[Construction and preliminary validation of a risk prediction model for the recurrence of diabetic foot ulcer in diabetic patients].

Objective: To develop a risk prediction model for the recurrence of diabetic foot ulcer (DFU) in diabetic patients and primarily validate its predictive value. Methods: Meta-analysis combined with retrospective cohort study was conducted. The Chinese and English papers on risk factors related to DFU recurrence publicly published in China Biology Medicine disc, China National Knowledge Infrastructure, Wanfang Database, VIP Database, and PubMed, Embase, Cochrane Library, and Web of Science, and the search time was from the establishment date of each database until March 31^st, 2022. The papers were screened and evaluated, the data were extracted, a meta-analysis was performed using RevMan 5.4.1 statistical software to screen risk factors for DFU recurrence, and Egger's linear regression was used to assess the publication bias of the study results. Risk factors for DFU recurrence mentioned in ≥3 studies and with statistically significant differences in the meta-analysis were selected as the independent variables to develop a logistic regression model for risk prediction of DFU recurrence. The medical records of 101 patients with DFU who met the inclusion criteria and were admitted to Affiliated Hospital of Guizhou Medical University from January 2019 to June 2022 were collected. There were 69 males and 32 females, aged (63±14) years. The receiver operating characteristic (ROC) curve of the predictive performance of the above constructed predictive model for DFU recurrence was drawn, and the area under the ROC curve, maximum Youden index, and sensitivity and specificity at the point were calculated. Dataset including data of 8 risk factors for DFU recurrence and the DFU recurrence rates of 10 000 cases was simulated using RStudio software and a scatter plot was drawn to determine two probabilities for risk division of DFU recurrence. Using the β coefficients corresponding to 8 DFU recurrence risk factors ×10 and taking the integer as the score of coefficient weight of each risk factor, the total score was obtained by summing up, and the cutoff scores for risk level division were calculated based on the total score × two probabilities for risk division of DFU recurrence. Results: Finally, 20 papers were included, including 3 case-control studies and 17 cohort studies, with a total of 4 238 cases and DFU recurrence rate of 22.7% to 71.2%. Meta-analysis showed that glycosylated hemoglobin >7.5% and with plantar ulcer, diabetic peripheral neuropathy, diabetic peripheral vascular disease, smoking, osteomyelitis, history of amputation/toe amputation, and multidrug-resistant bacterial infection were risk factors for the recurrence of DFU (with odds ratios of 3.27, 3.66, 4.05, 3.94, 1.98, 7.17, 11.96, 3.61, 95% confidence intervals of 2.79-3.84, 2.06-6.50, 2.50-6.58, 2.65-5.84, 1.65-2.38, 2.29-22.47, 4.60-31.14, 3.13-4.17, respectively, P<0.05). There were no statistically significant differences in publication biases of diabetic peripheral neuropathy, diabetic peripheral vascular disease, glycosylated hemoglobin >7.5%, plantar ulcer, smoking, multidrug-resistant bacterial infection, or osteomyelitis (P>0.05), but there was a statistically significant difference in the publication bias of amputation/toe amputation (t=-30.39, P<0.05). The area under the ROC curve of the predictive model was 0.81 (with 95% confidence interval of 0.71-0.91) and the maximum Youden index was 0.59, at which the sensitivity was 72% and the specificity was 86%. Ultimately, 29.0% and 44.8% were identified respectively as the cutoff for dividing the probability of low risk and medium risk, and medium risk and high risk for DFU recurrence, while the corresponding total scores of low, medium, and high risks of DFU recurrence were <37, 37-57, and 58-118, respectively. Conclusions: Eight risk factors for DFU recurrence are screened through meta-analysis and the risk prediction model for DFU recurrence is developed, which has moderate predictive accuracy and can provide guidance for healthcare workers to take interventions for patient with DFU recurrence risk.