AIMC Topic: Critical Illness

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Development of a risk prediction model for sepsis-related delirium based on multiple machine learning approaches and an online calculator.

PloS one
BACKGROUND: Sepsis-associated delirium (SAD) occurs due to disruptions in neurotransmission linked to inflammatory responses from infections. It poses significant challenges in clinical management and is associated with poor outcomes. Survivors often...

Interpretable prediction of hospital mortality in bleeding critically ill patients based on machine learning and SHAP.

BMC medical informatics and decision making
BACKGROUND: Hemorrhage is a prevalent and critical condition in the intensive care unit (ICU), characterized by high incidence, elevated mortality rates, and substantial therapeutic challenges. Accurate prediction of mortality in patients with hemorr...

Machine learning-based predictive tools and nomogram for in-hospital mortality in critically ill cancer patients: development and external validation using retrospective cohorts.

BMC medical informatics and decision making
BACKGROUND: The incidence of intensive care unit (ICU) admissions and the corresponding mortality rates among cancer patients are both high. However, the existing scoring systems all lack specificity. This research seeks to establish and validate a p...

Development and external validation of machine learning models for the early prediction of malnutrition in critically ill patients: a prospective observational study.

BMC medical informatics and decision making
BACKGROUND: Early detection of malnutrition in critically ill patients is crucial for timely intervention and improved clinical outcomes. However, identifying individuals at risk remains challenging due to the complexity and variability of patient co...

Development and validation of a modified SOFA score for mortality prediction in candidemia patients.

Scientific reports
Candidemia is a life-threatening bloodstream infection associated with high mortality rates, particularly in critically ill patients. Accurate risk stratification is crucial for timely intervention and could improve patient outcomes. This study aimed...

Methodological Review of Classification Trees for Risk Stratification: An Application Example in the Obesity Paradox.

Nutrients
BACKGROUND: Classification trees (CTs) are widely used machine learning algorithms with growing applications in clinical research, especially for risk stratification. Their ability to generate interpretable decision rules makes them attractive to hea...

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

BMC anesthesiology
OBJECTIVE: Muscle wasting in critically ill patients, particularly those with prolonged hospitalization, poses a significant challenge to recovery and long-term outcomes. The aim of this study was to characterize long-term muscle wasting trajectories...

Association between fibrinogen levels and prognosis in critically bleeding patients: exploration of the optimal therapeutic threshold.

European journal of trauma and emergency surgery : official publication of the European Trauma Society
BACKGROUND: Severe bleeding is a leading cause of ICU admission and mortality. Fibrinogen plays a crucial role in prognosis, yet optimal thresholds and supplementation targets remain unclear.

Development of a risk prediction model for secondary infection in severe/critical COVID-19 patients.

BMC infectious diseases
OBJECTIVE: This study aimed to develop a predictive model for secondary infections in patients with severe or critical COVID-19 by analyzing clinical characteristics and laboratory indicators.