Analyzing resuscitation conference content through the lens of the chain of survival

Background: Resuscitation science today often focuses on advanced topics such as extracorporeal cardiopulmonary resuscitation or targeted temperature management. However, the specific topics presented at resuscitation conferences have not been thoroughly analyzed. We thus analyzed resuscitation conferences abstracts using a chain of survival framework. Methods: Two major resuscitation conferences (Resuscitation in Greece and Resuscitation Science Symposium in the USA) took place in the fall of 2024. We categorized all abstracts using chain of survival framework, analyzing authors’ countries by geography and income. Additionally, artificial intelligence, deep learning, and machine learning approaches for data analysis were examined. Results: “Recognition and prevention” was the top category at both conferences, comprising 37% of topics at Resuscitation 2024 and 32% at Resuscitation Science Symposium 2024. “Early Call for Help”, “High-quality Cardiopulmonary Resuscitation”, and “Recovery and rehabilitation” were underrepresented, with each <8%. At Resuscitation Science Symposium 2024, “Post-cardiac arrest care” (31%) and “Early defibrillation and advanced life support” (26%) were emphasized, compared to 21% each at Resuscitation 2024 for both chains. Resuscitation 2024 featured participants from 51 countries while Resuscitation Science Symposium 2024 included participants from 19 countries, predominantly high-income ones. At Resuscitation 2024, 54 abstracts, and at Resuscitation Science Symposium 2024, 47 abstracts used machine learning, each with one employing artificial intelligence. None used deep learning. Conclusions: Conference abstracts aligned mainly with the early links of chain of survival and employing machine learning as a data analysis tool. Expanding participation from low-income countries could enhance inclusivity and contribute valuable perspectives to resuscitation science.