Risk prediction models for patients with recurrent diabetic foot ulcers: A systematic review

Abstract

Objectives: To systematically review published studies on risk prediction models for patients with recurrent diabetic foot ulcers.

Study design: Systematic review.

Methods: China National Knowledge Infrastructure (CNKI), Chinese Biomedical Literature Database (CBM), Wanfang Database, China Science and Technology Journal Database (VIP), PubMed, Web of Science, the Cochrane Library and Embase were searched from inception to November 5, 2023. Data from selected studies were extracted, including author, country, participants, study design, data source, sample size, outcome definition, predictors, model development and performance. The Prediction Model Risk of Bias Assessment Tool (PROBAST) checklist was used to assess the risk of bias and applicability.

Results: A total of 677 studies were retrieved, and after a screening process, eight predictive models from eight studies were included in this review. The studies utilized logistic regression, COX regression, and machine learning methods to develop risk prediction models for diabetic foot ulcer recurrence. The rate of diabetic foot ulcer recurrence was 20 %-41 %. The most commonly used predictors were HbA1c and DM duration. the reported area under the curve (AUC) ranged from 0.690 to 0.937. All studies were found to be at high risk of bias, mainly due to problems with outcome measures and poor reporting of analytic domains. the studies were not found to be at high risk of bias, mainly due to problems with outcome measures and poor reporting of analytic domains.

Conclusions: Although the performance of the diabetic foot ulcer recurrence prediction models included in the studies was decent, all of them were found to be at high risk of bias according to the PROBAST checklist. Future studies should focus on developing new models with larger samples, rigorous study designs, and multicenter external validation.

Keywords: Diabetic foot ulcer; Recurrence; Risk prediction model; Systematic review.