Effect of preoperative statins on respiratory complications after coronary artery bypass grafting

Abstract

Objectives Limited data exist on the effect of preoperative statin therapy on postoperative respiratory complications. Machine learning algorithms (MLA) can process large, heterogenous data, and have immensely improved the ability for risk prediction. In this study, we sought to examine the role of preoperative statins on respiratory complications in patients undergoing coronary artery bypass grafting (CABG) using MLA. Methods The study population contained the data of patients who underwent CABG between the years 2015 and 2019 (n = 5638). Three hundred and thirty‐seven independent variables were recorded and the data was randomly split with stratified sampling into training and testing data with 20% of the data (1113 records) reserved for model testing. Various models including linear models, Random forest, SVM, and XGboost were trained to predict the incidence of postoperative respiratory complications. Forty‐seven important features were found to impact model prediction (p ≤ .05) using the global surrogate model method. A conventional multivariable linear regression model was then used to identify predictors of respiratory complications. Results One thousand three hundred sixty‐two (24.5%) patients developed a respiratory complication in our series. The respiratory complication was seen in 561 (29.7%) of the patients who were not on statin compared to only 801 (21.8%) who were on a statin, p < .0001. The area under the curve for receiver operating characteristic curve using statins and respiratory complications was 0.706. Statins showed positive feature importance in all the MLA models. Conclusions MLA showed that statins impacted the prediction of respiratory complications in all the models studied. The study confirmed that preoperative statins reduced the risk of respiratory complications by 21%.