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| Content Provider | Springer Nature : BioMed Central |
|---|---|
| Author | Li, Zhi Song, Lihua Qin, Baoju Li, Kun Shi, Yingtao Wang, Hongqing Wang, Huiwang Ma, Nan Li, Jinlong Wang, Jitao Li, Chaozheng |
| 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. |
| Related Links | https://josr-online.biomedcentral.com/counter/pdf/10.1186/s13018-023-04473-2.pdf |
| Ending Page | 11 |
| Page Count | 11 |
| Starting Page | 1 |
| File Format | HTM / HTML |
| DOI | 10.1186/s13018-023-04473-2 |
| Journal | Journal of Orthopaedic Surgery and Research |
| Issue Number | 1 |
| Volume Number | 19 |
| Language | English |
| Publisher | BioMed Central |
| Publisher Date | 2024-01-05 |
| Access Restriction | Open |
| Subject Keyword | Orthopedics Surgical Orthopedics Elective clean orthopedic surgery Surgical site infection Nomogram Prediction model |
| Content Type | Text |
| Resource Type | Article |
| Subject | Surgery Orthopedics and Sports Medicine |
| Journal Impact Factor | 2.8/2023 |
| 5-Year Journal Impact Factor | 3/2023 |
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