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Evaluation of code-based algorithms to identify pulmonary arterial hypertension and chronic thromboembolic pulmonary hypertension patients in large administrative databases
Large administrative healthcare (including insurance claims) databases are used for various retrospective real-world evidence studies. However, in pulmonary arterial hypertension and chronic thromboembolic pulmonary hypertension, identifying patients retrospectively based on administrative codes rem...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7675881/ https://www.ncbi.nlm.nih.gov/pubmed/33240487 http://dx.doi.org/10.1177/2045894020961713 |
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author | Sprecher, Viviane P. Didden, Eva-Maria Swerdel, Joel N. Muller, Audrey |
author_facet | Sprecher, Viviane P. Didden, Eva-Maria Swerdel, Joel N. Muller, Audrey |
author_sort | Sprecher, Viviane P. |
collection | PubMed |
description | Large administrative healthcare (including insurance claims) databases are used for various retrospective real-world evidence studies. However, in pulmonary arterial hypertension and chronic thromboembolic pulmonary hypertension, identifying patients retrospectively based on administrative codes remains challenging, as it relies on code combinations (algorithms) and the accuracy for patient identification of most of them is unknown. This study aimed to assess the performance of various algorithms in correctly identifying patients with pulmonary arterial hypertension or chronic thromboembolic pulmonary hypertension in administrative databases. A systematic literature review was performed to find publications detailing code-based algorithms used to identify pulmonary arterial hypertension and chronic thromboembolic pulmonary hypertension patients. PheValuator, a diagnostic predictive modelling tool, was applied to three US claims databases, yielding models that estimated the probability of a patient having the disease. These models were used to evaluate the performance characteristics of selected pulmonary arterial hypertension and chronic thromboembolic pulmonary hypertension algorithms. With increasing algorithm complexity, average positive predictive value increased (pulmonary arterial hypertension: 13.4–66.0%; chronic thromboembolic pulmonary hypertension: 10.3–75.1%) and average sensitivity decreased (pulmonary arterial hypertension: 61.5–2.7%; chronic thromboembolic pulmonary hypertension: 20.7–0.2%). Specificities and negative predictive values were high (≥97.5%) for all algorithms. Several of the algorithms performed well overall when considering all of these four performance parameters, and all algorithms performed with similar accuracy across the three claims databases studied, even though most were designed for patient identification in a specific database. Therefore, it is the objective of a study that will determine which algorithm may be most suitable; one- or two-component algorithms are most inclusive and three- or four-component algorithms identify most precise pulmonary arterial hypertension or chronic thromboembolic pulmonary hypertension populations, respectively. |
format | Online Article Text |
id | pubmed-7675881 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-76758812020-11-24 Evaluation of code-based algorithms to identify pulmonary arterial hypertension and chronic thromboembolic pulmonary hypertension patients in large administrative databases Sprecher, Viviane P. Didden, Eva-Maria Swerdel, Joel N. Muller, Audrey Pulm Circ Research Article Large administrative healthcare (including insurance claims) databases are used for various retrospective real-world evidence studies. However, in pulmonary arterial hypertension and chronic thromboembolic pulmonary hypertension, identifying patients retrospectively based on administrative codes remains challenging, as it relies on code combinations (algorithms) and the accuracy for patient identification of most of them is unknown. This study aimed to assess the performance of various algorithms in correctly identifying patients with pulmonary arterial hypertension or chronic thromboembolic pulmonary hypertension in administrative databases. A systematic literature review was performed to find publications detailing code-based algorithms used to identify pulmonary arterial hypertension and chronic thromboembolic pulmonary hypertension patients. PheValuator, a diagnostic predictive modelling tool, was applied to three US claims databases, yielding models that estimated the probability of a patient having the disease. These models were used to evaluate the performance characteristics of selected pulmonary arterial hypertension and chronic thromboembolic pulmonary hypertension algorithms. With increasing algorithm complexity, average positive predictive value increased (pulmonary arterial hypertension: 13.4–66.0%; chronic thromboembolic pulmonary hypertension: 10.3–75.1%) and average sensitivity decreased (pulmonary arterial hypertension: 61.5–2.7%; chronic thromboembolic pulmonary hypertension: 20.7–0.2%). Specificities and negative predictive values were high (≥97.5%) for all algorithms. Several of the algorithms performed well overall when considering all of these four performance parameters, and all algorithms performed with similar accuracy across the three claims databases studied, even though most were designed for patient identification in a specific database. Therefore, it is the objective of a study that will determine which algorithm may be most suitable; one- or two-component algorithms are most inclusive and three- or four-component algorithms identify most precise pulmonary arterial hypertension or chronic thromboembolic pulmonary hypertension populations, respectively. SAGE Publications 2020-11-10 /pmc/articles/PMC7675881/ /pubmed/33240487 http://dx.doi.org/10.1177/2045894020961713 Text en © The Author(s) 2020 https://creativecommons.org/licenses/by-nc/4.0/ Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Research Article Sprecher, Viviane P. Didden, Eva-Maria Swerdel, Joel N. Muller, Audrey Evaluation of code-based algorithms to identify pulmonary arterial hypertension and chronic thromboembolic pulmonary hypertension patients in large administrative databases |
title | Evaluation of code-based algorithms to identify pulmonary arterial
hypertension and chronic thromboembolic pulmonary hypertension patients in large
administrative databases |
title_full | Evaluation of code-based algorithms to identify pulmonary arterial
hypertension and chronic thromboembolic pulmonary hypertension patients in large
administrative databases |
title_fullStr | Evaluation of code-based algorithms to identify pulmonary arterial
hypertension and chronic thromboembolic pulmonary hypertension patients in large
administrative databases |
title_full_unstemmed | Evaluation of code-based algorithms to identify pulmonary arterial
hypertension and chronic thromboembolic pulmonary hypertension patients in large
administrative databases |
title_short | Evaluation of code-based algorithms to identify pulmonary arterial
hypertension and chronic thromboembolic pulmonary hypertension patients in large
administrative databases |
title_sort | evaluation of code-based algorithms to identify pulmonary arterial
hypertension and chronic thromboembolic pulmonary hypertension patients in large
administrative databases |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7675881/ https://www.ncbi.nlm.nih.gov/pubmed/33240487 http://dx.doi.org/10.1177/2045894020961713 |
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