The Effect of Frailty on Discharge Location for Medicare Beneficiaries After Acute Stroke.

OBJECTIVE: To examine the effect of frailty on poststroke discharge location with respect to stroke severity and create a risk-adjusted model for understanding the effects of frailty on discharge to an inpatient rehabilitation facility. DESIGN: Retrospective cohort. SETTING: A 2014 5% Medicare sample. PARTICIPANTS: Patients hospitalized for a first-time acute ischemic stroke (N=7258). INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: A prehospitalization 6-month baseline was used to calculate a frailty score. Logistic regression to predict odds of discharge to inpatient rehabilitation was used to calculate for 3 levels of baseline frailty, controlling for patient demographics, stroke severity, and comorbidities. RESULTS: About 1603 patients were discharged to inpatient rehabilitation. Patients who were nonfrail (odds ratio [OR] 1.716; 95% confidence interval [95% CI], 1.463-2.013) or prefrail (OR 1.519; 95% CI, 1.296-1.779) were more likely to be discharged to inpatient rehabilitation. The final logistic regression model had a C-statistic of 0.63. Most of the patients discharged to inpatient rehabilitation were nonfrail (44.2%) and had moderate strokes (38.9%). Individuals who were frail and suffered a moderate (OR 0.78; 95% CI, 0.558-1.091) or severe stroke (OR 0.509; 95% CI, 0.358-0.721) were less likely to be discharged to an inpatient rehabilitation facility. CONCLUSIONS: A lack of a claims-based measure for prestroke functional ability makes it difficult to understand discharge decision-making patterns for individuals' poststroke. Prestroke frailty was found to have a significant effect on predicating inpatient rehabilitation discharge after an acute stroke when controlling for stroke severity, comorbidities, and age. Further investigation is warranted to examine differences in rehabilitation utilization based on frailty and to quantify the effect of rehabilitation on frailty status in individuals poststroke.

Effect of frailty on resource use and cost for Medicare patients.

Aim: The effects of frailty and multiple chronic conditions (MCCs) on cost of care are rarely disentangled in archival data studies. We identify the marginal contribution of frailty to medical care cost estimates using Medicare data. Materials & methods: Use of the Faurot frailty score to identify differences in acute medical events and cost of care for patients, controlling for MCCs and medication use. Results: Estimated marginal cost of frailty was US$10,690 after controlling for demographics, comorbid conditions, polypharmacy and use of potentially inappropriate medications. Conclusion: Frailty contributes greatly to cost of care, but while often correlated, is not synonymous with MCCs. Thus, it is important to control separately for frailty in studies that compare medical care use and cost.