Risk prediction models for recurrence of diabetic foot ulcers: a systematic review
10.3760/cma.j.cn115682-20231207-02480
- VernacularTitle:糖尿病足溃疡复发风险预测模型的系统评价
- Author:
Yusheng XIE
1
;
Rongrong HUANG
;
Yuhong LUO
;
Qiansha WANG
;
Yue MING
;
Yi XU
Author Information
1. 贵州医科大学护理学院，贵阳 550025
- Keywords:
Diabetic foot;
Recurrence;
Prediction model;
Systematic review
- From:
Chinese Journal of Modern Nursing
2024;30(11):1414-1421
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To systematically evaluate the recurrence risk prediction model of diabetic foot ulcers (DFU) .Methods:Research on DFU recurrence risk prediction models was electronically searched in PubMed, Embase, Web of Science, Cochrane Library, China National Knowledge Infrastructure, Wanfang Data, and China Biomedical Service System. The search period was from database establishment to July 20, 2023. Two researchers independently screened literature and conducted data extraction and quality evaluation using the prediction model research data extraction table and the Prediction Model Risk of Bias Assessment Tool (PROBAST) .Results:A total of 8 articles were included, including 14 models. The area under the receiver operating characteristic (ROC) curve included in the model ranged from 0.660 to 0.940. The most common five predictors in the model were ulcers location, glycosylated hemoglobin, smoking, combined peripheral neuropathy and diabetes course. All 8 articles had a high risk of bias, mainly due to insufficient sample size, improper handling and reporting of missing data, and a lack of internal validation, which might lead to overfitting of the model. Only one article was subjected to external validation.Conclusions:The research on DFU recurrence risk prediction models is still in the development stage, and the predictive performance of various studies is still acceptable, but there is a high risk of bias. Future research still needs to use rigorous statistical analysis methods to construct new risk prediction models and improve internal and external validation.
