Unveiling significant risk factors for intensive care unit-acquired weakness: Advancing preventive care

Abstract

In this editorial, we discuss an article titled, “Significant risk factors for intensive care unit-acquired weakness: A processing strategy based on repeated machine learning,” published in a recent issue of the World Journal of Clinical Cases. Intensive care unit-acquired weakness (ICU-AW) is a debilitating condition that affects critically ill patients, with significant implications for patient outcomes and their quality of life. This study explored the use of artificial intelligence and machine learning techniques to predict ICU-AW occurrence and identify key risk factors. Data from a cohort of 1063 adult intensive care unit (ICU) patients were analyzed, with a particular emphasis on variables such as duration of ICU stay, duration of mechanical ventilation, doses of sedatives and vasopressors, and underlying comorbidities. A multilayer perceptron neural network model was developed, which exhibited a remarkable impressive prediction accuracy of 86.2% on the training set and 85.5% on the test set. The study highlights the importance of early prediction and intervention in mitigating ICU-AW risk and improving patient outcomes.

Keywords: Intensive care unit-acquired weakness; Artificial intelligence; Machine learning; Neural network; Risk factors; Prediction; Critical care

Core Tip: This editorial comment on the published article related to the potential of artificial intelligence (AI) and machine learning in predicting and mitigating intensive care unit-acquired weakness (ICU-AW) in critically ill patients. By identifying key risk factors and developing a predictive model, clinicians can optimize patient care and improve outcomes. Early prediction and intervention based on AI-driven insights may lead to more personalized and effective strategies for preventing ICU-AW.