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

        Shorabuddin Syed,Ahmad Baghal,Fred Prior,Meredith Zozus,Shaymaa Al-Shukri,Hafsa Bareen Syeda,Maryam Garza,Salma Begum,Kim Gates,Mahanazuddin Syed,Kevin W. Sexton 대한의료정보학회 2020 Healthcare Informatics Research Vol.26 No.3

        Objectives: The time-dependent study of comorbidities provides insight into disease progression and trajectory. We hypothesizethat understanding longitudinal disease characteristics can lead to more timely intervention and improve clinicaloutcomes. 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 comorbidityindices using data from a clinical data repository (CDR). Then it was extended to the Informatics for IntegratingBiology and the Bedside (I2B2) and Observational Medical Outcomes Partnership (OMOP) CDMs. Results: At the Universityof Arkansas for Medical Sciences (UAMS), the TECI toolkit was successfully installed to compute the indices from CDRdata, and the scores were integrated into the I2B2 and OMOP CDMs. Comorbidity scores calculated by TECI were validatedagainst: scores available in the 2015 quarter 1–3 Nationwide Readmissions Database (NRD) and scores calculated usingthe comorbidities using a previously validated algorithm on the 2015 quarter 4 NRD. Furthermore, TECI identified 18,846UAMS patients that had changes in comorbidity scores over time (year 2013 to 2019). Comorbidities for a random sample ofpatients were independently reviewed, and in all cases, the results were found to be 100% accurate. Conclusions: TECI facilitatesthe study of comorbidities within a time-dependent context, allowing better understanding of disease associations andtrajectories, which has the potential to improve clinical outcomes.

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