title: The predictability of claim-data-based comorbidity-adjusted models could be improved by using medication data.
abstract: BACKGROUND
  Recently, claim-data-based comorbidity-adjusted methods such as the Charlson index and the Elixhauser comorbidity measures have been widely used among researchers. At the same time, there have been an increasing number of attempts to improve the predictability of comorbidity-adjusted models. We tried to improve the predictability of models using the Charlson and Elixhauser indices by using medication data; specifically, we used medication data to estimate omitted comorbidities in the claim data.
  
  
  METHODS
  We selected twelve major diseases (other than malignancies) that caused large numbers of in-hospital mortalities during 2008 in hospitals with 700 or more beds in South Korea. Then, we constructed prediction models for in-hospital mortality using the Charlson index and Elixhauser comorbidity measures, respectively. Inferring missed comorbidities using medication data, we built enhanced Charlson and Elixhauser comorbidity-measures-based prediction models, which included comorbidities inferred from medication data. We then compared the c-statistics of each model.
  
  
  RESULTS
  247,712 admission cases were enrolled. 55 generic drugs were used to infer 8 out of 17 Charlson comorbidities, and 106 generic drugs were used to infer 14 out of 31 Elixhauser comorbidities. Before the inclusion of comorbidities inferred from medication data, the c-statistics of models using the Charlson index were 0.633-0.882 and those of the Elixhauser index were 0.699-0.917. After the inclusion of comorbidities inferred from medication data, 9 of 12 models using the Charlson index and all of the models using the Elixhauser comorbidity measures were improved in predictability but, the differences were relatively small.
  
  
  CONCLUSIONS
  Prediction models using Charlson index or Elixhauser comorbidity measures might be improved by including comorbidities inferred from medication data.
authors.forename: Ji Hwan
authors.initials: JH
authors.lastname: Bang
authors.forename: Soo-Hee
authors.initials: SH
authors.lastname: Hwang
authors.forename: Eun-Jung
authors.initials: EJ
authors.lastname: Lee
authors.affiliation: Department of Health Policy and Management, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul 110-799, Korea. yoonkim@snu.ac.kr.
authors.forename: Yoon
authors.initials: Y
authors.lastname: Kim
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D002648:Child; D002675:Child, Preschool; D015897:Comorbidity; D011307:Drug Prescriptions / Q000706:statistics & numerical data*; D005260:Female; D017052:Hospital Mortality; D006760:Hospitalization / Q000706:statistics & numerical data*; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D011237:Predictive Value of Tests; D056910:Republic of Korea; D012720:Severity of Illness Index; D055815:Young Adult