Aarthi Kaviyarasu

In-hospital cardiac arrest (IHCA) affects over 200,000 patients annually in the United States, yet its detection through manual chart review remains resource-intensive and often delayed. We evaluated the performance of four open-source large language models (LLMs) and GPT-4o in identifying IHCA cases from 2,674 clinical notes across five hospitals. While GPT-4o achieved the highest performance (F1-score: 0.90, recall: 0.97), several open-source models demonstrated comparable capabilities, suggesting their viability for clinical applications. Our systematic analysis of model outputs revealed that performance was strongly influenced by site-specific documentation practices, with inter-site agreement rates varying by over 20%. Through detailed error analysis, we identified key challenges including medical terminology hallucinations and structural inconsistencies in model reasoning. These findings establish a framework for implementing LLM-based IHCA detection systems while highlighting critical considerations for their clinical deployment.

Background: Every year the American Heart Association's Resuscitation Science Symposium (ReSS) brings together a community of international resuscitation science researchers focused on advancing cardiac arrest care. Methods and Results: The American Heart Association's ReSS was held in Chicago, Illinois from November 4th to 6th, 2022. This annual narrative review summarizes ReSS programming, including awards, special sessions and scientific content organized by theme and plenary session. Conclusions: By exploring both the science of resuscitation and important related topics including survivorship, disparities, and community‐focused programs, this meeting provided important resuscitation updates.