Volume 25 - Issue 5, 2025

The intensive care unit (ICU) remains one of the most complex, data-rich, and time-pressured environments in healthcare. Artificial intelligence (AI) promises a profound transformation of critical care, offering the potential to improve diagnosis, monitoring, and therapeutic decision-making in ways that can reshape ICU workflows, enhance patient outcomes, and streamline resource use. One of the most...

Artificial intelligence holds unprecedented potential to transform healthcare, yet the current evidence base for AI applications in critical care remains limited. This article presents the ABCDEF framework to guide critical care clinicians in evaluating AI-based tools and demonstrate safety and effectiveness in real-world clinical environments. The "measure of a man" has been a topic of discussion...

Postoperative ICU admission is a complex clinical decision influenced by multiple factors, including patient characteristics, surgical type, intraoperative variables and organisational constraints. Traditional risk stratification tools have limitations in accuracy and consistency. Artificial intelligence models demonstrate promising predictive capability for ICU admission risk across various surgical c...

As intensive care units worldwide face mounting pressure from staffing shortages, technological complexity, and the need for improved patient outcomes, Mindray convened a roundtable of leading critical care experts to envision the future of the smart ICU. With ICUs around the world under growing strain from workforce shortages, expanding technological demands, and the constant pursuit of better patie...

The steps for development and deployment of the CODE-ICH framework and two AI-powered tools—HEADS-UP and SAHVAI—are detailed. Our goal was to transform acute ICH management through real-time detection, volumetric analysis, and predictive modelling. Intracerebral haemorrhage (ICH) remains one of the most devasting forms of stroke encountered in the neurocritical unit (NICU), with high early mort...

Pancreatic stone protein (PSP), an early sepsis biomarker measurable at the bedside, offers a complementary tool to enhance sepsis detection in emergency department patients who present with minimal clinical signs. When combined with existing clinical scoring systems, PSP improves early identification of high-risk patients, addressing a critical gap in sepsis management. Sepsis remains a global healt...

Artificial intelligence presents an unprecedented opportunity for critical care. But crucial questions about implementation, safety and responsibility demand immediate attention. Artificial intelligence (AI) has become ubiquitous across industries, with seemingly every technology now touting AI capabilities amongst its features. The exponential growth of AI-related publications—from a 36-fold inc...

Vasopressin, an adjunctive vasopressor agent in septic shock, is increasingly supported by evidence favouring early, targeted intervention. Recent research, from mechanistic reviews to artificial intelligence-driven modelling, converges on a coherent strategy: initiate vasopressin earlier, at lower norepinephrine doses, and before severe metabolic derangement occurs. For nearly two decades, adjunctiv...

We explore how echocardiography and cardiac output monitoring are becoming more accessible through AI-enabled ultrasound tools and the recent integration of pulse contour analysis into standard multiparameter bedside monitors. Echocardiography has become an indispensable diagnostic tool in the intensive care unit (ICU), providing critical insights into cardiac function and systemic haemodynamics (May...

Computer vision (CV) technology offers transformative potential for continuous patient monitoring in healthcare settings. By leveraging artificial intelligence to interpret visual data, CV systems can detect subtle physiological and behavioural changes that may be missed between routine observations. This article explores the evolution of CV from other industries to healthcare applications, examining its r...

Working in the ICU can be stressful, especially when the stakes are high, patients' conditions are complex and deteriorating, and life or death decisions need to be made quickly. Many challenges to patient safety can and do arise, often engaging one of the most difficult tasks: the need to escalate. Failures to escalate are a significant cause of errors and compromises to patient safety. Our goal is to des...

From ANDROMEDA-SHOCK 1 to ANDROMEDA-SHOCK 2, capillary refill time evolved from a bedside sign to a personalised, physiology-based resuscitation target with direct implications for clinical practice and ICU organisation. The evolution of haemodynamic resuscitation in septic shock reflects the identity of critical care as a field that balances protocolised pathways with individualised physiology. Fe...

Dynamic indices have been introduced as a complementary tool for haemodynamic monitoring, leveraging cardiopulmonary interactions to assess fluid responsiveness in mechanically ventilated patients. In patients with obesity, fluid management poses an even greater challenge due to marked cardiovascular and pulmonary alterations that can compromise the accuracy and reliability of these indices. Consequently,...

For a full listing of events visit https://iii.hm/icuevents2025 1-3 Critical Care Canada Forum Toronto, Canada https://iii.hm/1xy0 2-4 27th Refresher Course on Cardiovascular and Respiratory Physiology in Intensive Care Medicine Brussels, Belgium https://iii.hm/1xy1 3-5 DIVI25 Hamburg, Germany https://iii.hm/1xy2...

Issues/ Pages Volume 25, Issue 1 1-59 https://iii.hm/1uie Volume 25, Issue 2 60-128 https//iii.hm/1veh Volume 25, Issue 3 129-197 https//iii.hm/1w93 Volume 25, Issue 4 203-288 https//iii.hm/1xdg Volume 25, Issue 5 292-372 https//iii.hm/1xyp SUBJECT INDEX Acute Respiratory Distress Syndrome Fermín JL, Zamora Guevara IS, Alfaro López CI, Olguin Hernández JM, Cortes JR, Daniel de J...

Alapont VMI 168 https://iii.hm/1w9a Alejandre C 174 https://iii.hm/1w9b Alfaro-López CI 116 https://iii.hm/1vet Antolinez-Motta J 361 https://iii.hm/1xyo Barreto EF 296 https://iii.hm/1xyg Batista-Filho LAC 262 https://iii.hm/1xds Bauer SR 296 https://iii.hm/1xyg Berger E 133 https://iii.hm/1w95 Beauregard-Mora J 191 https://iii.hm/1w9e 245 https://iii.hm/1xdp Bellomo R 228 https://i...