AESurv: autoencoder survival analysis for accurate early prediction of coronary heart disease

Coronary heart disease (CHD) is one of the leading causes of mortality and morbidity in the United States. Accurate time-to-event CHD prediction models with high-dimensional DNA methylation and clinical features may assist with early prediction and intervention strategies. We developed a state-of-the-art deep learning autoencoder survival analysis model (AESurv) to effectively analyze high-dimensional blood DNA methylation features and traditional clinical risk factors by learning low-dimensional representation of participants for time-to-event CHD prediction. We demonstrated the utility of our model in two cohort studies: the Strong Heart Study cohort (SHS), a prospective cohort studying cardiovascular disease and its risk factors among American Indians adults; the Women's Health Initiative (WHI), a prospective cohort study including randomized clinical trials and observational study to improve postmenopausal women's health with one of the main focuses on cardiovascular disease. Our AESurv model effectively learned participant representations in low-dimensional latent space and achieved better model performance (concordance index-C index of 0.864 ± 0.009 and time-to-event mean area under the receiver operating characteristic curve-AUROC of 0.905 ± 0.009) than other survival analysis models (Cox proportional hazard, Cox proportional hazard deep neural network survival analysis, random survival forest, and gradient boosting survival analysis models) in the SHS. We further validated the AESurv model in WHI and also achieved the best model performance. The AESurv model can be used for accurate CHD prediction and assist health care professionals and patients to perform early intervention strategies. We suggest using AESurv model for future time-to-event CHD prediction based on DNA methylation features.

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Publication Details

Journal
Briefings in Bioinformatics
Published
2024-09-23
DOI
https://doi.org/10.1093/bib/bbae479
Citations
3
Primary Topic
Machine Learning in Healthcare
Type
article
Field-Weighted Citation Impact
1.04

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article

AESurv: autoencoder survival analysis for accurate early prediction of coronary heart disease

Steve Horvath, Allison Kupsco, Amanda M. Fretts, Eric A. Whitsel et al.
3 citations
Briefings in Bioinformatics
Machine Learning in Healthcare
1.04
article

AESurv: autoencoder survival analysis for accurate early prediction of coronary heart disease

Steve Horvath, Allison Kupsco, Amanda M. Fretts, Eric A. Whitsel, Yike Shen, Feng Gao, Ramon Casanova, Andrea Baccarelli, Jason G. Umans, Shelley A. Cole, Arce Domingo‐Relloso, Haotian Wu, Lisa Martin, Peter F. Schnatz, Ying Zhang, Ana Navas-Acien, Marianthi-Anna Kioumourtzoglou, Maria Tellez-Plaza, JoAnn E. Manson
article en
3 citations

Abstract

Coronary heart disease (CHD) is one of the leading causes of mortality and morbidity in the United States. Accurate time-to-event CHD prediction models with high-dimensional DNA methylation and clinical features may assist with early prediction and intervention strategies. We developed a state-of-the-art deep learning autoencoder survival analysis model (AESurv) to effectively analyze high-dimensional blood DNA methylation features and traditional clinical risk factors by learning low-dimensional representation of participants for time-to-event CHD prediction. We demonstrated the utility of our model in two cohort studies: the Strong Heart Study cohort (SHS), a prospective cohort studying cardiovascular disease and its risk factors among American Indians adults; the Women's Health Initiative (WHI), a prospective cohort study including randomized clinical trials and observational study to improve postmenopausal women's health with one of the main focuses on cardiovascular disease. Our AESurv model effectively learned participant representations in low-dimensional latent space and achieved better model performance (concordance index-C index of 0.864 ± 0.009 and time-to-event mean area under the receiver operating characteristic curve-AUROC of 0.905 ± 0.009) than other survival analysis models (Cox proportional hazard, Cox proportional hazard deep neural network survival analysis, random survival forest, and gradient boosting survival analysis models) in the SHS. We further validated the AESurv model in WHI and also achieved the best model performance. The AESurv model can be used for accurate CHD prediction and assist health care professionals and patients to perform early intervention strategies. We suggest using AESurv model for future time-to-event CHD prediction based on DNA methylation features.

Briefings in BioinformaticsVol. 25(6)
University of North Carolina at Chapel Hill (US), Brigham and Women's Hospital (US), Texas Biomedical Research Institute (US), Harvard University (US), Universitat de València (ES), University of California, Los Angeles (US), The University of Texas at Arlington (US), George Washington University (US), University of Washington (US), Reading Hospital (US), Instituto de Salud Carlos III (ES), Northwest Eye Surgeons (US), Georgetown-Howard Universities Center for Clinical and Translational Science (US), National Center for Epidemiology (HU), Wake Forest University (US), University of Oklahoma Health Sciences Center (US), Drexel University (US), Columbia University (US)
U.S. Department of Health and Human Services, Strong, National Institutes of Health, National Heart, Lung, and Blood Institute, National Institute of Environmental Health Sciences
Good health and well-being
Openalex Percentile: Top 19%
Machine Learning in Healthcare
1.04
DOIOpen Access PDFOpenAlex
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