Purpose: We aimed to develop a new scoring index based on decision-tree analysis to predict clinical outcomes of patients with community-acquired pneumonia (CAP) admitted to the intensive care unit (ICU).
Methods: Data of 3519 ICU patients with CAP were obtained from the Medical Information Mart for Intensive Care III (MIMIC III) 2001-2012 database and analysed between 30-d survivors and non-survivors. Accuracy, sensitivity, and specificity of the new decision tree model were compared with those of CURB-65 and SOAR.
Results: The newly developed classification and regression tree (CART) model identified coexisting illnesses as the most important single discriminating factor between survivors and non-survivors. The CART model area under the curve (AUC) 0.661 was superior to that of CURB-65 (0.609) and SOAR (0.589). CART sensitivity was 73.4%, and specificity 49.0%. CURB-65 and SOAR sensitivity for predicting 30-d mortality were 74.5 and 80.7%, and specificity was 42.3 and 33.9%, respectively. After smoothing, the CART model had higher sensitivity and specificity than both CURB-65 and SOAR.
Conclusions: The new CART prediction model has higher specificity and better receiver operating characteristics (ROC) curves than CURB-65 and SOAR score indices although its accuracy and sensitivity are only moderately better than the other systems. Key messages The new CART prediction model has higher specificity and better ROC curves than CURB-65 and SOAR score indices. However, accuracy and sensitivity of the new CART prediction model are only moderately better than the other systems in predicting 30-day mortality in CAP patients.
Keywords: Intensive care; community-acquired pneumonia; mortality.