Bouncing back: patterns and predictors of complicated transitions 30 days after hospitalization for acute ischemic stroke.
OBJECTIVE
To identify predictors of complicated transitions within 30 days after discharge from hospitalization for acute stroke.


METHODS
Retrospective analysis of administrative data.


METHODS
Four hundred twenty-two hospitals in the southern and eastern United States.


METHODS
Thirty-nine thousand three hundred eighty-four Medicare beneficiaries aged 65 and older discharged after acute ischemic stroke from 1998 to 2000.


METHODS
Complicated transition, defined as movement from less- to more-intensive care setting after hospital discharge, with hospital being most intensive and home without home health care being least intensive.


RESULTS
Twenty percent of patients experienced at least one complicated transition; 16% of those experienced more than one complicated transition. After adjustment using logistic regression, factors predicting any complicated transition included older age, African-American race, Medicaid enrollment, prior hospitalization, gastrostomy tube, chronic disease, length of stay, and discharge site. Patients with multiple complicated transitions were more likely to be African American (odds ratio (OR)=1.38, 95% confidence interval (CI)=1.13-1.68), be male (OR=1.21, 95% CI=1.04-1.40), have a prior diagnosis of fluid and electrolyte disorder (e.g., dehydration) (OR=1.23, 95% CI=1.07-1.43), have a prior hospitalization (OR=1.18, 95% CI=1.01-1.36), and be initially discharged to a skilled-nursing facility or long-term care (OR=1.22, 95% CI=1.04-1.44) than patients with only one complicated transition. They were less likely to be initially discharged to a rehabilitation center (OR=0.71, 95% CI=0.57-0.89).


CONCLUSIONS
Significant numbers of stroke patients experience complicated transitions soon after hospital discharge. Sociodemographic factors and initial discharge site distinguish patients with multiple complicated transitions. These factors may enable prospective identification and targeting of stroke patients at risk for "bouncing back."
Kind
Amy J H
AJ
Department of Population Health Sciences, Wisconsin, Madison, Wisconsin, WI 53705, USA. ajk@medicine.wisc.edu
Smith
Maureen A
MA
Frytak
Jennifer R
JR
Finch
Michael D
MD
D000208:Acute Disease; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D002544:Cerebral Infarction / Q000150:complications* / Q000453:epidemiology / Q000534:rehabilitation; D015897:Comorbidity; D003625:Data Collection / Q000706:statistics & numerical data; D005260:Female; D006279:Health Maintenance Organizations / Q000706:statistics & numerical data; D006699:Home Care Services / Q000706:statistics & numerical data; D006801:Humans; D007902:Length of Stay / Q000706:statistics & numerical data; D008134:Long-Term Care / Q000706:statistics & numerical data; D008297:Male; D006278:Medicare / Q000706:statistics & numerical data; D010351:Patient Discharge / Q000706:statistics & numerical data*; D010359:Patient Readmission / Q000706:statistics & numerical data*; D012008:Recurrence; D012047:Rehabilitation Centers / Q000706:statistics & numerical data; D012307:Risk Factors; D012737:Sex Factors; D012866:Skilled Nursing Facilities / Q000706:statistics & numerical data; D012959:Socioeconomic Factors; D014481:United States