title: Comparative Performance of Diagnosis-based and Prescription-based Comorbidity Scores to Predict Health-related Quality of Life.
abstract: OBJECTIVE
  To compare the performance of the health-related quality of life-comorbidity index (HRQoL-CI) with the diagnosis-based Charlson, Elixhauser, and combined comorbidity scores and the prescription-based chronic disease score (CDS) in predicting HRQoL in Agency of Healthcare Research and Quality priority conditions (asthma, breast cancer, diabetes, and heart failure).
  
  
  METHODS
  The Medical Expenditure Panel Survey (2005 and 2007-2011) data was used for this retrospective study. Four disease-specific cohorts were developed that included adult patients (age 18 y and above) with the particular disease condition. The outcome HRQoL [physical component score (PCS) and mental component score (MCS)] was measured using the Short Form Health Survey, Version 2 (SF-12v2). Multiple linear regression analyses were conducted with the PCS and MCS as dependent variables. Comorbidity scores were compared using adjusted R.
  
  
  RESULTS
  Of 140,046 adult participants, the study cohort included 7436 asthma (5.3%), 1054 breast cancer (0.8%), 13,829 diabetes (9.9%), and 937 heart failure (0.7%) patients. Among individual scores, HRQoL-CI was best at predicting PCS and MCS. Adding prescription-based comorbidity scores to HRQoL-CI in the same model improved prediction of PCS and MCS. HRQoL-CI+CDS performed the best in predicting PCS (adjusted R): asthma (43.7%), breast cancer (31.7%), diabetes (32.7%), and heart failure (20.0%). HRQoL-CI+CDS and Elixhauser+CDS had superior and comparable performance in predicting MCS (adjusted R): asthma (HRQoL-CI+CDS=20.1%; Elixhauser+CDS=19.6%), breast cancer (HRQoL-CI+CDS=12.9%; Elixhauser+CDS=14.1%), diabetes (HRQoL-CI+CDS=17.7%; Elixhauser+CDS=17.7%), and heart failure (HRQoL-CI+CDS=18.1%; Elixhauser+CDS=17.7%).
  
  
  CONCLUSIONS
  HRQoL-CI performed best in predicting HRQoL. Combining prescription-based scores to diagnosis-based scores improved the prediction of HRQoL.
authors.affiliation: *Department of Surgery, University of Texas Medical Branch, Galveston †Department of Pharmaceutical Health Outcomes and Policy, University of Houston, Houston, TX ‡Department of Surgery, University of Arizona, Tuscon, AZ.
authors.forename: Hemalkumar B
authors.initials: HB
authors.lastname: Mehta
authors.forename: Sneha D
authors.initials: SD
authors.lastname: Sura
authors.forename: Manvi
authors.initials: M
authors.lastname: Sharma
authors.forename: Michael L
authors.initials: ML
authors.lastname: Johnson
authors.forename: Taylor S
authors.initials: TS
authors.lastname: Riall
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D001249:Asthma / Q000503:physiopathology / Q000523:psychology; D001943:Breast Neoplasms / Q000503:physiopathology / Q000523:psychology; D002908:Chronic Disease; D015897:Comorbidity; D003625:Data Collection / Q000379:methods*; D003920:Diabetes Mellitus / Q000503:physiopathology / Q000523:psychology; D005260:Female; D006304:Health Status; D006333:Heart Failure / Q000503:physiopathology / Q000523:psychology; D006801:Humans; D008297:Male; D008875:Middle Aged; D011788:Quality of Life; D012189:Retrospective Studies; D055815:Young Adult