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Am J Respir Crit Care Med. 2000 Feb;161(2 Pt 1):432-9. doi: 10.1164/ajrccm.161.2.9901061.

An objective analysis of the pressure-volume curve in the acute respiratory distress syndrome

R Scott Harris, Dean R Hess, Jose G Venegas.

PMID: 10673182


To assess the interobserver and intraobserver variability in the clinical evaluation of the quasi-static pressure-volume (P-V) curve, we analyzed 24 sets of inflation and deflation P-V curves obtained from patients with ARDS. We used a recently described sigmoidal equation to curve-fit the P-V data sets and objectively define the point of maximum compliance increase of the inflation limb (P(mci, i)) and the true inflection point of the deflation limb (P(inf,d)). These points were compared with graphic determinations of lower Pflex by seven clinicians. The graphic and curve-fitting methods were also compared for their ability to reproduce the same parameter value in data sets with reduced number of data points. The sigmoidal equation fit the P-V data with great accuracy (R(2) = 0.9992). The average of Pflex determinations was found to be correlated with P(mci,i) (R = 0.89) and P(inf,d) (R = 0.76). Individual determinations of Pflex were less correlated with the corresponding objective parameters (R = 0.67 and 0.62, respectively). Pflex + 2 cm H(2)O was a more accurate estimator of P(inf,d) (2 SD = +/-6.05 cm H(2)O) than Pflex was of P(mci,i) (2 SD = +/-8.02 cm H(2)O). There was significant interobserver variability in Pflex, with a maximum difference of 11 cm H(2)O for the same patient (SD = 1.9 cm H(2)O). Clinicians had difficulty reproducing Pflex in smaller data sets with differences as great as 17 cm H(2)O (SD = 2.8 cm H(2)O). In contrast, the curve-fitting method reproduced P(mci,i) with great accuracy in reduced data sets (maximum difference of 1.5 cm H(2)O and SD = 0.3 cm H(2)O). We conclude that Pflex rarely coincided with the point of maximum compliance increase defined by a sigmoid curve-fit with large differences in Pflex seen both among and within observers. Calculating objective parameters such as P(mci,i) or P(inf,d) from curve-fitted P-V data can minimize this large variability.