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Updated: Apr 21, 2026
Manual Muscle Testing: A Method of Measuring Extremity Muscle Strength Applied to Critically Ill Patients
Published on: April 12, 2011
Early prediction of intensive care unit-acquired weakness using easily available parameters: a prospective
Luuk Wieske1, Esther Witteveen2, Camiel Verhamme3
1Department of Intensive Care Medicine, Academic Medical Center, Amsterdam, the Netherlands; Department of Neurology, Academic Medical Center, Amsterdam, the Netherlands; Laboratory of Experimental Anesthesiology and Intensive Care (L•E•I•C•A), Academic Medical Center, Amsterdam, the Netherlands.
Introduction:
An early diagnosis of Intensive Care Unit-acquired weakness (ICU-AW) using muscle strength assessment is not possible in most critically ill patients. We hypothesized that development of ICU-AW can be predicted reliably two days after ICU admission, using patient characteristics, early available clinical parameters, laboratory results and use of medication as parameters.
Methods:
Newly admitted ICU patients mechanically ventilated ≥2 days were included in this prospective observational cohort study. Manual muscle strength was measured according to the Medical Research Council (MRC) scale, when patients were awake and attentive. ICU-AW was defined as an average MRC score <4. A prediction model was developed by selecting predictors from an a-priori defined set of candidate predictors, based on known risk factors. Discriminative performance of the prediction model was evaluated, validated internally and compared to the APACHE IV and SOFA score.
Results:
Of 212 included patients, 103 developed ICU-AW. Highest lactate levels, treatment with any aminoglycoside in the first two days after admission and age were selected as predictors. The area under the receiver operating characteristic curve of the prediction model was 0.71 after internal validation. The new prediction model improved discrimination compared to the APACHE IV and the SOFA score.
Conclusion:
The new early prediction model for ICU-AW using a set of 3 easily available parameters has fair discriminative performance. This model needs external validation.
