Discussion
Although researches on the recurrent risk factors of DFU have become increasingly profound, there is currently no systematic model to integrate these risk factors and accurately predict recurrent probability.34 35 In this study, we developed and validated the first multidimensional prediction model for DFU recurrence, which integrates metabolic, hematological, inflammatory and clinical management factors using multicenter cohort data. The model includes 10 easily accessible predictors: smoking pack-years, HbA1c, ischemia, LDL-C, hemoglobin, age, BMI, serum creatinine, white blood cell count and hospitalization duration. This model provides a comprehensive tool that allows healthcare providers to assess and stratify the recurrence risk of DFU on an individual basis, enabling more personalized and effective management in routine clinical practice.
As previous studies have shown, chronic hyperglycemia accelerates advanced glycation end product (AGEs) accumulation, while tobacco smoke further promotes the generation of AGEs through the activation of oxidative stress, such as Nicotinamide Adenine Dinucleotide Phosphate (NADPH) oxidase, which synergistically impairs endothelial function and collagen remodeling.36 This ‘dual-engine’ driving mechanism of metabolic-oxidative stress is particularly prominent in diabetic microangiopathy, potentially explaining the double risk of ulcer recurrence observed in patients with concurrent smoking and hyperglycemia. This also supports our previous ML study, which demonstrated that patients with hyperglycemic crises tend to have worse outcomes.16 37
The predictors in our DFU recurrence model—anemia, ischemia, leukocytosis and elevated LDL-C—collectively delineate a hypoxia-inflammation-metabolic vicious cycle. Anemia combined with tissue ischemia disrupts oxygen supply–demand equilibrium,38 39 whereas leukocytosis indicates persistent microenvironmental inflammation that may deplete local oxygen reserves. Concurrently, elevated LDL-C exacerbates lower limb ischemia through foam cell formation and atherosclerotic plaque destabilization, establishing a metabolic-ischemic vicious cycle.40 This is consistent with the previous studies.41 42
Obesity is a well-known risk factor of plantar foot ulcer recurrence, as previous studies have shown.11 43 44 Our findings validate these results, demonstrating the substantial influence of weight on foot ulcer recurrence. Therefore, patients should be closely monitored in the clinic until their weight issues are addressed.
While prior DFU recurrence prediction models consistently overlooked hospitalization duration, our study found that short-term hospitalization (<7 days) may leave an increased risk of relapse due to residual biological risk of incomplete treatment (eg, debridement or inadequate course of antibiotics), whereas appropriate prolonged hospitalization reduces the risk of recurrence. This finding is consistent with the experience of the clinicians. Patients who require long-term hospitalization are often those with severe conditions and a higher risk of death. Appropriate duration of treatment increases the likelihood of being cured, hence reduces the risk of readmission. Thus, clinical practice demands dynamic stratification: enhancing transitional care for short-stay patients while initiating early rehabilitation in prolonged inpatients to disrupt the frailty-recurrence cycle. Thus, clinical practice demands dynamic stratification: enhancing transitional care for short-stay patients while initiating early rehabilitation in prolonged inpatients to disrupt the frailty-recurrence cycle.
Given the superior performance of ML techniques in managing high-dimensional and non-linear data, an increasing number of researchers have recently applied these approaches to the study of DFU. Both ML and deep learning algorithms have demonstrated remarkable predictive capabilities by utilizing clinical features. In this study, the XGBoost model exhibited exceptional predictive performance, achieving an AUROC of 0.924, with a 95% CI of 0.867 to 0.967. While traditional statistical models such as LR and Cox regression are prevalent in clinical prediction due to their interpretability, they encounter limitations when dealing with high-dimensional non-linear relationships. The LR model developed by Aan de Stegge et al (AUROC=0.690)45 and the Cox regression model by Wang and Pan (AUROC=0.796)46 demonstrated significantly lower performance compared with our model.
In studies specific to DFU, the conditional inference tree model developed by Stefanopoulos et al (AUROC=0.880)47 and the Naive Bayes model by Wang et al (AUROC=0.864)48demonstrated inferior performance compared with our model. This discrepancy may be attributed to variations in prediction endpoints: the aforementioned studies concentrated on ‘short-term healing’, whereas our research focused on the more complex endpoint of ‘recurrence’. The incorporation of 3-year longitudinal data significantly enhanced the accuracy of long-term risk prediction. Although the multimodel ensemble by Zhang et al (AUROC=0.937)49 exhibited marginally superior performance compared with our model, it did not report calibration metrics. In contrast, our model’s Brier score substantiates the reliability of its probability predictions, thereby rendering it more suitable for clinical risk quantification (online supplemental table 4).
In conclusion, our XGBoost model demonstrates markedly superior discriminative capability compared with traditional regression models and the majority of DFU-specific models. Its performance is on par with high-performance ML models, while offering calibration and interpretability that are more suited to clinical practice. These findings advocate for the use of ensemble learning models in predicting DFU recurrence, especially within patient populations characterized by complex comorbidities.
By integrating a range of clinical factors, including established disease scoring systems, into an ML framework, our study significantly enhances the predictive accuracy of the model, setting it apart from previous iterations. Furthermore, we have developed an online prediction tool designed to provide users with easy access to patient data and reliable prognostic assessments, potentially aiding general practitioners (GPs) in the early evaluation of DFU recurrence risk. Presently, the tool is deployed exclusively in Chongqing, with its initial development grounded in domestic research data. It is currently in the beta testing phase, during which local GPs and specialists are actively contributing to its ongoing training and refinement. Simultaneously, efforts are being made to broaden the data sources and optimize the tool, with the ultimate aim of extending its application to support diabetic foot patients on a global scale.
Our study has several limitations. First, the model was developed retrospectively using data from multiple hospitals in Southwest China, which may introduce potential biases. Second, the small sample size of the external validation set could impact the robustness of the model’s evaluation. Additionally, the variability in clinical data across different hospitals presents challenges in developing a universally applicable predictive model. To address these issues, future studies should include larger, multicenter validation cohorts to enhance the model’s generalizability. Third, although the model is designed to handle missing data, certain important features, such as the time between symptom onset and hospital admission, were frequently missing, which limits further in-depth analysis.
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