• SeelyAndrew J EAJEFaculty of Medicine Ottawa, University of Ottawa, Ottawa, ON, Canada. aseely@ohri.ca.Ottawa Hospital Research Institute, Ottawa, ON, Canada. aseely@ohri.ca.Department of Critical Care, The Ottawa Hospital, General Campus, 501 Smyth Road,, Kimberley Newman, Rashi Ramchandani, Christophe Herry, Nathan Scales, Natasha Hudek, Jamie Brehaut, Daniel Jones, Tim Ramsay, Doug Barnaby, Shannon Fernando, Jeffrey Perry, Sonny Dhanani, and BurnsKaren E AKEAInterdepartmental Division of Critical Care, University of Toronto, Toronto, ON, Canada.Division of Critical Care Medicine, Department of Medicine, Unity Health Toronto-St Michael's Hospital, Toronto, ON, Canada.Li Ka Shing Knowledge Inst.
• Faculty of Medicine Ottawa, University of Ottawa, Ottawa, ON, Canada. aseely@ohri.ca.
• Crit Care. 2024 Dec 5; 28 (1): 404404.

BackgroundContinuous waveform monitoring is standard-of-care for patients at risk for or with critically illness. Derived from waveforms, heart rate, respiratory rate and blood pressure variability contain useful diagnostic and prognostic information; and when combined with machine learning, can provide predictive indices relating to severity of illness and/or reduced physiologic reserve. Integration of predictive models into clinical decision support software (CDSS) tools represents a potential evolution of monitoring.MethodsWe perform a review and analysis of the multidisciplinary steps required to develop and rigorously evaluate predictive clinical decision support tools based on monitoring.ResultsDevelopment and evaluation of waveform-based variability-derived predictive models involves a multistep, multidisciplinary approach. The stepwise processes involves data science (data collection, waveform processing, variability analysis, statistical analysis, machine learning, predictive modelling), CDSS development (iterative research prototype evolution to commercial tool), and clinical research (observational and interventional implementation studies, followed by feasibility then definitive randomized controlled trials), and poses unique challenges (including technical, analytical, psychological, regulatory and commercial).ConclusionsThe proposed roadmap provides guidance for the development and evaluation of novel predictive CDSS tools with potential to help transform monitoring and improve care.© 2024. The Author(s).

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