Abstract
OBJECTIVE Despite persisting questions regarding its appropriateness, 30-day readmission is an increasingly common quality metric used to influence hospital compensation in the United States. However, there is currently insufficient evidence to identify which patients are at highest risk for readmission after aneurysmal subarachnoid hemorrhage (SAH). The objective of this study was to identify predictors of 30-day readmission after SAH, to focus preventative efforts, and to provide guidance to funding agencies seeking to risk-adjust comparisons among hospitals. METHODS The authors performed a case-control study of 30-day readmission among aneurysmal SAH patients treated at a single center between 2003 and 2013. To control for geographic distance from the hospital and year of treatment, the authors randomly matched each case (30-day readmission) with approximately 2 SAH controls (no readmission) based on home ZIP code and treatment year. They evaluated variables related to patient demographics, socioeconomic characteristics, comorbidities, presentation severity (e.g., Hunt and Hess grade), and clinical course (e.g., need for gastrostomy or tracheostomy, length of stay). Conditional logistic regression was used to identify significant predictors, accounting for the matched design of the study. RESULTS Among 82 SAH patients with unplanned 30-day readmission, the authors matched 78 patients with 153 nonreadmitted controls. Age, demographics, and socioeconomic factors were not associated with readmission. In univariate analysis, multiple variables were significantly associated with readmission, including Hunt and Hess grade (OR 3.0 for Grade IV/V vs I/II), need for gastrostomy placement (OR 2.0), length of hospital stay (OR 1.03 per day), discharge disposition (OR 3.2 for skilled nursing vs other disposition), and Charlson Comorbidity Index (OR 2.3 for score ≥ 2 vs 0). However, the only significant predictor in the multivariate analysis was discharge to a skilled nursing facility (OR 3.2), and the final model was sensitive to criteria used to enter and retain variables. Furthermore, despite the significant association between discharge disposition and readmission, less than 25% of readmitted patients were discharged to a skilled nursing facility. CONCLUSIONS Although discharge disposition remained significant in multivariate analysis, most routinely collected variables appeared to be weak independent predictors of 30-day readmission after SAH. Consequently, hospitals interested in decreasing readmission rates may consider multifaceted, cost-efficient interventions that can be broadly applied to most if not all SAH patients.