ArticleBMC neurology2024
Predictive modeling of ICU-AW inflammatory factors based on machine learning.
Article in BMC neurology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Efficacy of suspension-based lower-limb rehabilitation device in enhancing lower limb function among patients with ICU-acquired weakness: a self-controlled randomized clinical trial.Critical care (London, England) · 2026Trial
- Development and Validation of a Prediction Model for Intensive Care Unit-Acquired Weakness in Adult Patients with Sepsis: A Prospective, Observational, Single-Center Study.International journal of general medicine · 2026Article
- Integrating sarcopenia into ICU-acquired weakness risk stratification: a machine learning-based prediction model for critical care.Frontiers in nutrition · 2026Article
- Machine Learning-Based Classification of ICU-Acquired Neuromuscular Weakness: A Comparative Study in Survivors of Critical Illness.Life (Basel, Switzerland) · 2025Article
- Risk factors for ICU-acquired weakness in patients undergoing mechanical ventilation: a systematic review and meta-analysis.Journal of thoracic disease · 2025Article
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Authors and funding
3 authors.
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Abstract
backgroundICU-acquired weakness (ICU-AW) is a common complication among ICU patients. We used machine learning techniques to construct an ICU-AW inflammatory factor prediction model to predict the risk of disease development and reduce the incidence of ICU-AW.
methodsThe least absolute shrinkage and selection operator (LASSO) technique was used to screen key variables related to ICU-AW. Eleven indicators, such as the presence of sepsis, glucocorticoids (GC), neuromuscular blocking agents (NBAs), length of ICU stay, Acute Physiology and Chronic Health Evaluation (APACHE II) II score, and the levels of albumin (ALB), lactate (LAC), glucose (GLU), interleukin-1β (IL-1β), interleukin-6 (IL-6), and interleukin-10 (IL-10), were used as variables to establish the prediction model. We divided the data into a dataset that included inflammatory factors and a dataset that excluded inflammatory factors. Specifically, 70% of the participants in both datasets were used as the training set, and 30% of the participants were used as the test set. Three machine learning methods, logistic regression (LR), random forest (RF), and extreme gradient boosting (XGB), were used in the 70% participant training set to construct six different models, which were validated and evaluated in the remaining 30% of the participants as the test set. The optimal model was visualized for prediction using nomograms.
resultsThe logistic regression model including the inflammatory factors demonstrated excellent performance on the test set, with an area under the curve (AUC) of 82.1% and the best calibration curve fit, outperforming the other five models. The optimal model is represented visually in the nomograms.
conclusionThis study used easily accessible clinical characteristics and laboratory data that can aid in early clinical recognition of ICU-AW. The inflammatory factors IL-1β, IL-6, and IL-10 have high value for predicting ICU-AW.
trial registrationThe trial was registered at the Chinese Clinical Trial Registry with the registration number ChiCTR2300077968.
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