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Pressure support ventilation (PSV) should be titrated considering the pressure developed by the respiratory muscles (Pmusc) to prevent under- and over-assistance. The esophageal pressure (Pes) is the clinical gold standard for Pmusc assessment, but its use is limited by alleged invasiveness and complexity. The least square fitting method and the end-inspiratory occlusion method have been proposed as non-invasive alternatives for Pmusc assessment. The aims of this study were: (1) to compare the accuracy of Pmusc estimation using the end-inspiration occlusion (Pmusc,index) and the least square fitting (Pmusc,lsf) against the reference method based on Pes; (2) to test the accuracy of Pmusc,lsf and of Pmusc,index to detect overassistance, defined as Pmusc ≤ 1 cmH2O. We studied 18 patients at three different PSV levels. At each PSV level, Pmusc, Pmusc,lsf, Pmusc,index were calculated on the same breaths. Differences among Pmusc, Pmusc,lsf, Pmusc,index were analyzed with linear mixed effects models. Bias and agreement were assessed by Bland-Altman analysis for repeated measures. The ability of Pmusc,lsf and Pmusc,index to detect overassistance was assessed by the area under the receiver operating characteristics curve. Positive and negative predictive values were calculated using cutoff values that maximized the sum of sensitivity and specificity. At each PSV level, Pmusc,lsf was not different from Pmusc (p = 0.96), whereas Pmusc,index was significantly lower than Pmusc. The bias between Pmusc and Pmusc,lsf was zero, whereas Pmusc,index systematically underestimated Pmusc of 6 cmH2O. The limits of agreement between Pmusc and Pmusc,lsf and between Pmusc and Pmusc,index were ± 12 cmH2O across bias. Both Pmusc,lsf ≤ 4 cmH2O and Pmusc,index ≤ 1 cmH2O had excellent negative predictive value [0.98 (95% CI 0.94-1) and 0.96 (95% CI 0.91-0.99), respectively)] to identify over-assistance. The inspiratory effort during PSV could not be accurately estimated by the least square fitting or end-inspiratory occlusion method because the limits of agreement were far above the signal size. These non-invasive approaches, however, could be used to screen patients at risk for absent or minimal respiratory muscles activation to prevent the ventilator-induced diaphragmatic dysfunction.

Original publication





Journal of clinical monitoring and computing

Publication Date





913 - 921


Department of Intensive Care and Anesthesiology, Fondazione Poliambulanza, Brescia, Italy.


Respiratory Muscles, Humans, Work of Breathing, Respiration, Artificial, Positive-Pressure Respiration, Least-Squares Analysis, Respiratory Mechanics