Why the study?
The study was conducted to develop a model using vital sign (heart rate and arterial blood pressure) variability to predict time to death after withdrawal of life-supporting measures.
Does a predictive model using vital sign variability, clinical features, and physician's prediction accurately predict time to death in adult ICU patients after withdrawal of life-sustaining measures?
Does a predictive model using vital sign variability, clinical features, and physician's prediction accurately predict time to death in adult ICU patients after withdrawal of life-sustaining measures?
Machine learning models incorporating vital sign variability, clinical features, and physician prediction can accurately predict time to death after withdrawal of life-sustaining measures, which may aid in decision support for organ donation.
May aid ICU end-of-life timing; leaves open prospective validation before practice use.
No abstract yet. The journal hasn't published one we can show.
No takes yet. Share an insight, caveat, or question.
Scales et al. (2022) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: