title: External validation and comparison of two variants of the Elixhauser comorbidity measures for all-cause mortality.
abstract: Assessing prevalent comorbidities is a common approach in health research for identifying clinical differences between individuals. The objective of this study was to validate and compare the predictive performance of two variants of the Elixhauser comorbidity measures (ECM) for inhospital mortality at index and at 1-year in the Cerner Health Facts® (HF) U.S.
  
  
  METHODS
  We estimated the prevalence of select comorbidities for individuals 18 to 89 years of age who received care at Cerner contributing health facilities between 2002 and 2011 using the AHRQ (version 3.7) and the Quan Enhanced ICD-9-CM ECMs. External validation of the ECMs was assessed with measures of discrimination [c-statistics], calibration [Hosmer-Lemeshow goodness-of-fit test, Brier Score, calibration curves], added predictive ability [Net Reclassification Improvement], and overall model performance [R2]. Of 3,273,298 patients with a mean age of 43.9 years and a female composition of 53.8%, 1.0% died during their index encounter and 1.5% were deceased at 1-year. Calibration measures were equivalent between the two ECMs. Calibration performance was acceptable when predicting inhospital mortality at index, although recalibration is recommended for predicting inhospital mortality at 1 year. Discrimination was marginally better with the Quan ECM compared the AHRQ ECM when predicting inhospital mortality at index (cQuan = 0.887, 95% CI: 0.885-0.889 vs. cAHRQ = 0.880, 95% CI: 0.878-0.882; p < .0001) and at 1-year (cQuan = 0.884, 95% CI: 0.883-0.886 vs. cAHRQ = 0.880, 95% CI: 0.878-0.881, p < .0001). Both the Quan and the AHRQ ECMs demonstrated excellent discrimination for inhospital mortality of all-causes in Cerner Health Facts®, a HIPAA compliant observational research and privacy-protected data warehouse. While differences in discrimination performance between the ECMs were statistically significant, they are not likely clinically meaningful.
authors.affiliation: McLaughlin Centre for Population Health Risk Assessment, University of Ottawa, Ottawa, Ontario, Canada.|School of Epidemiology, Public Health and Preventive Medicine, University of Ottawa, Ottawa, Ontario, Canada.
authors.forename: Yannick
authors.identifier: http://orcid.org/0000-0001-7300-9549
authors.initials: Y
authors.lastname: Fortin
authors.affiliation: McLaughlin Centre for Population Health Risk Assessment, University of Ottawa, Ottawa, Ontario, Canada.|School of Epidemiology, Public Health and Preventive Medicine, University of Ottawa, Ottawa, Ontario, Canada.|Fulbright Canada Student, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
authors.forename: James A G
authors.initials: JA
authors.lastname: Crispo
authors.affiliation: School of Epidemiology, Public Health and Preventive Medicine, University of Ottawa, Ottawa, Ontario, Canada.|Canadian Population Health Initiative (CPHI), Canadian Institute for Health Information (CIHI), Ottawa, Ontario, Canada.|Institute for Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada.
authors.forename: Deborah
authors.initials: D
authors.lastname: Cohen
authors.affiliation: Cerner Corporation, Kansas City, Missouri, United States of America.
authors.forename: Douglas S
authors.initials: DS
authors.lastname: McNair
authors.affiliation: McLaughlin Centre for Population Health Risk Assessment, University of Ottawa, Ottawa, Ontario, Canada.|Risk Sciences International, Ottawa, Ontario, Canada.
authors.forename: Donald R
authors.initials: DR
authors.lastname: Mattison
authors.affiliation: McLaughlin Centre for Population Health Risk Assessment, University of Ottawa, Ottawa, Ontario, Canada.|School of Epidemiology, Public Health and Preventive Medicine, University of Ottawa, Ottawa, Ontario, Canada.|Risk Sciences International, Ottawa, Ontario, Canada.
authors.forename: Daniel
authors.initials: D
authors.lastname: Krewski
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D015897:Comorbidity; D005260:Female; D017052:Hospital Mortality; D006801:Humans; D038801:International Classification of Diseases; D008297:Male; D008875:Middle Aged; D012372:ROC Curve; D018570:Risk Assessment; D055815:Young Adult