Advertisement

Can machine learning predict recurrent cardiac arrest?

      Out-of-hospital cardiac arrest (OHCA) treated by emergency medical services (EMS) is an important global health issue, affecting an estimated 30–97 individuals per 100,000 population annually.
      • Kiguchi T.
      • Okubo M.
      • Nishyama C.
      • et al.
      Out-of-hospital cardiac arrest across the World: First report from the International Liaison Committee on Resuscitation (ILCOR).
      Advancements in resuscitation science have led to incremental improvements in survival, though overall outcomes remain relatively poor.
      • Berdowski J.
      • Berg R.A.
      • Tijssen J.G.P.
      • Koster R.W.
      Global incidences of out-of-hospital cardia arrest and survival rates: Systematic review of 67 prospective studies.
      Consequently, much of the scientific literature has focused on identifying and modifying the factors that affect short-term outcomes such as return of spontaneous circulation (ROSC), survival to hospital admission or discharge, and short-term neurofunctional status. Relatively little work has addressed long-term outcomes, and there is in particular a paucity of literature exploring recurrent OHCA, which may affect more than 10% of this population.
      • Held E.P.
      • Reinier K.
      • Chugh H.
      • et al.
      Recurrent Out-of-Hospital Sudden Cardiac Arrest: Prevalence and Clinical Factors.
      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Resuscitation
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Kiguchi T.
        • Okubo M.
        • Nishyama C.
        • et al.
        Out-of-hospital cardiac arrest across the World: First report from the International Liaison Committee on Resuscitation (ILCOR).
        Resuscitation. 2020; 152: 39-49
        • Berdowski J.
        • Berg R.A.
        • Tijssen J.G.P.
        • Koster R.W.
        Global incidences of out-of-hospital cardia arrest and survival rates: Systematic review of 67 prospective studies.
        Resuscitation. 2010; 81: 1479-1487
        • Held E.P.
        • Reinier K.
        • Chugh H.
        • et al.
        Recurrent Out-of-Hospital Sudden Cardiac Arrest: Prevalence and Clinical Factors.
        Circ Arrhythm Electrophysiol. 2022; 15: 793-800
        • Hellsén G.
        • Rawshani A.
        • Skoglund K.
        • et al.
        Predicting recurrent cardiac arrest in individuals surviving Out-of-Hospital cardiac arrest.
        Resuscitation. 2023;
        • Blomberg S.N.
        • Folke F.
        • Ersboll A.K.
        • et al.
        Machine learning as a supportive tool to recognize cardiac arrest in emergency calls.
        Resuscitation. 2019; 138: 322-329
        • Kwon J.
        • Jeon K.
        • Kim H.M.
        • et al.
        Deep-learning-based out-of-hospital cardiac arrest prognostic system to predict clinical outcomes.
        Resuscitation. 2019; 139: 84-91
        • Hirano Y.
        • Kondo Y.
        • Sueyoshi K.
        • et al.
        Early outcome prediction for out-of-hospital cardiac arrest with initial shockable rhythm using machine learning models.
        Resuscitation. 2021; 158: 49-56
        • Lu T.C.
        • Wang C.H.
        • Chou F.Y.
        • et al.
        Machine learning to predict in-hospital cardiac arrest from patients presenting to the emergency department.
        Intern Emerg Med. 2022; (Epub ahead of print)
        • Wu T.T.
        • Lin X.Q.
        • Mu Y.
        • Li H.
        • Guo Y.S.
        Machine learning for early prediction of in-hospital cardiac arrest in patients with acute coronary syndrome.
        Clin Cardiol. 2021; 44: 249-256
        • Moffat L.M.
        • Xu D.
        Accuracy of machine learning models to predict in-hospital cardiac arrest.
        Clin Nurse Spec. 2022; 36: 29-44
        • Mayampurath A.
        • Hagopian R.
        • Venable L.
        • et al.
        Comparison of machine learning methods for predicting outcomes after in-hospital cardiac arrest.
        Crit Care Med. 2022; 50: e162-e172
        • Stevens R.D.
        Machine learning to decode the electroencephalography for post cardiac arrest neuroprognostication.
        Crit Care Med. 2019; 47: 1474-1476
        • Kaul V.
        • Enslin S.
        • Gross S.A.
        History of artificial intelligence in medicine.
        Gastroinest Endosc. 2020; 92: 807-812
        • Oakes J.
        • Rossi P.H.
        The measurement of SES in health research: current practice and steps toward a new approach.
        Social Sci Med. 2003; 56: 769-784
        • van Nieuwenhuizen B.P.
        • Oving I.
        • Kunst A.E.
        • et al.
        Socioeconomic differences in incidence, bystander cardiopulmonary resuscitation and survival from out-of-hospital cardiac arrest: a systematic review.
        Resuscitation. 2019; 141: 44-62
        • Lee S.Y.
        • Park J.H.
        • Lee J.
        • et al.
        Individual socioeconomic status and risk of out-of-hospital cardiac arrest: a nationwide case-control analysis.
        Acad Emerg Med. 2022; 12: 1438-1446
      1. OECD. Income inequality (indicator). doi: 10.1787/459aa7f1-en. Accessed 10 January 2023.

        • van der Lingen A.C.J.
        • Woudstra J.
        • Becker M.A.J.
        • et al.
        Recurrent ventricular arrhythmias and mortality in cardiac arrest survivors with a reversible cause with and without an implantable cardioverter defibrillator: a systematic review.
        Resuscitation. 2022; 173: 76-90