ArticleFrontiers in public health2026
Development and validation of a prediction model for infection in chronic nonhealing wounds: a two-center retrospective study with external validation.
Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Purpose: To develop and externally validate a pragmatic and interpretable model that predicts infection risk in chronic nonhealing wounds using routine electronic medical record data. Methods: We conducted a two-center retrospective study at two tertiary hospitals in China. The primary cohort ( Results: Six consensus predictors were retained: smoking, diabetes duration, wound depth, elevated C-reactive protein, elevated procalcitonin, and hypoalbuminemia. The RF achieved AUROC 0.884 (95% CI 0.841-0.928) in the testing cohort with a calibration slope of 1.00 (95% CI 0.73-1.27) and higher net benefit than treat-all and treat-none across broad thresholds. External validation showed AUROC 0.855 (95% CI 0.807-0.904) with a calibration slope of 1.00 (95% CI 0.74-1.26) and similar decision utility. SHAP indicated hypoalbuminemia and inflammatory markers as dominant drivers, consistent with clinical reasoning. Conclusion: A six-variable RF model based on readily available data provides accurate, well-calibrated, and clinically useful prediction of infection in chronic nonhealing wounds, with transparent explanations to support bedside use. To facilitate immediate clinical application, this model has been deployed as a free, user-friendly web calculator. Prospective validation and impact evaluation across diverse settings are warranted.
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