biostudies-literatureUnknown9(1)Edmunds M<h4>Objectives</h4>To develop a multimodal pediatric critical care datamart supporting predictive modeling and decision support tool development, integrating high-resolution physiologic and clinical data and future clinical deployment.<h4>Materials and methods</h4>We developed a continuously expanding datamart integrating electronic health record data, high-resolution telemetry, and extracorporeal membrane oxygenation (ECMO)-domain datasets. The platform links static and longitudinal time-series variables with expert-curated neurologic outcomes for both ECMO and non-ECMO patients, enabling trajectory-based analyses.<h4>Results</h4>The datamart currently includes 25 762 pediatric patients, of whom 395 received ECMO support. The datamart captures granular longitudinal physiologic, laboratory, medication, and telemetry data suitable for dynamic predictive modeling.<h4>Discussion</h4>Existing ECMO prognostication tools rely on static variables and lack appropriate control cohorts. This datamart enables trajectory-based multimodal modeling, that reflects evolving physiology and neurologic outcomes.<h4>Conclusion</h4>This platform provides a scalable foundation for predictive modeling across pediatric critical care, beyond ECMO, to support precision decision-making and outcomes research.JAMIA openooag011https://www.ebi.ac.uk/biostudies/studies/S-EPMC12907016biostudies-literatureMulti-modal pediatric critical care datamart for extracorporeal support prediction and decision support.PMC12907016Said AEdmunds MGupta AKaster LOh IZhuge YMichelson APayne PSagel JfalseMulti-modal pediatric critical care datamart for extracorporeal support prediction and decision support.<h4>Objectives</h4>To develop a multimodal pediatric critical care datamart supporting predictive modeling and decision support tool development, integrating high-resolution physiologic and clinical data and future clinical deployment.<h4>Materials and methods</h4>We developed a continuously expanding datamart integrating electronic health record data, high-resolution telemetry, and extracorporeal membrane oxygenation (ECMO)-domain datasets. The platform links static and longitudinal time-series variables with expert-curated neurologic outcomes for both ECMO and non-ECMO patients, enabling trajectory-based analyses.<h4>Results</h4>The datamart currently includes 25 762 pediatric patients, of whom 395 received ECMO support. The datamart captures granular longitudinal physiologic, laboratory, medication, and telemetry data suitable for dynamic predictive modeling.<h4>Discussion</h4>Existing ECMO prognostication tools rely on static variables and lack appropriate control cohorts. This datamart enables trajectory-based multimodal modeling, that reflects evolving physiology and neurologic outcomes.<h4>Conclusion</h4>This platform provides a scalable foundation for predictive modeling across pediatric critical care, beyond ECMO, to support precision decision-making and outcomes research.2026-01-01T00:00:00Z2026 Feb2026-07-10T03:17:23.418Z2026-07-10T03:08:48.3ZS-EPMC129070164170522510.1093/jamiaopen/ooag011