Artificial Intelligence in Cardiovascular Clinical Trials

Jonathan W Cunningham¹, William T Abraham², Ankeet S Bhatt³, Jessilyn Dunn⁴, G Michael Felker⁵, Sneha S Jain⁶, Christopher J Lindsell⁷, Matthew Mace⁸, Trejeeve Martyn⁹, Rashmee U Shah¹⁰, Geoffrey H Tison¹¹, Tala Fakhouri¹², Mitchell A Psotka¹³, Harlan Krumholz¹⁴, Mona Fiuzat¹⁵, Christopher M O'Connor¹⁶, Scott D Solomon¹⁷; Heart Failure Collaboratory

Affiliations

¹ Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
² The Ohio State University College of Medicine, Columbus, Ohio, USA.
³ Division of Research, Kaiser Permanente Northern California, San Francisco, California, USA; Division of Cardiovascular Medicine, Stanford University, Stanford, California, USA.
⁴ Department of Biostatistics and Bioinformatics and Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA.
⁵ Duke Clinical Research Institute, Durham, North Carolina, USA; Division of Cardiology, Duke University Medical Center, Durham, North Carolina, USA.
⁶ Division of Cardiovascular Medicine, Stanford University, Stanford, California, USA.
⁷ Department of Biostatistics and Bioinformatics and Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA; Duke Clinical Research Institute, Durham, North Carolina, USA.
⁸ Academy for HealthCare Science (AHCS), Lutterworth, United Kingdom; Acorai AB, Helsingborg, Sweden.
⁹ Department of Cardiovascular Medicine, Cleveland Clinic, Cleveland, Ohio, USA.
¹⁰ University of Utah School of Medicine, Salt Lake City, Utah, USA.
¹¹ Division of Cardiology, University of California-San Francisco, San Francisco, California, USA.
¹² Office of Medical Policy, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA.
¹³ Inova Schar Heart and Vascular, Falls Church, Virginia, USA.
¹⁴ Yale University School of Medicine, New Haven, Connecticut, USA.
¹⁵ Division of Cardiology, Duke University Medical Center, Durham, North Carolina, USA.
¹⁶ Division of Cardiology, Duke University Medical Center, Durham, North Carolina, USA; Inova Schar Heart and Vascular, Falls Church, Virginia, USA.
¹⁷ Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA. Electronic address: ssolomon@bwh.harvard.edu.

PMID: 39505413
PMCID: PMC12178241
DOI: 10.1016/j.jacc.2024.08.069

Review

Artificial Intelligence in Cardiovascular Clinical Trials

Jonathan W Cunningham et al. J Am Coll Cardiol. 2024.

. 2024 Nov 12;84(20):2051-2062.

doi: 10.1016/j.jacc.2024.08.069.

Authors

Affiliations

¹ Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
² The Ohio State University College of Medicine, Columbus, Ohio, USA.
³ Division of Research, Kaiser Permanente Northern California, San Francisco, California, USA; Division of Cardiovascular Medicine, Stanford University, Stanford, California, USA.
⁴ Department of Biostatistics and Bioinformatics and Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA.
⁵ Duke Clinical Research Institute, Durham, North Carolina, USA; Division of Cardiology, Duke University Medical Center, Durham, North Carolina, USA.
⁶ Division of Cardiovascular Medicine, Stanford University, Stanford, California, USA.
⁷ Department of Biostatistics and Bioinformatics and Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA; Duke Clinical Research Institute, Durham, North Carolina, USA.
⁸ Academy for HealthCare Science (AHCS), Lutterworth, United Kingdom; Acorai AB, Helsingborg, Sweden.
⁹ Department of Cardiovascular Medicine, Cleveland Clinic, Cleveland, Ohio, USA.
¹⁰ University of Utah School of Medicine, Salt Lake City, Utah, USA.
¹¹ Division of Cardiology, University of California-San Francisco, San Francisco, California, USA.
¹² Office of Medical Policy, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA.
¹³ Inova Schar Heart and Vascular, Falls Church, Virginia, USA.
¹⁴ Yale University School of Medicine, New Haven, Connecticut, USA.
¹⁵ Division of Cardiology, Duke University Medical Center, Durham, North Carolina, USA.
¹⁶ Division of Cardiology, Duke University Medical Center, Durham, North Carolina, USA; Inova Schar Heart and Vascular, Falls Church, Virginia, USA.
¹⁷ Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA. Electronic address: ssolomon@bwh.harvard.edu.

PMID: 39505413
PMCID: PMC12178241
DOI: 10.1016/j.jacc.2024.08.069

Abstract

Randomized clinical trials are the gold standard for establishing the efficacy and safety of cardiovascular therapies. However, current pivotal trials are expensive, lengthy, and insufficiently diverse. Emerging artificial intelligence (AI) technologies can potentially automate and streamline clinical trial operations. This review describes opportunities to integrate AI throughout a trial's life cycle, including designing the trial, identifying eligible patients, obtaining informed consent, ascertaining physiological and clinical event outcomes, interpreting imaging, and analyzing or disseminating the results. Nevertheless, AI poses risks, including generating inaccurate results, amplifying biases against underrepresented groups, and violating patient privacy. Medical journals and regulators are developing new frameworks to evaluate AI research tools and the data they generate. Given the high-stakes role of randomized trials in medical decision making, AI must be integrated carefully and transparently to protect the validity of trial results.

Keywords: artificial intelligence; automated; large language model; randomized.

PubMed Disclaimer

Conflict of interest statement

Funding Support and Author Disclosures Dr Cunningham has received support from the KL2/Harvard Catalyst Medical Research Investigator Training program and the American Heart Association (23CDA1052151); and has received consulting fees from Roche Diagnosis, Edgewise Therapeutics, KCK, and Occlutech. Dr Abraham has served as a consultant to Boehringer Ingelheim, CVRx, Impulse Dynamics, Sensible Medical, Vectorious, V-Wave, and Zoll Respicardia. Dr Bhatt has received research grant support to his institution from the National Institutes of Health (NIH) National Heart, Lung, and Blood Institute (NHLBI) and National Institute on Aging, the American College of Cardiology Foundation, and the Centers for Disease Control and Prevention (CDC); and has received consulting fees from the Kinetix Group, Merck, Sanofi Pasteur, and Novo Nordisk. Dr Dunn has served as a scientific advisor to Veri. Dr Felker has received research grants from NIH, Bayer, Bristol Myers Squibb, Novartis, Daxor, Merck, Cytokinetics, and CSL-Behring; has acted as a consultant to Novartis, Bristol Myers Squibb, Cytokinetics, Innolife, Boehringer Ingelheim, Abbott, Sanofi, Regeneron, Myovant, Sequana, Windtree Therapeutics, and Whiteswell; and has served on clinical endpoint committees or data safety monitoring boards for Merck, Medtronic, EBR Systems, Rocket Pharma, V-Wave, and LivaNova. Dr Jain has received consulting fees from Bristol Myers Squibb, ARTIS Ventures, and Broadview Ventures outside of the submitted work. Dr Lindsell has received grants and contracts from the NIH, the U.S. Department of Defense, CDC, Biomeme, Novartis, bioMérieux, AstraZeneca, AbbVie, Entegrion, and Endpoint Health, all outside of the submitted work; has obtained patents for risk stratification in sepsis and septic shock issued to Cincinnati Children’s Hospital Medical Center; has served on data safety monitoring boards unrelated to the current work; has held stock options in Bioscape Digital unrelated to the current work; and has served as Editor-in-Chief of the Journal of Clinical and Translational Science. Mr Mace is an employee of Acorai AB; and has held stock interest in Abbott Laboratories. Dr Martyn has served as an advisor to or has received consulting fees from Fire1, Cleveland Clinic/American Well Joint Venture, Boehringer Ingelheim/Eli Lilly, NIRSense, Novo Nordisk, AstraZeneca, and Apricity Robotics; and has received grant support from Ionis Therapeutics, AstraZeneca, and the Heart Failure Society of America. Dr Shah is an employee of Meta, which had no role in this work or providing financial support. Dr Tison has received research grants from MyoKardia, a wholly owned subsidiary of Bristol Myers Squibb, and Janssen Pharmaceuticals; and is an advisor to Viz.ai and Prolaio. Dr Fakhouri is an employee of the Office of Medical Policy, Center for Drug Evaluation and Research, U.S. Food and Drug Administration; the views expressed in this article are those of the authors and do not necessarily represent the views or policies of the U.S. Food and Drug Administration. Dr Krumholz has received options from Element Science and Identifeye; has received payments from F-Prime for advisory roles; has co-founded and held equity in Hugo Health, Refactor Health, and ENSIGHT-AI; and has been associated with research contracts through Yale University from Janssen, Kenvue, and Pfizer. Dr O’Connor has received consulting fees from Merck, Abiomed, and Zealcare. Dr Solomon has received research grants from Alexion, Alnylam, AstraZeneca, Bellerophon, Bayer, Bristol Myers Squibb, Boston Scientific, Cytokinetics, Edgewise, Eidos, Gossamer, GSK, Ionis, Lilly, MyoKardia, NIH NHLBI, Novartis, Novo Nordisk, Respicardia, Sanofi Pasteur, Theracos, and US2.AI; and has consulted for Abbott, Action, Akros, Alexion, Alnylam, Amgen, Arena, AstraZeneca, Bayer, Boehringer Ingelheim, Bristol Myers Squibb, Cardior, Cardurion, Corvia, Cytokinetics, Daiichi-Sankyo, GSK, Lilly, Merck, MyoKardia, Novartis, Roche, Theracos, Quantum Genomics, Janssen, Cardiac Dimensions, Tenaya, Sanofi-Pasteur, Dinaqor, Tremeau, CellProThera, Moderna, American Regent, Sarepta, Lexicon, AnaCardio, Akros, and Valo. Drs Psotka and Fiuzat have reported that they have no relationships relevant to the contents of this paper to disclose.

Figures

**FIGURE 1. NLP for Automated Endpoint Adjudication in the INVESTED Trial**
A natural language processing (NLP) model developed to identify heart failure (HF) hospitalizations in a single-center electronic health record (EHR) cohort was externally validated in the multicenter INVESTED (Influenza Vaccine to Effectively Stop Cardio Thoracic Events and Decompensated Heart Failure) clinical trial. CEC = clinical event committee.

**FIGURE 2. Applications of Digital Health Technologies for Remote Data Collection**
Digital health technologies may aid in the collection of physiological and patient-reported outcomes. GPS = global positioning system

See this image and copyright information in PMC

References

1. Collins R, Bowman L, Landray M, Peto R. The magic of randomization versus the myth of real-world evidence. N Engl J Med. 2020;382(7):674–678. 10.1056/NEJMsb1901642 - DOI - PubMed
1. Freemantle N, Marston L, Walters K, Wood J, Reynolds MR, Petersen I. Making inferences on treatment effects from real world data: propensity scores, confounding by indication, and other perils for the unwary in observational research. BMJ. 2013;347:f6409. 10.1136/bmj.f6409 - DOI - PubMed
1. McMurray JJV. Only trials tell the truth about treatment effects. J Am Coll Cardiol. 2018;71(23):2640–2642. 10.1016/j.jacc.2018.04.019 - DOI - PubMed
1. O’Connor CM, Psotka MA, Fiuzat M, et al. improving heart failure therapeutics development in the United States. J Am Coll Cardiol. 2018;71(4):443–453. 10.1016/j.jacc.2017.11.048 - DOI - PubMed
1. Moore TJ, Zhang H, Anderson G, Alexander GC. Estimated costs of pivotal trials for novel therapeutic agents approved by the US Food and Drug Administration, 2015–2016. JAMA Intern Med. 2018;178(11):1451–1457. 10.1001/jamainternmed.2018.3931 - DOI - PMC - PubMed

Publication types

Actions

MeSH terms

Actions
Actions
Actions
Actions
Actions

Grants and funding

LinkOut - more resources

Full Text Sources

Save citation to file

Email citation

Add to Collections

Add to My Bibliography

Your saved search

Create a file for external citation management software

Your RSS Feed

Artificial Intelligence in Cardiovascular Clinical Trials

Affiliations

Artificial Intelligence in Cardiovascular Clinical Trials

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

References

Publication types

MeSH terms

Grants and funding

LinkOut - more resources

Full Text Sources