A Machine Learning Prediction Model for Non-cardiogenic Out-of-hospital Cardiac Arrest with Initial Non-shockable Rhythm

Objectives The purpose of this study was to develop and validate a machine learning prediction model for the prognosis of non-cardiogenic out-of-hospital cardiac arrest (OHCA) with an initial non-shockable rhythm.Design Data were obtained from a nationwide OHCA registry in Japan. Overall, 222,056 pa...

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Bibliographic Details
Published in:Juntendo Iji Zasshi = Juntendo Medical Journal 2023, Vol.69(3), pp.222-230
Main Authors: KARATSU, SHINSUKE, HIRANO, YOHEI, KONDO, YUTAKA, OKAMOTO, KEN, TANAKA, HIROSHI
Format: Article
Language:English
Subjects:
cardiac arrest
machine learning
non-shockable rhythm
Original
prediction model
termination of resuscitation
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Summary:Objectives The purpose of this study was to develop and validate a machine learning prediction model for the prognosis of non-cardiogenic out-of-hospital cardiac arrest (OHCA) with an initial non-shockable rhythm.Design Data were obtained from a nationwide OHCA registry in Japan. Overall, 222,056 patients with OHCA and an initial non-shockable rhythm were identified from the registry in 2016 and 2017. Patients aged
ISSN:2187-9737
2188-2126
DOI:10.14789/jmj.JMJ22-0035-OA