Intensive care unit-acquired weakness: Unveiling significant risk factors and preemptive strategies through machine learning

Abstract

This editorial discusses an article recently published in the World Journal of Clinical Cases, focusing on risk factors associated with intensive care unit-acquired weakness (ICU-AW). ICU-AW is a serious neuromuscular complication seen in critically ill patients, characterized by muscle dysfunction, weakness, and sensory impairments. Post-discharge, patients may encounter various obstacles impacting their quality of life. The pathogenesis involves intricate changes in muscle and nerve function, potentially leading to significant disabilities. Given its global significance, ICU-AW has become a key research area. The study identified critical risk factors using a multilayer perceptron neural network model, highlighting the impact of intensive care unit stay duration and mechanical ventilation duration on ICU-AW. Recommendations were provided for preventing ICU-AW, emphasizing comprehensive interventions and risk factor mitigation. This editorial stresses the importance of external validation, cross-validation, and model transparency to enhance model reliability. Moreover, the application of machine learning in clinical medicine has demonstrated clear benefits in improving disease understanding and treatment decisions. While machine learning presents opportunities, challenges such as model reliability and data management necessitate thorough validation and ethical considerations. In conclusion, integrating machine learning into healthcare offers significant potential and challenges. Enhancing data management, validating models, and upholding ethical standards are crucial for maximizing the benefits of machine learning in clinical practice.

Keywords: Clinical medicine; Intensive care unit-acquired weakness; Machine learning; Risk factors; Treatment decision.