Risk stratification model for post-stroke pneumonia in patients with acute ischemic stroke

Abstract

Background: Post-stroke pneumonia (PSP) has been implicated in the morbidity, mortality, and increased medical costs after acute ischemic stroke. Aim: The aim of this study was to develop a prediction model for PSP in patients with acute ischemic stroke. Methods: A retrospective, case-control, secondary analysis study was conducted using data for 10,034 patients with ischemic stroke who presented to the hospital within 24 hours of onset of stroke symptoms. The predictive factors for PSP were analyzed using multivariate logistic regression and classification and regression tree (CART) analyses. Results: Among the study population, 546 patients (5.4%) had PSP. Multivariate logistic regression revealed that age, atrial fibrillation, smoking habit, body temperature at admission, pulse rate at admission, National Institute of Health Stroke Scale (NIHSS) score upon admission, white blood cell count, and blood urea nitrogen level were major predictive factors of PSP. CART analysis identified NIHSS score at admission, pulse rate at admission, and percentage of lymphocyte as important factors for PSP to stratify the patients into subgroups. The subgroup of patients with an NIHSS score >14 at admission and pulse rate >111 beats per minute at admission and those with an NIHSS score >14, pulse rate ⩽111 beats per minute at admission, and percentage of lymphocyte ⩽9.2% had a relatively high risk of PSP (39.6% and 35.5%, respectively). Conclusions: In this study, CART analysis has a similar predictive value of PSP as compared with a logistic regression model. In addition, decision rules generated by CART can easily be interpreted and applied in clinical practice.