ArticleInfection and drug resistance2026
Clinical Nomogram for Early Identification of Necrotizing Soft Tissue Infections: Development and Internal Validation in Hospitalized Patients.
Article in Infection and drug resistance, 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 validate a predictive model for necrotizing soft tissue infections (NSTIs) in hospitalized patients with skin and soft tissue infections (SSTIs). Patients and Methods: In this retrospective single-center study, 131 patients with SSTIs admitted between April 2021 and March 2024 were divided into NSTI (n=72) and non-NSTI (n=59) groups. Demographic information, clinical characteristics, and key severity scores were collected. LASSO regression was used to select variables, and a logistic regression model was then developed. Model performance was evaluated using AUC, sensitivity, specificity, and the Hosmer-Lemeshow test. Internal validation was performed using 1000 bootstrap resamples. Model discrimination, calibration, and clinical utility were assessed through ROC curve, calibration curve, and decision curve analysis (DCA), respectively. Results: Four independent predictors-age, temperature, depth of infection, and SOFA score-were identified. A nomogram constructed based on these variables exhibited good discriminative performance, with an AUC of 0.832 (95% CI: 0.763-0.900), a sensitivity of 0.722, and a specificity of 0.797. Internal validation revealed an AUC of 0.832 (95% CI: 0.752-0.897). The model passed the H-L test (p = 0.322). The calibration curve showed close alignment between predicted and observed outcomes (slope = 0.916), indicating good calibration. DCA demonstrated net clinical benefit across a wide range of threshold probabilities (0.1-0.9). Conclusion: The predictive model is simple and practical, with good discriminative and calibration performance. It may serve as a useful decision-support tool for early identification of NSTIs. However, future multicenter prospective studies are still needed for external validation to assess its practical application value.
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