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PMHnet-alpha: development and validation of a neural network based discrete-time survival model for mortality prediction in ischemic heart disease
BACKGROUND: Current risk prediction models in ischemic heart disease (IHD) use a small set of well-known risk factors, have limited predictive capabilities, and are largely the same as they were twenty years ago. We developed and externally validated PMHnet-alpha, a neural-network based survival mod...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9779768/ http://dx.doi.org/10.1093/ehjdh/ztac076.2785 |
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author | Holm, P Haue, A D Westergaard, D Banasik, K Koeber, L Brunak, S Bundgaard, H |
author_facet | Holm, P Haue, A D Westergaard, D Banasik, K Koeber, L Brunak, S Bundgaard, H |
author_sort | Holm, P |
collection | PubMed |
description | BACKGROUND: Current risk prediction models in ischemic heart disease (IHD) use a small set of well-known risk factors, have limited predictive capabilities, and are largely the same as they were twenty years ago. We developed and externally validated PMHnet-alpha, a neural-network based survival model for risk-stratification in ischemic heart disease that leverages the multitude of clinical features available in modern electronical health records. METHODS: We included 39,746 IHD patients from the regional Heart Registry that had been subjected to a coronary angiography between 2006 and 2017 with confirmed coronary artery disease. Clinical data was extracted from the Danish National Patient Registry, and electronic health records. 595 different features, consisting of diagnosis codes, procedure codes, biochemical test results, and clinical measurements were used as model inputs. Prior to model development, patients were randomly divided into a training set (n=34,746) and a tesing set (n=5,000). The testing set was not used for model development. Model performance was evaluated at six months, one years, three-, and five years of follow-up using time-dependent ROC curve analysis and Harrels' C-index. Lastly, we also assessed the calibration of the model. We benchmarked the performance of PMHnet-alpha against the GRACE Risk Score 2.0, which is widely considered to the best-performing model in current clinical use. We explored the importance of individual features using SHAP values on the trained models. FINDINGS: PMHnet-alpha had very high model discrimination on the testing data with time-dependent AUCs of 0.88 (95% CI 0.86–0.90) at six months, 0.88 (95% CI 0.86–0.90) at one year, 0.84 (95% CI 0.82–0.86) at three years, and 0.82 (95% CI 0.80–0.84) at five years. The discrimination of the benchmark model GRACE2.0 on the same data was considerably lower, 0.77 (95% CI 0.73–0.80) at six months, 0.77 (95% CI 0.74–0.80) at one year, and 0.73 (95% CI 0.70–0.75) at three years. PMHnet-alpha is undergoing external validation in other nordic countries. We identified that on-average, age, coronary pathology and smoking status were the most impactful features. INTERPRETATION: Here we present a significant improvement of the state of the art in cardiac risk prediction. PMHnet-alpha supports better and optimized use of available healthcare data, signified by the vast improvement compared to GRACE2.0. This also signifies an important paradigm shift in which data-driven strategies are necessary to transform the increasing amount of data generated in the modern healthcare system into evidence-based clinical decision making. FUNDING ACKNOWLEDGEMENT: Type of funding sources: Foundation. Main funding source(s): The Novo Nordisk Foundation, NordForsk |
format | Online Article Text |
id | pubmed-9779768 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-97797682023-01-27 PMHnet-alpha: development and validation of a neural network based discrete-time survival model for mortality prediction in ischemic heart disease Holm, P Haue, A D Westergaard, D Banasik, K Koeber, L Brunak, S Bundgaard, H Eur Heart J Digit Health Abstracts BACKGROUND: Current risk prediction models in ischemic heart disease (IHD) use a small set of well-known risk factors, have limited predictive capabilities, and are largely the same as they were twenty years ago. We developed and externally validated PMHnet-alpha, a neural-network based survival model for risk-stratification in ischemic heart disease that leverages the multitude of clinical features available in modern electronical health records. METHODS: We included 39,746 IHD patients from the regional Heart Registry that had been subjected to a coronary angiography between 2006 and 2017 with confirmed coronary artery disease. Clinical data was extracted from the Danish National Patient Registry, and electronic health records. 595 different features, consisting of diagnosis codes, procedure codes, biochemical test results, and clinical measurements were used as model inputs. Prior to model development, patients were randomly divided into a training set (n=34,746) and a tesing set (n=5,000). The testing set was not used for model development. Model performance was evaluated at six months, one years, three-, and five years of follow-up using time-dependent ROC curve analysis and Harrels' C-index. Lastly, we also assessed the calibration of the model. We benchmarked the performance of PMHnet-alpha against the GRACE Risk Score 2.0, which is widely considered to the best-performing model in current clinical use. We explored the importance of individual features using SHAP values on the trained models. FINDINGS: PMHnet-alpha had very high model discrimination on the testing data with time-dependent AUCs of 0.88 (95% CI 0.86–0.90) at six months, 0.88 (95% CI 0.86–0.90) at one year, 0.84 (95% CI 0.82–0.86) at three years, and 0.82 (95% CI 0.80–0.84) at five years. The discrimination of the benchmark model GRACE2.0 on the same data was considerably lower, 0.77 (95% CI 0.73–0.80) at six months, 0.77 (95% CI 0.74–0.80) at one year, and 0.73 (95% CI 0.70–0.75) at three years. PMHnet-alpha is undergoing external validation in other nordic countries. We identified that on-average, age, coronary pathology and smoking status were the most impactful features. INTERPRETATION: Here we present a significant improvement of the state of the art in cardiac risk prediction. PMHnet-alpha supports better and optimized use of available healthcare data, signified by the vast improvement compared to GRACE2.0. This also signifies an important paradigm shift in which data-driven strategies are necessary to transform the increasing amount of data generated in the modern healthcare system into evidence-based clinical decision making. FUNDING ACKNOWLEDGEMENT: Type of funding sources: Foundation. Main funding source(s): The Novo Nordisk Foundation, NordForsk Oxford University Press 2022-12-22 /pmc/articles/PMC9779768/ http://dx.doi.org/10.1093/ehjdh/ztac076.2785 Text en Reproduced from: European Heart Journal, Volume 43, Issue Supplement_2, October 2022, ehac544.2785, https://doi.org/10.1093/eurheartj/ehac544.2785 by permission of Oxford University Press on behalf of the European Society of Cardiology. The opinions expressed in the Journal item reproduced as this reprint are those of the authors and contributors, and do not necessarily reflect those of the European Society of Cardiology, the editors, the editorial board, Oxford University Press or the organization to which the authors are affiliated. The mention of trade names, commercial products or organizations, and the inclusion of advertisements in this reprint do not imply endorsement by the Journal, the editors, the editorial board, Oxford University Press or the organization to which the authors are affiliated. The editors and publishers have taken all reasonable precautions to verify drug names and doses, the results of experimental work and clinical findings published in the Journal. The ultimate responsibility for the use and dosage of drugs mentioned in this reprint and in interpretation of published material lies with the medical practitioner, and the editors and publisher cannot accept liability for damages arising from any error or omissions in the Journal or in this reprint. Please inform the editors of any errors. © The Author(s) 2022. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Abstracts Holm, P Haue, A D Westergaard, D Banasik, K Koeber, L Brunak, S Bundgaard, H PMHnet-alpha: development and validation of a neural network based discrete-time survival model for mortality prediction in ischemic heart disease |
title | PMHnet-alpha: development and validation of a neural network based discrete-time survival model for mortality prediction in ischemic heart disease |
title_full | PMHnet-alpha: development and validation of a neural network based discrete-time survival model for mortality prediction in ischemic heart disease |
title_fullStr | PMHnet-alpha: development and validation of a neural network based discrete-time survival model for mortality prediction in ischemic heart disease |
title_full_unstemmed | PMHnet-alpha: development and validation of a neural network based discrete-time survival model for mortality prediction in ischemic heart disease |
title_short | PMHnet-alpha: development and validation of a neural network based discrete-time survival model for mortality prediction in ischemic heart disease |
title_sort | pmhnet-alpha: development and validation of a neural network based discrete-time survival model for mortality prediction in ischemic heart disease |
topic | Abstracts |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9779768/ http://dx.doi.org/10.1093/ehjdh/ztac076.2785 |
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