Real-world evaluation of an algorithmic machine-learning-guided testing approach in stable chest pain: a multinational, multicohort study

Evangelos K Oikonomou; Arya Aminorroaya; Lovedeep S Dhingra; Caitlin Partridge; Eric J Velazquez; Nihar R Desai; Harlan M Krumholz; Edward J Miller; Rohan Khera

doi:10.1093/ehjdh/ztae023

Real-world evaluation of an algorithmic machine-learning-guided testing approach in stable chest pain: a multinational, multicohort study

Eur Heart J Digit Health. 2024 Apr 8;5(3):303-313. doi: 10.1093/ehjdh/ztae023. eCollection 2024 May.

Authors

Affiliations

¹ Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, 333 Cedar Street, PO Box 208017, New Haven, 06520-8017 CT, USA.
² Yale Center for Clinical Investigation, 2 Church Street South, New Haven, 06519 CT, USA.
³ Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, 195 Church Street 5th Floor, New Haven, 06510 CT, USA.
⁴ Section of Biomedical Informatics and Data Science, Yale School of Medicine, 100 College Street, New Haven, 06511 CT, USA.
⁵ Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, 06510 CT, USA.

Abstract

Aims: An algorithmic strategy for anatomical vs. functional testing in suspected coronary artery disease (CAD) (Anatomical vs. Stress teSting decIsion Support Tool; ASSIST) is associated with better outcomes than random selection. However, in the real world, this decision is rarely random. We explored the agreement between a provider-driven vs. simulated algorithmic approach to cardiac testing and its association with outcomes across multinational cohorts.

Methods and results: In two cohorts of functional vs. anatomical testing in a US hospital health system [Yale; 2013-2023; n = 130 196 (97.0%) vs. n = 4020 (3.0%), respectively], and the UK Biobank [n = 3320 (85.1%) vs. n = 581 (14.9%), respectively], we examined outcomes stratified by agreement between the real-world and ASSIST-recommended strategies. Younger age, female sex, Black race, and diabetes history were independently associated with lower odds of ASSIST-aligned testing. Over a median of 4.9 (interquartile range [IQR]: 2.4-7.1) and 5.4 (IQR: 2.6-8.8) years, referral to the ASSIST-recommended strategy was associated with a lower risk of acute myocardial infarction or death (hazard ratio_adjusted: 0.81, 95% confidence interval [CI] 0.77-0.85, P < 0.001 and 0.74 [95% CI 0.60-0.90], P = 0.003, respectively), an effect that remained significant across years, test types, and risk profiles. In post hoc analyses of anatomical-first testing in the Prospective Multicentre Imaging Study for Evaluation of Chest Pain (PROMISE) trial, alignment with ASSIST was independently associated with a 17% and 30% higher risk of detecting CAD in any vessel or the left main artery/proximal left anterior descending coronary artery, respectively.

Conclusion: In cohorts where historical practices largely favour functional testing, alignment with an algorithmic approach to cardiac testing defined by ASSIST was associated with a lower risk of adverse outcomes. This highlights the potential utility of a data-driven approach in the diagnostic management of CAD.

Keywords: Artificial intelligence; Chest pain; Clinical decision support; Machine learning.

Grants and funding

F32 HL170592/HL/NHLBI NIH HHS/United States