Predictive models for hospital readmission rates are in high demand because of 
the Centers for Medicare & Medicaid Services (CMS) Hospital Readmission 
Reduction Program (HRRP). The LACE index is one of the most popular predictive 
tools among hospitals in the United States. The LACE index is a simple tool with 
4 parameters: Length of stay, Acuity of admission, Comorbidity, and Emergency 
visits in the previous 6 months. The authors applied logistic regression to 
develop a predictive model for a medium-sized not-for-profit community hospital 
in California using patient-level data with more specific patient information 
(including 13 explanatory variables). Specifically, the logistic regression is 
applied to 2 populations: a general population including all patients and the 
specific group of patients targeted by the CMS penalty (characterized as ages 65 
or older with select conditions). The 2 resulting logistic regression models 
have a higher sensitivity rate compared to the sensitivity of the LACE index. 
The C statistic values of the model applied to both populations demonstrate 
moderate levels of predictive power. The authors also build an economic model to 
demonstrate the potential financial impact of the use of the model for targeting 
high-risk patients in a sample hospital and demonstrate that, on balance, 
whether the hospital gains or loses from reducing readmissions depends on its 
margin and the extent of its readmission penalties.