Leveraging Large Language Models to Identify In-Hospital Cardiac Arrest

Abstract Manual chart abstraction is the gold standard for identifying in-hospital cardiac arrest (IHCA) but is resource-intensive. Diagnosis codes are a widely used alternative given their accessibility and automated nature, but this method has poor sensitivity and positive predictive value. We present a novel large language model (LLM) approach to identify IHCA and location, highlighting the potential of LLMs for rapid, accurate, and automated IHCA identification from clinical notes. Competing Interest Statement The authors have declared no competing interest. Funding Statement This study did not receive funding. Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study was approved by the Cedars-Sinai Medical Center Institutional Review Board. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data Availability Prompts are available upon reasonable request.