Deep neural survival networks for cardiovascular risk prediction: The Multi-Ethnic Study of Atherosclerosis (MESA)

Quincy A Hathaway; Naveena Yanamala; Matthew J Budoff; Partho P Sengupta; Irfan Zeb

doi:10.1016/j.compbiomed.2021.104983

Deep neural survival networks for cardiovascular risk prediction: The Multi-Ethnic Study of Atherosclerosis (MESA)

Comput Biol Med. 2021 Dec:139:104983. doi: 10.1016/j.compbiomed.2021.104983. Epub 2021 Oct 29.

Authors

Quincy A Hathaway¹, Naveena Yanamala², Matthew J Budoff³, Partho P Sengupta⁴, Irfan Zeb¹

Affiliations

¹ Heart and Vascular Institute, West Virginia University, Morgantown, WV, USA.
² Heart and Vascular Institute, West Virginia University, Morgantown, WV, USA; Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA.
³ Lundquist Institute, Harbor-University of California, Los Angeles, Torrance, CA, USA.
⁴ Heart and Vascular Institute, West Virginia University, Morgantown, WV, USA; Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA. Electronic address: partho.sengupta@rutgers.edu.

PMID: 34749095
DOI: 10.1016/j.compbiomed.2021.104983

Abstract

Background: There is growing interest in utilizing machine learning techniques for routine atherosclerotic cardiovascular disease (ASCVD) risk prediction. We investigated whether novel deep learning survival models can augment ASCVD risk prediction over existing statistical and machine learning approaches.

Methods: 6814 participants from the Multi-Ethnic Study of Atherosclerosis (MESA) were followed over 16 years to assess incidence of all-cause mortality (mortality) or a composite of major adverse events (MAE). Features were evaluated within the categories of traditional risk factors, inflammatory biomarkers, and imaging markers. Data was split into an internal training/testing (four centers) and external validation (two centers). Both machine learning (COXPH, RSF, and lSVM) and deep learning (nMTLR and DeepSurv) models were evaluated.

Results: In comparison to the COXPH model, DeepSurv significantly improved ASCVD risk prediction for MAE (AUC: 0.82 vs. 0.80, P ≤ 0.001) and mortality (AUC: 0.87 vs. 0.84, P ≤ 0.001) with traditional risk factors alone. Implementing non-categorical NRI, we noted a >40% increase in correct reclassification compared to the COXPH model for both MAE and mortality (P ≤ 0.05). Assessing the relative risk of participants, DeepSurv was the only learning algorithm to develop a significantly improved risk score criteria, which outcompeted COXPH for both MAE (4.22 vs. 3.61, P = 0.043) and mortality (6.81 vs. 5.52, P = 0.044). The addition of inflammatory or imaging biomarkers to traditional risk factors showed minimal/no significant improvement in model prediction.

Conclusion: DeepSurv can leverage simple office-based clinical features alone to accurately predict ASCVD risk and cardiovascular outcomes, without the need for additional features, such as inflammatory and imaging biomarkers.

Keywords: Biomarkers; Deep neural survival networks; Event prediction; MESA; Machine learning; Risk factors.

Publication types

Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Atherosclerosis* / epidemiology
Cardiovascular Diseases* / epidemiology
Coronary Artery Disease*
Ethnicity
Heart Disease Risk Factors
Humans
Risk Assessment
Risk Factors