*Identical offset values for signal alignment applied to reloaded and
collected data
Kruskal–Wallis nonparametric tests compared LCI values. As differences
between the three methods were normally distributed, Bland Altman
analysis assessed agreement, and as an additional comparison 95% limits
of agreement (LA) were compared to within-session repeatability between
the two 3.3.1 collected trials.
All 34 participants contributed complete sets of technically acceptable
data: mean (SD; range) age 15 (9;5-47) years, 28 with CF. LCI agreement
is summarised in Figure 1. In agreement with Short et al, there was a
difference between migrated values vs 3.3.1 collected LCI, albeit
smaller in magnitude: 6.50(5.72-9.78) vs 6.79(6.09-10.94),p=0.049; mean
(95% LA) difference -0.37 (-1.17-0.66). But unlike in that study, the
additional optimisation of methodology resulted in reloaded values which
were not significantly different from those directly collected in 3.3.1:
6.86(5.87-10.09),p=0.819; actual difference 0.01(-0.85,0.75) and
0.13(-9.57,9.83)% change. Differences to both migrated and reloaded
3.1.6 data were almost entirely within this study’s within-session
repeatability of 3.3.1 collected data [mean 95% LA difference
0.11(-0.83,1.05) and -0.98(-12.0,14.0%), respectively]. We did find
evidence of a within-subject software version sequence effect on LCI
difference within this dataset, in the same direction of the difference
described by Short et al, supporting the randomisation approach taken in
this study’s methodology (data not shown).
Collectively the CORCs believe these data together with the three
previous datasets3-5 are now sufficient to makes some
recommendations to the community. Firstly, that reloaded legacy 3.1.6
data agree strongly with contemporaneously collected 3.3.1 data when
study design carefully ensures alignment of all the
signal processing factors previously discussed, including ensuring
consistent offset values across comparison groups. Secondly, all these
studies have demonstrated that, whilst data migration does generate LCI
values that differ from prospectively collected 3.3.1 data (due to the
lack of O2 drift correction and DDC application),
magnitude of difference is relatively small (<0.4 TO, or 5%)
and usually within the within-test repeatability. Depending on the
reasons for researchers wishing to use longitudinal, legacy data, these
differences may be deemed acceptable. Thirdly, variation in findings
across these studies highlights that the methodology used to update
legacy data can impact results due to varying application of signal
processing factors, and care is needed when updating legacy data
(whether by migration or reloading), to ensure the method used is fit
for purpose, and the process used must be fully transparent and
described. The strong agreement with optimised, reloaded data within
this study is reassuring for the current GLI normative data project
which now specifies that legacy data is reloaded by contributing sites
using a detailed protocol to minimise between site differences in
approach. As the amount of 3.3.1 collected data contributed to the GLI
database increases, the ability to evaluate agreement with legacy
reloaded 3.1.6 data will be created and should be evaluated.