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已发表论文
早期识别坏死性软组织感染的临床列线图：住院患者中的开发与内部验证
Authors Xia Y, Wang Y, Tao Z, Lu Z
Received 26 August 2025
Accepted for publication 30 January 2026
Published 7 February 2026 Volume 2026:19 559267
DOI https://doi.org/10.2147/IDR.S559267
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Oliver Planz
Yu Xia,1,2 Yiping Wang,1,2 Zhangbin Tao,1,2 Zhongqiu Lu1,2
1Department of Emergency Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, People’s Republic of China; 2Wenzhou Key Laboratory of Emergency and Disaster Medicine, Wenzhou, 325000, People’s Republic of China
Correspondence: Zhongqiu Lu, Department of Emergency Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, People’s Republic of China, Email lzq_640815@163.com
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.
Keywords: skin and soft tissue infections, necrotizing soft tissue infections, sepsis, prediction model
1Department of Emergency Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, People’s Republic of China; 2Wenzhou Key Laboratory of Emergency and Disaster Medicine, Wenzhou, 325000, People’s Republic of China
Correspondence: Zhongqiu Lu, Department of Emergency Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, People’s Republic of China, Email lzq_640815@163.com
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.
Keywords: skin and soft tissue infections, necrotizing soft tissue infections, sepsis, prediction model