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\begin{document}

\title{Reply to Migration is not the perfect answer: Optimised methodology to
assess LCI agreement between corrected legacy multiple breath nitrogen
washout data and that directly collected on updated software.}



\author[1]{Paul Robinson}%
\author[1]{Ediane De Queiroz Andrade}%
\author[1]{Brooke Bailey}%
\author[2]{Jane Davies}%
\author[3]{Renee Jensen}%
\author[3]{Felix Ratjen}%
\author[2]{Clare Saunders}%
\author[2]{Christopher Short}%
\affil[1]{The Children's Hospital at Westmead}%
\affil[2]{Royal Brompton and Harefield Hospitals}%
\affil[3]{The Hospital for Sick Children}%


\vspace{-1em}



  
  \date{June 17, 2024}


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\textbf{Title:} Reply to Migration is not the perfect answer: Optimised
methodology to assess LCI agreement between corrected legacy multiple
breath nitrogen washout data and that directly collected on updated
software.

\textbf{Authors:}

Ediane De Queiroz Andrade\textsuperscript{1,2}

Brooke Bailey\textsuperscript{1}

Jane C. Davies\textsuperscript{3,4,5}

Renee Jensen\textsuperscript{6,7,8}

Felix Ratjen\textsuperscript{6,7,8,9}

Clare J. Saunders\textsuperscript{3,4,5}

Christopher Short\textsuperscript{3,4,5}

Paul D Robinson\textsuperscript{1,2,10,11}

\textbf{Affiliations:}

\textsuperscript{1}Department of Respiratory Medicine, The Children's
Hospital at Westmead, Sydney, New South Wales, Australia;

\textsuperscript{2}Australian Multiple Breath Washout Central
Over-Reading Centre, Sydney, Australia

\textsuperscript{3}Royal Brompton and Harefield Hospitals, Guys and St
Thomas' Trust, London, UK;

\textsuperscript{4}National Heart and Lung Institute, Imperial College
London, London, UK;

\textsuperscript{5}European Cystic Fibrosis Society Lung Clearance Index
Core Facility, London, United Kingdom

\textsuperscript{6}Translational Medicine, Hospital for Sick Children,
Toronto, Ontario, Canada;

\textsuperscript{7}Division of Respiratory Medicine, Hospital for Sick
Children, Toronto, Ontario, Canada;

\textsuperscript{8}Cystic Fibrosis Foundation Multiple Breath Washout
National Resource Centre, Toronto, Canada

\textsuperscript{9}University of Toronto, Toronto, Canada;

\textsuperscript{10}Discipline of Paediatrics and Child Health,
University of Sydney, Sydney, New South Wales, Australia;

\textsuperscript{11}Airway Physiology and Imaging group, Woolcock
Medical Research Institute, Sydney, New South Wales, Australia;

\textbf{Word count:} 987 (up to 1000 words permitted)

\textbf{CORRESPONDING AUTHOR}

Professor Paul D Robinson

The Children's Hospital at Westmead The Sydney Children's Hospital
Network Respiratory Medicine Department Cnr Hawkesbury Road and
Hainsworth Street Locked Bag 4001, Westmead 2145, NSW Australia

t: (02) 9845 3395/97 \textbar{} f: (02) 9845 3396 \textbar{} e:
dr.pdrobinson@gmail.com

To the Editor,

The recent identification of a sensor crosstalk (XT) error in the
Exhalyser D MBW device\textsuperscript{1} led to the release of new
software (3.3.1). Understanding impacts of transition between
MBWN\textsubscript{2} software updates, and how to best handle
historical data, is critical\textsuperscript{2}. Should users simply
migrate legacy data, by uploading into the new software version to
enable XT correction (XTC), or perform a more time-consuming reload of
raw data A-files to apply \emph{all} advances between that and
historical software versions (3.1.6): XTC, O\textsubscript{2} drift
correction and novel gas signal flow synchronisation methodology
(dynamic delay correction, DDC)? What are the differences and which
approach most accurately reflects MBWN\textsubscript{2} Lung Clearance
Index (LCI) values collected within 3.3.1?

This issue has been investigated to varying degrees across three studies
published in 2022. Two studies have drawn differing conclusions
comparing agreement between migration and reloading. Oestreich et
al\textsuperscript{3} (n=44, healthy and Cystic Fibrosis, CF) reported a
mean (95\% CI) LCI difference of -0.16 (-0.27 to -0.04) turnovers (TO)
or -1.3\%, with an observed magnitude-dependent bias, which they felt
supported a recommendation to reload data (and not migrate). Jensen et
al.\textsuperscript{4}, in a reply to that letter, ``respectfully
disagreed''. In their independent analysis of a separate, similarly
sized cohort, in which they attempted to isolate the impact of XTC on
3.1.6 data, mean (95\% CI) difference between migration and reload was
-0.10 (-0.26, 0.06), or -1\%, was not statistically significant, without
a magnitude-dependent bias when correctly expressed as relative
difference.

In the~next~study to be published,~by Short et al, this discussion was
extended by introducing \emph{direct} comparison to 3.3.1 collected
data. Subjects (n=19, healthy and CF) performed [?]2 technically
acceptable trials on both 3.1.6 and 3.3.1 in a fixed order (mandated by
study protocol). They confirmed the non-significant difference between
migrated and reloaded data reported by Jensen, but also showed a large
difference between collected 3.3.1 data and 3.1.6-collected data
`corrected' via either migration or reloading into 3.3.1: mean absolute
difference 0.5-0.8, relative difference 7.2-8.5\%. The authors felt
these differences raised concerns about `corrected' 3.1.6 data being
used by the current GLI normative values project to derive reference
equations applicable for 3.3.1, and proposed that, ideally, only data
collected in 3.3.1 should be included. This conclusion would have
significant implications on the available data to derive these
equations.

Here we present results from an independent Australian dataset that the
3 CORCs have scrutinised and agree offers methodological improvements to
help maximise the utility of legacy data. In this study, performed under
local ethics approval (2021/ETH11854), recruited participants performed
[?]2 technically acceptable trials in both 3.1.6 and 3.3.1, in a
randomised order, on a single Exhalyser D device at a single visit.
3.1.6 data were both migrated and reloaded in 3.3.1 to generate
`corrected' LCI values to compare to data collected directly on version
3.3.1. Aspects of processing within 3.3.1 were identical for reloaded
and 3.3.1 collected data, as done by Short et al, but with the addition
of consistent O\textsubscript{2} and CO\textsubscript{2} sensor offset
values. The latter was not performed in the study by Short et al where
instead, software and equipment specific offsets were used to produce
effective synchronisation (assessed visually) during the washout phase.

Summary of signal processes applied:\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
& Migrated & Reloaded & Collected\tabularnewline
Fixed signal offset & & x & x\tabularnewline
DDC & x & * & *\tabularnewline
O\textsubscript{2} drift correction & x & &\tabularnewline
XTC & & &\tabularnewline
\bottomrule
\end{longtable}

*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 datasets\textsuperscript{3-5} 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
O\textsubscript{2} drift correction and DDC application), magnitude of
difference is relatively small (\textless{}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.

\textbf{Figure 1.} Agreement between MBWN2 data: Collected and processed
in 3.1.6, later migrated to 3.3.1, reloaded in 3.3.1 in comparison to
data collected and processed in 3.3.1 (panel A). Bland Altman plots
outlining the percent difference between data collected in 3.1.6 and
migrated to (panel B) or reloaded in (panel C) 3.3.1, and data collected
in 3.3.1, in comparison to within-test repeatability of 3.3.1 collected
data.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Footnote: *p\textless{}0.05, **p\textless{}0.01, ***p\textless{}0.001
vs. 3.3.1 collected values. Band Altman plots show mean difference as
solid horizontal lines, 95\% limits of agreement (95\% LA) as wide
dashed horizontal lines, and comparison 95\% LA values for
within-session repeatability of 3.3.1 collected data as narrow dashed
lines.



\textbf{References}

1. Wyler F, Oestreich MA, Frauchiger BS, Ramsey KA, Latzin P. Correction
of sensor crosstalk error in Exhalyzer D multiple-breath washout device
significantly impacts outcomes in children with cystic fibrosis. Journal
of applied physiology 2021;131(3):1148-1156.

2. Robinson PD, Latzin P, Verbanck S, Hall GL, Horsley A, Gappa M,
Thamrin C, Arets HG, Aurora P, Fuchs S\emph{, et al} . ERS/ATS Consensus
statement for inert gas washout measurement using multiple and single
breath tests. Eur Respir J 2013;41(3):507-522.

3. Oestreich MA, Wyler F, Latzin P, Ramsey KA. Impact of Spiroware
re-analysis method on multiple-breath washout outcomes in children with
cystic fibrosis. J Cyst Fibros 2022;21(3):e208-e209.

4. Jensen R, Shaw M, Phanthanourak A, Ratjen F. Re: Impact of spiroware
re-analysis method on multiple-breath washout outcomes in children with
cystic fibrosis; M.A. Oestreich, F. Wyler, P. Latzin et al. J Cyst
Fibros 2022;21(3):e210-e211.

5. Short C, Abkir M, Pinnell S, Proctor O, Saunders CJ, Davies JC.
Migration is not the perfect answer: How the cross-talk error correction
for multiple breath nitrogen washout (MBWN(2) ) parameters differs on
directly collected vs. legacy data. Pediatr Pulmonol 2023;58(1):328-331.

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