ArticleHealthcare informatics research2020
Toolkit to Compute Time-Based Elixhauser Comorbidity Indices and Extension to Common Data Models.
Article in Healthcare informatics research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Common data models and data standards for tabular health data: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Bayesian Analysis of Postoperative Complication Risk Associated With Preoperative Exposure to Fine Particulate Matter: A Single-Center Cohort Study.Acta anaesthesiologica Scandinavica · 2026Article
- Registered nurse effect on long length of stay in the heart failure hospitalizations of African Americans.PloS one · 2025Article
- The association of workforce configurations with length of stay and charges in hospitalized patients with congestive heart failure.Frontiers in health services · 2024Article
- Simple Excel and ICD-10 based dataset calculator for the Charlson and Elixhauser comorbidity indices.BMC medical research methodology · 2022Article
- Patient characteristics and procedural outcomes of moderate sedation for endoscopic procedures in patients with obesity: A retrospective, propensity score-matched study.Endoscopy international open · 2021Article
- Machine Learning Approach to Optimize Sedation Use in Endoscopic Procedures.Studies in health technology and informatics · 2021Article
- Role of Machine Learning Techniques to Tackle the COVID-19 Crisis: Systematic Review.JMIR medical informatics · 2021Review
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Authors and funding
11 authors.
Funding
Abstract
objectiveThe 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).
methodsA 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.
resultsAt 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.
conclusionTECI 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.
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