum creatinine value; X 2 Indicates the patient's maximum serum creatinine value; X 3 represents the patient's age; X 4 Indicates the total burn area of ​​the patient; X 5 represents the patient's third-degree burn area; P represents the mortality risk probability of severe burns combined with hypoxic hepatitis.

[0087] Using the internal validation method, all clinical characteristic data of the above HH patients were randomly divided into a training set (70%) and a test set (30%). The model was then trained with the training set and validated with the test set. Figures 3 to 8 shown.

[0088] in, Figure 3 The training set subject cohort operating characteristic curve for the logistic regression model; Figure 4 The figure is the receiver operating characteristic curve of the test set cohort of the logistic regression model. The AUC of the training set and test set of this model are 0.955 and 0.600 respectively, and the prediction performance is good. Figure 5 Results of calibration curve analysis for the training set cohort of the nomogram model; Figure 6 This is the result of the calibration curve analysis of the test set cohort of the nomogram model. Calibration is an indicator of the goodness of fit of the model, which measures the consistency between the actual probability of the outcome and the probability predicted by the model, and reflects the accuracy of the model in predicting absolute risk. The calibration curve in the result graph of this model is close to the ideal line, indicating that the predicted probability of the model matches the actual probability of occurrence. Figure 7 The results of decision curve analysis for the training set cohort of the nomogram model; Figure 8 This is the result of the test set cohort decision curve analysis of the nomogram model. In the result graph of this model, the overall benefit value of the prediction model in the range of 0 to 1 is basically higher than the all line and the none line at the corresponding threshold probability, indicating that the model has practical value.

[0089] Frequency distribution histograms were created for the maximum and mean values ​​of ALT and AST for the deceased population (a total of 19 individuals), as shown in Fig. 9 shown.

[0090] from Fig. 9 From the analysis, we can see that the maximum ALT value is concentrated in 200-400, the maximum AST value is concentrated in 600, the average ALT value is concentrated in 200-400, and the average AST value is concentrated in 600. Therefore, it can be concluded that the AST and ALT values ​​of patients with hypoxic hepatitis generally exceed the normal value by 10 times.

[0091] In some embodiments, a mortality risk prediction model for severe burns combined with hypoxic hepatitis constructed by the construction method in any of the above embodiments is also provided, comprising:

[0092] The data collection and preprocessing module is used to collect clinical characteristic data of patients who died and those who survived from severe burns complicated with hypoxic hepatitis, and to preprocess the clinical characteristic data;

[0093] The feature data screening module is used to divide the preprocessed clinical feature data into a training set and a validation set, and use the LASSO regression model to analyze and screen the clinical feature data of the training set with a p value less than 0.05 and whether the patient died, and screen out the clinical feature data related to severe burns combined with hypoxic hepatitis and death;

[0094] The prediction model construction module constructs a death risk prediction model for severe burns combined with hypoxic hepatitis based on the screened clinical feature data related to death.

[0095] In some embodiments, a computer-readable storage medium is also provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the construction method in any of the above embodiments are implemented.

[0096] In summary, the nomogram model for predicting death in severe burns combined with hypoxic hepatitis of the present invention can comprehensively evaluate the clinical characteristic indicators and laboratory data of patients, and reduce misjudgment caused by one-sided information. When a severe burn patient has already developed hypoxic hepatitis, the nomogram model based on the characteristics of clinical characteristic data can be used to assist in predicting whether the severe burn patient combined with hypoxic hepatitis will die. The model of the present invention is simple, intuitive, and easy to promote and apply. It can effectively allow clinicians to conduct accurate individualized assessments of severe burn patients when they are receiving treatment, bringing better survival benefits to patients. It has important value for the effective application of prognosis and monitoring of severe burns combined with hypoxic hepatitis, and provides personalized and professional health management plans for different stratifications. It has promotion and application value in the field of medical diagnostic technology.

[0097] The above embodiments are only preferred embodiments for fully illustrating the present invention, and the protection scope of the present invention is not limited thereto. Any equivalent substitution or change made by a person skilled in the art based on the present invention is within the protection scope of the present invention.

Claims

1. A method for constructing a mortality risk prediction model for severe burns combined with hypoxic hepatitis, characterized in that: The following steps are involved: S1. Data collection and preprocessing: The clinical characteristic data of patients with severe burns and hypoxic hepatitis who died and those who survived were collected and preprocessed; S2. Feature data screening: The preprocessed clinical feature data were divided into a training set and a validation set. The LASSO regression model was used to analyze and screen the clinical feature data with a p value less than 0.05 and whether the patient died, and the clinical feature data related to death in severe burns combined with hypoxic hepatitis were screened out. S3. Model construction: Based on the screened clinical characteristic data related to death, a death risk prediction model for severe burns combined with hypoxic hepatitis was constructed.

2. The method for constructing a mortality risk prediction model for severe burns combined with hypoxic hepatitis according to claim 1, characterized in that: The clinical characteristic data included sex, age, cause of burns, past medical history, smoking history, drinking history, total burn area, third-degree burn area, BMI, burn site, number of liver injuries before HH, hospitalization duration, ICU stay duration, mortality, AST, ALT, GGT, ALP, direct bilirubin, indirect bilirubin, LDH, albumin, prealbumin, fibrinogen, admission values ​​of serum creatinine, and maximum value of serum creatinine; And / or, the screened clinical characteristic data related to death of severe burns combined with hypoxic hepatitis include the patient's age, the patient's total burn area, the patient's third-degree burn area, the patient's admission blood creatinine value and the patient's maximum blood creatinine value.

3. The method for constructing a mortality risk prediction model for severe burns combined with hypoxic hepatitis according to claim 2, characterized in that: The death risk prediction model is a nomogram model.

4. The method for constructing a mortality risk prediction model for severe burns combined with hypoxic hepatitis according to claim 3, characterized in that: In the nomogram model: The first column is the score scale, ranging from 0 to 100; the second column is the patient's admission blood creatinine value, ranging from 50 to 600; the third column is the patient's maximum blood creatinine value, ranging from 50 to 650; the fourth column is the patient's age, ranging from 20 to 80; the fifth column is the patient's total burn area, ranging from 20 to 100; the sixth column is the patient's third-degree burn area, ranging from 0 to 70; the seventh column is the total score, ranging from 0 to 130; the eighth column is the probability of death risk, ranging from 0.01 to 0.

99.

5. The method for constructing a mortality risk prediction model for severe burns combined with hypoxic hepatitis according to claim 3, characterized in that: The death risk prediction model also includes a death risk value formula: In formula (I), X1 represents the patient's blood creatinine value at admission; X2 represents the patient's maximum blood creatinine value; X3 represents the patient's age; X4 represents the patient's total burn area; X5 represents the patient's third-degree burn area; and P represents the probability of death risk for severe burns combined with hypoxic hepatitis.

6. The method for constructing a mortality risk prediction model for severe burns combined with hypoxic hepatitis according to claim 3, characterized in that: The maximum AUROC value of the nomogram model was 0.

945.

7. The method for constructing a mortality risk prediction model for severe burns combined with hypoxic hepatitis according to claim 1, characterized in that: The p value is obtained by t test or chi-square test; And / or, in S1, the clinical characteristic data is randomly divided into a 70% training set and a 30% test set; the model is trained by the training set to obtain a death risk prediction model; The death risk prediction model was validated using the test set.

8. A mortality risk prediction model for severe burns combined with hypoxic hepatitis constructed by the construction method according to any one of claims 1 to 7.

9. The mortality risk prediction model for severe burns combined with hypoxic hepatitis according to claim 8, characterized in that: include: The data collection and preprocessing module is used to collect clinical characteristic data of patients who died and those who survived from severe burns complicated with hypoxic hepatitis, and to preprocess the clinical characteristic data; The feature data screening module is used to divide the preprocessed clinical feature data into a training set and a validation set, and use the LASSO regression model to analyze and screen the clinical feature data of the training set with a p value less than 0.05 and whether the patient died, and screen out the clinical feature data related to severe burns combined with hypoxic hepatitis and death; The prediction model construction module constructs a death risk prediction model for severe burns combined with hypoxic hepatitis based on the screened clinical feature data related to death.

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 construction method according to any one of claims 1 to 7 are implemented.

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