Predicting Risk of Infection After Rhinoplasty with Autogenous Costal Cartilage: A Cohort Study.

Abstract

The study was aimed to develop and validate a nomogram to predict risk of postoperative infection after costal cartilage-based rhinoplasty METHODS: The primary cohort of this study consisted of 672 patients who were appraised between October 2018 and December 2020. The least absolute shrinkage and selection operator (LASSO) regression model was used for data reduction and selection. Multivariable logistic regression analysis was used to develop the predicting model. The calibration curve and C-index were used to evaluate the accuracy of the nomogram, while DCA was used to assess the clinical value. Internal validation was evaluated and an independent validation cohort contained 118 consecutive patients from January 2021 to June 2021. Twenty-one features were reduced to 10 potential predictors on the basis of 672 patients in the primary cohort using LASSO regression. Thus, the predictive nomogram finally contained ten clinical features-age, number of nose operations, length of hospital stay, operation time, history of nose trauma, history of animal contact after operation, smoking after operation in one month, drinking after operation in one month, history of nose infection, and spicy food after operation in one month with the most essential factor. The model showed good discrimination with a C-index of 0.987 (95% CI, 0.978-0.996) (internal validation of 0.967) and good calibration. In addition, the model also had the highest sensitivity due to the AUC of the model was 0.987. Application of the nomogram in the validation cohort still gave good discrimination (C-index, 0.935 [95% CI, 0.910-0.960]). Decision curve analysis demonstrated that the nomogram was clinically helpful. This is the first study to develop a nomogram to predict infection after rhinoplasty with autologous costal cartilage. Use of this nomogram might help surgeons with early identification of patients at high risk of infection. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .