Resuscitation
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Growth of machine learning (ML) in healthcare has increased potential for observational data to guide clinical practice systematically. This can create self-fulfilling prophecies (SFPs), which arise when prediction of an outcome increases the chance that the outcome occurs. ⋯ There is a need for broad recognition of SFPs as ML is increasingly applied in resuscitation science and across medicine. Acknowledging this challenge is crucial to inform research and practice that can transform ML from a tool that risks obfuscating and compounding SFPs into one that sheds light on and mitigates SFPs.
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This study aimed to describe the characteristics of cases of out-of-hospital cardiac arrest (OHCA) with an initial asystole rhythm in which extracorporeal cardiopulmonary resuscitation (ECPR) was introduced and discuss the clinical indications for ECPR in such patients. ⋯ A total of 202 ECPR cases with an initial asystole rhythm, including 12 patients with favourable neurological outcomes, were described. Even if the initial cardiac rhythm is asystole, ECPR could be considered if certain conditions are met.