In Plastic and reconstructive surgery ; h5-index 62.0
BACKGROUND : Body contouring is a common procedure, but it is worth attention due to concerns for a variety of complications, and even potential for death. As a result, the purpose of this study was to determine the key predictors following body contouring and create models for the risk of mortality using diverse machine learning models.
METHODS : The National Inpatient Sample (NIS) database from 2015 to 2017 was queried to identify patients undergoing body contouring. Candidate predictors such as demographics, comorbidities, personal history, postoperative complications, operative features were included. The outcome was the in-hospital mortality. Models were compared on area under the curve (AUC), accuracy, sensitivity, specificity, positive and negative predictive values, and decision curve analysis (DCA) curve.
RESULTS : Overall, 8214 patients undergoing body contouring were identified, among whom 141 (1.72%) patients died in the hospital. Variable importance plot demonstrated that sepsis was the variable with greatest importance across all machine learning algorithms, followed by Elixhauser Comorbidity Index (ECI), cardiac arrest (CA), and so forth. Naïve Bayes (NB) had a higher predictive performance (AUC 0.898, 95% CI 0.884 to 0.911) among these eight machine learning models. Similarly, in the DCA curve, the NB also demonstrated a higher net benefit (ie, the correct classification of in-hospital deaths considering a trade-off between false-negatives and false-positives)-over the other seven models across a range of threshold probability values.
CONCLUSIONS : The machine learning models, as indicated by our study, can be used to predict in-hospital deaths for patients at risk who underwent body contouring.
Peng Chi, Yang Fan, Jian Yu, Peng Liwei, Zhang Chenxu, Chen Chenxin, Lin Zhen, Li Yuejun, He Jia, Jin Zhichao
2023-Mar-21
