AIMC Topic: Intensive Care Units

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Online machine learning model for predicting delirium risk in elderly patients with chronic kidney disease: development and preliminary validation.

European journal of medical research
BACKGROUND: Delirium frequently complicates elderly chronic kidney disease (CKD) patients due to multifactorial vulnerability. Early detection in geriatric intensive care unit (ICU) settings is challenged by traditional assessments' communication def...

Joint impact of stress hyperglycaemic ratio and glycaemic variability in patients with ischaemic stroke and machine learning for mortality prediction.

BMC neurology
BACKGROUND: The global burden of ischaemic stroke (IS) is high, which is potentially relevant to stress hyperglycemia ratio (SHR) and glycaemic variability (GV). This study aims to evaluate the combined effect of the SHR and GV with predict short-ter...

Albumin-corrected anion gap as a predictor of 28-day mortality in acute respiratory distress syndrome: A machine learning-based retrospective study.

PloS one
BACKGROUND: Acute Respiratory Distress Syndrome (ARDS) remains a critical condition associated with high mortality rates, prolonged hospitalization, and reduced quality of life despite advances in critical care. The albumin-corrected anion gap (ACAG)...

Impact of COVID-19 isolation measures on ICU microbial resistance dynamics: simulation-based statistical modeling analysis.

Antimicrobial resistance and infection control
BACKGROUND: The transmission of antibiotic-resistant bacteria in intensive care units (ICUs) poses a significant challenge to infection control and patient safety. While direct patient-to-patient transmission is well documented, the relative contribu...

Using machine learning for early prediction of in-hospital mortality during ICU admission in liver cancer patients.

Scientific reports
Liver cancer has a high incidence and mortality rate globally, particularly in patients requiring intensive care unit (ICU) admission. Early prediction of in-hospital mortality for these patients is crucial, yet lacking reliable tools. This study aim...

Ability of the hypotension prediction index to predict hypotension in patients with septic shock in the intensive care unit.

Scientific reports
The hypotension prediction index (HPI) is a machine learning-based model for predicting hypotension. It provides good performance for predicting intraoperative hypotension but has rarely been studied in critically ill patients admitted to the intensi...

Machine learning glucose forecasting models for septic patients.

Scientific reports
Sepsis-induced glucose fluctuations present major challenges in critical care, underscoring the importance of accurate glucose monitoring and forecasting to improve patient outcomes. This study introduces a suite of forecasting models trained using c...

Critical care challenges after gastrointestinal surgery.

Current opinion in critical care
PURPOSE OF REVIEW: This review addresses the critical care challenges encountered after gastrointestinal surgery, emphasizing the high incidence of postoperative complications and the pivotal role of early recognition and management in improving pati...

A large language model for delirium prediction in the intensive care unit using structured electronic health records.

Scientific reports
Delirium is an acute syndrome characterized by fluctuating attention, cognitive impairment, and severe disorganization of behavior, which has been shown to affect up to 31% of patients in the intensive care unit (ICU). Early detection can enable time...

Impact of blood culture positivity at intensive care unit admission on mortality in infective endocarditis: Machine learning and deep learning-based causal inference models.

PloS one
BACKGROUND: Infective endocarditis (IE) carries high in-hospital mortality, particularly among intensive care unit (ICU) patients. The predictive role of blood culture positivity in these patients remains unclear.