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Multicenter Study Observational Study
Predicting early diagnosis of intensive care unit-acquired weakness in septic patients using critical ultrasound and biological markers.
- Ling Lei, Liang He, Tongjuan Zou, Jun Qiu, Yi Li, Ran Zhou, Yao Qin, and Wanhong Yin.
- Department of Critical Care Medicine, West China Hospital, Sichuan University, 37 Guo Xue Xiang St, Chengdu, 610041, Sichuan, China.
- BMC Anesthesiol. 2025 Jan 25; 25 (1): 3939.
ObjectiveEarly diagnosis of intensive care unit-acquired weakness (ICUAW) is crucial for improving the outcomes of critically ill patients. Hence, this study was designed to identify predisposing factors for ICUAW and establish a predictive model for the early diagnosis of ICUAW.MethodsThis prospective observational multicenter study included septic patients from the comprehensive ICUs of West China Hospital of Sichuan University and 10 other hospitals between September and November 2023. Inclusion criteria were as follows: age over 18 years; expected ICU stay longer than 3 days; and voluntary informed consent. Patients were classified into ICUAW (MRC score < 48) and non-ICUAW (MRC score ≥ 48) groups based on muscle strength assessments. The analyzed key predictive factors encompassed demographic data, SOFA and APACHE II scores, inflammatory markers (PCT, IL-6, and CRP), and ultrasound measurements of muscle thickness and cross-sectional area. Logistic regression analysis was conducted for variable selection and nomogram model construction.ResultsA total of 116 septic patients were included, comprising 77 males and 39 females (mean age: 56.94 ± 19.90 years). A nomogram model predicting ICUAW probability was developed, which involved vastus intermedius diameter, rectus femoris cross-sectional area, IL-6, and CRP. The AUC of the composite diagnostic ROC curve was 0.966 (95%CI: 0.936 - 0.996), with a sensitivity of 88% and a specificity of 95.8%.ConclusionsConclusively, a nomogram model is constructed for diagnosing ICUAW in septic patients, which is simple and rapid and allows for visual representation, with excellent diagnostic capability.© 2025. The Author(s).
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