Predictors of length of stay in the coronary care unit in patient with acute coronary syndrome based on data mining methods

Abstract

IntroductionAssessing the possibility of patient discharge based on data-mining models is one of the common, user-friendly approaches to optimally exploit the limited capacity of hospital beds. ObjectiveThe aim of this study was to determine the predictors of length of stay (LOS) in cardiologic care wards developed and carried out based on data-mining approaches. MethodsData from 136 patient records were evaluated using data-mining analysis approaches including the Multilayer perceptron artificial neural network (MLP-ANN),Quick unbiased and efficient statistical tree (QUEST), support vector machines (SVM), classification and regression tree (CRT), Advanced decision tree (C5.0), Auto Classifier (AC) and Logistic Regression models. ResultsThe median and mean LOS was 4 and 4.15 days (95% CI [3.99, 4.30]), respectively. Predictors are associated with increase in LOS (more than 4 days) were: the ST segment elevation myocardial infarction (STEMI) diagnosis at the time of referral, being in the 50–70 years old group, history of smoking, high blood lipids, history of hypertension, hypertension at the time of admission, and high serum troponin levels. ConclusionUsing classical models to explain the predictors of aoutcome is inefficient when the number of predictors is high and sample size is low. Therefore, the analysis based on new data-mining approaches is a desirable alternative solution. Behavioral factors, especially smoking, are among the important factors in determining the long-term stay in the heart care ward.