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