AI-powered electrocardiography alert intervention and all-cause mortality: a pragmatic randomized clinical trial

  • Adhikari, NK, Fowler, RA, Bhagwanjee, S. & Rubenfeld, GD Critical care and the global burden of critical illness in adults. The Lancet 3761339–1346 (2010).

    Article PubMed PubMed Central Google Scholar

  • Pronovost, PJ et al. Physician staffing patterns and clinical outcomes in critically ill patients: a systematic review. Jama 2882151–2162 (2002).

    Article PubMed Google Scholar

  • Chalfin, DB, Trzeciak, S., Likourezos, A., Baumann, BM & Dellinger, RP Effect of delayed transfer of critically ill patients from the emergency department to the intensive care unit. Criticism Care Med. 351477–1483 (2007).

    Article PubMed Google Scholar

  • Hodgetts, TJ et al. Incidence, location and causes of in-hospital cardiac arrest in District General Hospital. Resuscitation 54115–123 (2002).

    Article PubMed Google Scholar

  • Jones, DA, DeVita, MA & Bellomo, R. Rapid response teams. N Engl J Med 365139–146 (2011).

    Article CAS PubMed Google Scholar

  • Goldstein, BA, Navar, AM, Pencina, MJ & Ioannidis, JPA Opportunities and challenges in developing risk prediction models with electronic health record data: a systematic review. JM Med. inform. Association 24198–208 (2017).

    Article PubMed Google Scholar

  • Smith, GB, Prytherch, DR, Schmidt, PE, Featherstone, PI & Higgins, B. A review, and performance evaluation, of single parameter 'track and trigger' systems. Resuscitation 7911–21 (2008).

    Article PubMed Google Scholar

  • Smith, GB, Prytherch, DR, Meredith, P., Schmidt, PE & Featherstone, PI Ability of the National Early Warning Score (NEWS) to discriminate patients at risk of sudden cardiac arrest. Intensive care unit admissions, and death. Resuscitation 84465–470 (2013).

    Article PubMed Google Scholar

  • Subbe, CP, Kruger, M., Rutherford, P. & Gemmel, L. Validation of a modified early warning score in clinical admissions. QJM 94521–526 (2001).

    Article CAS PubMed Google Scholar

  • Mann, KD et al. Predicting patient morbidity: a review of tools in the digital hospital setting. J Med Internet Res. 23e28209 (2021).

    Article PubMed PubMed Central Google Scholar

  • Romero-Brufau, S. et al. Widely Used Track and Trigger Score: Are They Practically Ready for Automation? Resuscitation 85549–552 (2014).

    Article PubMed Google Scholar

  • Ludikhuize, J. et al. Standardized measurement of a modified early warning score results in improved implementation of a rapid response system: a quasi-experimental study. Resuscitation 85676–682 (2014).

    Article PubMed Google Scholar

  • Chan, PS, Jain, R., Nallmothu, BK, Berg, RA & Sasson, C. Rapid response teams: a systematic review and meta-analysis. Arch Intern Med. 17018–26 (2010).

    Article PubMed Google Scholar

  • Tsai, DJ. ET. The electrocardiogram as an informative indicator of cardiovascular disease in predicting the risk of death. Digit. Health 920552076231187247 (2023).

    Article PubMed PubMed Central Google Scholar

  • Raghunath, S. et al. Mortality prediction from 12-lead electrocardiogram voltage data using a deep neural network. Net Med. 26886–891 (2020).

    Article CAS PubMed Google Scholar

  • Plana, D. et al. Randomized clinical trials of machine learning interventions in healthcare: a systematic review. JAMA NATO. open 5e2233946 (2022).

    Article PubMed PubMed Central Google Scholar

  • Atiya, Z.I. et al. Screening for cardiac contractile dysfunction using artificial intelligence-driven electrocardiogram. Net Med. 2570–74 (2019).

    Article CAS PubMed Google Scholar

  • Atiya, ZI et al. An artificial intelligence-driven ECG algorithm for identifying patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. The Lancet 394861–867 (2019).

    Article PubMed Google Scholar

  • Downey, CL, Tahir, W, Randall, R, Brown, JM & Jain, DG Powers and limitations of early warning scores: a systematic review and narrative synthesis. Int J Nurse Connect. 76106–119 (2017).

    Article CAS PubMed Google Scholar

  • van der Sies, H., Aarts, J., Wilto, A. and Berg, M. Overriding Drug Safety Alerts in Computerized Physician Order Entry. JM Med. inform. Association 13138–147 (2006).

    Article PubMed PubMed Central Google Scholar

  • Bedoya, AD et al. Minimal impact of an implemented early warning score and best practice alert for patient deterioration. Criticism Care Med. 4749–55 (2019).

    Article PubMed PubMed Central Google Scholar

  • Embey, PJ and Leonard, EHR-based alert fatigue estimator over time for clinical trial alerts: results from a randomized controlled study. JM Med. inform. Association 19e145–e148 (2012).

    Article PubMed PubMed Central Google Scholar

  • Lyons, PG, Adelson, DP and Chorpek, MM Rapid Response System. Resuscitation 128191–197 (2018).

    Article PubMed PubMed Central Google Scholar

  • Iung, B. & Vahanian, A. Epidemiology of valvular heart disease in adults. Net Rev. Cardiol. 8162–172 (2011).

    Article PubMed Google Scholar

  • De Lemos, JA, McGuire, DK and Drazner, MH B-type natriuretic peptide in cardiovascular disease. The Lancet 362316–322 (2003).

    Article CAS PubMed Google Scholar

  • Coliff, MH et al. A randomized trial of real-time automated medical deterioration alerts sent to a rapid response team. J. Hosp. Med. 9424–429 (2014).

    Article PubMed Google Scholar

  • Perkins, GD, Temple, RM & George, R. Time to intervene: lessons from the NCEPOD report. Resuscitation 831305–1306 (2012).

    Article PubMed Google Scholar

  • Yao, X. et al. Artificial intelligence-driven electrocardiograms to identify patients with low ejection fraction: a practical, randomized clinical trial. Net Med. 27815–819 (2021).

    Article CAS PubMed Google Scholar

  • Austrian, J. et al. Applying A/B testing to clinical decision support: rapid randomized controlled trials. J Med Internet Res. 23e16651 (2021).

    Article PubMed PubMed Central Google Scholar

  • Horwitz, LI, Kuznetsova, M. & Jones, SA Building a learning health system through rapid, randomized testing. N Engl J Med 3811175–1179 (2019).

    Article PubMed Google Scholar

  • Simon, GE, Platt, R. and Hernandez, AF evidence from clinical trials during routine care – Leaning towards a learning health system. N Engl J Med 3821488–1491 (2020).

    Article PubMed Google Scholar

  • Liu, X., Cruz Rivera, S., Moher, D., Calvert, MJ & Denniston, AK Reporting Guidelines for Clinical Trial Reports Involving Artificial Intelligence: CONSORT-AI Extension. Net Med. 261364–1374 (2020).

    Article CAS PubMed PubMed Central Google Scholar

  • Crawford, MH et al. ACC/AHA Guidelines for Ambulatory Electrocardiography. A report by the American College of Cardiology/American Heart Association Task Force on Practice Guidelines prepared in collaboration with the North American Society for Pacing and Electrophysiology. JM Coll Cardiol. 34912–948 (1999).

    Article CAS PubMed Google Scholar

  • Attia, ZI, Harmon, DM, Behr, ER and Friedman, PA Application of artificial intelligence to the electrocardiogram. Your Heart J. 424717–4730 (2021).

    Article CAS PubMed Google Scholar

  • Huang, P.-F., Kung, P.-T., Chou, W.-Y. and Tsai, W.-C. Characteristics and associated factors of emergency department visits, readmissions, and hospital transfers under a DRG-based payment system: a nationwide cohort study. Plus one 15e0243373 (2020).

    Article CAS PubMed PubMed Central Google Scholar

  • Ribeiro, ALP et al. Sami Tropp: 12-lead ECG with interpretations of age and mortality. Zenodo https://doi.org/10.5281/zenodo.4905617 (2021).

  • Ribeiro, AH et al. CODE-15%: a large-scale annotated dataset of 12-lead ECGs. Zenodo https://doi.org/10.5281/zenodo.4916205 (2021).

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