Precision Care in Cardiac Arrest: ICECAP (PRECICECAP) Study Protocol and Informatics Approach

Area of Science:

• Critical care medicine
• Machine learning applications in healthcare
• Neurocritical care

Background:

• Cardiac arrest presents significant heterogeneity, leading to neutral trial outcomes due to varied patient responses to treatment.
• Personalized medicine approaches are needed to optimize treatment efficacy in critical care settings.
• The Precision Care in Cardiac Arrest: Influence of Cooling duration on Efficacy in Cardiac Arrest Patients (PRECICECAP) study addresses this by applying advanced analytics.

Purpose of the Study:

• To discover novel biomarker signatures for predicting optimal therapeutic hypothermia duration in cardiac arrest survivors.
• To predict 90-day functional outcomes using machine learning on high-resolution patient data.
• To develop a freely available software platform for standardized intensive care unit data curation for machine learning.

Main Methods:

• Utilizing data from the Influence of Cooling duration on Efficacy in Cardiac Arrest Patients (ICECAP) study, including waveforms and DICOMs.
• Employing an autoencoder neural network to represent raw waveform data.
• Developing a supervised deep learning algorithm to predict functional outcomes based on comprehensive patient features.

Main Results:

• The PRECICECAP study is currently enrolling participants.
• Completion of the study is anticipated in late 2025.
• Data analysis will focus on identifying predictive biomarkers and optimizing hypothermia duration.

Conclusions:

• PRECICECAP aims to personalize neurocritical care for cardiac arrest patients based on individual needs and treatment response.
• The study will advance the goal of tailored treatment strategies in critical care.
• The developed software platform will have broad applications in hospital-based research for acute conditions.