Intensivists

Association between ePWV and delirium risk in icu sepsis patients: a retrospective analysis based on the MIMIC database and development of a predictive model.

BACKGROUND: Sepsis, frequently complicated by delirium with poor prognosis, is common in the Intensive Care Unit (ICU). Estimated Pulse Wave Velocity (ePWV), a non-invasive arterial stiffness marker, remains unexplored regarding delirium risk in ICU sepsis patients. This study investigated ePWV's association with sepsis-linked delirium and developed a predictive model. METHODS: This retrospective ...

Unsupervised machine learning with Cluster Analysis in patients with rheumatoid arthritis: insights in CV risk phenotypes from a multicentre Italian cohort.

Rheumatoid arthritis (RA) patients face increased cardiovascular (CV) risk, yet existing 10-year estimation tools often underperform. This study evaluated whether unsupervised machine learning (uML) clustering can identify distinct RA patient phenotypes with varying major cardiovascular events (MACE) potentially improving risk stratification. A cross-sectional, multi-center cohort of RA patients m...

Research progress in traditional Chinese medicine for lymphoma: multi-pathway and multi-drug combination approaches.

OBJECTIVES: Lymphoma is a prevalent hematologic malignancy with complex pathogenesis involving dysregulated signaling pathways and high treatment resi...

Dedicated Endoscopy for Barrett's Oesophagus With Higher Dysplasia Yield May Reduce Seattle Protocol Biopsies: Results From UK Multicentre Study.

BACKGROUND: Dedicated service for Barrett's oesophagus (BO) surveillance may be more effective than conventional service, according to some single cen...

Harnessing Artificial Intelligence to Strengthen Acute and Critical Care Nursing Practice: A Systematic Review.

BACKGROUND: Artificial intelligence (AI) is reshaping clinical decision support systems (CDSSs). In acute and critical care, nurses provide continuous...

The Role of Machine Learning and Artificial Intelligence in Enhancing Critical Care Nursing Practice: A Scoping Review.

BACKGROUND: Artificial intelligence (AI) and machine learning (ML) are emerging as transformative tools in healthcare, with significant potential to e...

An Interpretable Machine Learning Approach to Ecologically Characterize Soil Carbon and Structure From Multi-Kingdom Microbiome, Texture and Climate.

Soil physical structure is a critical determinant of agricultural landscape resilience, yet standard pedotransfer functions estimate soil hydraulic an...

Multi-level Transcriptomic and Machine-learning Analyses Identify MZT1 as a Proliferation-associated Prognostic Marker in Lung Adenocarcinoma.

BACKGROUND/AIM: Lung adenocarcinoma (LUAD) exhibits substantial molecular heterogeneity and variable clinical outcomes, highlighting the need for biom...

Artificial intelligence in emergency medicine: from algorithmic performance to real-world clinical integration - understanding the implementation gap.

BACKGROUND: Emergency departments (EDs) operate under time pressure, diagnostic uncertainty, and cognitive overload. Artificial intelligence (AI)-driv...

Prospective economic evaluation of a predictive artificial intelligence model for sepsis: Effects on hospital costs and return on investment.

Early sepsis detection is essential for improving outcomes and reducing costs, but traditional rule-based systems have limited accuracy and real-world...

Glucose-to-albumin ratio and mortality in ICU patients with acute pancreatitis.

OBJECTIVES: Acute pancreatitis (AP) carries a high mortality risk in ICU patients. The glucose-to-albumin ratio (GAR), reflecting metabolic stress and...

ESM2-Kcr: a novel model combining protein language model and deep learning methods for predicting lysine crotonylation sites.

Lysine crotonylation (Kcr) is a vital posttranslational modification that plays a significant role in diverse biological processes such as DNA replica...

Real-Time Dynamic Prediction of Mortality and Renal Replacement Therapy After Cardiac Surgery Using a Time-Series Deep Learning Model.

OBJECTIVE: Predicting postoperative deterioration following cardiac surgery remains challenging. Conventional risk scores rely on static variables and...

Development of a machine-learning risk stratification tool for vasoactive medication need after two-bolus fluid resuscitation in pediatric suspected sepsis.

BACKGROUND: Timely vasopressor initiation is critical in fluid-refractory pediatric septic shock, yet clinicians lack objective tools to identify chil...

The LoVe score: Development and validation of a bedside clinical index to identify patients at risk for prolonged invasive mechanical ventilation.

BACKGROUND: Early management decisions after intubation, such as humidification strategy or initiation of prevention bundles for ventilator-associated...

Traumatic Brain Injury: Multi-Omics Insights into Brain Aging, Neurodegeneration, and Precision Therapeutics.

Traumatic brain injury (TBI) is a major cause of mortality and persistent neurological disability and is increasingly recognized as a potential contri...

Comparison of Machine Learning-Based Reporting with Surgeon Reporting of Postoperative Complication in Cardiothoracic Morbidity and Mortality Conferences.

BACKGROUND: Traditional morbidity and mortality (M&M) conferences incompletely capture postoperative complications, potentially limiting quality impro...

Completely free-breathing cardiac MRI using deep learning reconstruction reduces sedation and scan time in children.

BACKGROUND: Traditional cardiac magnetic resonance (CMR) imaging scan times are long and require breath-holds, often necessitating intubation and mech...

Development and Validation of an Interpretable Machine Learning Model for Predicting In-Hospital Mortality in Diabetic Patients with Sepsis-Associated Acute Kidney Injury.

BackgroundSepsis-associated acute kidney injury (SA-AKI) is a common and severe complication in critically ill patients, with poor prognosis. Diabetes...

Leveraging ECG foundation models in critical care for sinus rhythm and atrial fibrillation classification.

BACKGROUND: Recent advances in deep learning have led to the development of ECG foundation models (ECG-FMs) trained with self-supervised learning, whi...