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Toolkit to Compute Time-Based Elixhauser Comorbidity Indices and Extension to Common Data Models
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 ea...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Korean Society of Medical Informatics
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7438698/ https://www.ncbi.nlm.nih.gov/pubmed/32819037 http://dx.doi.org/10.4258/hir.2020.26.3.193 |
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author | Syed, Shorabuddin Baghal, Ahmad Prior, Fred Zozus, Meredith Al-Shukri, Shaymaa Syeda, Hafsa Bareen Garza, Maryam Begum, Salma Gates, Kim Syed, Mahanazuddin Sexton, Kevin W. |
author_facet | Syed, Shorabuddin Baghal, Ahmad Prior, Fred Zozus, Meredith Al-Shukri, Shaymaa Syeda, Hafsa Bareen Garza, Maryam Begum, Salma Gates, Kim Syed, Mahanazuddin Sexton, Kevin W. |
author_sort | Syed, Shorabuddin |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7438698 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Korean Society of Medical Informatics |
record_format | MEDLINE/PubMed |
spelling | pubmed-74386982020-08-25 Toolkit to Compute Time-Based Elixhauser Comorbidity Indices and Extension to Common Data Models Syed, Shorabuddin Baghal, Ahmad Prior, Fred Zozus, Meredith Al-Shukri, Shaymaa Syeda, Hafsa Bareen Garza, Maryam Begum, Salma Gates, Kim Syed, Mahanazuddin Sexton, Kevin W. Healthc Inform Res Original Article 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. Korean Society of Medical Informatics 2020-07 2020-07-31 /pmc/articles/PMC7438698/ /pubmed/32819037 http://dx.doi.org/10.4258/hir.2020.26.3.193 Text en © 2020 The Korean Society of Medical Informatics This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Article Syed, Shorabuddin Baghal, Ahmad Prior, Fred Zozus, Meredith Al-Shukri, Shaymaa Syeda, Hafsa Bareen Garza, Maryam Begum, Salma Gates, Kim Syed, Mahanazuddin Sexton, Kevin W. Toolkit to Compute Time-Based Elixhauser Comorbidity Indices and Extension to Common Data Models |
title | Toolkit to Compute Time-Based Elixhauser Comorbidity Indices and Extension to Common Data Models |
title_full | Toolkit to Compute Time-Based Elixhauser Comorbidity Indices and Extension to Common Data Models |
title_fullStr | Toolkit to Compute Time-Based Elixhauser Comorbidity Indices and Extension to Common Data Models |
title_full_unstemmed | Toolkit to Compute Time-Based Elixhauser Comorbidity Indices and Extension to Common Data Models |
title_short | Toolkit to Compute Time-Based Elixhauser Comorbidity Indices and Extension to Common Data Models |
title_sort | toolkit to compute time-based elixhauser comorbidity indices and extension to common data models |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7438698/ https://www.ncbi.nlm.nih.gov/pubmed/32819037 http://dx.doi.org/10.4258/hir.2020.26.3.193 |
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