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Journal of Orthopaedic Surgery and Research

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Open Access 01-12-2024 | Nosocomial Infection | Research article

A predictive nomogram for surgical site infection in patients who received clean orthopedic surgery: a retrospective study

Authors: Zhi Li, Lihua Song, Baoju Qin, Kun Li, Yingtao Shi, Hongqing Wang, Huiwang Wang, Nan Ma, Jinlong Li, Jitao Wang, Chaozheng Li

Published in: Journal of Orthopaedic Surgery and Research | Issue 1/2024

Abstract

Background

Surgical site infection (SSI) is a common and serious complication of elective clean orthopedic surgery that can lead to severe adverse outcomes. However, the prognostic efficacy of the current staging systems remains uncertain for patients undergoing elective aseptic orthopedic procedures. This study aimed to identify high-risk factors independently associated with SSI and develop a nomogram prediction model to accurately predict the occurrence of SSI.

Methods

A total of 20,960 patients underwent elective clean orthopedic surgery in our hospital between January 2020 and December 2021, of whom 39 developed SSI; we selected all 39 patients with a postoperative diagnosis of SSI and 305 patients who did not develop postoperative SSI for the final analysis. The patients were randomly divided into training and validation cohorts in a 7:3 ratio. Univariate and multivariate logistic regression analyses were conducted in the training cohort to screen for independent risk factors of SSI, and a nomogram prediction model was developed. The predictive performance of the nomogram was compared with that of the National Nosocomial Infections Surveillance (NNIS) system. Decision curve analysis (DCA) was used to assess the clinical decision-making value of the nomogram.

Results

The SSI incidence was 0.186%. Univariate and multivariate logistic regression analysis identified the American Society of Anesthesiology (ASA) class (odds ratio [OR] 1.564 [95% confidence interval (CI) 1.029–5.99, P = 0.046]), operative time (OR 1.003 [95% CI 1.006–1.019, P < 0.001]), and D-dimer level (OR 1.055 [95% CI 1.022–1.29, P = 0.046]) as risk factors for postoperative SSI. We constructed a nomogram prediction model based on these independent risk factors. In the training and validation cohorts, our predictive model had concordance indices (C-indices) of 0.777 (95% CI 0.672–0.882) and 0.732 (95% CI 0.603–0.861), respectively, both of which were superior to the C-indices of the NNIS system (0.668 and 0.543, respectively). Calibration curves and DCA confirmed that our nomogram model had good consistency and clinical predictive value, respectively.

Conclusions

Operative time, ASA class, and D-dimer levels are important clinical predictive indicators of postoperative SSI in patients undergoing elective clean orthopedic surgery. The nomogram predictive model based on the three clinical features demonstrated strong predictive performance, calibration capabilities, and clinical decision-making abilities for SSI.

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Title: A predictive nomogram for surgical site infection in patients who received clean orthopedic surgery: a retrospective study
Authors: Zhi Li
Lihua Song
Baoju Qin
Kun Li
Yingtao Shi
Hongqing Wang
Huiwang Wang
Nan Ma
Jinlong Li
Jitao Wang
Chaozheng Li
Publication date: 01-12-2024
Publisher: BioMed Central
Keyword: Nosocomial Infection
Published in: Journal of Orthopaedic Surgery and Research / Issue 1/2024
Electronic ISSN: 1749-799X
DOI: https://doi.org/10.1186/s13018-023-04473-2

Keynote webinar | Spotlight on medication adherence

Springer Medicine

A predictive nomogram for surgical site infection in patients who received clean orthopedic surgery: a retrospective study

Abstract

Background

Methods

Results

Conclusions

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Methods

Results

Conclusions

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