ArticleWorld journal of clinical cases2024
Significant risk factors for intensive care unit-acquired weakness: A processing strategy based on repeated machine learning.
Article in World journal of clinical cases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Development and Validation of an Interpretable Machine Learning Model for Predicting ICU-Acquired Weakness in Postoperative Patients.Nursing in critical care · 2026Article
- Factors Influencing Functional Recovery in People After Chronic Critical Illness During Early Neurological Rehabilitation.Journal of multidisciplinary healthcare · 2026Article
- Risk Factors and Prediction Model for ICU-Acquired Weakness in Severe Acute Pancreatitis: A Retrospective Cohort Study.Journal of inflammation research · 2026Article
- IL-1βCell death discovery · 2025Article
- Risk factors of ICU-acquired weakness in sepsis patients.Pakistan journal of medical sciences · 2025Article
- Predicting outcomes using neural networks in the intensive care unit.World journal of clinical cases · 2025Article
- Intensive care unit-acquired weakness: Unveiling significant risk factors and preemptive strategies through machine learning.World journal of clinical cases · 2024Article
- Acute Skeletal Muscle Wasting is Associated with Prolonged Hospital Stay in Critical Illness with Brain Injury.Neurocritical care · 2024Observational
- Advancing critical care recovery: The pivotal role of machine learning in early detection of intensive care unit-acquired weakness.World journal of clinical cases · 2024Article
- Risk factors for intensive-care-unit-acquired weakness.World journal of clinical cases · 2024Article
- Intensive care unit-acquired weakness - preventive, and therapeutic aspects; future directions and special focus on lung transplantation.World journal of clinical cases · 2024Article
- Machine learning insights on intensive care unit-acquired weakness.World journal of clinical cases · 2024Article
- Unveiling significant risk factors for intensive care unit-acquired weakness: Advancing preventive care.World journal of clinical cases · 2024Article
- Intensive care unit-acquired weakness and mechanical ventilation: A reciprocal relationship.World journal of clinical cases · 2024Article
- Risk factors for ICU-acquired weakness in sepsis patients: A retrospective study of 264 patients.Heliyon · 2024Article
- Pioneering role of machine learning in unveiling intensive care unit-acquired weakness.World journal of clinical cases · 2024Article
- Predicting intensive care unit-acquired weakness: A multilayer perceptron neural network approach.World journal of clinical cases · 2024Article
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2 authors.
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Abstract
backgroundIntensive care unit-acquired weakness (ICU-AW) is a common complication that significantly impacts the patient's recovery process, even leading to adverse outcomes. Currently, there is a lack of effective preventive measures.
aimTo identify significant risk factors for ICU-AW through iterative machine learning techniques and offer recommendations for its prevention and treatment.
methodsPatients were categorized into ICU-AW and non-ICU-AW groups on the 14th day post-ICU admission. Relevant data from the initial 14 d of ICU stay, such as age, comorbidities, sedative dosage, vasopressor dosage, duration of mechanical ventilation, length of ICU stay, and rehabilitation therapy, were gathered. The relationships between these variables and ICU-AW were examined. Utilizing iterative machine learning techniques, a multilayer perceptron neural network model was developed, and its predictive performance for ICU-AW was assessed using the receiver operating characteristic curve.
resultsWithin the ICU-AW group, age, duration of mechanical ventilation, lorazepam dosage, adrenaline dosage, and length of ICU stay were significantly higher than in the non-ICU-AW group. Additionally, sepsis, multiple organ dysfunction syndrome, hypoalbuminemia, acute heart failure, respiratory failure, acute kidney injury, anemia, stress-related gastrointestinal bleeding, shock, hypertension, coronary artery disease, malignant tumors, and rehabilitation therapy ratios were significantly higher in the ICU-AW group, demonstrating statistical significance. The most influential factors contributing to ICU-AW were identified as the length of ICU stay (100.0%) and the duration of mechanical ventilation (54.9%). The neural network model predicted ICU-AW with an area under the curve of 0.941, sensitivity of 92.2%, and specificity of 82.7%.
conclusionThe main factors influencing ICU-AW are the length of ICU stay and the duration of mechanical ventilation. A primary preventive strategy, when feasible, involves minimizing both ICU stay and mechanical ventilation duration.
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