Model-based Prediction of Length of Stay for Rehabilitating Stroke Patients

Abstract

Accurate length-of-stay (LOS) estimates have an impact on medical costs for stroke patients. Most studies have reported only descriptive sample means or have provided linear-model-based estimates for LOS. This study calculated point and interval estimates by treating hospital discharge as an event, and utilizing the proportional hazards (PH) model to provide the estimation of hospital stay for first-ever stroke patients in a rehabilitation department of a clinical center. Methods: Pairwise analysis for correlations between age, sex, comorbidity status, modified Barthel index (MBI) and functional independence measure (FIM) was performed. These explanatory variables are used in the K-sample comparisons, the c2 test for association, the PH regression analysis, and log-transformed linear (LTL) regression. Results: The PH model gave a prediction on estimated mean LOS, with an absolute bias of 0.85 days, by combining MBI and FIM into a single variable, or a bias of 1.15 days and 1.16 days with MBI and FIM variables, respectively. The LTL-based estimation generated a bias of 5.91 days. The PH model has relatively shorter confidence intervals than those obtained by sample-mean and LTL methods. Conclusion: We recommend using the PH model for predicting mean LOS when the PH assumption for patients with different clinical characteristics is satisfied. However, the proposed method only applies to rehabilitating stroke patients.

Keywords

• length of stay
• linear models
• log-transform
• proportional hazards models
• rehabilitation