PREcision Care In Cardiac ArrEst - ICECAP (PRECICECAP)

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Project Summary: The PREcision Care In Cardiac ArrEst - ICECAP (PRECICECAP) parent R01 study aims to discover novel biomarker signatures after cardiac arrest that predict treatment responsiveness and long-term recovery. Cardiac arrest is a major public health problem and top cause of morbidity and mortality nationally. Improving survival and functional recovery is of critical importance. We hypothesize that through innovative, multi-parametric data driven approaches we will be able to identify distinct clinically relevant subgroups of patients. Our primary analytical plan allows us to achieve our aims but is not readily generalizable or usable by others. An ever-growing number of NIH-funded projects amass and analyze similar datasets and develop their own custom solutions. This ad hoc approach is costly, inefficient, and threatens both rigor and reproducibility. The objective of this supplement to PRECICECAP is to complete a publicly available software platform that allows end-to-end curation of complex neurocritical care data to create artificial intelligence/machine learning (AI/ML) ready analytical datasets from raw waveform data. Building on our previous work, we will address the remaining gaps needed to achieve this goal by: 1) developing standard processes to ensure data quality, segmentation, and sampling; 2) feature engineering; and, 3) external validation of our entire pipeline. This work aligns with and supports NIH goals for modernizing the biomedical research data ecosystem by developing a software product that can handle AI/ML on this type of complex data. It will also allow the sharing of a finalized analytical data set for use by others. By continuing our proven collaboration between clinician investigators, data scientists and industry, we will take NIH-supported data from the PRECICECAP study and make it broadly available and easily usable. The project delivers an important software tool that can be used by others conducting similar research, advancing the NIH’s mission to make complex data FAIR (Findable, Accessible, Interoperable, and Reusable). The result will facilitate an open, wide collaboration between scientists, using similar data.