Development and validation of an incidence risk prediction model for early foot ulcer in diabetes based on a high evidence systematic review and meta-analysis

医学 接收机工作特性 荟萃分析 糖尿病足溃疡 内科学 累积发病率 糖尿病 入射(几何) 相对风险 切断 风险评估 队列研究 曲线下面积 队列 2型糖尿病 置信区间 糖尿病足 量子力学 光学 物理 内分泌学 计算机科学 计算机安全
作者
Dong Chen,Yan Wang,Xin Shang,Xixi Liu,Xinbang Liu,Tiantian Ge,Qiuyue Ren,Xiaoxia Ren,Xin Song,Hongmei Xu,Mingyan Sun,Hongmei Zhou,Bai Chang
出处
期刊:Diabetes Research and Clinical Practice [Elsevier]
卷期号:180: 109040-109040 被引量:30
标识
DOI:10.1016/j.diabres.2021.109040
摘要

Abstract

Objectives

To develop and validate a model for predicting the risk of early diabetic foot ulcer (DFU) based on systematic review and meta-analysis.

Methods

Data were analyzed from the risk factors of DFU with their corresponding risk ratio (RR) by meta-analysis. The DFU prediction model included statistically significant risk factors from the meta-analysis, all of which were scored by its weightings, and the prediction model was externally validated using a validation cohort from China. The occurrence of early DFU was defined as patients with type 2 diabetes who were free of DFU at baseline and diagnosed with DFU at follow-up. Evaluation of model performance was based on the area under the discrimination receiver operating characteristic curve (ROC), with optimal cutoff point determined by calculation of sensitivity and specificity. Kaplan–Meier curve were performed tocompare the cumulative risk of different groups.

Results

Our meta-analysis confirmed a cumulative incidence of approximately 6.0% in 46,521 patients with diabetes. The final risk prediction model included Sex, BMI, HbA1c, Smoker, DN, DR, DPN, Intermittent Claudication, Foot care, and their RRs were 1.87, 1.08, 1.21, 1.77, 2.97, 2.98, 2.76, 3.77, 0.38, respectively. The total score of all risk factors was 80 points according to their weightings. The prediction model showed good discrimination with AUC = 0.798 (95 %CI 0.738–0.858). At the optimal cut-off value of 46.5 points, the sensitivity, specificity and Youden index were 0.769, 0.798 and 0.567, respectively. The final model stratified the validation cohort into low, low-intermediate, high-intermediate and high-risk groups; Compared with low-risk group, the RR with 95 %CI of developing DFU in high-intermediate and high-risk group were 17.23 (5.12–58.02), p < 0.01 and 46.11 (5.16–91.74), p < 0.01, respectively.

Conclusion

We have developed a simple tool to facilitates early identification of patients with diabetes at high risk of developing DFU based on scores. This simple tool may improve clinical decision-making and potentially guide early intervention.
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