Nursing in critical care · 2025
An optimal cut-off point for the Chelsea Critical Care Physical Assessment Tool to diagnose intensive care unit-acquired weakness in patients with mechanical ventilation: A multicentre observational study.
СтатьяМногоцентровое исследованиеНаблюдательное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Wu Yuchen, Liu Yatao, Wang Guoqiang, Zheng Ang, Zhang Zhigang, Wei Huaping, Wang Xin
Аннотация
BACKGROUND: The Chelsea Critical Care Physical Assessment Tool (CPAx) may be an optimal tool for diagnosing intensive care unit-acquired weakness (ICU-AW). However, we do not know the cut-off point for the diagnosis of ICU-AW.
AIM: To ascertain the best cut-off point for CPAx to diagnose ICU-AW in adult patients with mechanical ventilation.
STUDY DESIGN: A multicentre, prospective cross-sectional study. Participants were recruited from five ICUs in China that ranged from 20 June 2021 to 31 July 2023. The Medical Research Council Sum Scale (MRC-ss) <48 was taken as the standard to calculate the area under the curve (AUC) of CPAx. The cut-off point was determined by the maximum value of Youden Index (YImax). The kappa (κ) test was used to test the consistency of the MRC-ss and CPAx.
RESULTS: The AUC at baseline, weaning and discharge time point was 0.87 (95% CI 0.81-0.93), 0.96 (95% CI 0.92-0.99) and 0.91 (95% CI 0.86-0.96), respectively. Correspondingly, The YImax was 0.62, 0.91 and 0.65. The best cut-off point of CPAx score to diagnose ICU-AW at baseline, weaning and discharge time point was 30.5 (sensitivity = 72%, specificity = 89%), 31.5 (sensitivity = 95%, specificity = 90%) and 31.5 (sensitivity = 94%, specificity = 71%), respectively. Due to the CPAx being an ordinal scale, it was determined preliminarily that the cut-off point for the CPAx to diagnose ICU-AW was 31 points. We took CPAx ≤31 and MRC-ss <48 as criteria to diagnose ICU-AW and test the consistency of MRC-ss and CPAx. The results showed that there was no significant difference in the incidence of ICU-AW at different time points.
CONCLUSIONS: The optimal cut-off point for the CPAx to diagnose ICU-AW is a score of 31 and it has good sensitivity and specificity.
RELEVANCE TO CLINICAL PRACTICE: The CPAx ≤31 score to diagnose ICU-acquired weakness (ICU-AW) has good sensitivity and specificity, and it can help to predict the risk of ICU-AW and guide medical personnel to make interventions.
AIM: To ascertain the best cut-off point for CPAx to diagnose ICU-AW in adult patients with mechanical ventilation.
STUDY DESIGN: A multicentre, prospective cross-sectional study. Participants were recruited from five ICUs in China that ranged from 20 June 2021 to 31 July 2023. The Medical Research Council Sum Scale (MRC-ss) <48 was taken as the standard to calculate the area under the curve (AUC) of CPAx. The cut-off point was determined by the maximum value of Youden Index (YImax). The kappa (κ) test was used to test the consistency of the MRC-ss and CPAx.
RESULTS: The AUC at baseline, weaning and discharge time point was 0.87 (95% CI 0.81-0.93), 0.96 (95% CI 0.92-0.99) and 0.91 (95% CI 0.86-0.96), respectively. Correspondingly, The YImax was 0.62, 0.91 and 0.65. The best cut-off point of CPAx score to diagnose ICU-AW at baseline, weaning and discharge time point was 30.5 (sensitivity = 72%, specificity = 89%), 31.5 (sensitivity = 95%, specificity = 90%) and 31.5 (sensitivity = 94%, specificity = 71%), respectively. Due to the CPAx being an ordinal scale, it was determined preliminarily that the cut-off point for the CPAx to diagnose ICU-AW was 31 points. We took CPAx ≤31 and MRC-ss <48 as criteria to diagnose ICU-AW and test the consistency of MRC-ss and CPAx. The results showed that there was no significant difference in the incidence of ICU-AW at different time points.
CONCLUSIONS: The optimal cut-off point for the CPAx to diagnose ICU-AW is a score of 31 and it has good sensitivity and specificity.
RELEVANCE TO CLINICAL PRACTICE: The CPAx ≤31 score to diagnose ICU-acquired weakness (ICU-AW) has good sensitivity and specificity, and it can help to predict the risk of ICU-AW and guide medical personnel to make interventions.