Critical Care

Integrative multi-omics and machine learning identify mitochondrial biomarkers for pathogen-specific sepsis stratification and translational prioritization.

BACKGROUND: Sepsis is a leading cause of critical illness and mortality, yet substantial heterogeneity limits risk stratification and biomarker translation. Mitochondrial dysfunction is widely implicated in sepsis, but genetically supported, multi-layer regulatory features and their clinical relevance remain incompletely characterized. METHODS: We integrated publicly available sepsis GWAS summary ...

Early Prediction of Standing at Discharge in Moderate-to-Severe TBI: A Clinical Machine Learning Model Integrating Modifiable and Nonmodifiable Factors.

OBJECTIVE: To develop and internally validate a machine learning model to predict favorable standing ability at hospital discharge in patients with moderate-to-severe traumatic brain injury (TBI), incorporating both modifiable and nonmodifiable clinical factors. DESIGN: Retrospective cohort study. SETTING: A tertiary academic medical center in Taiwan. PARTICIPANTS: A total of 248 adults with moder...

Screening of Respiratory Toxicity of Environmental Compounds Based on Multimodal Feature Fusion Model.

The respiratory system constitutes the primary interface between the human body and the external environment, demonstrating particular vulnerability t...

Field Foresight-Predicting the Need for Massive Transfusion, ICU Utilization, and Mechanical Ventilation for Traumatically Injured Patients.

INTRODUCTION: Expeditiously predicting outcomes is essential to allocating blood and intensive care resources. We hypothesize the use of external inju...

Differentiation of Benign and Malignant Cervical Lymph Nodes Using a Multi-Modal Ultrasound-Based Machine Learning Model with SHAP Interpretability.

OBJECTIVES: To evaluate the diagnostic value of a machine learning (ML) model based on multi-modal ultrasound features in differentiating benign from ...

Highlights from the Italian National Congress of Imaging in Pulmonology 2025: fostering implementation of advanced technologies for precision patient-centered care.

The Italian National Congress of Imaging in Pulmonology, held in Milan on November 21st, provided a unique educational platform exploring the evolving...

AI-Driven Analysis of Cardiopulmonary Exercise Tests to Identify Gas Exchange and Ventilatory Thresholds.

BACKGROUND: A cardiopulmonary exercise test (CPET) provides the estimated lactate threshold (θLT) and respiratory compensation point (RCP) through vis...

Peak Oxygen Uptake Prediction From Resting and Submaximal Variables of Cardiopulmonary Exercise Testing.

BACKGROUND: Cardiorespiratory fitness, as measured by peak oxygen uptake during cardiopulmonary exercise testing, is a prognostic indicator. We aim to...

Speech-to-Speech Voice-Cloning Care (SVCC) for improving ICU-acquired anxiety for critically ill patients in a tertiary hospital in Beijing, China: protocol of a randomised, controlled trial.

INTRODUCTION: Intensive care unit (ICU) visiting restrictions in hospitals, implemented due to infection control and other factors, limited contact be...

Thoracic muscle loss increases the use of mechanical ventilation in elderly patients with pulmonary embolism: constructing and validating a machine learning model on a two-center cohort.

OBJECTIVE: This study aimed to develop and validate a machine learning (ML) model to predict the need for mechanical ventilation (MV) in elderly patie...

Association between deep learning-based atrial fibrillation burden and in-hospital mortality.

Despite its clinical significance, research on atrial fibrillation (AF) burden as a dynamic, real-time predictor of adverse outcomes in patients with ...

Development of an interpretable machine learning model-based online tool for risk identification of anxiety symptoms in Chinese older adults.

BACKGROUND: As the population aging process has accelerated, anxiety symptoms (AS) among older adults have become a critical concern. In addition to d...

Predictive accuracy of Early Warning Score Systems for Detecting Critically Ill Patients in an Outpatient Setting.

Background No systematic methods exist for triaging outpatients with severe conditions. Our previous pilot study suggested that the National Early War...

In-Hospital Mortality Prediction of Patients Requiring Extracorporeal Membrane Oxygenation Using Composite Lactate Metrics and Weight of Evidence Modeling.

BACKGROUND: Mortality prognostication in adult patients requiring extracorporeal membrane oxygenation (ECMO) is not accurate or established. We hypoth...

Smart Garment for Continuous Respiration Monitoring in Canines.

There is a growing need for at-home respiration monitoring in canines, who are prone to respiratory issues due to breed-specific anatomy and active li...

Development and multicenter validation of an explainable machine learning diagnostic criteria for pediatric abdominal sepsis.

Accurate identification of early pediatric abdominal sepsis (PAS) is essential to improving outcomes, yet most existing pediatric sepsis criteria and ...

Evaluating deep learning sepsis prediction models in ICUs under distribution shift: a multi-centre retrospective cohort study.

Sepsis prediction models trained on ICU data often fail to generalize under external validation because of distribution shift. Prior studies have focu...

Prediction of high-flow nasal cannula failure in critically ill patients: a narrative review.

High-flow nasal cannula (HFNC) therapy is widely used for respiratory support in critically ill patients, offering benefits such as improved oxygenati...

Predicting short-term mortality in severe cirrhosis: An interpretable machine learning model integrating routine clinical indicators.

BACKGROUND: The critical need for precise risk stratification in severe liver cirrhosis is underscored by its substantial 30-day mortality rates, dema...

ARIMA-based forecasting of cerebral physiologic signals in acute traumatic brain injury: a CAnadian high-resolution TBI (CAHR-TBI) cohort study.

BACKGROUND: Traumatic brain injury (TBI) remains a major global health issue, with limited progress in reducing morbidity and mortality for TBI patien...