Intensivists

Harnessing AI in critical care: opportunities, challenges and key steps for success.

BACKGROUND: The integration of artificial intelligence (AI) into critical care offers significant potential to enhance early diagnosis, predict patient deterioration, personalise treatment and inform clinical decision-making. Despite this promise, AI adoption in the intensive care unit (ICU) faces challenges, as illustrated by the limited number of AI tools which have been approved for clinical us...

Chemokine-like factor-like MARVEL transmembrane domain-containing 1 identified as a novel target protein for immune dysregulation in sepsis: A machine learning and molecular dynamics framework for diagnostic biomarkers and therapeutic exploration.

BACKGROUND: Sepsis, a systemic inflammatory response syndrome precipitated by infection, involves critical immune dysregulation. Here, we aimed to evaluate immune-derived diagnostic markers, investigate immune cell infiltration, and explore targeted pharmacological interventions.

Exploring the molecular mechanisms of lactylation-related biological functions and immune regulation in sepsis-associated acute kidney injury.

Lactylation, a novel post-translational modification, has been implicated in various pathophysiological processes; however, its role in sepsis-associa...

Interpretable Machine Learning Prediction Model for Predicting Mortality Risk of ICU Patients With Pressure Ulcers Based on the Braden Scale: A Clinical Study Based on MIMIC-IV.

AIMS: This study was to create an interpretable machine learning model to predict the risk of mortality within 90 days for ICU patients suffering from...

Predicting carbapenem-resistant Pseudomonas aeruginosa infection risk using XGBoost model and explainability.

The prevalence and spread of carbapenem-resistant Pseudomonas aeruginosa (CRPA) is a global public health problem. This study aims to identify the ris...

Machine learning models for predicting severe acute kidney injury in patients with sepsis-induced myocardial injury.

Severe acute kidney injury (sAKI) is a prevalent and serious complication among patients with sepsis-induced myocardial injury (SIMI). Prompt and earl...

Machine learning-based prediction of respiratory depression during sedation for liposuction.

Procedural sedation is often performed by non-anesthesiologists in various settings and can lead to respiratory depression. A tool that enables early ...

A machine learning-based prediction model for sepsis-associated delirium in intensive care unit patients with sepsis-associated acute kidney injury.

Sepsis-associated acute kidney injury (SA-AKI) patients in the ICU often suffer from sepsis-associated delirium (SAD), which is linked to unfavorable ...

DeepCR: predicting cytokine receptor proteins through pretrained language models and deep learning networks.

Cytokine receptors play a pivotal role in mediating the immune response and are critical in cytokine storms, which underlie the pathogenesis of condit...

Cell death-related signature genes: risk-predictive biomarkers and potential therapeutic targets in severe sepsis.

Sepsis is a systemic inflammatory response syndrome that predisposes to severe lung infections (SeALAR) such as sepsis-associated acute lung injury (S...

Discovery of CYP1A1 Inhibitors for Host-Directed Therapy against Sepsis.

Bacterial sepsis remains a leading cause of death globally, exacerbated by the rise of multidrug resistance (MDR). Host-directed therapy (HDT) has eme...

Impact of analytical bias on machine learning models for sepsis prediction using laboratory data.

OBJECTIVES: Machine learning (ML) models, using laboratory data, support early sepsis prediction. However, analytical bias in laboratory measurements ...

A panting behavior-driven assessment framework for summer ventilation quality optimization in layer houses.

Ventilation quality in summer layer houses is critical for heat stress prevention, production performance, and poultry welfare. Addressing the issue o...

Advanced predictive modeling for enhanced mortality prediction in ICU stroke patients using clinical data.

Background Stroke is second-leading cause of disability and death among adults. Approximately 17 million people suffer from a stroke annually, with ab...

Immunogenic cell death biomarkers for sepsis diagnosis and mechanism via integrated bioinformatics.

Immunogenic cell death (ICD) has been implicated in sepsis, a condition with high mortality, through mechanisms involving endoplasmic reticulum stress...

Novel machine learning models for the prediction of acute respiratory distress syndrome after liver transplantation.

Early prediction of acute respiratory distress syndrome (ARDS) after liver transplantation (LT) facilitates timely intervention. We aimed to develop a...

The extent of Skeletal muscle wasting in prolonged critical illness and its association with survival: insights from a retrospective single-center study.

OBJECTIVE: Muscle wasting in critically ill patients, particularly those with prolonged hospitalization, poses a significant challenge to recovery and...

Study protocol for the Rainbow Austrian Longitudinal Family (RALF) study: a longitudinal, multi-method, multi-rater investigation of risk and resilience factors in Austrian LGBTQ+ parent families.

BACKGROUND: Research on LGBTQ+ parent families is evolving to include a growing range of family systems, identities, methodologies, and topics. Howeve...

Analysis of the heterogeneous treatment effect of vasoactive drug dosage and time on hospital mortality across different sepsis phenotypes: a retrospective cohort study.

BACKGROUND: The heterogeneity of sepsis poses challenges for the individualized treatment of vasoactive drugs.

Development and validation of a multi-omics hemorrhagic transformation model based on hyperattenuated imaging markers following mechanical thrombectomy.

This study aimed to develop a predictive model integrating clinical, radiomics, and deep learning (DL) features of hyperattenuated imaging markers (HI...