External validation of a claims-based model to predict left ventricular ejection fraction class in patients with heart failure.

External validation of a claims-based model to predict left ventricular ejection fraction class in patients with heart failure.

<h4>Background</h4>Ejection fraction (EF) is an important prognostic factor in heart failure (HF), but administrative claims databases lack information on EF. We previously developed a model to predict EF class from Medicare claims. Here, we evaluated the performance of this model in an...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Mufaddal Mahesri, Kristyn Chin, Abheenava Kumar, Aditya Barve, Rachel Studer, Raquel Lahoz, Rishi J Desai
Formato: article
Lenguaje:EN
Publicado: Public Library of Science (PLoS) 2021
Materias:
Medicine
R
Science
Q
Acceso en línea:https://doaj.org/article/937e1ffa85004b9e9322b447c29ed62d
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
id oai:doaj.org-article:937e1ffa85004b9e9322b447c29ed62d
record_format dspace
spelling oai:doaj.org-article:937e1ffa85004b9e9322b447c29ed62d2021-12-02T20:11:01ZExternal validation of a claims-based model to predict left ventricular ejection fraction class in patients with heart failure.1932-620310.1371/journal.pone.0252903https://doaj.org/article/937e1ffa85004b9e9322b447c29ed62d2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0252903https://doaj.org/toc/1932-6203<h4>Background</h4>Ejection fraction (EF) is an important prognostic factor in heart failure (HF), but administrative claims databases lack information on EF. We previously developed a model to predict EF class from Medicare claims. Here, we evaluated the performance of this model in an external validation sample of commercial insurance enrollees.<h4>Methods</h4>Truven MarketScan claims linked to electronic medical records (EMR) data (IBM Explorys) containing EF measurements were used to identify a cohort of US patients with HF between 01-01-2012 and 10-31-2019. By applying the previously developed model, patients were classified into HF with reduced EF (HFrEF) or preserved EF (HFpEF). EF values recorded in EMR data were used to define gold-standard HFpEF (LVEF ≥45%) and HFrEF (LVEF<45%). Model performance was reported in terms of overall accuracy, positive predicted values (PPV), and sensitivity for HFrEF and HFpEF.<h4>Results</h4>A total of 7,001 HF patients with an average age of 71 years were identified, 1,700 (24.3%) of whom had HFrEF. An overall accuracy of 0.81 (95% CI: 0.80-0.82) was seen in this external validation sample. For HFpEF, the model had sensitivity of 0.96 (95%CI, 0.95-0.97) and PPV of 0.81 (95% CI, 0.81-0.82); while for HFrEF, the sensitivity was 0.32 (95%CI, 0.30-0.34) and PPV was 0.73 (95%CI, 0.69-0.76). These results were consistent with what was previously published in US Medicare claims data.<h4>Conclusions</h4>The successful validation of the Medicare claims-based model provides evidence that this model may be used to identify patient subgroups with specific EF class in commercial claims databases as well.Mufaddal MahesriKristyn ChinAbheenava KumarAditya BarveRachel StuderRaquel LahozRishi J DesaiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 6, p e0252903 (2021)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Mufaddal Mahesri
Kristyn Chin
Abheenava Kumar
Aditya Barve
Rachel Studer
Raquel Lahoz
Rishi J Desai
External validation of a claims-based model to predict left ventricular ejection fraction class in patients with heart failure.
description <h4>Background</h4>Ejection fraction (EF) is an important prognostic factor in heart failure (HF), but administrative claims databases lack information on EF. We previously developed a model to predict EF class from Medicare claims. Here, we evaluated the performance of this model in an external validation sample of commercial insurance enrollees.<h4>Methods</h4>Truven MarketScan claims linked to electronic medical records (EMR) data (IBM Explorys) containing EF measurements were used to identify a cohort of US patients with HF between 01-01-2012 and 10-31-2019. By applying the previously developed model, patients were classified into HF with reduced EF (HFrEF) or preserved EF (HFpEF). EF values recorded in EMR data were used to define gold-standard HFpEF (LVEF ≥45%) and HFrEF (LVEF<45%). Model performance was reported in terms of overall accuracy, positive predicted values (PPV), and sensitivity for HFrEF and HFpEF.<h4>Results</h4>A total of 7,001 HF patients with an average age of 71 years were identified, 1,700 (24.3%) of whom had HFrEF. An overall accuracy of 0.81 (95% CI: 0.80-0.82) was seen in this external validation sample. For HFpEF, the model had sensitivity of 0.96 (95%CI, 0.95-0.97) and PPV of 0.81 (95% CI, 0.81-0.82); while for HFrEF, the sensitivity was 0.32 (95%CI, 0.30-0.34) and PPV was 0.73 (95%CI, 0.69-0.76). These results were consistent with what was previously published in US Medicare claims data.<h4>Conclusions</h4>The successful validation of the Medicare claims-based model provides evidence that this model may be used to identify patient subgroups with specific EF class in commercial claims databases as well.
format article
author Mufaddal Mahesri
Kristyn Chin
Abheenava Kumar
Aditya Barve
Rachel Studer
Raquel Lahoz
Rishi J Desai
author_facet Mufaddal Mahesri
Kristyn Chin
Abheenava Kumar
Aditya Barve
Rachel Studer
Raquel Lahoz
Rishi J Desai
author_sort Mufaddal Mahesri
title External validation of a claims-based model to predict left ventricular ejection fraction class in patients with heart failure.
title_short External validation of a claims-based model to predict left ventricular ejection fraction class in patients with heart failure.
title_full External validation of a claims-based model to predict left ventricular ejection fraction class in patients with heart failure.
title_fullStr External validation of a claims-based model to predict left ventricular ejection fraction class in patients with heart failure.
title_full_unstemmed External validation of a claims-based model to predict left ventricular ejection fraction class in patients with heart failure.
title_sort external validation of a claims-based model to predict left ventricular ejection fraction class in patients with heart failure.
publisher Public Library of Science (PLoS)
publishDate 2021
url https://doaj.org/article/937e1ffa85004b9e9322b447c29ed62d
work_keys_str_mv AT mufaddalmahesri externalvalidationofaclaimsbasedmodeltopredictleftventricularejectionfractionclassinpatientswithheartfailure
AT kristynchin externalvalidationofaclaimsbasedmodeltopredictleftventricularejectionfractionclassinpatientswithheartfailure
AT abheenavakumar externalvalidationofaclaimsbasedmodeltopredictleftventricularejectionfractionclassinpatientswithheartfailure
AT adityabarve externalvalidationofaclaimsbasedmodeltopredictleftventricularejectionfractionclassinpatientswithheartfailure
AT rachelstuder externalvalidationofaclaimsbasedmodeltopredictleftventricularejectionfractionclassinpatientswithheartfailure
AT raquellahoz externalvalidationofaclaimsbasedmodeltopredictleftventricularejectionfractionclassinpatientswithheartfailure
AT rishijdesai externalvalidationofaclaimsbasedmodeltopredictleftventricularejectionfractionclassinpatientswithheartfailure
_version_ 1718374960168173568

Ejemplares similares