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Evaluation of claims‐based computable phenotypes to identify heart failure patients with preserved ejection fraction

The purpose of this analysis was to develop and validate computable phenotypes for heart failure (HF) with preserved ejection fraction (HFpEF) using claims‐type measures using the Rochester Epidemiology Project. This retrospective study utilized an existing cohort of Olmsted County, Minnesota reside...

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Autores principales: Cohen, Sarah S., Roger, Véronique L., Weston, Susan A., Jiang, Ruoxiang, Movva, Naimisha, Yusuf, Akeem A., Chamberlain, Alanna M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7596663/
https://www.ncbi.nlm.nih.gov/pubmed/33124771
http://dx.doi.org/10.1002/prp2.676
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author Cohen, Sarah S.
Roger, Véronique L.
Weston, Susan A.
Jiang, Ruoxiang
Movva, Naimisha
Yusuf, Akeem A.
Chamberlain, Alanna M.
author_facet Cohen, Sarah S.
Roger, Véronique L.
Weston, Susan A.
Jiang, Ruoxiang
Movva, Naimisha
Yusuf, Akeem A.
Chamberlain, Alanna M.
author_sort Cohen, Sarah S.
collection PubMed
description The purpose of this analysis was to develop and validate computable phenotypes for heart failure (HF) with preserved ejection fraction (HFpEF) using claims‐type measures using the Rochester Epidemiology Project. This retrospective study utilized an existing cohort of Olmsted County, Minnesota residents aged ≥ 20 years diagnosed with HF between 2007 and 2015. The gold standard definition of HFpEF included meeting the validated Framingham criteria for HF and having an LVEF ≥ 50%. Computable phenotypes of claims‐type data elements (including ICD‐9/ICD‐10 diagnostic codes and lab test codes) both individually and in combinations were assessed via sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) with respect to the gold standard. In the Framingham‐validated cohort, 2,035 patients had HF; 1,172 (58%) had HFpEF. One in‐patient or two out‐patient diagnosis codes of ICD‐9 428.3X or ICD‐10 I50.3X had 46% sensitivity, 88% specificity, 84% PPV, and 54% NPV. The addition of a BNP/NT‐proBNP test code reduced sensitivity to 35% while increasing specificity to 91% (PPV = 84%, NPV = 51%). Broadening the diagnostic codes to ICD‐9 428.0, 428.3X, and 428.9/ICD‐10 I50.3X and I50.9 increased sensitivity at the expense of decreasing specificity (diagnostic code‐only model: 87% sensitivity, 8% specificity, 56% PPV, 30% NPV; diagnostic code and BNP lab code model: 61% sensitivity, 43% specificity, 60% PPV, 45% NPV). In an analysis conducted to mimic real‐world use of the computable phenotypes, any one in‐patient or out‐patient code of ICD‐9 428/ICD‐10 150 among the broader population (N = 3,755) resulted in lower PPV values compared with the Framingham cohort. However, one in‐patient or two out‐patient instances of ICD‐9 428.0, 428.9, or 428.3X/ICD‐10 150.3X or 150.9 brought the PPV values from the two cohorts closer together. While some misclassification remains, the computable phenotypes defined here may be used in claims databases to identify HFpEF patients and to gain a greater understanding of the characteristics of patients with HFpEF.
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spelling pubmed-75966632020-11-05 Evaluation of claims‐based computable phenotypes to identify heart failure patients with preserved ejection fraction Cohen, Sarah S. Roger, Véronique L. Weston, Susan A. Jiang, Ruoxiang Movva, Naimisha Yusuf, Akeem A. Chamberlain, Alanna M. Pharmacol Res Perspect Original Articles The purpose of this analysis was to develop and validate computable phenotypes for heart failure (HF) with preserved ejection fraction (HFpEF) using claims‐type measures using the Rochester Epidemiology Project. This retrospective study utilized an existing cohort of Olmsted County, Minnesota residents aged ≥ 20 years diagnosed with HF between 2007 and 2015. The gold standard definition of HFpEF included meeting the validated Framingham criteria for HF and having an LVEF ≥ 50%. Computable phenotypes of claims‐type data elements (including ICD‐9/ICD‐10 diagnostic codes and lab test codes) both individually and in combinations were assessed via sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) with respect to the gold standard. In the Framingham‐validated cohort, 2,035 patients had HF; 1,172 (58%) had HFpEF. One in‐patient or two out‐patient diagnosis codes of ICD‐9 428.3X or ICD‐10 I50.3X had 46% sensitivity, 88% specificity, 84% PPV, and 54% NPV. The addition of a BNP/NT‐proBNP test code reduced sensitivity to 35% while increasing specificity to 91% (PPV = 84%, NPV = 51%). Broadening the diagnostic codes to ICD‐9 428.0, 428.3X, and 428.9/ICD‐10 I50.3X and I50.9 increased sensitivity at the expense of decreasing specificity (diagnostic code‐only model: 87% sensitivity, 8% specificity, 56% PPV, 30% NPV; diagnostic code and BNP lab code model: 61% sensitivity, 43% specificity, 60% PPV, 45% NPV). In an analysis conducted to mimic real‐world use of the computable phenotypes, any one in‐patient or out‐patient code of ICD‐9 428/ICD‐10 150 among the broader population (N = 3,755) resulted in lower PPV values compared with the Framingham cohort. However, one in‐patient or two out‐patient instances of ICD‐9 428.0, 428.9, or 428.3X/ICD‐10 150.3X or 150.9 brought the PPV values from the two cohorts closer together. While some misclassification remains, the computable phenotypes defined here may be used in claims databases to identify HFpEF patients and to gain a greater understanding of the characteristics of patients with HFpEF. John Wiley and Sons Inc. 2020-10-30 /pmc/articles/PMC7596663/ /pubmed/33124771 http://dx.doi.org/10.1002/prp2.676 Text en © 2020 The Authors. Pharmacology Research & Perspectives published by John Wiley & Sons Ltd, British Pharmacological Society and American Society for Pharmacology and Experimental Therapeutics. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
spellingShingle Original Articles
Cohen, Sarah S.
Roger, Véronique L.
Weston, Susan A.
Jiang, Ruoxiang
Movva, Naimisha
Yusuf, Akeem A.
Chamberlain, Alanna M.
Evaluation of claims‐based computable phenotypes to identify heart failure patients with preserved ejection fraction
title Evaluation of claims‐based computable phenotypes to identify heart failure patients with preserved ejection fraction
title_full Evaluation of claims‐based computable phenotypes to identify heart failure patients with preserved ejection fraction
title_fullStr Evaluation of claims‐based computable phenotypes to identify heart failure patients with preserved ejection fraction
title_full_unstemmed Evaluation of claims‐based computable phenotypes to identify heart failure patients with preserved ejection fraction
title_short Evaluation of claims‐based computable phenotypes to identify heart failure patients with preserved ejection fraction
title_sort evaluation of claims‐based computable phenotypes to identify heart failure patients with preserved ejection fraction
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7596663/
https://www.ncbi.nlm.nih.gov/pubmed/33124771
http://dx.doi.org/10.1002/prp2.676
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