title: Apples and Oranges: Four Definitions of Multiple Chronic Conditions and their Relationship to 30-Day Hospital Readmission.
abstract: OBJECTIVE
  To determine the extent of agreement between four commonly used definitions of multiple chronic conditions (MCCs) and compare each definition's ability to predict 30-day hospital readmissions.
  
  
  METHODS
  Retrospective cohort study.
  
  
  METHODS
  National Medicare claims data.
  
  
  METHODS
  Random sample of Medicare beneficiaries discharged from the hospital from 2005 to 2009 (n = 710,609).
  
  
  METHODS
  Baseline chronic conditions were determined for each participant using four definitions of MCC. The primary outcome was all-cause 30-day hospital readmission. Agreement between MCC definitions was measured, and sensitivities and specificities for each definition's ability to identify patients experiencing a future readmission were calculated. Logistic regression was used to assess the ability of each MCC definition to predict 30-day hospital readmission.
  
  
  RESULTS
  The sample prevalence of hospitalized Medicare beneficiaries with two or more chronic conditions ranged from 18.6% (Johns Hopkins Adjusted Clinical Groups (ACG) Case-Mix System software) to 92.9% (Medicare Chronic Condition Warehouse (CCW)). There was slight to moderate agreement (kappa = 0.03-0.44) between pair-wise combinations of MCC definitions. CCW-defined MCC was the most sensitive (sensitivity 95.4%, specificity 7.4%), and ACG-defined MCC was the most specific (sensitivity 32.7%, specificity 83.2%) predictor of being readmitted. In the fully adjusted model, the risk of readmission was higher for those with chronic condition Special Needs Plan (c-SNP)-defined MCCs (odds ratio (OR) = 1.50, 95% confidence interval (CI) = 1.47-1.52), Charlson Comorbidity Index-defined MCCs (OR = 1.45, 95% CI = 1.42-1.47), ACG-defined MCCs (OR = 1.22, 95% CI = 1.19-1.25), and CCW-defined MCCs (OR = 1.15, 95% CI = 1.11-1.19) than for those without MCCs.
  
  
  CONCLUSIONS
  MCC definitions demonstrate poor agreement and should not be used interchangeably. The two definitions with the greatest agreement (CCI, c-SNP) were also the best predictors of 30-day hospital readmissions.
authors.affiliation: Geriatric Research, Education, and Clinical Center, Department of Veterans Affairs, William S. Middleton Memorial Veterans Affairs Hospital, Madison, Wisconsin.|Division of Geriatrics, Department of Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin.
authors.forename: Melissa
authors.initials: M
authors.lastname: Dattalo
authors.affiliation: Department of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin.
authors.forename: Eva
authors.initials: E
authors.lastname: DuGoff
authors.affiliation: Department of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin.
authors.forename: Katie
authors.initials: K
authors.lastname: Ronk
authors.affiliation: Geriatric Research, Education, and Clinical Center, Department of Veterans Affairs, William S. Middleton Memorial Veterans Affairs Hospital, Madison, Wisconsin.|Division of Geriatrics, Department of Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin.|School of Pharmacy, University of Wisconsin, Madison, Wisconsin.
authors.forename: Korey
authors.initials: K
authors.lastname: Kennelty
authors.affiliation: Geriatric Research, Education, and Clinical Center, Department of Veterans Affairs, William S. Middleton Memorial Veterans Affairs Hospital, Madison, Wisconsin.|School of Nursing, University of Wisconsin, Madison, Wisconsin.
authors.forename: Andrea
authors.initials: A
authors.lastname: Gilmore-Bykovskyi
authors.affiliation: Geriatric Research, Education, and Clinical Center, Department of Veterans Affairs, William S. Middleton Memorial Veterans Affairs Hospital, Madison, Wisconsin.|Division of Geriatrics, Department of Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin.|School of Pharmacy, University of Wisconsin, Madison, Wisconsin.|School of Nursing, University of Wisconsin, Madison, Wisconsin.
authors.forename: Amy J
authors.initials: AJ
authors.lastname: Kind
mesh_terms: D000368:Aged; D005260:Female; D006801:Humans; D008297:Male; D006278:Medicare; D000071069:Multiple Chronic Conditions / Q000145:classification* / Q000453:epidemiology; D010359:Patient Readmission / Q000706:statistics & numerical data*; D015995:Prevalence; D012189:Retrospective Studies; D012307:Risk Factors; D014481:United States / Q000453:epidemiology
keywords: Medicare; chronic care management; multimorbidity; multiple chronic conditions; readmissions