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An improved comorbidity summary score for measuring disease burden and predicting mortality with applications to two national cohorts

Author

Listed:
  • Ralph C. Ward
  • Leonard Egede
  • Viswanathan Ramakrishnan
  • Lewis Frey
  • Robert Neal Axon
  • Clara Libby E. Dismuke
  • Kelly J. Hunt
  • Mulugeta Gebregziabher

Abstract

Research involving administrative healthcare data to study patient outcomes requires the investigator to account for the patient’s disease burden in order to reduce the potential for biased results. Here we develop a comorbidity summary score based on variable importance measures derived from several statistical and machine learning methods and show it has superior predictive performance to the Elixhauser and Charlson indices when used to predict 1-year, 5-year, and 10-year mortality. We used two large Veterans Administration cohorts to develop and validate the summary score and compared predictive performance using the area under ROC curve (AUC) and the Brier score.

Suggested Citation

  • Ralph C. Ward & Leonard Egede & Viswanathan Ramakrishnan & Lewis Frey & Robert Neal Axon & Clara Libby E. Dismuke & Kelly J. Hunt & Mulugeta Gebregziabher, 2019. "An improved comorbidity summary score for measuring disease burden and predicting mortality with applications to two national cohorts," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 48(18), pages 4642-4655, September.
    • Handle: RePEc:taf:lstaxx:v:48:y:2019:i:18:p:4642-4655
    DOI: 10.1080/03610926.2018.1498896
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