#CriticalCare

www.stemlynsblog.org

JC: Peri-intubation cardiovascular collapse in critically ill patients.

In the INTUBATE study across 29 countries and thousands of RSIs, Propofol was associated with cardiovascular collapse. @stemlyns

emcrit.org

PulmCrit Wee: Four reasons that hyperangulated videolaryngoscopy should be the mainstay of ICU intubations

As glorious as direct laryngoscopy was, it's time to transition to hyperangulated videolaryngoscopy as the mainstay of intubation among critically ill

www.nursingtimes.net

Nursing Times Awards 2025: Critical and Emergency Care Nursing winner and shortlist

Find out more about the winner and shortlisted entries in the Critical and Emergency Care Nursing category at the 2025 Nursing Times Awards.

www.xiahepublishing.com

Artificial Intelligence for Personalized Critical Care

Critical care medicine requires rapid, high-stakes decisions informed by dynamic and complex streams of patient data. Traditional predictive models have shown value in forecasting deterioration and identifying subphenotypes. However, this leaves a critical gap between anticipating adverse outcomes and guiding therapeutic interventions. Achieving true personalization demands moving beyond generalized protocols toward individualized strategies that account for patient heterogeneity and consequences of alternative clinical actions. Emerging methods in prescriptive artificial intelligence, particularly causal machine learning (causal ML) and reinforcement learning (RL), are beginning to bridge this gap. Causal ML enables estimation of individualized treatment effects by addressing confounding and enabling counterfactual reasoning, allowing clinicians to ask whether a specific intervention is likely to help or harm a given patient. RL can generate adaptive treatment policies that evolve with patient state. The objective of this review is to examine how critical care can progress from generalized prediction to true personalization through the development of prescriptive artificial intelligence. The review contributes by (1) surveying the achievements and limitations of current predictive models, (2) detailing how causal ML and RL can generate individualized treatment effects and sequential decision strategies, (3) identifying the major translational, technical, clinical, ethical, and regulatory barriers to implementation, and (4) outlining future pathways such as digital twins and clinician in the loop systems that may enable safe and actionable personalized decision support at the bedside.