中国全科医学 ›› 2024, Vol. 27 ›› Issue (03): 357-363.DOI: 10.12114/j.issn.1007-9572.2023.0451

所属专题: 内分泌代谢性疾病最新文章合集

• 论著·医学循证 • 上一篇    下一篇

糖尿病足发病风险预测模型的系统评价

林令君, 郭俊, 王俊伟, 高杨, 陈惠盈, 万永丽*()   

  1. 300134 天津市,天津医科大学朱宪彝纪念医院 天津市内分泌研究所 国家卫健委激素与发育重点实验室 天津市代谢性疾病重点实验室
  • 收稿日期:2023-06-10 修回日期:2023-08-15 出版日期:2024-01-20 发布日期:2023-10-23
  • 通讯作者: 万永丽

  • 作者贡献:林令君提出研究思路,负责文献检索及文章撰写;林令君、郭俊、王俊伟负责文献筛选,资料的提取与整理;郭俊、高杨、陈惠盈负责文献偏倚风险和适用性评估;高杨、万永丽负责统计分析;万永丽负责论文的修订、质量控制及审查。
  • 基金资助:
    天津市卫生健康委员会中西医结合重点项目(2021034); 天津市医学重点学科(专科)建设项目资助(TJYXZDXK-032A)

A Systematic Review of Risk Prediction Models for Diabetic Foot Development

LIN Lingjun, GUO Jun, WANG Junwei, GAO Yang, CHEN Huiying, WAN Yongli*()   

  1. Tianjin Medical University, Chu Hsien-I Memorial Hospital/Tianjin Institute of Endocrinology/NHC Key Laboratory of Hormones and Development/Tianjin Key Laboratory of Metabolic Diseases, Tianjin 300134, china
  • Received:2023-06-10 Revised:2023-08-15 Published:2024-01-20 Online:2023-10-23
  • Contact: WAN Yongli

摘要: 背景 糖尿病足是糖尿病患者常见并发症,多数患者病情重,疾病进展快。性能良好的糖尿病足发病风险预测模型可以帮助医务人员识别高危患者,尽早采取干预措施。目的 系统评价糖尿病足发病风险预测模型,为模型的构建和优化提供参考。方法 检索PubMed、Cochrane Library、Embase、Web of Science、中国知网及万方数据知识服务平台发表的关于糖尿病足风险预测模型的相关文献,检索期限为建库至2023-05-15。由研究者独立筛选文献,并提取文献数据,使用预测模型研究的偏倚风险评估工具(PROBAST)对模型进行质量评价。使用Stata 17.0软件对模型中预测因子进行Meta分析。结果 共纳入13篇文献,包含13个模型,其中12个模型的曲线下面积(AUC)>0.7。7个模型进行了模型校准,8个模型进行了验证。PROBAST评估结果显示,纳入的13篇文献中有1篇为低偏倚风险,其余12篇均为高偏倚风险;模型适用性方面,1篇为低适用性。Meta分析结果显示,年龄(OR=1.13,95%CI=1.04~1.24)、糖化血红蛋白(OR=1.56,95%CI=1.26~1.94)、足溃疡史(OR=5.93,95%CI=2.85~12.37)、足截肢史(OR=7.79,95%CI=2.74~22.17)、单丝试验敏感性减弱(OR=1.59,95%CI=1.42~1.78)、足真菌感染(OR=6.14,95%CI=1.71~22.04)、肾病(OR=2.09,95%CI=1.65~2.65)是糖尿病足发病风险的影响因素(P<0.05)。结论 糖尿病足风险预测模型仍存在不足,未来风险预测模型的建立可重点关注年龄、糖化血红蛋白水平、足溃疡史、足截肢史、单丝试验敏感性、足真菌感染、肾病等预测因子。

关键词: 糖尿病足, 足溃疡, 危险性评估, 预测, 模型, 系统评价

Abstract:

Backgroud

Diabetic foot is a common complication of diabetes patients, most of whom are seriously ill with rapid disease progression. A well-performing risk prediction model for the development of diabetic foot can help healthcare professionals to identify high-risk patients and take early interventions.

Objective

To systematically review the risk prediction models for diabetic foot, and provide reference for the construction and optimization of the model.

Methods

PubMed, Cochrane Library, Embase, Web of Science, CNKI and Wanfang Data were searched to collect the related studies on risk prediction models for diabetic foot from inception to May 15th, 2022. Two reviewers independently screened the literature, extracted data and evaluated the quality of models using prediction model risk of bias assessment tool (PROBAST). Meta-analysis of the predictors in the model was performed using Stata 17.0 software.

Results

A total of 13 papers were included, containing 13 models, 12 of which had AUC>0.7. Model calibration was performed on 7 models and 8 models were validated. PROBAST results showed that 1 of the 13 included papers was at low risk of bias and the remaining 12 were at high risk of bias; for model applicability, only 1 was of low applicability. The results of Meta-analysis showed that age (OR=1.13, 95%CI=1.04-1.24), glycated hemoglobin (OR=1.56, 95%CI=1.26-1.94), foot ulcer history (OR=5.93, 95%CI=2.85-12.37), previous amputation (OR=7.79, 95%CI=2.74-22.17), diminished sensitivity of the monofilament test (OR=1.59, 95%CI=1.42-1.78), foot fungal infection (OR=6.14, 95%CI=1.71-22.01), and kidney disease (OR=2.09, 95%CI= 1.65-2.65) were independent influencing factors for diabetic foot (P<0.05) .

Conclusion

The risk prediction models for diabetic foot was still inadequate, and the future risk prediction model should focus on age, glycated hemoglobin level, foot ulcer history, amputation history, monofilament test sensitivity, foot fungal infection and kidney disease.

Key words: Diabetic foot, Foot ulcer, Risk assessment, Forecasting, Model, Systematic review