Development and validation of a risk prediction model for the recurrence of foot ulcer with type 2 diabetes in China: A longitudinal cohort study based on a systematic review and meta‐analysis

Abstract Aims To develop and validate a risk prediction model for Chinese patients with type 2 diabetes with the recurrence of diabetic foot ulcers (DFUs) based on a systematic review and meta‐analysis. Methods A prospective analysis was performed with 1333 participants and followed up for 60 months. Three models were analysed using a derived cohort. The risk factors were screened using meta‐analysis and logistic regression, and the missing variables were interpolated by multiple imputation. The internal validation was performed using the bootstrap procedure, and the validation cohort was applied to the external validation. The performance of the model was evaluated in the area under the discrimination Receiver Operating Characteristic Curve (ROC). Calibration and discrimination methods were used for the validation cohort. The variables were selected according to their clinical and statistical importance to construct the nomograms. Results Three models were developed and validated. Model 1 included seven social and clinical indicators like sex, diabetes mellitus duration, previous DFU, location of ulcer, smoking, history of amputation, and foot deformity. Model 2 included four more indicators besides those in Model 1, which were statin agents used, antiplatelet agents used, systolic blood pressure, and body mass index. Model 3 added further laboratory indicators to Model 2, such as LDL‐C, HbA1C, fibrinogen, and blood urea nitrogen. In the derivation cohort, 20.1% (206/1027) participants with DFU recurred as compared to the validation cohort, which was 38.2% (117/306). The areas under the curve in the derivation cohort for Models 1–3 were 0.781 (0.744–0.817), 0.843 (0.813–0.873), and 0.899 (0.876–0.922), respectively. The Youden indexes for Models 1–3 were 0.430, 0.559, and 0.653, respectively. Model 3 showed the highest sensitivity and specificity. All models performed well for both discrimination and calibration. Conclusions Models 1–2 were non‐invasive, which indicate their role in general screening for patients at a high risk of recurrence of DFU. However, Model 3 offers a more specific screening due to its best performance in predicting the risk of DFU recurrence amongst the three models.