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Finding missed cases of familial hypercholesterolemia in health systems using machine learning
Familial hypercholesterolemia (FH) is an underdiagnosed dominant genetic condition affecting approximately 0.4% of the population and has up to a 20-fold increased risk of coronary artery disease if untreated. Simple screening strategies have false positive rates greater than 95%. As part of the FH...
Autores principales: | , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6550268/ https://www.ncbi.nlm.nih.gov/pubmed/31304370 http://dx.doi.org/10.1038/s41746-019-0101-5 |
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author | Banda, Juan M. Sarraju, Ashish Abbasi, Fahim Parizo, Justin Pariani, Mitchel Ison, Hannah Briskin, Elinor Wand, Hannah Dubois, Sebastien Jung, Kenneth Myers, Seth A. Rader, Daniel J. Leader, Joseph B. Murray, Michael F. Myers, Kelly D. Wilemon, Katherine Shah, Nigam H. Knowles, Joshua W. |
author_facet | Banda, Juan M. Sarraju, Ashish Abbasi, Fahim Parizo, Justin Pariani, Mitchel Ison, Hannah Briskin, Elinor Wand, Hannah Dubois, Sebastien Jung, Kenneth Myers, Seth A. Rader, Daniel J. Leader, Joseph B. Murray, Michael F. Myers, Kelly D. Wilemon, Katherine Shah, Nigam H. Knowles, Joshua W. |
author_sort | Banda, Juan M. |
collection | PubMed |
description | Familial hypercholesterolemia (FH) is an underdiagnosed dominant genetic condition affecting approximately 0.4% of the population and has up to a 20-fold increased risk of coronary artery disease if untreated. Simple screening strategies have false positive rates greater than 95%. As part of the FH Foundation′s FIND FH initiative, we developed a classifier to identify potential FH patients using electronic health record (EHR) data at Stanford Health Care. We trained a random forest classifier using data from known patients (n = 197) and matched non-cases (n = 6590). Our classifier obtained a positive predictive value (PPV) of 0.88 and sensitivity of 0.75 on a held-out test-set. We evaluated the accuracy of the classifier′s predictions by chart review of 100 patients at risk of FH not included in the original dataset. The classifier correctly flagged 84% of patients at the highest probability threshold, with decreasing performance as the threshold lowers. In external validation on 466 FH patients (236 with genetically proven FH) and 5000 matched non-cases from the Geisinger Healthcare System our FH classifier achieved a PPV of 0.85. Our EHR-derived FH classifier is effective in finding candidate patients for further FH screening. Such machine learning guided strategies can lead to effective identification of the highest risk patients for enhanced management strategies. |
format | Online Article Text |
id | pubmed-6550268 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-65502682019-07-12 Finding missed cases of familial hypercholesterolemia in health systems using machine learning Banda, Juan M. Sarraju, Ashish Abbasi, Fahim Parizo, Justin Pariani, Mitchel Ison, Hannah Briskin, Elinor Wand, Hannah Dubois, Sebastien Jung, Kenneth Myers, Seth A. Rader, Daniel J. Leader, Joseph B. Murray, Michael F. Myers, Kelly D. Wilemon, Katherine Shah, Nigam H. Knowles, Joshua W. NPJ Digit Med Article Familial hypercholesterolemia (FH) is an underdiagnosed dominant genetic condition affecting approximately 0.4% of the population and has up to a 20-fold increased risk of coronary artery disease if untreated. Simple screening strategies have false positive rates greater than 95%. As part of the FH Foundation′s FIND FH initiative, we developed a classifier to identify potential FH patients using electronic health record (EHR) data at Stanford Health Care. We trained a random forest classifier using data from known patients (n = 197) and matched non-cases (n = 6590). Our classifier obtained a positive predictive value (PPV) of 0.88 and sensitivity of 0.75 on a held-out test-set. We evaluated the accuracy of the classifier′s predictions by chart review of 100 patients at risk of FH not included in the original dataset. The classifier correctly flagged 84% of patients at the highest probability threshold, with decreasing performance as the threshold lowers. In external validation on 466 FH patients (236 with genetically proven FH) and 5000 matched non-cases from the Geisinger Healthcare System our FH classifier achieved a PPV of 0.85. Our EHR-derived FH classifier is effective in finding candidate patients for further FH screening. Such machine learning guided strategies can lead to effective identification of the highest risk patients for enhanced management strategies. Nature Publishing Group UK 2019-04-11 /pmc/articles/PMC6550268/ /pubmed/31304370 http://dx.doi.org/10.1038/s41746-019-0101-5 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Banda, Juan M. Sarraju, Ashish Abbasi, Fahim Parizo, Justin Pariani, Mitchel Ison, Hannah Briskin, Elinor Wand, Hannah Dubois, Sebastien Jung, Kenneth Myers, Seth A. Rader, Daniel J. Leader, Joseph B. Murray, Michael F. Myers, Kelly D. Wilemon, Katherine Shah, Nigam H. Knowles, Joshua W. Finding missed cases of familial hypercholesterolemia in health systems using machine learning |
title | Finding missed cases of familial hypercholesterolemia in health systems using machine learning |
title_full | Finding missed cases of familial hypercholesterolemia in health systems using machine learning |
title_fullStr | Finding missed cases of familial hypercholesterolemia in health systems using machine learning |
title_full_unstemmed | Finding missed cases of familial hypercholesterolemia in health systems using machine learning |
title_short | Finding missed cases of familial hypercholesterolemia in health systems using machine learning |
title_sort | finding missed cases of familial hypercholesterolemia in health systems using machine learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6550268/ https://www.ncbi.nlm.nih.gov/pubmed/31304370 http://dx.doi.org/10.1038/s41746-019-0101-5 |
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