Zhou Zitong, Jia Yu, Yan Hong, Xu Jialan, Wang Siyu, Wen Jun
School of Nursing, Chengdu University of Traditional Chinese Medicine, Sichuan, Chengdu, 610075, China.
Public Health. 2025 Jul;244:105744. doi: 10.1016/j.puhe.2025.105744. Epub 2025 May 9.
To systematically review published studies on risk prediction models for patients with recurrent diabetic foot ulcers.
Systematic review.
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.
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.
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.
系统评价已发表的关于复发性糖尿病足溃疡患者风险预测模型的研究。
系统评价。
检索中国知网（CNKI）、中国生物医学文献数据库（CBM）、万方数据库、维普中文科技期刊数据库（VIP）、PubMed、Web of Science、考克兰图书馆和Embase，检索时间范围从建库至2023年11月5日。提取所选研究的数据，包括作者、国家、参与者、研究设计、数据来源、样本量、结局定义、预测因素、模型开发和性能。使用预测模型偏倚风险评估工具（PROBAST）清单评估偏倚风险和适用性。
共检索到677项研究，经过筛选过程，本评价纳入了8项研究中的8个预测模型。这些研究利用逻辑回归、COX回归和机器学习方法开发糖尿病足溃疡复发风险预测模型。糖尿病足溃疡复发率为20% - 41%。最常用的预测因素是糖化血红蛋白（HbA1c）和糖尿病病程。报告的曲线下面积（AUC）范围为0.690至0.937。所有研究均被发现存在高偏倚风险，主要原因是结局测量问题和分析领域报告不佳。
尽管纳入研究中的糖尿病足溃疡复发预测模型性能尚可，但根据PROBAST清单，所有模型均被发现存在高偏倚风险。未来研究应侧重于开发样本量更大、研究设计严谨且经过多中心外部验证的新模型。
