title: Readmissions after unauthorized discharges in the cardiovascular setting.
abstract: BACKGROUND
  Patients who left against medical advice (AMA) may be at higher risk for a hospital readmission if the unauthorized discharge was premature. The objective of this study is to examine the relationship between discharges AMA from nonfederal acute care hospitals and cardiovascular disease (CVD) hospital readmissions while addressing bias due to potential confounding, selection, and hospital-level clustering.
  
  
  METHODS
  This cross-sectional study used hospital discharge data covering the period between 2000 and 2005. The outcome variables captured readmissions for a CVD-related condition following an index CVD-related discharge. The covariate of interest was an indicator for a discharge AMA in the index hospitalization. The relationship between discharges AMA and 7-day, 31-day, and 180-day readmissions was examined using multivariate models with adjustment for clustering and selection bias.
  
  
  RESULTS
  The sample included 348,572 patients, of which 7001 (2%), 19,779 (6%), and 48,855 (14%) were readmitted within 7, 31, and 180 days, respectively. The percentage of patients who were readmitted (7 days; 31 days; 180 days) was higher among the AMA group versus the non-AMA group (2.2% vs. 1%, P < 0.002; 1.3% vs. 1%, P < 0.001; 1.2% vs. 1%, P = 0.02). The adjusted odds of a CVD-related readmission to the same hospital within 7 days, 31 days, and 180 days were 154% (P < 0.001), 51% (P < 0.001), and 19% (P = 0.004) higher, respectively, for patients who left AMA. Results were robust in examining readmissions to any hospital as well as corrections for observable selection bias through propensity score analysis.
  
  
  CONCLUSIONS
  A discharge AMA among patients with a discharge diagnosis for CVD during the index hospitalization was predictive of CVD-related readmissions. The strength of the association between a discharge AMA and readmission was greatest within the first week after discharge.
authors.affiliation: Department of Pharmaceutical Health Services Research, School of Pharmacy, University of Maryland, Baltimore, MD 21201, USA. eonukwug@rx.umaryland.edu
authors.forename: Eberechukwu
authors.initials: E
authors.lastname: Onukwugha
authors.forename: C Daniel
authors.initials: CD
authors.lastname: Mullins
authors.forename: F Ellen
authors.initials: FE
authors.lastname: Loh
authors.forename: Elijah
authors.initials: E
authors.lastname: Saunders
authors.forename: Fadia T
authors.initials: FT
authors.lastname: Shaya
authors.forename: Matthew R
authors.initials: MR
authors.lastname: Weir
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002318:Cardiovascular Diseases / Q000453:epidemiology / Q000628:therapy; D016009:Chi-Square Distribution; D016000:Cluster Analysis; D015986:Confounding Factors, Epidemiologic; D003430:Cross-Sectional Studies; D005260:Female; D019538:Health Care Surveys; D006801:Humans; D016015:Logistic Models; D008297:Male; D008396:Maryland / Q000453:epidemiology; D008875:Middle Aged; D015999:Multivariate Analysis; D017063:Outcome Assessment, Health Care; D010349:Patient Compliance / Q000706:statistics & numerical data*; D010351:Patient Discharge / Q000706:statistics & numerical data*; D010359:Patient Readmission / Q000706:statistics & numerical data*; D015983:Selection Bias; D013997:Time Factors