A Meaningful Machine Learning Model for Predicting Amputation Rate of Patients with Diabetic Foot Ulcer

A Meaningful Machine Learning Model for Predicting Amputation Rate of Patients with Diabetic Foot Ulcer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A Meaningful Machine Learning Model for Predicting Amputation Rate of Patients with Diabetic Foot Ulcer Zixuan Liu, Siyang Han, Lei Gao, Jiangning Wang, Qi Yao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4642735/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background . Diabetic foot (DF) disease, which includes ulcers, infections and gangrene of the feet, is one of the leading causes of disability worldwide. Due to the high disability rate and expensive treatment cost of diabetic foot, doctors and patients all hope to forecast the prognosis in time and give early intervention. With the development of artificial intelligence technology, more and more methods are used in the diagnosis and prognosis prediction of chronic diseases. Machine learning, a type of artificial intelligence, has excellent predictive effects with a certain accuracy. 1 The results of diabetic foot are affected by many factors, so it is necessary for the machine learning to reasonably predict the relationship between input variables and output variables, and to correct and tolerate faults. 2 Objective . To develop an accurate and applicable predictive model for diabetic foot amputation and use it to guide clinical diagnosis and treatment, indicating the direction for the prevention of diabetic foot amputation. Methods and Materials . This retrospective study collected the basic data of 150 patients with DFU who met the study criteria in Beijing Shijitan Hospital from January 2019 to December 2022. Above all, We divided them into amputation group and non-amputation group based on prognostic outcome. Then we used Lasso algorithm to screen relevant risk factors, and predictive models were built with support vector mechanism(SVM) to input risk factors and predict amputation. Besides, we divided the test set and training set by 5-fold cross-validation. The area under the receiver operating characteristic (ROC) curves of the model were 0.89. This model’s calibration capability was 19.614 through Hosmer-Lemeshow test (p=0.012). Conclusion . In summary, our survey data suggested that C-reactive protein (CRP) in the infection index and the Wagner scale of the affected foot might play a vital role in predicting diabetic foot amputation. The predictive model we constructed can accurately estimate the rate of amputation during hospitalization in DFU patients. In addition, the model allows for personalized analysis of patients' risk factors. Health sciences/Diseases Health sciences/Endocrinology Health sciences/Medical research Health sciences/Risk factors Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction The prevalence of type 2 diabetes (T2DM) that is a complex and inordinate metabolic disease, is becoming more and more greater. 3 DFU is a major global challenge for older people. It is one of the leading causes of disability worldwide. 4 Life expectancy is affected due to complications such as infections and amputations.People with diabetes have a 25 percent lifetime risk of developing foot ulcers, and 14 to 24 percent require severe gangrene or minor lower limb amputation. 5 Therefore, early and accurate prediction of amputation has guiding significance for improving the quality of life and survival rate of DFU patients. It is necessary to establish a predictive model to predict the risk of amputation in DFU patients. The decision integration of clinical diagnosis and treatment with long-term prognosis was attempted to improve the quality of prognosis, reduce the amputation rate of DFU patients, and reduce treatment costs. The classification of the severity of diabetic foot infection has been the focus of international diabetic foot scholars. Meggitt-Wagner classification, which is the most widely used rating system for evaluating the development of diabetic foot. 6 The higher the Wagner rating, the greater the likelihood of amputation, and the lower the cure and recovery rate. 7 University of Texas diabetic wound classification is an improvement on the Wagner scale, which associated wound depth with ischemic infection. 8 WiFi classification has been improved on the basis of the earlier classification, integrating ulcer area, ischemia index and foot infection degree. 9 It can assess the severity of diabetic foot patients from a multi-dimensional perspective, which is one of the most widely used classification systems at present. Alb also reflect nutritional status to some extent, and relevant studies have shown that Alb level is negatively correlated with the severity of diabetic foot. Therefore, Alb is an outstanding indicator for predicting the risk of amputation of diabetic foot ulcers. 10 These methods are common tools for clinical diagnosis and long-term prediction of diabetic foot patients, but these are not the gold standard for clinical diagnosis. Hence, the prediction of diabetic foot amputation will be a comprehensive consideration of blood supply, wound, nutrition, infection and other factors, not simply relying on subjective judgment, more rely on scientific analysis. In addition, the current classification system does not take into account the basic information of the patient, such as age, gender, medical history, which affects the accuracy and scientific nature of clinical decision-making to a certain extent. Early identification and targeted prevention of diabetic foot are of great significance for improving patient prognosis and reducing medical burden. 11 Risk prediction model refers to estimating the probability or risk of the existence (diagnostic model) or future occurrence (prognostic model) of a specific disease or condition through mathematical formulas. 12,13 Previous investigator used traditional statistical methods (eg. Multiple logistic regression analysis, COX proportional risk model) to predict the risk of amputation in DFU patients. 14 However, due to the diversity and unpredictability of the influencing factors, the prediction range of these methods is limited. 15,16 In recent years, with the continuous improvement of the understanding of medical big data and in-depth research on statistical methods, machine learning (ML) algorithm can predict the occurrence and prognosis of diseases. 17–19 This provides a new idea for our clinical treatment, and raises the efficiency of prediction.Support vector machine (SVM) is a supervised learning algorithm which can be used to solve regression analysis and binary classification problems. It reduces the error caused by empirical classification and increases the margin, also known as the maximum margin classifier. 20 At present, SVM has been widely used in the medical field, but it is mainly used in the prognosis assessment of cancer patients, and rarely used in the field of survival analysis, especially in the field of chronic diseases. 21–23 In summary, we intend to develop a machine learning model based on SVM algorithm that can predict the amputation rate of diabetic foot ulcers. In addition, we try to integrate clinical diagnosis and treatment with long-term prognosis decision-making, providing scientific guidance for clinical decision-making and nursing work of diabetic foot ulcer, improving the quality of prognosis and reducing the rate of amputation. 2. Methods 2.1 Inclusion and exclusion criteria for subjects We randomly selected diabetic foot infection patients admitted to the Diabetic Foot Centre Department of Beijing Shijitan Hospital Affiliated to Capital Medical University from January 2019 to December 2022 as the study subjects to carry out this retrospective cohort study. The study included patients who met the following criteria : a) Admitted to hospital with a diagnosis that met DFU's clinical diagnostic criteria b) Diabetic foot patients above Wagner level 1 c) Routine laboratory tests and auxiliary examinations have been completed after admission d) Surgical treatment has been performed during the visit e) The number of hospitalizations of the patient within the investigation range ≤ 2 times f) This study protocol was known to the patient, and the patient himself was informed and consented. We also excluded subjects according to the following criteria: a) Amputation cases with low nutritional indexes (albumin ≤ 30g/L). b) Amputation cases with severe insufficiency of lower limb blood supply (B-ultrasonography showed vascular stenosis ≥ 75%). c) Patients with other infectious diseases. d) Patients with malignant tumors. e) Patients younger than 18 years f) Patients transferred to other healthcare facilities during treatment The prognosis of the patients was split into two types according to the surgical method : (a) Amputation group; (b) Non-amputee group. According to the plane of amputation, amputation can be divided into minor amputation, which are considered to be below the ankle amputation, and severe amputation that above the ankle amputation. All participants gave informed consent to the data collected in this study. This project has been approved by the Ethics Committee of Beijing Shijitan Hospital Affiliated to Capital Medical University. 2.2 Subject inclusion index We collected the basic information of 150 patients (including hospitalization number, number of admissions, date of hospitalization, gender, age), clinical data (eg. history of hyperlipidemia, Wagner grade of affected foot, wound location, prognosis) and auxiliary examination (C-reactive protein, procalcitonin, hemameba, albumin, and degree of arteriosclerosis of lower extremity) as electronic medical records. The units of laboratory tests collected for each patient were the same, C-reactive protein was mg/L, procalcitonin was ng/mL, hemameba was 10^9/L, albumin was g/L, and blood vessel occlusion degree of lower limbs was referred to the results of color Doppler examination of both lower limbs. The comprehensive analysis results of infection indicators in diabetic foot amputation patients are shown in Table 1 . 2.3 Data preprocessing In order to ensure the accuracy and scientificity of input variables and reduce systematic errors, we first fit the prediction model. The Lasso(Least Absolute Shrinkage and Selection Operator)algorithm can initially screen the predictors and obtain a model with good performance and simplicity. We take the lambda value corresponding to the cross-validation error within 1 standard deviation of the minimum error as the optimal penalty coefficient of the model. Then, multivariate logistic regression analysis was carried out on the variables selected by LASSO regression using backward likelihood method to determine the final predictors and construct the nomogram model. 2.4 Statistical analyses Descriptive statistical analysis of the data was performed for each of the two groups separately. Besides, continuous variables were expressed as mean ± standard deviation.For the variables analyzed by the Kolmogorov-Smirnov normality test, we compared the significance of differences in qualitative features between groups using the Wilson rank sum test and Chi-square test, respectively. In addition, categorical variables are expressed as counts (n) and percentages (%). A P value < 0.05 was considered statistically significant.All the above calculations were performed using SPSS 21.0. 2.5 Model development This paper uses machine learning as the analysis method of prediction model, which includes input variables, establishment of mapping relationship and output function results. We screened patient data by inclusion and exclusion measures and used them as input variables to the model. However, due to too many related factors, it is necessary to use the lasso algorithm to preprocess the included data and conduct correlation analysis. In terms of modeling, the data is divided into training sets and test sets by using 5-fold cross-validation. Then LDA, K neighborhood, SVM and other mature models were constructed to predict condition of diabetic foot amputation. We used MATLAB2019A to analyze the data. After modeling, we drawed the corresponding confusion matrix to estimate the effectiveness of predictive model, and selected the model with the best prediction ability and the corresponding function. 2.6 Model evaluation Three evaluation measures were used to evaluate the performance of the model in each category: area under the subject operating characteristic curve (AUC), sensitivity, and specificity. The model calibration curve is drawn to observe the fitting results between the ideal model and the real results. Decision analysis curve can reflect the impact of the predictive model on the patient's net benefit.The nomogram of the prediction model contributes to analyze the risk weights of individual risk factors. 3. Results 3.1 Statistical test result Table 1 Overall analysis of infection indicators in diabetic foot amputation patients Age Gender CRP PCT Hemameba Mean(SD) Median[Min,Max] 1 2 Mean(SD) Median[Min,Max] Mean(SD) Median[Min,Max] Mean(SD) Median[Min,Max] Training Dataset 0 (n = 66) 64.8 (11.9) 65.0 [34.0,91.0] 47 (71.2%) 19 (28.8%) 36.0 (51.9) 14.0 [0.400,292] 0.249 (0.704) 0.05 [0.01,4.98] 9.09 (3.80) 7.83 [2.00,26.4] 1 (n = 54) 63.7 (11.5) 64.0 [39.0,88.0] 41 (75.9%) 13 (24.1%) 137 (93.5) 122 [1.15,411] 0.859 (1.99) 0.175 [0.02,11.1] 12.0 (5.84) 11.4 [3.96,31.1] P-values 0.601 0.561 <0.001 <0.001 0.0042 Validation Dataset 0 (n = 15) 67.2 (14.1) 67.0 [42.0,88.0] 9 (60.0%) 6 (40%) 48.2 (72.7) 18.2 [1.23,265] 0.438 (0.934) 0.08 [0.03,3.59] 10.2 (5.88) 8.67 [4.93,24.9] 1 (n = 14) 66.4 (16.6) 65.5 [39.0,96.0] 8 (57.1%) 6 (42.9%) 125 (88.7) 106 [2.46,332] 0.311(0.729) 0.045 [0.02,2.76] 12.2 (5.60) 9.85 [5.14,22.8] P-values 0.884 1 0.006 0.154 0.183 Albumin Hyperlipemia Wagner rating Angiosclerosis Degree of occlusion Mean(SD) Median[Min,Max] 0 1 2 3 4 0 1 2 0 50 75 99 Training Dataset 0 (n = 66) 34.6 (5.95) 34.7 [17.1,46.4] 50 (75.8%) 16 (24.2%) 29 (43.9%) 23 (34.8%) 14 (21.2%) 4 (6.1%) 2 (3.0%) 60 (90.9%) 33 (50.0%) 2 (3.0%) 4 (6.1%) 27 (40.9%) 1 (n = 54) 33.7 (4.68) 33.7 [20.5,45.1] 37 (68.5%) 17 (31.5%) 1 (1.9%) 11 (20.4%) 42 (77.8%) 4 (7.4%) 0 (0%) 50 (92.6%) 18 (33.3%) 0 (0%) 9 (16.7%) 27 (50.0%) P-values 0.385 0.377 <0.001 0.647 0.0609 Validation Dataset 0 (n = 15) 33.4 (5.84) 34.7 [23.0,43.0] 11 (73.3%) 4 (26.7%) 5 (33.3%) 7 (46.7%) 3 (20.0%) 0 (0%) 1 (6.7%) 14 (93.3%) 7 (46.7%) 0 (0%) 0 (0%) 8 (53.3%) 1 (n = 14) 32.7 (6.14) 32.8 [22.7,44.4] 9 (64.3%) 5 (35.7%) 0 (0%) 2 (14.3%) 12 (85.7%) 0 (0%) 0 (0%) 14 (100%) 7 (50.0%) 0 (0%) 3 (21.4%) 4 (28.6%) P-values 0.756 0.7 0.00103 1 0.16 Albumin Hyperlipemia Wagner rating Angiosclerosis Degree of occlusion Mean(SD) Median[Min,Max] 0 1 2 3 4 0 1 2 0 50 75 99 Training Dataset 0 (n = 66) 34.6 (5.95) 34.7 [17.1,46.4] 50 (75.8%) 16 (24.2%) 29 (43.9%) 23 (34.8%) 14 (21.2%) 4 (6.1%) 2 (3.0%) 60 (90.9%) 33 (50.0%) 2 (3.0%) 4 (6.1%) 27 (40.9%) 1 (n = 54) 33.7 (4.68) 33.7 [20.5,45.1] 37 (68.5%) 17 (31.5%) 1 (1.9%) 11 (20.4%) 42 (77.8%) 4 (7.4%) 0 (0%) 50 (92.6%) 18 (33.3%) 0 (0%) 9 (16.7%) 27 (50.0%) P-values 0.385 0.377 <0.001 0.647 0.0609 Validation Dataset 0 (n = 15) 33.4 (5.84) 34.7 [23.0,43.0] 11 (73.3%) 4 (26.7%) 5 (33.3%) 7 (46.7%) 3 (20.0%) 0 (0%) 1 (6.7%) 14 (93.3%) 7 (46.7%) 0 (0%) 0 (0%) 8 (53.3%) 1 (n = 14) 32.7 (6.14) 32.8 [22.7,44.4] 9 (64.3%) 5 (35.7%) 0 (0%) 2 (14.3%) 12 (85.7%) 0 (0%) 0 (0%) 14 (100%) 7 (50.0%) 0 (0%) 3 (21.4%) 4 (28.6%) P-values 0.756 0.7 0.00103 1 0.16 The information distribution of patients is shown in Table 1 . In the drawing,we used t test to analyze continuous variables and Chi-square test to analyze categorical variables. Among them, variables CRP, PCT and Hemameba of training set and test set did not conform to normality, so U test was used for analysis. According to the analysis results, in the training set, there were statistically significant differences between CRP, PCT, Hemameba and Wagner_rating in terms of generalized amputation scores (P<0.05, see overall data status) Table 2 Multivariate analysis of infection index in diabetic foot amputation patients β Odds Ratio(95%CI) P (Intercept) -5.1546 Angiosclerosis1 -17.8435 <0.001 0.994 Angiosclerosis2 0.3903 1.477[0.119,18.306] 0.761 CRP 0.0207 1.021[1.010,1.032] <0.001 Degree of occlusion 50 -16.5541 <0.001 0.994 Degree of occlusion 75 1.1200 3.065[0.367,25.597] 0.301 Degree of occlusion 99 -0.0199 0.980[0.277,3.473] 0.975 Wagner rating3 2.7226 15.220[1.535,150.867] 0.020 Wagner rating4 4.3520 77.630[8.152,739.265] <0.001 Multivariate analysis results (Table 2 ) showed that CRP, Wagner_rating3 and Wagner_rating4 were independent predictors of generalized amputation (all P<0.05). The degree of vascular sclerosis and vascular occlusion in one or both lower limbs did not affect the prognosis of amputation. 3.2 Determine input variable The path diagram of the LASSO shrinkage coefficient is shown in Fig. 1 . It shows the noose coefficient contours for 10 texture features. The horizontal coordinate is the penalty coefficient, and the vertical coordinate is the gene coefficient. A coefficient profile is plotted against the log(λ) sequence, and the vertical line is plotted at selected values that are cross-validated by a factor of 10, where the optimal λ yields four non-zero coefficients. This is basically consistent with the results of statistical analysis. LASSO regression was used to screen the relevant risk factors,and then we calculated the corresponding regression coefficients to improve the robustness of the model. 24 The LASSO regression analysis cross-validation curve we plotted is shown in Fig. 2 . It shows the cross verification curve of LASSO regression analysis, where the horizontal coordinate is the penalty coefficient and the vertical coordinate represents the cross verification error. The smaller the value of the vertical axis, the better the LASSO fitting effect. Using the minimum criterion and one standard error of the minimum criterion (1-se criterion), the vertical dotted line is drawn at the optimal value.After 10-fold cross-verification, the λ value is 0.0276, and the log(λ) is 0.122(1-SE criterion).At the same time, the upper horizontal coordinate corresponding to this point is the number of variables that can be used for analysis. 3.3 Model performance This is a confusion matrix, plotted by a predictive model built from the data set (Fig. 3 ). The formulation of confusion matrix makes the prediction result more intuitive and convenient for clinical workers to make judgment and decision. 25 As shown in the figure, we can observe that in the green part, the predicted results are consistent with the actual amputation results, while in the pink part, the two are completely opposite.We can roughly estimate that the prediction accuracy of the model is about 81.2% through the confusion matrix, which basically conforms to the prediction.The true positive rates of predicted amputation and non-amputation, respectively were 79% and 83%. We try to use SVM method to build a prediction model, run the prediction model according to the customized function, calculate the accuracy of the model, and select the function with the highest accuracy as the modeling decision tree. The prediction accuracy of our function is up to 82.4%, which is basically in line with expectations. This proves that the prediction model we constructed has a certain guiding effect on clinical work. 3.4 Model evaluation We use ROC curve analysis (Fig. 4 ) to evaluate the accuracy of the model. The ROC is plotted based on whether generalized amputation or not, where the Area Under Curve (AUC) is 0.89. The closer the curve area is to 1, the higher the accuracy of the proof model. The maximum approximate entry index (Max(sensitivity + specificity − 1)) under the ROC curve is supreme when the tangent point is 0.21, corresponding to 0.83. To further fit the model, we designed the calibration curve (Fig. 5). The calibration ability of the model was 19.614(p = 0.012) through Hosmer-Lemeshow test. Figure (a) is the calibration curve of the model on the training set, Figure (b) is the calibration curve of the model on the test set. Calibration curves are drawn based on the agreement between the observed predicted risk of amputation and the actual results. The Y-axis represents the actual amputation outcome, and the X-axis represents the predicted risk of amputation. The gray diagonal line represents an ideal perfect model, while the solid black line represents the performance of the model, and the closer the two lines are, the better the fit. 3.5 Multimodal analysis The nomogram of the prediction model based on whether generalized amputation or not is shown in Fig. 6 , integrating all independent predictors. We use it to determine the relative weights of relevant infectious factors. Integrated risk factor assessment to determine the impact of decision analysis on patient net benefit (Fig. 7 ). The graph Decisioncurveanalysis shows the net benefit of the model to the patient as the threshold selection changes. When the threshold value is selected as 0.302 derived from the Jorden index, the model is able to generate a net gain of 0.351. 4. Discussion In this study, we developed an amputation prediction model that incorporates 10 baseline features to predict the probability of amputation in DFU patients. The AUC and calibration capability of the prediction model were 0.89 and 19.614, respectively. At the same time, according to the decision analysis curve, when the threshold is 0.302, the net benefit is 0.351. This shows that the prediction model has a good clinical application prospect, and that the predicted probability of the model is in reliable agreement with the actual probability. Numerous studies have shown that the more severe the infection, the higher the amputation rate in DFU patients. 26–27 In the prediction model of amputation prognosis of DFU patients, a machine learning model with infection severity, lower limb blood supply and systemic nutrition as input variables was established. We found that in the patient cases we collected, the prognosis of patients with amputation was affected by multiple factors, such as severe infection, lower limb vascular occlusion, poor nutritional status, etc., which was basically consistent with clinical experience and related studies. 28–29 In addition, it is worth noting that hyperlipidemia also increases the risk of amputation to some extent. The results are meaningful and generally in line with our expectations. The Wagner classification system is widely used in clinical practice to assess the severity of foot ulcers in diabetic patients. It categorizes foot ulcers into six grades . 30 The classification of wounds is based on depth, extent and degree of infection, which are just what we need to observe. This additional information helps to improve the accuracy of the model in predicting the risk of amputation in DFU patients. We hope to build a prediction model for amputation risk and further improve the DFU classification system from multiple aspects. 31–33 Overall, incorporating the Wagner classification system into the predictive model allows for a more comprehensive assessment of the risk of amputation in DFU patients.It enhances the predictive power of the model and provides valuable clinical information for personalized treatment and management of diabetic foot disease. In addition, simple linear models (COX regression, multiple Logistic regression, COX proportional risk model) have certain limitations in assessing the prognosis of DFU patients due to the diversity and unpredictability of influencing factors. Boyko et al. Boyko et al. 35 first carried out a prospective cohort study on diabetic patients in 2006, applied Cox proportional risk model to screen independent influencing factors, and finally formed a scoring system model. The AUC of this model was 0.81, indicating good differentiation. But the study was not externally verified. In 2019, British scholar Heald 36 et al. analyzed diabetic patients through retrospective cohort study and Logistic multiple regression, finally included 5 risk factors, and built a calculation equation for the risk probability of diabetic foot based on the regression coefficients of each factor. The model AUC was 0.65. Although the model has certain clinical practicability, the accuracy rate is still low. Tomita et al. 37 conducted a case-control study, and the AUC of the constructed model was 0.865. The emergence of artificial intelligence provides new ideas and methods for diabetes risk prediction. In 2021, Peng et al. 38 constructed a model to predict the risk of diabetic foot amputation, and adopted the nomogram to visually compare the risk weights of each risk factor. Nonetheless, the sample size and input variables of this experiment are insufficient. The model constructed is not verified. Deng et al. 39 used XGBoost algorithm and COX regression to evaluate the impact of hyperglycemic crisis and other risk factors on the mortality of DFU patients. The model’s AUC is 0.680. A prospective study by Lv et al. 40 established a DFU risk model based on risk factors and presented it in the form of the nomogram and web calculator. The AUC of its model was 0.741. Therefore, our study used ANN to establish the model. Machine learning algorithms adopt a multivariate, non-parametric approach that can use non-normal distributions and strongly correlated data to build robust models and identify complex patterns. 41–42 Compared with the statistical methods in previous relevant studies, the prediction model we constructed includes more relevant factors, has higher accuracy and more intuitive prediction results.We believe that our model is convincing for the guidance of clinical work. The combination of model prediction and clinical decision making is another feature of our experiment. In this study, we intend to construct a database of diabetic foot - diabetic foot amputation patients, study the real world of diabetic foot amputation patients, and validate predictive models to guide clinical decision making. Obviously, whether the predictive model based on retrospective data analysis can be applied to clinical practice needs to be further verified. Therefore, we hope to collect the information of newly included diabetic foot patients, compared the model prediction results, optimized the internal algorithm of the model, further screened the relevant risk factors, and understood the distribution of diabetic foot characteristics in the current survey population. Based on the relevant risk factors we have screened, we should proceed with the multi-disciplinary combined diagnosis and treatment of diabetic foot patients from the four aspects of nutrition, blood circulation, wound surface and infection. It is essential for the early prevention, timely intervention and scientific individualized treatment of DFU. 40 However, there are some limitations to our study. Although we use SVM algorithms to build models, we still lack sufficient clinical validation and cohort studies.Although we use SVM algorithms to build models, we still lack sufficient clinical validation and cohort studies. It is true that machine learning models primarily establish mapping relationships between input variables and predicted outcomes, rather than capturing direct causal relationships. Identifying causal relationships in complex medical conditions such as foot ulcers is challenging. Machine learning models can provide insights into the associations between patient characteristics and predicted outcomes, but further investigation and studies are needed to establish causality. It is essential to consider the timing and effectiveness of treatment interventions when interpreting the predictions made by the model. The fluctuation of the infection index of a single patient's second admission is influenced by the treatment measures. At the same time, we can not ignore that the decision of diabetic foot amputation surgery is affected by many aspects, such as the patient's economic status, wound status, and the subjective judgment of clinicians, and it is difficult to predict whether amputation is possible only through observational indicators. We want to develop a model that differ from the existing DFU taxonomy system and can be optimized and refined on the basis of the Wagner classification. In this way, our research constructs highly accurate and practical models to predict amputation rates in DFU patients and attempts to combine clinical care with long-term prognosis. 5. Conclusion In summary, we built an intelligent model which can be used to forecast the risk of inpatient amputation in DFU patients and analyzed the real world of newly enrolled diabetic foot patients. Our experimental results show that the machine learning model not only has accurate predictive power, but also provides new ideas for the formulation of personalized treatment plans for patients. Declarations Conflicts of Interest The authors declare that they have no conflicts of interest. Consent for publication This study protocol was known to the patient, and the patient provided informed consent. Ethical review The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Beijing Shijitan Hospital Affiliated to Capital Medical University (protocol code sjtky11-1x-2022(087) and date of approval August 2022). Author Contribution All co-authors listed are fully responsible for the integrity and accuracy of the article. Zixuan Liu and Siyang Han contributed equally to this work, they are co-first authors. Zixuan Liu participated in the collection of clinical cases and the writing of papers. Siyang Han was involved in data collation and chart production. Qi Yao, Jiangning Wang, and Lei Gao are the co-corresponding authors of this manuscript, they have made similar outstanding contributions in the process of writing. Qi Yao partook in the designation of the experimental program, chaired the seminar, and put forward constructive suggestions for the follow-up of the research. Jiangning Wang was responsible for guiding the construction of the model and checking its accuracy. Lei Gao was in charge of providing clinical cases, establishing cohort studies, and providing technical support for the development of predictive models. Data Availability Data availabilityThe reader can access the data by correspondence with the corresponding author. The data are currently under embargo while the research findings are commercialized.Requests for data,12 months after publication of this article, will be considered by the corresponding author. Patient’s privacy in hospital is protected by the Ethics Committee. References Chen M, Yang J, Zhou J, et al. 5G -smart diabetes: toward personalized diabetes diagnosis with healthcare big data clouds [J]. IEEE Communications Magazine, 2018, 56(4):16–23. Ye Huarong, Yang Yi, Lin Xuan, et al. Application of BP neural network in the diagnosis of breast cancer with high-frequency color ultrasound [J]. China Health Statistics, 2016,33 (1): 71–72. Sun H, Saeedi P, Karuranga S, et al. IDF Diabetes Atlas: global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045 [J]. Diabetes Res Clin Pract. 2022;183:109119. Global disability burdens of diabetes-related lower-extremity complications in 1990 and 2016. Zhang Y, Lazzarini PA, McPhail SM, et al [J]. Diabetes Care. 2020;43:964–974. Xuan Liao, Sheng-Hong Li, Mariya Mohamad El Akkawi,et al. Surgical amputation for patients with diabetic foot ulcers: A Chinese expert panel consensus treatment guide Front Surg. 2022; 9: 1003339. Diabetic podiatry Group, Peripheral Vascular Diseases Committee of Chinese Microcirculation Society. Expert Consensus on diabetic foot wound repair and treatment [J]. Chinese Journal of Diabetes, 2018, 10(5):305–309. Chinese Medical Association Diabetes Society, Chinese Medical Association Infectious Diseases Society, Chinese Medical Association Tissue Repair and Regeneration Society. Chinese Guidelines for the Prevention and Treatment of diabetic foot (2019 edition) (Ⅳ). Chinese Journal of Diabetes, 2019, 11(5):316–327. Li Zhihong, Guo Shuqin, Li Tingting, et al. Comparison of Wagner grading method for diabetic foot and University of TEXAS classification [J]. Chinese Journal of Diabetes, 2012,4 (8): 469–73. International Diabetic Foot Working Group: Classification of diabetic foot ulcers Part of the International Diabetic Foot Working Group: International Guidelines for Diabetic Foot Management (2019) [J]. Infection, Inflammation and Repair, 2019, 20(4):231–237. Shi Hongyan, Xu Zhangrong, Jiang Yufeng, et al. Serum albumin is a good clinical indicator for predicting the risk of amputation and medical cost of diabetic foot ulcer [J]. Chinese Journal of geriatric multi-organ Diseases, 2013,12 (12): 919–923. Xie Xiaoran, Xu Rong. A systematic evaluation of risk prediction model for diabetic foot [J]. Chinese Journal of Nursing, 21, 56 (1): 124–131 Ranstam J,Cook J A,Collins G S. Clinical prediction models [J]. Br J Surg,2016,103(13):1886. Moons K G,Altman D G,Reitsma J B,et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis(TRIPOD):explanation and elaboration [J]. Ann Intern Med,2015,162(1):W1-73. Heald A,Lunt M,Rutter M K,et al. Developing a foot ulcer risk model:what is needed to do this in a real-world primary care setting? [J]. Diabet Med,2019,36(11):1412–1416. Basu A, Manning WG, Mullahy J. Comparing alternative models: log vs cox proportional hazard? [J]. Health Econ. 2004;13(8):749–765. Wright RE. Logistic regression. Reading and Understanding Multivariate Statistics [J]. Washington DC: American Psychological Association; 1995:217–244. Nanda R, Nath A, Patel S, et al. Machine learning algorithm to evaluate risk factors of diabetic foot ulcers and its severity [J]. Med Biol Eng Comput, 2022, 60(8): 2349–2357. Zale A, Mathioudakis N. Machine learning models for inpatient glucose prediction[J]. Curr Diab Rep, 2022, 22(8): 353–364. Deng L, Xie P, Chen Y, et al. Impact of acute hyperglycemic crisis episode on survival in individuals with diabetic foot ulcer using a machine learning approach [J]. Front Endocrinol (Lausanne), 2022, 13: 974063. Brereton RG, Lloyd GR. Support vector machines for classification and regression [J]. Analyst, 2010, 135(2): 230–267. Ansari D, Nilsson J, Andersson R, et al. Artificial neural networks predict survival from pancreatic cancer after radical surgery [J]. Am J Surg, 2013, 205(1): 1–7. Amiri Z, Mohammad K, Mahmoudi M, et al. Assessing the effect of quantitative and qualitative predictors on gastric cancer individuals survival using hierarchical artifcial neural network models [J]. Iran Red Cresc Med J, 2013, 15(1): 42–48. Qiao G, Li J, Huang A, et al. Artificial neural networking model for the prediction of post -hepatectomy survival of patients with early hepatocellular carcinoma [J]. J Gastroenterol Hepatol, 2014, 29(12): 2014–2020. Zhang Lijin, Wei Xiayan, Lu Jiaqi, et al. Lasso regression: From interpretation to prediction [J]. Advances in Psychological Science, 2019,28(10):1777–1788. Valero-Carreras Daniel, Alcaraz Javier, Landete Mercedes, et al. Comparing two SVM models through different metrics based on the confusion matrix [J]. Computers and Operations Research. Volume 152, Issue. 2023 Musa IR, Ahmed MON, Sabir EI, et al. Factors associated with amputation among patients with diabetic foot ulcers in a Saudi population[J]. BMC Research Notes, 2018, 11(1): 260. Hu Huiping, Chen Mingwei. Analysis of related factors of diabetic foot ulcer healing and amputation [J]. Journal of Anhui Medical University, 2016,51 (11): 1634–1637. Sun J-H, Tsai J-S, Huang C-H, et al. Risk factors for lower extremity amputation in diabetic foot disease categorized by Wagner classification[J]. Diabetes Res Clin Pract. 2012;95(3):358–363. Zhan LX, Branco BC, Armstrong DG, Mills JL Sr. The Society for Vascular Surgery lower extremity threatened limb classification system based on wound, ischemia, and foot infection(WIfI) correlates with risk of major amputation and time to wound healing[J]. J Vasc Surg. 2015;61(4):939–944. Gau B R,Chen H Y,Hung S Y,et al.The impact of nutritional status on treatment outcomes of patients with limb-threatening diabetic foot ulcers [J].J Diabetes Complications,2016,30 (1):138–42. Yang Q,Wang JH,Huang DD,et al. Clinical significance of analysis of the level of blood fat,CRP and hemorheological indicators in the diagnosis of elder coronary heart disease [J]. Saudi J Biol Sci,2018,25 (8):1812–1816. Mehraj M, Shah I. A review of Wagner classification and current concepts in management of diabetic foot. Int J Orthop Sci.2018;4(1):933–935. Lu S, Chen R, Wei W, Lu X. Understanding heart-failure patients EHR clinical features via SHAP interpretation of tree-based machine learning model predictions. arXiv preprint arXiv:210311254; 2021. Jeon BJ, Choi HJ, Kang JS, Tak MS, Park ES. Comparison of five systems of classification of diabetic foot ulcers and predictive factors for amputation[J]. Int Wound J. 2017;14(3):537–545. Santema TB, Lenselink EA, Balm R, Ubbink DT. Comparing the Meggitt-Wagner and the University of Texas wound classification systems for diabetic foot ulcers: inter-observer analyses[J].Int Wound J. 2016;13(6):1137–1141. Beckert S, Witte M, Wicke C, Königsrainer A, Coerper S. A new wound-based severity score for diabetic foot ulcers: a prospective analysis of 1,000 patients [J]. Diabetes Care. 2006;29(5):988–992. BOYKO E J,AHRONI J H,COHEN V,et al. Prediction of diabetic foot ulcer occurrence using commonly available clinical information:the seattle diabetic foot study [J]. Diabetes Care,2006,29(6):1202–1207. HEALD A,LUNT M,RUTTER M K,et al. Developing a foot ulcer risk model:what is needed to do this in a real-world primary care setting? [J]. Diabet Med, 2019,36(11):1412–1416. TOMITA M,KABEYA Y,OKISUGI M,et al.Development and assessment of a simple scoring system for the risk of developing diabetic foot [J]. Diabetol Int, 2015,6(3):212–218. Bo P, Rui M, Yi L,et al. Development of Predictive Nomograms for Clinical Use to Quantify the Risk of Amputation in Patients with Diabetic Foot Ulcer[J]. Diabetes Res.2021,6621035(2021). Deng L, Xie P, Chen Y, et al. Impact of acute hyperglycemic crisis episode on survival in individuals with diabetic foot ulcer using a machine learning approach [J]. Front Endocrinol (Lausanne), 2022, 13: 974063. Lv, J. et al. Development and validation of a risk prediction model for foot ulcers in diabetic patients.[J]. Diabetes Res. 2023, 1199885 (2023). Ripatti S,Tikkanen E,Orho-Melander M,et al. A multilocus genetic risk score for coronary heart disease: Case -control and prospective cohort analyses [J]. The Lancet, 2010, 376(9750):1393–1400. Silver M, Chen P, Li R,et al. Pathways-driven sparse regression identifies pathways and genes associated with high - density lipoprotein cholesterol in two asiancohorts [J]. PLoS Genetics, 2013, 9(11): e1003939 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4642735","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":329544851,"identity":"b08b1cf1-c27b-470a-b382-7f1ee67322e8","order_by":0,"name":"Zixuan Liu","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zixuan","middleName":"","lastName":"Liu","suffix":""},{"id":329544852,"identity":"da455194-60b6-4ff8-840d-f8d3881a736d","order_by":1,"name":"Siyang Han","email":"","orcid":"","institution":"Beijing Shijitan Hospital, Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Siyang","middleName":"","lastName":"Han","suffix":""},{"id":329544853,"identity":"953f023f-796c-48e7-8481-168a1d3e75ad","order_by":2,"name":"Lei Gao","email":"","orcid":"","institution":"Beijing Shijitan Hospital, Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Gao","suffix":""},{"id":329544854,"identity":"21cd0968-3a70-45ab-be97-7e754967dd51","order_by":3,"name":"Jiangning Wang","email":"","orcid":"","institution":"Beijing Shijitan Hospital, Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiangning","middleName":"","lastName":"Wang","suffix":""},{"id":329544855,"identity":"e862f5af-ddc6-4944-9cc4-59e818ecf906","order_by":4,"name":"Qi Yao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwUlEQVRIiWNgGAWjYHCCNIYPBhI8/MzMhx8QrYVxRoWFnGQ7W5oBsVrYmHnOVBgbnOdRkCBKvcHxhGcPeNskEjcf5mEwYKixiSas5cyDdANJoJZth3kPPGA4lpbbQFDLjYQ0CUOwFr4EA8aGw0RqSQQ5rJnHQIJ4LQfOSBgbMBOrRfLMgzTJhgoJOYnDwEBOIMYvfMdz0qT/GNTx8PcfPvzgQ40NYS0KB3ISELwEXMqQgXxD+gFi1I2CUTAKRsFIBgCr+kIMb+ztHwAAAABJRU5ErkJggg==","orcid":"","institution":"Beijing Shijitan Hospital, Capital Medical University","correspondingAuthor":true,"prefix":"","firstName":"Qi","middleName":"","lastName":"Yao","suffix":""}],"badges":[],"createdAt":"2024-06-26 12:29:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4642735/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4642735/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61005739,"identity":"cdf49f6f-dc6b-415f-a125-3c4afe4089fe","added_by":"auto","created_at":"2024-07-24 13:46:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":118489,"visible":true,"origin":"","legend":"\u003cp\u003eLASSO shrinkage coefficient path diagram\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4642735/v1/8e0c7f20d774857a9d272f0c.png"},{"id":61005743,"identity":"2cc45e88-a7fc-47d0-9f89-4f3f90123842","added_by":"auto","created_at":"2024-07-24 13:46:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":112040,"visible":true,"origin":"","legend":"\u003cp\u003eLASSO regression analysis cross-validation curve\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4642735/v1/d5426f4bd82259ada621cf3d.png"},{"id":61005736,"identity":"d1d88aec-850b-4f1a-813e-855acd71e71a","added_by":"auto","created_at":"2024-07-24 13:46:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":9616,"visible":true,"origin":"","legend":"\u003cp\u003eConfusion matrix drawn from prediction model\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4642735/v1/8b966e3dde68afab26c12946.png"},{"id":61007913,"identity":"8555b2c9-41e5-4c66-833e-699fe2381d1a","added_by":"auto","created_at":"2024-07-24 14:02:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":20799,"visible":true,"origin":"","legend":"\u003cp\u003eAccuracy and calibration performance of the predictable model\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4642735/v1/45e7956e67805031b0e9bbef.png"},{"id":61005744,"identity":"2a94befb-e408-477c-8248-9ba87867e332","added_by":"auto","created_at":"2024-07-24 13:46:17","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":40407,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curve of amputation prediction model\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4642735/v1/9f9736dd320edb1993a32815.png"},{"id":61006698,"identity":"17e8d4f2-a2fb-4297-a109-e20644efc858","added_by":"auto","created_at":"2024-07-24 13:54:17","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":29959,"visible":true,"origin":"","legend":"\u003cp\u003eA nomogram of the amputation prediction model\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4642735/v1/91d6c5fd287b36ab0e427331.png"},{"id":61005740,"identity":"c8b86458-29e1-4224-9d76-edcaa6baf101","added_by":"auto","created_at":"2024-07-24 13:46:17","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":37584,"visible":true,"origin":"","legend":"\u003cp\u003eDecision analysis curve of diabetic foot amputation\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4642735/v1/2e029c9df82a299fd9b5b0aa.png"},{"id":61005741,"identity":"004d9a3c-9a8e-4c3d-ac82-135f9a6095ac","added_by":"auto","created_at":"2024-07-24 13:46:17","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":78847,"visible":true,"origin":"","legend":"\u003cp\u003eUnnumbered image in the Methods section.\u003c/p\u003e","description":"","filename":"Flow.png","url":"https://assets-eu.researchsquare.com/files/rs-4642735/v1/8b3a4ede6fa3953f51c0b862.png"},{"id":70455568,"identity":"9d74b90d-2c5f-4b2e-b688-1f9d4706bbc7","added_by":"auto","created_at":"2024-12-03 10:32:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":977504,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4642735/v1/d6a2f672-8bd4-4ab8-ad11-cf7f3a0c0e6a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Meaningful Machine Learning Model for Predicting Amputation Rate of Patients with Diabetic Foot Ulcer","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe prevalence of type 2 diabetes (T2DM) that is a complex and inordinate metabolic disease, is becoming more and more greater.\u003csup\u003e3\u003c/sup\u003e DFU is a major global challenge for older people. It is one of the leading causes of disability worldwide.\u003csup\u003e4\u003c/sup\u003e Life expectancy is affected due to complications such as infections and amputations.People with diabetes have a 25 percent lifetime risk of developing foot ulcers, and 14 to 24 percent require severe gangrene or minor lower limb amputation.\u003csup\u003e5\u003c/sup\u003e Therefore, early and accurate prediction of amputation has guiding significance for improving the quality of life and survival rate of DFU patients. It is necessary to establish a predictive model to predict the risk of amputation in DFU patients. The decision integration of clinical diagnosis and treatment with long-term prognosis was attempted to improve the quality of prognosis, reduce the amputation rate of DFU patients, and reduce treatment costs.\u003c/p\u003e \u003cp\u003eThe classification of the severity of diabetic foot infection has been the focus of international diabetic foot scholars. Meggitt-Wagner classification, which is the most widely used rating system for evaluating the development of diabetic foot.\u003csup\u003e6\u003c/sup\u003e The higher the Wagner rating, the greater the likelihood of amputation, and the lower the cure and recovery rate.\u003csup\u003e7\u003c/sup\u003e University of Texas diabetic wound classification is an improvement on the Wagner scale, which associated wound depth with ischemic infection.\u003csup\u003e8\u003c/sup\u003e WiFi classification has been improved on the basis of the earlier classification, integrating ulcer area, ischemia index and foot infection degree.\u003csup\u003e9\u003c/sup\u003e It can assess the severity of diabetic foot patients from a multi-dimensional perspective, which is one of the most widely used classification systems at present. Alb also reflect nutritional status to some extent, and relevant studies have shown that Alb level is negatively correlated with the severity of diabetic foot. Therefore, Alb is an outstanding indicator for predicting the risk of amputation of diabetic foot ulcers.\u003csup\u003e10\u003c/sup\u003e These methods are common tools for clinical diagnosis and long-term prediction of diabetic foot patients, but these are not the gold standard for clinical diagnosis. Hence, the prediction of diabetic foot amputation will be a comprehensive consideration of blood supply, wound, nutrition, infection and other factors, not simply relying on subjective judgment, more rely on scientific analysis. In addition, the current classification system does not take into account the basic information of the patient, such as age, gender, medical history, which affects the accuracy and scientific nature of clinical decision-making to a certain extent.\u003c/p\u003e \u003cp\u003eEarly identification and targeted prevention of diabetic foot are of great significance for improving patient prognosis and reducing medical burden.\u003csup\u003e11\u003c/sup\u003e Risk prediction model refers to estimating the probability or risk of the existence (diagnostic model) or future occurrence (prognostic model) of a specific disease or condition through mathematical formulas.\u003csup\u003e12,13\u003c/sup\u003e Previous investigator used traditional statistical methods (eg. Multiple logistic regression analysis, COX proportional risk model) to predict the risk of amputation in DFU patients.\u003csup\u003e14\u003c/sup\u003e However, due to the diversity and unpredictability of the influencing factors, the prediction range of these methods is limited.\u003csup\u003e15,16\u003c/sup\u003e In recent years, with the continuous improvement of the understanding of medical big data and in-depth research on statistical methods, machine learning (ML) algorithm can predict the occurrence and prognosis of diseases.\u003csup\u003e17\u0026ndash;19\u003c/sup\u003e This provides a new idea for our clinical treatment, and raises the efficiency of prediction.Support vector machine (SVM) is a supervised learning algorithm which can be used to solve regression analysis and binary classification problems. It reduces the error caused by empirical classification and increases the margin, also known as the maximum margin classifier.\u003csup\u003e20\u003c/sup\u003e At present, SVM has been widely used in the medical field, but it is mainly used in the prognosis assessment of cancer patients, and rarely used in the field of survival analysis, especially in the field of chronic diseases.\u003csup\u003e21\u0026ndash;23\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn summary, we intend to develop a machine learning model based on SVM algorithm that can predict the amputation rate of diabetic foot ulcers. In addition, we try to integrate clinical diagnosis and treatment with long-term prognosis decision-making, providing scientific guidance for clinical decision-making and nursing work of diabetic foot ulcer, improving the quality of prognosis and reducing the rate of amputation.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003e2.1 Inclusion and exclusion criteria for subjects\u003c/h2\u003e\n\u003cp\u003eWe randomly selected diabetic foot infection patients admitted to the Diabetic Foot Centre Department of Beijing Shijitan Hospital Affiliated to Capital Medical University from January 2019 to December 2022 as the study subjects to carry out this retrospective cohort study.\u003c/p\u003e\n\u003cp\u003eThe study included patients who met the following criteria :\u003c/p\u003e\na) Admitted to hospital with a diagnosis that met DFU's clinical diagnostic criteria\n\u003cp\u003eb) Diabetic foot patients above Wagner level 1\u003c/p\u003e\n\u003cp\u003ec) Routine laboratory tests and auxiliary examinations have been completed after admission\u003c/p\u003e\n\u003cp\u003ed) Surgical treatment has been performed during the visit\u003c/p\u003e\n\u003cp\u003ee) The number of hospitalizations of the patient within the investigation range\u0026thinsp;\u0026le;\u0026thinsp;2 times\u003c/p\u003e\n\u003cp\u003ef) This study protocol was known to the patient, and the patient himself was informed and consented.\u003c/p\u003e\n\u003cp\u003eWe also excluded subjects according to the following criteria:\u003c/p\u003e\na) Amputation cases with low nutritional indexes (albumin\u0026thinsp;\u0026le;\u0026thinsp;30g/L).\n\u003cp\u003eb) Amputation cases with severe insufficiency of lower limb blood supply (B-ultrasonography showed vascular stenosis\u0026thinsp;\u0026ge;\u0026thinsp;75%).\u003c/p\u003e\n\u003cp\u003ec) Patients with other infectious diseases.\u003c/p\u003e\n\u003cp\u003ed) Patients with malignant tumors.\u003c/p\u003e\n\u003cp\u003ee) Patients younger than 18 years\u003c/p\u003e\n\u003cp\u003ef) Patients transferred to other healthcare facilities during treatment\u003c/p\u003e\n\u003cp\u003eThe prognosis of the patients was split into two types according to the surgical method : (a) Amputation group; (b) Non-amputee group. According to the plane of amputation, amputation can be divided into minor amputation, which are considered to be below the ankle amputation, and severe amputation that above the ankle amputation. All participants gave informed consent to the data collected in this study. This project has been approved by the Ethics Committee of Beijing Shijitan Hospital Affiliated to Capital Medical University.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003e2.2 Subject inclusion index\u003c/h2\u003e\n\u003cp\u003eWe collected the basic information of 150 patients (including hospitalization number, number of admissions, date of hospitalization, gender, age), clinical data (eg. history of hyperlipidemia, Wagner grade of affected foot, wound location, prognosis) and auxiliary examination (C-reactive protein, procalcitonin, hemameba, albumin, and degree of arteriosclerosis of lower extremity) as electronic medical records.\u003c/p\u003e\n\u003cp\u003eThe units of laboratory tests collected for each patient were the same, C-reactive protein was mg/L, procalcitonin was ng/mL, hemameba was 10^9/L, albumin was g/L, and blood vessel occlusion degree of lower limbs was referred to the results of color Doppler examination of both lower limbs.\u003c/p\u003e\n\u003cp\u003eThe comprehensive analysis results of infection indicators in diabetic foot amputation patients are shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003e2.3 Data preprocessing\u003c/h2\u003e\n\u003cp\u003eIn order to ensure the accuracy and scientificity of input variables and reduce systematic errors, we first fit the prediction model. The Lasso(Least Absolute Shrinkage and Selection Operator)algorithm can initially screen the predictors and obtain a model with good performance and simplicity. We take the lambda value corresponding to the cross-validation error within 1 standard deviation of the minimum error as the optimal penalty coefficient of the model. Then, multivariate logistic regression analysis was carried out on the variables selected by LASSO regression using backward likelihood method to determine the final predictors and construct the nomogram model.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003ch2\u003e2.4 Statistical analyses\u003c/h2\u003e\n\u003cp\u003eDescriptive statistical analysis of the data was performed for each of the two groups separately. Besides, continuous variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation.For the variables analyzed by the Kolmogorov-Smirnov normality test, we compared the significance of differences in qualitative features between groups using the Wilson rank sum test and Chi-square test, respectively. In addition, categorical variables are expressed as counts (n) and percentages (%). A P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.All the above calculations were performed using SPSS 21.0.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003e2.5 Model development\u003c/h2\u003e\n\u003cp\u003eThis paper uses machine learning as the analysis method of prediction model, which includes input variables, establishment of mapping relationship and output function results. We screened patient data by inclusion and exclusion measures and used them as input variables to the model. However, due to too many related factors, it is necessary to use the lasso algorithm to preprocess the included data and conduct correlation analysis. In terms of modeling, the data is divided into training sets and test sets by using 5-fold cross-validation. Then LDA, K neighborhood, SVM and other mature models were constructed to predict condition of diabetic foot amputation. We used MATLAB2019A to analyze the data. After modeling, we drawed the corresponding confusion matrix to estimate the effectiveness of predictive model, and selected the model with the best prediction ability and the corresponding function.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003e2.6 Model evaluation\u003c/h2\u003e\n\u003cp\u003eThree evaluation measures were used to evaluate the performance of the model in each category: area under the subject operating characteristic curve (AUC), sensitivity, and specificity. The model calibration curve is drawn to observe the fitting results between the ideal model and the real results. Decision analysis curve can reflect the impact of the predictive model on the patient's net benefit.The nomogram of the prediction model contributes to analyze the risk weights of individual risk factors.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003e3.1 Statistical test result\u003c/h2\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\n\u003cp\u003eTable 1 Overall analysis of infection indicators in diabetic foot amputation patients\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Taba\" border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAge\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eGender\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCRP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePCT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eHemameba\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean(SD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedian[Min,Max]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean(SD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedian[Min,Max]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean(SD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedian[Min,Max]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean(SD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedian[Min,Max]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eTraining\u003c/p\u003e\n\u003cp\u003eDataset\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;66)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e64.8\u003c/p\u003e\n\u003cp\u003e(11.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e65.0\u003c/p\u003e\n\u003cp\u003e[34.0,91.0]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e47\u003c/p\u003e\n\u003cp\u003e(71.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19\u003c/p\u003e\n\u003cp\u003e(28.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36.0\u003c/p\u003e\n\u003cp\u003e(51.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14.0\u003c/p\u003e\n\u003cp\u003e[0.400,292]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.249\u003c/p\u003e\n\u003cp\u003e(0.704)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.05\u003c/p\u003e\n\u003cp\u003e[0.01,4.98]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.09\u003c/p\u003e\n\u003cp\u003e(3.80)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.83\u003c/p\u003e\n\u003cp\u003e[2.00,26.4]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;54)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e63.7\u003c/p\u003e\n\u003cp\u003e(11.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e64.0\u003c/p\u003e\n\u003cp\u003e[39.0,88.0]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e41\u003c/p\u003e\n\u003cp\u003e(75.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003cp\u003e(24.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e137\u003c/p\u003e\n\u003cp\u003e(93.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e122\u003c/p\u003e\n\u003cp\u003e[1.15,411]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.859\u003c/p\u003e\n\u003cp\u003e(1.99)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.175\u003c/p\u003e\n\u003cp\u003e[0.02,11.1]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.0\u003c/p\u003e\n\u003cp\u003e(5.84)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.4\u003c/p\u003e\n\u003cp\u003e[3.96,31.1]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP-values\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.601\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.561\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.0042\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eValidation\u003c/p\u003e\n\u003cp\u003eDataset\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;15)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e67.2\u003c/p\u003e\n\u003cp\u003e(14.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e67.0\u003c/p\u003e\n\u003cp\u003e[42.0,88.0]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003cp\u003e(60.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003cp\u003e(40%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48.2\u003c/p\u003e\n\u003cp\u003e(72.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.2\u003c/p\u003e\n\u003cp\u003e[1.23,265]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.438\u003c/p\u003e\n\u003cp\u003e(0.934)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.08\u003c/p\u003e\n\u003cp\u003e[0.03,3.59]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.2\u003c/p\u003e\n\u003cp\u003e(5.88)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.67\u003c/p\u003e\n\u003cp\u003e[4.93,24.9]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;14)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e66.4\u003c/p\u003e\n\u003cp\u003e(16.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e65.5\u003c/p\u003e\n\u003cp\u003e[39.0,96.0]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003cp\u003e(57.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003cp\u003e(42.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e125\u003c/p\u003e\n\u003cp\u003e(88.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e106\u003c/p\u003e\n\u003cp\u003e[2.46,332]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.311(0.729)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.045\u003c/p\u003e\n\u003cp\u003e[0.02,2.76]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.2\u003c/p\u003e\n\u003cp\u003e(5.60)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.85\u003c/p\u003e\n\u003cp\u003e[5.14,22.8]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP-values\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.884\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.006\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.154\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.183\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\u0026nbsp;\u003c/caption\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAlbumin\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eHyperlipemia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eWagner rating\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"5\" align=\"left\"\u003e\n\u003cp\u003eAngiosclerosis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"7\" align=\"left\"\u003e\n\u003cp\u003eDegree of occlusion\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean(SD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedian[Min,Max]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e99\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eTraining\u003c/p\u003e\n\u003cp\u003eDataset\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;66)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34.6\u003c/p\u003e\n\u003cp\u003e(5.95)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34.7\u003c/p\u003e\n\u003cp\u003e[17.1,46.4]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50\u003c/p\u003e\n\u003cp\u003e(75.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003cp\u003e(24.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29\u003c/p\u003e\n\u003cp\u003e(43.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23\u003c/p\u003e\n\u003cp\u003e(34.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003cp\u003e(21.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003cp\u003e(6.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003cp\u003e(3.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e60\u003c/p\u003e\n\u003cp\u003e(90.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33\u003c/p\u003e\n\u003cp\u003e(50.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003cp\u003e(3.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003cp\u003e(6.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e27\u003c/p\u003e\n\u003cp\u003e(40.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;54)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33.7\u003c/p\u003e\n\u003cp\u003e(4.68)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33.7\u003c/p\u003e\n\u003cp\u003e[20.5,45.1]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37\u003c/p\u003e\n\u003cp\u003e(68.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17\u003c/p\u003e\n\u003cp\u003e(31.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003cp\u003e(1.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003cp\u003e(20.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e42\u003c/p\u003e\n\u003cp\u003e(77.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003cp\u003e(7.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003cp\u003e(0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e50\u003c/p\u003e\n\u003cp\u003e(92.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18\u003c/p\u003e\n\u003cp\u003e(33.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003cp\u003e(0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003cp\u003e(16.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e27\u003c/p\u003e\n\u003cp\u003e(50.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP-values\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.385\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.377\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"5\" align=\"left\"\u003e\n\u003cp\u003e0.647\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"7\" align=\"left\"\u003e\n\u003cp\u003e0.0609\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eValidation\u003c/p\u003e\n\u003cp\u003eDataset\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;15)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33.4\u003c/p\u003e\n\u003cp\u003e(5.84)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34.7\u003c/p\u003e\n\u003cp\u003e[23.0,43.0]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003cp\u003e(73.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003cp\u003e(26.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003cp\u003e(33.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003cp\u003e(46.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003cp\u003e(20.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003cp\u003e(0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003cp\u003e(6.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003cp\u003e(93.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003cp\u003e(46.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003cp\u003e(0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003cp\u003e(0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003cp\u003e(53.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;14)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32.7\u003c/p\u003e\n\u003cp\u003e(6.14)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32.8\u003c/p\u003e\n\u003cp\u003e[22.7,44.4]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003cp\u003e(64.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003cp\u003e(35.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003cp\u003e(0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003cp\u003e(14.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003cp\u003e(85.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003cp\u003e(0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003cp\u003e(0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003cp\u003e(100%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003cp\u003e(50.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003cp\u003e(0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003cp\u003e(21.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003cp\u003e(28.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP-values\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.756\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.00103\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"5\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"7\" align=\"left\"\u003e\n\u003cp\u003e0.16\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAlbumin\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eHyperlipemia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eWagner rating\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"5\" align=\"left\"\u003e\n\u003cp\u003eAngiosclerosis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"7\" align=\"left\"\u003e\n\u003cp\u003eDegree of occlusion\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean(SD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedian[Min,Max]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e99\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eTraining\u003c/p\u003e\n\u003cp\u003eDataset\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;66)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34.6\u003c/p\u003e\n\u003cp\u003e(5.95)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34.7\u003c/p\u003e\n\u003cp\u003e[17.1,46.4]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50\u003c/p\u003e\n\u003cp\u003e(75.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003cp\u003e(24.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29\u003c/p\u003e\n\u003cp\u003e(43.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23\u003c/p\u003e\n\u003cp\u003e(34.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003cp\u003e(21.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003cp\u003e(6.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003cp\u003e(3.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e60\u003c/p\u003e\n\u003cp\u003e(90.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33\u003c/p\u003e\n\u003cp\u003e(50.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003cp\u003e(3.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003cp\u003e(6.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e27\u003c/p\u003e\n\u003cp\u003e(40.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;54)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33.7\u003c/p\u003e\n\u003cp\u003e(4.68)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33.7\u003c/p\u003e\n\u003cp\u003e[20.5,45.1]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37\u003c/p\u003e\n\u003cp\u003e(68.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17\u003c/p\u003e\n\u003cp\u003e(31.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003cp\u003e(1.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003cp\u003e(20.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e42\u003c/p\u003e\n\u003cp\u003e(77.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003cp\u003e(7.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003cp\u003e(0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e50\u003c/p\u003e\n\u003cp\u003e(92.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18\u003c/p\u003e\n\u003cp\u003e(33.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003cp\u003e(0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003cp\u003e(16.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e27\u003c/p\u003e\n\u003cp\u003e(50.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP-values\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.385\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.377\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"5\" align=\"left\"\u003e\n\u003cp\u003e0.647\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"7\" align=\"left\"\u003e\n\u003cp\u003e0.0609\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eValidation\u003c/p\u003e\n\u003cp\u003eDataset\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;15)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33.4\u003c/p\u003e\n\u003cp\u003e(5.84)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34.7\u003c/p\u003e\n\u003cp\u003e[23.0,43.0]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003cp\u003e(73.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003cp\u003e(26.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003cp\u003e(33.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003cp\u003e(46.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003cp\u003e(20.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003cp\u003e(0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003cp\u003e(6.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003cp\u003e(93.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003cp\u003e(46.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003cp\u003e(0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003cp\u003e(0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003cp\u003e(53.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;14)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32.7\u003c/p\u003e\n\u003cp\u003e(6.14)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32.8\u003c/p\u003e\n\u003cp\u003e[22.7,44.4]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003cp\u003e(64.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003cp\u003e(35.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003cp\u003e(0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003cp\u003e(14.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003cp\u003e(85.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003cp\u003e(0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003cp\u003e(0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003cp\u003e(100%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003cp\u003e(50.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003cp\u003e(0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003cp\u003e(21.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003cp\u003e(28.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP-values\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.756\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.00103\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"5\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"7\" align=\"left\"\u003e\n\u003cp\u003e0.16\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;The information distribution of patients is shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. In the drawing,we used t test to analyze continuous variables and Chi-square test to analyze categorical variables. Among them, variables CRP, PCT and Hemameba of training set and test set did not conform to normality, so U test was used for analysis. According to the analysis results, in the training set, there were statistically significant differences between CRP, PCT, Hemameba and Wagner_rating in terms of generalized amputation scores (P\u0026lt;0.05, see overall data status)\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMultivariate analysis of infection index in diabetic foot amputation patients\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u0026beta;\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eOdds Ratio(95%CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(Intercept)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-5.1546\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAngiosclerosis1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-17.8435\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.994\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAngiosclerosis2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.3903\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.477[0.119,18.306]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.761\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCRP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0207\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.021[1.010,1.032]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDegree of occlusion 50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-16.5541\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.994\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDegree of occlusion 75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.1200\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.065[0.367,25.597]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.301\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDegree of occlusion 99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.0199\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.980[0.277,3.473]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.975\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWagner rating3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.7226\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.220[1.535,150.867]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.020\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWagner rating4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.3520\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e77.630[8.152,739.265]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eMultivariate analysis results (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) showed that CRP, Wagner_rating3 and Wagner_rating4 were independent predictors of generalized amputation (all P\u0026lt;0.05). The degree of vascular sclerosis and vascular occlusion in one or both lower limbs did not affect the prognosis of amputation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003e3.2 Determine input variable\u003c/h2\u003e\n\u003cp\u003eThe path diagram of the LASSO shrinkage coefficient is shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. It shows the noose coefficient contours for 10 texture features. The horizontal coordinate is the penalty coefficient, and the vertical coordinate is the gene coefficient. A coefficient profile is plotted against the log(\u0026lambda;) sequence, and the vertical line is plotted at selected values that are cross-validated by a factor of 10, where the optimal \u0026lambda; yields four non-zero coefficients. This is basically consistent with the results of statistical analysis. LASSO regression was used to screen the relevant risk factors,and then we calculated the corresponding regression coefficients to improve the robustness of the model.\u003csup\u003e24\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThe LASSO regression analysis cross-validation curve we plotted is shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. It shows the cross verification curve of LASSO regression analysis, where the horizontal coordinate is the penalty coefficient and the vertical coordinate represents the cross verification error. The smaller the value of the vertical axis, the better the LASSO fitting effect. Using the minimum criterion and one standard error of the minimum criterion (1-se criterion), the vertical dotted line is drawn at the optimal value.After 10-fold cross-verification, the \u0026lambda; value is 0.0276, and the log(\u0026lambda;) is 0.122(1-SE criterion).At the same time, the upper horizontal coordinate corresponding to this point is the number of variables that can be used for analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003e3.3 Model performance\u003c/h2\u003e\n\u003cp\u003eThis is a confusion matrix, plotted by a predictive model built from the data set (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The formulation of confusion matrix makes the prediction result more intuitive and convenient for clinical workers to make judgment and decision.\u003csup\u003e25\u003c/sup\u003e As shown in the figure, we can observe that in the green part, the predicted results are consistent with the actual amputation results, while in the pink part, the two are completely opposite.We can roughly estimate that the prediction accuracy of the model is about 81.2% through the confusion matrix, which basically conforms to the prediction.The true positive rates of predicted amputation and non-amputation, respectively were 79% and 83%.\u003c/p\u003e\n\u003cp\u003eWe try to use SVM method to build a prediction model, run the prediction model according to the customized function, calculate the accuracy of the model, and select the function with the highest accuracy as the modeling decision tree. The prediction accuracy of our function is up to 82.4%, which is basically in line with expectations. This proves that the prediction model we constructed has a certain guiding effect on clinical work.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003e3.4 Model evaluation\u003c/h2\u003e\n\u003cp\u003eWe use ROC curve analysis (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) to evaluate the accuracy of the model. The ROC is plotted based on whether generalized amputation or not, where the Area Under Curve (AUC) is 0.89. The closer the curve area is to 1, the higher the accuracy of the proof model. The maximum approximate entry index (Max(sensitivity\u0026thinsp;+\u0026thinsp;specificity \u0026minus;\u0026thinsp;1)) under the ROC curve is supreme when the tangent point is 0.21, corresponding to 0.83.\u003c/p\u003e\n\u003cp\u003eTo further fit the model, we designed the calibration curve (Fig.\u0026nbsp;5). The calibration ability of the model was 19.614(p\u0026thinsp;=\u0026thinsp;0.012) through Hosmer-Lemeshow test. Figure (a) is the calibration curve of the model on the training set, Figure (b) is the calibration curve of the model on the test set. Calibration curves are drawn based on the agreement between the observed predicted risk of amputation and the actual results. The Y-axis represents the actual amputation outcome, and the X-axis represents the predicted risk of amputation. The gray diagonal line represents an ideal perfect model, while the solid black line represents the performance of the model, and the closer the two lines are, the better the fit.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n\u003ch2\u003e3.5 Multimodal analysis\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe nomogram of the prediction model based on whether generalized amputation or not is shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, integrating all independent predictors. We use it to determine the relative weights of relevant infectious factors.\u003c/p\u003e\n\u003cp\u003eIntegrated risk factor assessment to determine the impact of decision analysis on patient net benefit (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). The graph Decisioncurveanalysis shows the net benefit of the model to the patient as the threshold selection changes. When the threshold value is selected as 0.302 derived from the Jorden index, the model is able to generate a net gain of 0.351.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study, we developed an amputation prediction model that incorporates 10 baseline features to predict the probability of amputation in DFU patients. The AUC and calibration capability of the prediction model were 0.89 and 19.614, respectively. At the same time, according to the decision analysis curve, when the threshold is 0.302, the net benefit is 0.351. This shows that the prediction model has a good clinical application prospect, and that the predicted probability of the model is in reliable agreement with the actual probability. Numerous studies have shown that the more severe the infection, the higher the amputation rate in DFU patients.\u003csup\u003e26\u0026ndash;27\u003c/sup\u003e In the prediction model of amputation prognosis of DFU patients, a machine learning model with infection severity, lower limb blood supply and systemic nutrition as input variables was established. We found that in the patient cases we collected, the prognosis of patients with amputation was affected by multiple factors, such as severe infection, lower limb vascular occlusion, poor nutritional status, etc., which was basically consistent with clinical experience and related studies. \u003csup\u003e28\u0026ndash;29\u003c/sup\u003e In addition, it is worth noting that hyperlipidemia also increases the risk of amputation to some extent. The results are meaningful and generally in line with our expectations.\u003c/p\u003e \u003cp\u003eThe Wagner classification system is widely used in clinical practice to assess the severity of foot ulcers in diabetic patients. It categorizes foot ulcers into six grades .\u003csup\u003e30\u003c/sup\u003e The classification of wounds is based on depth, extent and degree of infection, which are just what we need to observe. This additional information helps to improve the accuracy of the model in predicting the risk of amputation in DFU patients. We hope to build a prediction model for amputation risk and further improve the DFU classification system from multiple aspects. \u003csup\u003e31\u0026ndash;33\u003c/sup\u003e Overall, incorporating the Wagner classification system into the predictive model allows for a more comprehensive assessment of the risk of amputation in DFU patients.It enhances the predictive power of the model and provides valuable clinical information for personalized treatment and management of diabetic foot disease.\u003c/p\u003e \u003cp\u003eIn addition, simple linear models (COX regression, multiple Logistic regression, COX proportional risk model) have certain limitations in assessing the prognosis of DFU patients due to the diversity and unpredictability of influencing factors. Boyko et al. Boyko et al.\u003csup\u003e35\u003c/sup\u003e first carried out a prospective cohort study on diabetic patients in 2006, applied Cox proportional risk model to screen independent influencing factors, and finally formed a scoring system model. The AUC of this model was 0.81, indicating good differentiation. But the study was not externally verified. In 2019, British scholar Heald \u003csup\u003e36\u003c/sup\u003e et al. analyzed diabetic patients through retrospective cohort study and Logistic multiple regression, finally included 5 risk factors, and built a calculation equation for the risk probability of diabetic foot based on the regression coefficients of each factor. The model AUC was 0.65. Although the model has certain clinical practicability, the accuracy rate is still low. Tomita et al. \u003csup\u003e37\u003c/sup\u003econducted a case-control study, and the AUC of the constructed model was 0.865. The emergence of artificial intelligence provides new ideas and methods for diabetes risk prediction. In 2021, Peng et al.\u003csup\u003e38\u003c/sup\u003e constructed a model to predict the risk of diabetic foot amputation, and adopted the nomogram to visually compare the risk weights of each risk factor. Nonetheless, the sample size and input variables of this experiment are insufficient. The model constructed is not verified. Deng et al.\u003csup\u003e39\u003c/sup\u003eused XGBoost algorithm and COX regression to evaluate the impact of hyperglycemic crisis and other risk factors on the mortality of DFU patients. The model\u0026rsquo;s AUC is 0.680. A prospective study by Lv et al. \u003csup\u003e40\u003c/sup\u003e established a DFU risk model based on risk factors and presented it in the form of the nomogram and web calculator. The AUC of its model was 0.741. Therefore, our study used ANN to establish the model. Machine learning algorithms adopt a multivariate, non-parametric approach that can use non-normal distributions and strongly correlated data to build robust models and identify complex patterns.\u003csup\u003e41\u0026ndash;42\u003c/sup\u003e Compared with the statistical methods in previous relevant studies, the prediction model we constructed includes more relevant factors, has higher accuracy and more intuitive prediction results.We believe that our model is convincing for the guidance of clinical work.\u003c/p\u003e \u003cp\u003eThe combination of model prediction and clinical decision making is another feature of our experiment. In this study, we intend to construct a database of diabetic foot - diabetic foot amputation patients, study the real world of diabetic foot amputation patients, and validate predictive models to guide clinical decision making. Obviously, whether the predictive model based on retrospective data analysis can be applied to clinical practice needs to be further verified. Therefore, we hope to collect the information of newly included diabetic foot patients, compared the model prediction results, optimized the internal algorithm of the model, further screened the relevant risk factors, and understood the distribution of diabetic foot characteristics in the current survey population. Based on the relevant risk factors we have screened, we should proceed with the multi-disciplinary combined diagnosis and treatment of diabetic foot patients from the four aspects of nutrition, blood circulation, wound surface and infection. It is essential for the early prevention, timely intervention and scientific individualized treatment of DFU.\u003csup\u003e40\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eHowever, there are some limitations to our study. Although we use SVM algorithms to build models, we still lack sufficient clinical validation and cohort studies.Although we use SVM algorithms to build models, we still lack sufficient clinical validation and cohort studies. It is true that machine learning models primarily establish mapping relationships between input variables and predicted outcomes, rather than capturing direct causal relationships. Identifying causal relationships in complex medical conditions such as foot ulcers is challenging. Machine learning models can provide insights into the associations between patient characteristics and predicted outcomes, but further investigation and studies are needed to establish causality. It is essential to consider the timing and effectiveness of treatment interventions when interpreting the predictions made by the model. The fluctuation of the infection index of a single patient's second admission is influenced by the treatment measures. At the same time, we can not ignore that the decision of diabetic foot amputation surgery is affected by many aspects, such as the patient's economic status, wound status, and the subjective judgment of clinicians, and it is difficult to predict whether amputation is possible only through observational indicators. We want to develop a model that differ from the existing DFU taxonomy system and can be optimized and refined on the basis of the Wagner classification. In this way, our research constructs highly accurate and practical models to predict amputation rates in DFU patients and attempts to combine clinical care with long-term prognosis.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn summary, we built an intelligent model which can be used to forecast the risk of inpatient amputation in DFU patients and analyzed the real world of newly enrolled diabetic foot patients. Our experimental results show that the machine learning model not only has accurate predictive power, but also provides new ideas for the formulation of personalized treatment plans for patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflicts of Interest\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConsent for publication\u003c/h2\u003e \u003cp\u003eThis study protocol was known to the patient, and the patient provided informed consent.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthical review\u003c/h2\u003e \u003cp\u003e The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Beijing Shijitan Hospital Affiliated to Capital Medical University (protocol code sjtky11-1x-2022(087) and date of approval August 2022).\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll co-authors listed are fully responsible for the integrity and accuracy of the article. Zixuan Liu and Siyang Han contributed equally to this work, they are co-first authors. Zixuan Liu participated in the collection of clinical cases and the writing of papers. Siyang Han was involved in data collation and chart production. Qi Yao, Jiangning Wang, and Lei Gao are the co-corresponding authors of this manuscript, they have made similar outstanding contributions in the process of writing. Qi Yao partook in the designation of the experimental program, chaired the seminar, and put forward constructive suggestions for the follow-up of the research. Jiangning Wang was responsible for guiding the construction of the model and checking its accuracy. Lei Gao was in charge of providing clinical cases, establishing cohort studies, and providing technical support for the development of predictive models.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData availabilityThe reader can access the data by correspondence with the corresponding author. The data are currently under embargo while the research findings are commercialized.Requests for data,12 months after publication of this article, will be considered by the corresponding author. Patient\u0026rsquo;s privacy in hospital is protected by the Ethics Committee.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChen M, Yang J, Zhou J, et al. 5G -smart diabetes: toward personalized diabetes diagnosis with healthcare big data clouds [J]. IEEE Communications Magazine, 2018, 56(4):16\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYe Huarong, Yang Yi, Lin Xuan, et al. Application of BP neural network in the diagnosis of breast cancer with high-frequency color ultrasound [J]. China Health Statistics, 2016,33 (1): 71\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun H, Saeedi P, Karuranga S, et al. IDF Diabetes Atlas: global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045 [J]. Diabetes Res Clin Pract. 2022;183:109119.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlobal disability burdens of diabetes-related lower-extremity complications in 1990 and 2016. Zhang Y, Lazzarini PA, McPhail SM, et al [J]. Diabetes Care. 2020;43:964\u0026ndash;974.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXuan Liao, Sheng-Hong Li, Mariya Mohamad El Akkawi,et al. Surgical amputation for patients with diabetic foot ulcers: A Chinese expert panel consensus treatment guide Front Surg. 2022; 9: 1003339.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiabetic podiatry Group, Peripheral Vascular Diseases Committee of Chinese Microcirculation Society. Expert Consensus on diabetic foot wound repair and treatment [J]. Chinese Journal of Diabetes, 2018, 10(5):305\u0026ndash;309.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChinese Medical Association Diabetes Society, Chinese Medical Association Infectious Diseases Society, Chinese Medical Association Tissue Repair and Regeneration Society. Chinese Guidelines for the Prevention and Treatment of diabetic foot (2019 edition) (Ⅳ). Chinese Journal of Diabetes, 2019, 11(5):316\u0026ndash;327.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Zhihong, Guo Shuqin, Li Tingting, et al. Comparison of Wagner grading method for diabetic foot and University of TEXAS classification [J]. Chinese Journal of Diabetes, 2012,4 (8): 469\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInternational Diabetic Foot Working Group: Classification of diabetic foot ulcers Part of the International Diabetic Foot Working Group: International Guidelines for Diabetic Foot Management (2019) [J]. Infection, Inflammation and Repair, 2019, 20(4):231\u0026ndash;237.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi Hongyan, Xu Zhangrong, Jiang Yufeng, et al. Serum albumin is a good clinical indicator for predicting the risk of amputation and medical cost of diabetic foot ulcer [J]. Chinese Journal of geriatric multi-organ Diseases, 2013,12 (12): 919\u0026ndash;923.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXie Xiaoran, Xu Rong. A systematic evaluation of risk prediction model for diabetic foot [J]. Chinese Journal of Nursing, 21, 56 (1): 124\u0026ndash;131\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRanstam J,Cook J A,Collins G S. Clinical prediction models [J]. Br J Surg,2016,103(13):1886.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoons K G,Altman D G,Reitsma J B,et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis(TRIPOD):explanation and elaboration [J]. Ann Intern Med,2015,162(1):W1-73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeald A,Lunt M,Rutter M K,et al. Developing a foot ulcer risk model:what is needed to do this in a real-world primary care setting? [J]. Diabet Med,2019,36(11):1412\u0026ndash;1416.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBasu A, Manning WG, Mullahy J. Comparing alternative models: log vs cox proportional hazard? [J]. Health Econ. 2004;13(8):749\u0026ndash;765.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWright RE. Logistic regression. Reading and Understanding Multivariate Statistics [J]. Washington DC: American Psychological Association; 1995:217\u0026ndash;244.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNanda R, Nath A, Patel S, et al. Machine learning algorithm to evaluate risk factors of diabetic foot ulcers and its severity [J]. Med Biol Eng Comput, 2022, 60(8): 2349\u0026ndash;2357.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZale A, Mathioudakis N. Machine learning models for inpatient glucose prediction[J]. Curr Diab Rep, 2022, 22(8): 353\u0026ndash;364.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeng L, Xie P, Chen Y, et al. Impact of acute hyperglycemic crisis episode on survival in individuals with diabetic foot ulcer using a machine learning approach [J]. Front Endocrinol (Lausanne), 2022, 13: 974063.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrereton RG, Lloyd GR. Support vector machines for classification and regression [J]. Analyst, 2010, 135(2): 230\u0026ndash;267.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnsari D, Nilsson J, Andersson R, et al. Artificial neural networks predict survival from pancreatic cancer after radical surgery [J]. Am J Surg, 2013, 205(1): 1\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmiri Z, Mohammad K, Mahmoudi M, et al. Assessing the effect of quantitative and qualitative predictors on gastric cancer individuals survival using hierarchical artifcial neural network models [J]. Iran Red Cresc Med J, 2013, 15(1): 42\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQiao G, Li J, Huang A, et al. Artificial neural networking model for the prediction of post -hepatectomy survival of patients with early hepatocellular carcinoma [J]. J Gastroenterol Hepatol, 2014, 29(12): 2014\u0026ndash;2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Lijin, Wei Xiayan, Lu Jiaqi, et al. Lasso regression: From interpretation to prediction [J]. Advances in Psychological Science, 2019,28(10):1777\u0026ndash;1788.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eValero-Carreras Daniel, Alcaraz Javier, Landete Mercedes, et al. Comparing two SVM models through different metrics based on the confusion matrix [J]. Computers and Operations Research. Volume 152, Issue. 2023\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMusa IR, Ahmed MON, Sabir EI, et al. Factors associated with amputation among patients with diabetic foot ulcers in a Saudi population[J]. BMC Research Notes, 2018, 11(1): 260.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu Huiping, Chen Mingwei. Analysis of related factors of diabetic foot ulcer healing and amputation [J]. Journal of Anhui Medical University, 2016,51 (11): 1634\u0026ndash;1637.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun J-H, Tsai J-S, Huang C-H, et al. Risk factors for lower extremity amputation in diabetic foot disease categorized by Wagner classification[J]. Diabetes Res Clin Pract. 2012;95(3):358\u0026ndash;363.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhan LX, Branco BC, Armstrong DG, Mills JL Sr. The Society for Vascular Surgery lower extremity threatened limb classification system based on wound, ischemia, and foot infection(WIfI) correlates with risk of major amputation and time to wound healing[J]. J Vasc Surg. 2015;61(4):939\u0026ndash;944.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGau B R,Chen H Y,Hung S Y,et al.The impact of nutritional status on treatment outcomes of patients with limb-threatening diabetic foot ulcers [J].J Diabetes Complications,2016,30 (1):138\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang Q,Wang JH,Huang DD,et al. Clinical significance of analysis of the level of blood fat,CRP and hemorheological indicators in the diagnosis of elder coronary heart disease [J]. Saudi J Biol Sci,2018,25 (8):1812\u0026ndash;1816.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMehraj M, Shah I. A review of Wagner classification and current concepts in management of diabetic foot. Int J Orthop Sci.2018;4(1):933\u0026ndash;935.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu S, Chen R, Wei W, Lu X. Understanding heart-failure patients EHR clinical features via SHAP interpretation of tree-based machine learning model predictions. arXiv preprint arXiv:210311254; 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJeon BJ, Choi HJ, Kang JS, Tak MS, Park ES. Comparison of five systems of classification of diabetic foot ulcers and predictive factors for amputation[J]. Int Wound J. 2017;14(3):537\u0026ndash;545.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSantema TB, Lenselink EA, Balm R, Ubbink DT. Comparing the Meggitt-Wagner and the University of Texas wound classification systems for diabetic foot ulcers: inter-observer analyses[J].Int Wound J. 2016;13(6):1137\u0026ndash;1141.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeckert S, Witte M, Wicke C, K\u0026ouml;nigsrainer A, Coerper S. A new wound-based severity score for diabetic foot ulcers: a prospective analysis of 1,000 patients [J]. Diabetes Care. 2006;29(5):988\u0026ndash;992.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBOYKO E J,AHRONI J H,COHEN V,et al. Prediction of diabetic foot ulcer occurrence using commonly available clinical information:the seattle diabetic foot study [J]. Diabetes Care,2006,29(6):1202\u0026ndash;1207.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHEALD A,LUNT M,RUTTER M K,et al. Developing a foot ulcer risk model:what is needed to do this in a real-world primary care setting? [J]. Diabet Med, 2019,36(11):1412\u0026ndash;1416.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTOMITA M,KABEYA Y,OKISUGI M,et al.Development and assessment of a simple scoring system for the risk of developing diabetic foot [J]. Diabetol Int, 2015,6(3):212\u0026ndash;218.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBo P, Rui M, Yi L,et al. Development of Predictive Nomograms for Clinical Use to Quantify the Risk of Amputation in Patients with Diabetic Foot Ulcer[J]. Diabetes Res.2021,6621035(2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeng L, Xie P, Chen Y, et al. Impact of acute hyperglycemic crisis episode on survival in individuals with diabetic foot ulcer using a machine learning approach [J]. Front Endocrinol (Lausanne), 2022, 13: 974063.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLv, J. et al. Development and validation of a risk prediction model for foot ulcers in diabetic patients.[J]. Diabetes Res. 2023, 1199885 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRipatti S,Tikkanen E,Orho-Melander M,et al. A multilocus genetic risk score for coronary heart disease: Case -control and prospective cohort analyses [J]. The Lancet, 2010, 376(9750):1393\u0026ndash;1400.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSilver M, Chen P, Li R,et al. Pathways-driven sparse regression identifies pathways and genes associated with high - density lipoprotein cholesterol in two asiancohorts [J]. PLoS Genetics, 2013, 9(11): e1003939\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4642735/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4642735/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eBackground\u003c/em\u003e. Diabetic foot (DF) disease, which includes ulcers, infections and gangrene of the feet, is one of the leading causes of disability worldwide. Due to the high disability rate and expensive treatment cost of diabetic foot, doctors and patients all hope to forecast the prognosis in time and give early intervention. With the development of artificial intelligence technology, more and more methods are used in the diagnosis and prognosis prediction of chronic diseases. Machine learning, a type of artificial intelligence, has excellent predictive effects with a certain accuracy.\u003csup\u003e1 \u003c/sup\u003eThe results of diabetic foot are affected by many factors, so it is necessary for the machine learning to reasonably predict the relationship between input variables and output variables, and to correct and tolerate faults.\u003csup\u003e2 \u003c/sup\u003e\u003cem\u003eObjective\u003c/em\u003e. To develop an accurate and applicable predictive model for diabetic foot amputation and use it to guide clinical diagnosis and treatment, indicating the direction for the prevention of diabetic foot amputation.\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMethods and Materials\u003c/em\u003e. This retrospective study collected the basic data of 150 patients with DFU who met the study criteria in Beijing Shijitan Hospital from January 2019 to December 2022. Above all, We divided them into amputation group and non-amputation group based on prognostic outcome. Then we used Lasso algorithm to screen relevant risk factors, and predictive models were built with support vector mechanism(SVM) to input risk factors and predict amputation. Besides, we divided the test set and training set by 5-fold cross-validation. The area under the receiver operating characteristic (ROC) curves of the model were 0.89. This model’s calibration capability was 19.614 through Hosmer-Lemeshow test (p=0.012).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConclusion\u003c/em\u003e. In summary, our survey data suggested that C-reactive protein (CRP) in the infection index and the Wagner scale of the affected foot might play a vital role in predicting diabetic foot amputation. The predictive model we constructed can accurately estimate the rate of amputation during hospitalization in DFU patients. In addition, the model allows for personalized analysis of patients' risk factors.\u003c/p\u003e","manuscriptTitle":"A Meaningful Machine Learning Model for Predicting Amputation Rate of Patients with Diabetic Foot Ulcer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-24 13:46:12","doi":"10.21203/rs.3.rs-4642735/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3f3486b1-f7ea-481f-87c4-5800687e25b2","owner":[],"postedDate":"July 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":34886264,"name":"Health sciences/Diseases"},{"id":34886265,"name":"Health sciences/Endocrinology"},{"id":34886266,"name":"Health sciences/Medical research"},{"id":34886267,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2024-12-03T10:24:03+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-24 13:46:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4642735","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4642735","identity":"rs-4642735","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}