Healthc Inform Res.  2020 Jul;26(3):193-200. 10.4258/hir.2020.26.3.193.

Toolkit to Compute Time-Based Elixhauser Comorbidity Indices and Extension to Common Data Models

Affiliations
  • 1Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA
  • 2Department of Population Health Sciences, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
  • 3Department of Information Technology, University of Arkansas for Medical Sciences, Little Rock, AR, USA
  • 4Department of Surgery, University of Arkansas for Medical Sciences, Little Rock, AR, USA
  • 5Department of Health Policy and Management, University of Arkansas for Medical Sciences, Little Rock, AR, USA

Abstract


Objectives
The time-dependent study of comorbidities provides insight into disease progression and trajectory. We hypothesize that understanding longitudinal disease characteristics can lead to more timely intervention and improve clinical outcomes. As a first step, we developed an efficient and easy-to-install toolkit, the Time-based Elixhauser Comorbidity Index (TECI), which pre-calculates time-based Elixhauser comorbidities and can be extended to common data models (CDMs).
Methods
A Structured Query Language (SQL)-based toolkit, TECI, was built to pre-calculate time-specific Elixhauser comorbidity indices using data from a clinical data repository (CDR). Then it was extended to the Informatics for Integrating Biology and the Bedside (I2B2) and Observational Medical Outcomes Partnership (OMOP) CDMs.
Results
At the University of Arkansas for Medical Sciences (UAMS), the TECI toolkit was successfully installed to compute the indices from CDR data, and the scores were integrated into the I2B2 and OMOP CDMs. Comorbidity scores calculated by TECI were validated against: scores available in the 2015 quarter 1–3 Nationwide Readmissions Database (NRD) and scores calculated using the comorbidities using a previously validated algorithm on the 2015 quarter 4 NRD. Furthermore, TECI identified 18,846 UAMS patients that had changes in comorbidity scores over time (year 2013 to 2019). Comorbidities for a random sample of patients were independently reviewed, and in all cases, the results were found to be 100% accurate.
Conclusions
TECI facilitates the study of comorbidities within a time-dependent context, allowing better understanding of disease associations and trajectories, which has the potential to improve clinical outcomes.

Keyword

Comorbidity; Multimorbidity; Data Warehouse; Quality of Care; Retrospective Studies; Risk Assessments; Risk Adjustment
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