Lung function measurements in children - copsac

Discussion
Between-center variation
sRaw offers a method for clinical monitoring and research during the critical period of
growth and development early in life. The method is feasible from the age of two years
and the precision is high (10-12). Our aim was to examine center agreement between six
centers in Denmark currently using identical hardware equipment with different
software versions (JLAB 4.51, 4.53 with different sub versions, 4.65 and 4.67). The
study was conducted in a “real” setting of a multi-center study; therefore we did not
update the centers software into the same version. We assumed the manufacturer had
checked the equipments and made adjustments for possible variations in sRaw
measurements (also electronic BTPS correction) after releasing new software. The
starting point of the study was that there would be no difference in sRaw measurements
between the centers despite different software versions.
However, the present study showed that the accuracy of sRaw measurements in young
children was flawed from errors. A technician from the company (Cardinal Health) was
sent to identify the problems in the deviating centers (center no. 1, 2 and 6). This
revealed incorrect setting of the “ASC Compensation” at center no. 1. “Time delay for
compensation” was set to 20 milliseconds and should have been 50, which resulted in
19-32% lower values. This was a factory setting not accessible for the operator. The
technician found no reason for the deviating measurements at the other centers (center
no. 2 and 6). Center no. 6 subsequently updated the whole-body plethysmograph
software after this study. It was not possible to re-analyse the data because the software
saved the sRaw values after the primary calculation of sRaw and the original loops was
lost, which was a great limitation of the software. To optimize the study design we
could have made prints of the original breath curves and have a third person evaluate
the quality of the loops to avoid bias of the operator. In future software updates or
development, it would be optimal if the software could determine the best breath curves
from algorithms as flow and respiratory rates or encourage the child (with animated biofeedback)
to breath with optimal respiratory rates and flow. Currently, it relies on the
operator who estimates by visual judgement when the breath curves are representatively
and decides when the computer should analyze the loops.
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