Abstract
BACKGROUND: Reducing hospital readmissions is a key component of reforms for stroke care. Current readmission prediction models lack accuracy and are limited by data being from only acute hospitalizations. We hypothesized that patient-level factors from a nationwide post-acute care database would improve prediction modeling.
METHODS AND RESULTS: Medicare inpatient claims for the year 2008 that used International Classification of Diseases, Ninth Revision codes were used to identify ischemic stroke patients older than age 65. Unique individuals were linked to comprehensive post-acute care assessments through use of the Minimum Data Set (MDS). Logistic regression was used to construct risk-adjusted readmission models. Covariates were derived from MDS variables. Among 39 178 patients directly admitted to nursing homes after hospitalization due to acute stroke, there were 29 338 (75%) with complete MDS assessments. Crude rates of readmission and death at 30 days were 8448 (21%) and 2791 (7%), respectively. Risk-adjusted models identified multiple independent predictors of all-cause 30-day readmission. Model performance of the readmission model using MDS data had a c-statistic of 0.65 (95% CI 0.64 to 0.66). Higher levels of social engagement, a marker of nursing home quality, were associated with progressively lower odds of readmission (odds ratio 0.71, 95% CI 0.55 to 0.92).
CONCLUSIONS: Individual clinical characteristics from the post-acute care setting resulted in only modest improvement in the c-statistic relative to previous models that used only Medicare Part A data. Individual-level characteristics do not sufficiently account for the risk of acute hospital readmission.