A risk prediction model was developed to predict the risk of major amputation in patients with diabetic foot ulcer (DFU) on admission, and instruct patients to prevent and control early, and guide doctors to make clinical decisions. We used data from the Electronic Medical Record (EMR) database of the First Teaching Hospital of Tianjin University of Traditional Chinese Medicine from February 2014 to July 2020. DFU patients were divided into major amputation group and non-major amputation group, and nested case-control study method was used to identify case group and control group. The results of the first laboratory tests, imaging examinations, complications and other information of DFU patients at admission were collected, and initial predictive variables were selected. Logistic regression and LASSO regression in R software were used to develop a clinical prediction model for DFU patients with major amputation, which was displayed in the form of nomographs, and the model was evaluated by internal validation. A total of 3654 patients were diagnosed as DFU, 695 patients were included in the study on the development of risk prediction model of DFU major amputation, 139 patients in the case group and 556 patients in the control group. 9 variables (WBC, Hb, ALB, Wagner grade, amputation history, smoking, ABI 1 month, HbA1c) screened by logistic regression and LASSO regression were used as predictors of major amputation in DFU patients. The internal validation showed that the C index adjusted by Bootstrap method was 0.91 (95% CI, 0.894–0.943), the average absolute error of the prediction model for drawing the calibration curve was 0.01, and the brier score was 0.08. The clinical risk prediction model of major amputation in DFU patients developed in this study has good discrimination and calibration, can accurately predict the outcome events, can be used as an effective tool to guide doctors to make clinical decisions, and enrich and improve the content of DFU prevention and control work, but the promotion and use of the model still needs further verification of external data. Not applicable.
Lu et al. (Thu,) studied this question.