AUTHOR=Siva Kumar Shruti , Al-Kindi Sadeer , Tashtish Nour , Rajagopalan Varun , Fu Pingfu , Rajagopalan Sanjay , Madabhushi Anant 

TITLE=Machine learning derived ECG risk score improves cardiovascular risk assessment in conjunction with coronary artery calcium scoring

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=Volume 9 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2022.976769

DOI=10.3389/fcvm.2022.976769

ISSN=2297-055X

ABSTRACT=BACKGROUND: Precision estimation of cardiovascular risk remains the cornerstone of atherosclerotic cardiovascular disease (ASCVD) prevention. While coronary artery calcium (CAC) scoring is the best available non-invasive quantitative modality to evaluate risk of ASCVD, it excludes risk related to prior myocardial infarction, cardiomyopathy, and arrhythmia which are implicated in ASCVD. The high-dimensional and inter-correlated nature of ECG data makes it a good candidate for analysis using machine learning techniques and may provide additional prognostic information not captured by CAC. 
METHODS: We analyzed 5864 patients with at least 1 cardiovascular risk factor who underwent CAC scoring and a standard ECG as part of the CLARIFY study (ClinicalTrials.gov Identifier: NCT04075162). A Cox-proportional hazards model was used to assess the benefit of using an eRiS alone (Mecg), CAC alone (Mcac) and a combination of eRiS and CAC (Mecg+cac) for MACE prediction. A nomogram (Mnom) was further constructed by integrating eRiS with CAC and demographics (age and sex). 
FINDINGS: Over a median follow-up of 14 months, 494 patients had MACE. The Mecg model, comprising of eRiS alone was found to be significantly associated with MACE and had good discrimination of MACE (C-Index: 0.7, p=<2e-16). Model comparison showed that Mecg+cac was superior to Mecg (p=1.8e-10) or Mcac (p<2.2e-16) alone. The Mnom, comprising of eRiS, CAC, age and sex was associated with MACE. eRiS had the most significant contribution, followed by CAC score and other clinical variables. Further, Mnom was able to identify unique patient risk-groups based on eRiS, CAC and clinical variables.
CONCLUSION: The use of ECG features in conjunction with CAC may allow for improved prognostication and identification of populations at risk. Future directions will involve prospective validation of the risk score and the nomogram across diverse populations with a heterogeneity of treatment effects.