In the last decade, large datasets have been widely available for clinical research, thanks to the increasing use of digital health records systems. It was recently reported the healthcare data volume is increasing at a rate of almost 50% annually  and is expected to reach over 2000 exabytes (1018 bytes) in 2020. This development, combined with increased computational power, offers an interesting perspective.
As an example, the Medical Information Mart for Intensive Care (MIMIC-III) database includes over 54 000 hospital admissions in critical care , with vital signs, laboratory tests, reports, medications, ICD (International Classification of Diseases) codes. Similarly, the eICU Collaborative Research Database includes patients admitted to critical care units with vital signs, laboratory tests and medications . Similar databases, applied to cardiac arrest patients, might offer a perspective to describe more accurately subgroups of patients with their early prognostic factors. Indeed, as recently described in a review by Narayan et al.[29▪▪] establishing a better definition of sudden cardiac arrest, and identifying subgroups of phenotypes (instead of considering cardiac arrest as a monolithic event) is crucial to better address its pathology. Especially, regarding early prognostication at admission, a definition of precise phenotypes could enhance previously established models, and addition of initial vital signs and/or laboratory results could dramatically increase the predictive power of existing models. Tailoring specific treatments in the post-ROSC period (such as coronary angiogram or targeted temperature management), according to predicted prognosis (as previously described for coronary angiogram according to CAHP score ) could help to optimize efficiency of care in this setting, and to avoid unnecessary treatments.
In addition to these promising elements, management of massive datasets requires a specific, careful data management. A major pitfall can result from unlabelled and/or uncorrected data, with uncertain quality. Considering the usually automated gathering of data in dataset (for example, regarding Electronic Health Records or vital parameters), medical accuracy of data must be assessed. Moreover, several data cannot be used because they are unstructured (for example, free-text reports). Use of data from electronic medical records, thus requires prior assessment of their reliability.
In order to provide more personalized care, and thanks to big datasets previously described, a new approach, such as machine learning could be used  These techniques have been previously described in other cardiologic diseases (such as electrocardiogram analysis [32,33] or echocardiogram ) and in the critical care setting (for example, to identify optimal treatment strategies for sepsis in the ICU [35▪]). To the best of our knowledge, the use of machine learning has not been reported in the aim of establishing new prediction models in cardiac arrest research.
Different approaches could be used. Machine learning can be either supervised or unsupervised [36▪]. In a supervised approach, the algorithm aims to accurately establish and predict a specific characteristic, such as outcome. Supervised machine learning could be used to predict early outcome (i.e. death or unfavourable neurological outcome at hospital discharge) after cardiac arrest, using parameters available during the first hours (Utstein parameters, but also clinical and biological data). By contrast, unsupervised methods can have exploratory goals, to identify hidden clusters or relations between variables. Unsupervised learning could be useful to identify phenotypes of patients with a certain risk profile after hospital admission.
Overall, enrichment of datasets offers great potential for new approaches in the field of cardiac arrest research, including machine learning (either with or without preconditions, thanks to unsupervised approach). Potential applications are promising, and development of large datasets should be promoted to allow research in cardiac arrest using an approach previously validated in other fields of cardiovascular research. However, whether development of artificial intelligence will make ‘doctors obsolete’ remains highly uncertain .
Papers of particular interest, published within the annual period of review, have been highlighted as:
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5. Chelly J, Mongardon N, Dumas F, et al. Benefit of an early and systematic imaging procedure after cardiac arrest
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: a statement for healthcare professionals from a task force of the International Liaison Committee on Resuscitation (American Heart Association, European Resuscitation Council, Australian and New Zealand Council on Resuscitation, Heart and Stroke Foundation of Canada, InterAmerican Heart Foundation, Resuscitation Council of Southern Africa, Resuscitation Council of Asia); and the American Heart Association Emergency Cardiovascular Care Committee and the Council on Cardiopulmonary, Critical Care, Perioperative and Resuscitation. Circulation 2015; 132:1286–1300.
8. Dumas F, Rea TD, Fahrenbruch C, et al. Chest compression alone cardiopulmonary resuscitation is associated with better long-term survival compared with standard cardiopulmonary resuscitation. Circulation 2013; 127:435–441.
9. Geri G, Dumas F, Bougouin W, et al. Immediate percutaneous coronary intervention is associated with improved short- and long-term survival after out-of-hospital cardiac arrest
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18. Maupain C, Bougouin W, Lamhaut L, et al. The CAHP (Cardiac Arrest
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19. Nielsen N, Wetterslev J, Cronberg T, et al. Targeted temperature management at 33°C versus 36°C after cardiac arrest
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21. Nishikimi M, Matsuda N, Matsui K, et al. CAST: a new score for early prediction
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22. Nishikimi M, Matsuda N, Matsui K, et al. A novel scoring system for predicting the neurologic prognosis prior to the initiation of induced hypothermia in cases of postcardiac arrest syndrome: the CAST score. Scand J Trauma Resusc Emerg Med 2017; 25:49.
23▪. Bascom KE, Dziodzio J, Vasaiwala S, et al. Derivation and validation of the CREST model for very early prediction
of circulatory etiology death in patients without ST-segment-elevation myocardial infarction after cardiac arrest
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Prediction model including patients from 44 international centres.
24. Hunziker S, Bivens MJ, Cocchi MN, et al. International validation of the out-of-hospital cardiac arrest
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25. Rea TD, Cook AJ, Stiell IG, et al. Resuscitation Outcomes Consortium Investigators. Predicting survival after out-of-hospital cardiac arrest
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26. Stanford Medicine 2017 Health Trends Report - Harnessing the Power of Data in Health.
27. Johnson AEW, Pollard TJ, Shen L, et al. MIMIC-III, a freely accessible critical care database. Sci Data 2016; 3:160035.
29▪▪. Narayan SM, Wang PJ, Daubert JP. New concepts in sudden cardiac arrest
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Very interesting review with a state-of-the-art in the field of sudden death, identifying future areas of research.
30. Bougouin W, Dumas F, Karam N, et al. Sudden Death Expertise Center. Should we perform an immediate coronary angiogram in all patients after cardiac arrest
?: Insights from a large French Registry. JACC Cardiovasc Interv 2018; 11:249–256.
31. Beam AL, Kohane IS. Big data and machine learning in healthcare. JAMA 2018; 319:1317–1318.
32. Hannun AY, Rajpurkar P, Haghpanahi M, et al. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat Med 2019; 25:65–69.
33. Attia ZI, Kapa S, Lopez-Jimenez F, et al. Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram. Nat Med 2019; 25:70–74.
34. Zhang J, Gajjala S, Agrawal P, et al. Fully automated echocardiogram interpretation in clinical practice. Circulation 2018; 138:1623–1635.
35▪. Komorowski M, Celi LA, Badawi O, et al. The artificial intelligence clinician learns optimal treatment strategies for sepsis in intensive care. Nat Med 2018; 24:1716–1720.
Nice example of the potential use of artificial intelligence in the treatment of sepsis, based on a large US database.
36▪. Johnson KW, Torres Soto J, Glicksberg BS, et al. Artificial intelligence in cardiology. J Am Coll Cardiol 2018; 71:2668–2679.
In this review, application of artificial intelligence is described in general, and especially its application in cardiology.
37. Goldhahn J, Rampton V, Spinas GA. Could artificial intelligence make doctors obsolete? BMJ 2018; 363:k4563.