AIMC Topic: Critical Illness
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Relationship between atherogenic index of plasma and length of stay in critically ill patients with atherosclerotic cardiovascular disease: a retrospective cohort study and predictive modeling based on machine learning.
Cardiovascular diabetology
Feb 28, 2025
BACKGROUND: The atherogenic index of plasma (AIP) is considered an important marker of atherosclerosis and cardiovascular risk. However, its potential role in predicting length of stay (LOS), especially in patients with atherosclerotic cardiovascular...
Triglycerides
Humans
Machine Learning
Decision Support Techniques
Databases, Factual
Male
Middle Aged
Intensive Care Units
Risk Assessment
Time Factors
Female
Biomarkers
Predictive Value of Tests
Risk Factors
Prognosis
Length of Stay
Atherosclerosis
Aged
Retrospective Studies
Cholesterol, HDL
Critical Illness
Risk factors and an interpretability tool of in-hospital mortality in critically ill patients with acute myocardial infarction.
Clinical medicine (London, England)
Feb 27, 2025
OBJECTIVE: We aim to develop and validate an interpretable machine-learning model that can provide critical information for the clinical treatment of critically ill patients with acute myocardial infarction (AMI).
Prediction of mortality risk in critically ill patients with systemic lupus erythematosus: a machine learning approach using the MIMIC-IV database.
Lupus science & medicine
Feb 13, 2025
OBJECTIVE: Early prediction of long-term outcomes in patients with systemic lupus erythematosus (SLE) remains a great challenge in clinical practice. Our study aims to develop and validate predictive models for the mortality risk.
Tailoring ventilation and respiratory management in pediatric critical care: optimizing care with precision medicine.
Current opinion in pediatrics
Feb 12, 2025
PURPOSE OF REVIEW: Critically ill children admitted to the intensive care unit frequently need respiratory care to support the lung function. Mechanical ventilation is a complex field with multiples parameters to set. The development of precision med...
Methods for estimating resting energy expenditure in intensive care patients: A comparative study of predictive equations with machine learning and deep learning approaches.
Computer methods and programs in biomedicine
Feb 9, 2025
BACKGROUND: Accurate estimation of resting energy expenditure (REE) is critical for guiding nutritional therapy in critically ill patients. While indirect calorimetry (IC) is the gold standard for REE measurement, it is not routinely feasible in clin...
Interoperable Models for Identifying Critically Ill Children at Risk of Neurologic Morbidity.
JAMA network open
Feb 3, 2025
IMPORTANCE: Decreasing mortality in the field of pediatric critical care medicine has shifted practicing clinicians' attention to preserving patients' neurodevelopmental potential as a main objective. Earlier identification of critically ill children...
The Liver Intensive Care Unit.
Clinics in liver disease
Jan 30, 2025
Major advances in managing critically ill patients with liver disease have improved their prognosis and access to intensive care facilities. Acute-on-chronic liver failure (ACLF) is now a well-defined disease and these patients can be fast-tracked fo...
Development and validation of machine-learning models for predicting the risk of hypertriglyceridemia in critically ill patients receiving propofol sedation using retrospective data: a protocol.
BMJ open
Jan 21, 2025
INTRODUCTION: Propofol is a widely used sedative-hypnotic agent for critically ill patients requiring invasive mechanical ventilation (IMV). Despite its clinical benefits, propofol is associated with increased risks of hypertriglyceridemia. Early ide...
Deep learning model to identify and validate hypotension endotypes in surgical and critically ill patients.
British journal of anaesthesia
Jan 8, 2025
BACKGROUND: Hypotension is associated with organ injury and death in surgical and critically ill patients. In clinical practice, treating hypotension remains challenging because it can be caused by various underlying haemodynamic alterations. We aime...
Machine learning-based 28-day mortality prediction model for elderly neurocritically Ill patients.
Computer methods and programs in biomedicine
Jan 6, 2025
BACKGROUND: The growing population of elderly neurocritically ill patients highlights the need for effective prognosis prediction tools. This study aims to develop and validate machine learning (ML) models for predicting 28-day mortality in intensive...
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