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|Title:||Clinical prediction score for superficial surgical site infection after appendectomy in adults with complicated appendicitis|
Mahidol University. Faculty of Medicine, Ramathibodi Hospital. Section for Clinical Epidemiology and Biostatistics
|Keywords:||Appendicitis;Surgical site infection;Wound infection;Risk factors;Prediction scores;Open Access article|
|Citation:||World Journal of Emergency Surgery. Vol.13 (2018), 23|
|Abstract:||Background: Superficial surgical site infection (SSI) is common after appendectomy. This study aims to determine a clinical prediction score for SSI after appendectomy in complicated appendicitis. Methods: Data from randomized controlled trial of delayed versus primary wound closures in complicated appendicitis was used. Nineteen patient- and operative-related predictors were selected in the logit model. Clinical prediction score was then constructed using coefficients of significant predictors. Risk stratification was done by receiver operating characteristic (ROC) curve analysis. Bootstrap technique was used to internal validate the score. Results: Among 607 patients, the SSI incidence was 8.7% (95% CI 6.4, 11.2). Four predictors were significantly associated with SSI, i.e., presence of diabetes, incisional length > 7 cm, fecal contamination, and operative time > 75 min with the odds ratio of 2.6 (95% CI 1.2, 5.9), 2.8 (1.5, 5.4), 3.6 (1.9, 6.8), and 3.4 (1.8, 6.5), respectively. Clinical prediction score ranged from 0 to 4.5 with its discrimination concordance (C) statistic of 0.74 (95% CI 0.66, 0.81). Risk stratification classified patients into very low, low, moderate, and high risk groups for SSI when none, one, two, and more than two risk factors were presented with positive likelihood ratio of 1.00, 1.45, 3.32, and 9.28, respectively. A bootstrap demonstrated well calibration and thus good internal validation. Conclusions: Diabetes, incisional length, fecal contamination, and operative time could be used to predict SSI with acceptable discrimination. This clinical risk prediction should be useful in prediction of SSI. However, external validation should be performed.|
|Appears in Collections:||RA-Article|
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