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\begin{document}

\title{Do clinimetric properties of LCI change after correction of signal
processing?}



\author[1]{Bettina Frauchiger}%
\author[2]{Marc-Alexander Oestreich}%
\author[3]{Florian Wyler}%
\author[4]{Nathalie Monney}%
\author[4]{Corin Willers}%
\author[1]{Sophie Yammine}%
\author[1]{Philipp Latzin}%
\affil[1]{Inselspital University Hospital Bern}%
\affil[2]{Inselspital Bern Universitatsklinik fur Kinderheilkunde}%
\affil[3]{Inselspital Bern Universitätsklinik für Kinderheilkunde}%
\affil[4]{Inselspital Universitatsspital Bern}%


\vspace{-1em}



  
  \date{June 11, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Background: The recently described sensor-crosstalk error in the
multiple-breath washout (MBW) device (Exhalyzer D, Eco Medics AG,
Duernten, Switzerland) could highly influence clinimetric properties and
the current interpretation of MBW results. This study reanalyzes MBW
data from clinical routine in the corrected software version Spiroware\selectlanguage{ngerman}®
3.3.1 and evaluates the effect on outcomes. Methods: We included
nitrogen-MBW data from healthy children and children with CF from
previously published trials and ongoing cohort studies. We specifically
compared LCI analyzed in Spiroware 3.2.1 and 3.3.1 with regards to i)
feasibility, ii) repeatability and iii) validity as outcome parameters
in children with CF. Results: (i) All previously collected measurements
could be reanalyzed and resulted in unchanged feasibility in Spiroware
3.3.1. (ii) Short- and midterm repeatability of LCI was similar in both
software versions. (iii) Clinical validity of LCI remained similar in
Spiroware 3.3.1, however, resulted in lower values. Discrimination
between health and disease was comparable between both software
versions. The increase in LCI over time was less pronounced with 0.16
LCI units/year (95\% CI 0.08; 0.24) vs. 0.30 LCI units/year (95\% CI
0.21; 0.38) in 3.2.1. Response to intervention in children receiving
CFTR-modulator therapy resulted in a comparable improvement in LCI in
both Spiroware versions. Conclusion: Our study confirms that clinimetric
properties of LCI remain unaffected after correction for the
cross-sensitivity error in Spiroware software.%
\end{abstract}\selectlanguage{ngerman}%



\sloppy


\textbf{Do clinimetric properties of LCI change after correction of
signal processing?}

Bettina Sarah
Frauchiger\textsuperscript{1+}(https://orcid.org/0000-0002-9519-9328)

Marc-Alexander
Oestreich\textsuperscript{1,2+}(https://orcid.org/0000-0001-9641-3691)

Florian Wyler\textsuperscript{1} (https://orcid.org/0000-0002-1232-1392)

Nathalie
Monney\textsuperscript{1}(https://orcid.org/0000-0002-0627-6732)

Corin Willers\textsuperscript{1} (https://orcid.org/0000-0003-1140-9437)

Sophie Yammine\textsuperscript{1}(https://orcid.org/0000-0001-7720-3445)

Philipp Latzin\textsuperscript{1}(https://orcid.org/0000-0002-5239-1571)

\textsuperscript{+} Authors contributed equally to this work as first
author.

\textsuperscript{1} Division of Paediatric Respiratory Medicine and
Allergology, Department of Paediatrics, Inselspital, Bern University
Hospital, University of Bern, Switzerland

\textsuperscript{2} Graduate School for Health Sciences, University of
Bern, Switzerland.

\textbf{Corresponding author:}

Prof. Dr. med. Philipp Latzin, PhD

Inselspital, University Children's Hospital of Bern

Freiburgstrasse 15, 3010 Bern, Switzerland

E-mail: philipp.latzin@insel.ch

Phone: +41 31 632 9353

\textbf{Keywords:} Multiple-breath washout; Cystic fibrosis, pulmonary
function testing

\textbf{Running title:} LCI properties hold after correction of sensor
error

\textbf{Word count:} 2942/3500

\textbf{Figures and tables:} 5/6 (1 Table, 4 Figures)

\textbf{References:} 24/40

\textbf{Online Supplement:} This manuscript has supplemental material.

\textbf{Abstract} (221/250)

\textbf{Background:} The recently described sensor-crosstalk error in
the multiple-breath washout (MBW) device (Exhalyzer D, Eco Medics AG,
Duernten, Switzerland) could highly influence clinimetric properties and
the current interpretation of MBW results. This study reanalyzes MBW
data from clinical routine in the corrected software version Spiroware\selectlanguage{ngerman}®
3.3.1 and evaluates the effect on outcomes.

\textbf{Methods:} We included nitrogen-MBW data from healthy children
and children with CF from previously published trials and ongoing cohort
studies. We specifically compared LCI analyzed in Spiroware 3.2.1 and
3.3.1 with regards to i) feasibility, ii) repeatability and iii)
validity as outcome parameters in children with CF.

\textbf{Results:} (i) All previously collected measurements could be
reanalyzed and resulted in unchanged feasibility in Spiroware 3.3.1.
(ii) Short- and midterm repeatability of LCI was similar in both
software versions. (iii) Clinical validity of LCI remained similar in
Spiroware 3.3.1, however, resulted in lower values. Discrimination
between health and disease was comparable between both software
versions. The increase in LCI over time was less pronounced with 0.16
LCI units/year (95\% CI 0.08; 0.24) vs. 0.30 LCI units/year (95\% CI
0.21; 0.38) in 3.2.1. Response to intervention in children receiving
CFTR-modulator therapy resulted in a comparable improvement in LCI in
both Spiroware versions.

\textbf{Conclusion:} Our study confirms that clinimetric properties of
LCI remain unaffected after correction for the cross-sensitivity error
in Spiroware software.

\textbf{Introduction}

We recently described and characterized a substantial sensor-crosstalk
error in a commercially available and widely used multiple-breath
washout (MBW) device (Exhalyzer D, Eco Medics AG, Duernten, Switzerland)
and suggested a possible correction which is now available in an updated
version of Spiroware analysis software (Spiroware 3.3.1, Eco Medics AG,
Duernten, Switzerland) \textsuperscript{1}. There is an obvious sense of
concern in the MBW community as the potential impact of these findings
on existing and ongoing studies could highly influence clinimetric
properties of MBW results and their current clinical
interpretation\textsuperscript{2}.

MBW has become an important diagnostic tool in cystic fibrosis (CF) for
both, clinical follow-up of the patients and as an endpoint for clinical
trials of new therapies \textsuperscript{3-8}. The lung clearance index
(LCI) is a feasible, repeatable, and sensitive marker of ventilation
inhomogeneity that correlates well with structural lung disease and
tracks disease progression in children with
CF\textsuperscript{5,6,9,10}. LCI is calculated as the ratio of the
cumulative expired volume divided by the functional residual capacity
(FRC), based on indirect calculation of nitrogen (N\textsubscript{2})
from oxygen (O\textsubscript{2}) and carbon dioxide
(CO\textsubscript{2}) signals. This results in inherent sensitivity to
measurement errors in these signals, and the error described above
previously resulted in a prolongation of the washout with falsely
elevated LCI values \textsuperscript{1}.

To evaluate the potential clinical impact of these findings, this study
aims to reanalyze published and collected MBW data from clinical routine
by applying the corrected signal processing algorithm and evaluating the
effect on outcomes. We assessed if previously described clinimetric
properties of LCI hold for corrected results. We specifically examined
corrected LCI with regards to i) feasibility, ii) repeatability, and
iii) validity as outcome parameters in children with CF.

\textbf{Methods}

\textbf{2.1 Study design and population}

In this observational study combining several existing trials, we
included N\textsubscript{2}-MBW data from healthy children and children
with CF from previously published trials and two ongoing cohort studies
(Basel\selectlanguage{english}-Bern Infant Lung Development (BILD) cohort
\textsuperscript{11}and Swiss Cystic Fibrosis Infant Lung Development
(SCILD) cohort\textsuperscript{12}). The Ethics Committee of the Canton
of Bern, Switzerland approved the study protocols (B2019\selectlanguage{english}-01072,
PB\_2017\selectlanguage{english}-02139, 2017-00279, 2017-00088) and parents gave written
consent. We included MBW data from 275 study participants that were
reported in previous studies and 28 participants from an ongoing trial
(Table 1). Using the corrected results of these measurements, we
evaluated the effect of the correction on MBW outcomes and consequently
if clinimetric properties of LCI were affected.

\textbf{2.2 MBW measurements}

N\textsubscript{2}-MBW tests were performed using the Exhalyzer D device
and Spiroware analysis software (v3.2.1, Eco Medics AG, Duernten,
Switzerland) with weight-adjusted dead spaces and settings according to
current consensus guidelines \textsuperscript{13,14}. Testing was
performed with patients sitting upright and breathing through a
mouthpiece. Quality control was performed according to current
guidelines and tests with at least two acceptable MBW trials were
included in our analysis \textsuperscript{14-16}.

\textbf{2.3 Correction of signal processing in MBW}

In brief, the sensor-crosstalk error in the Exhalyzer D device
underestimates O\textsubscript{2} and CO\textsubscript{2} gas
concentrations and thus overestimates end-expiratory concentrations of
N\textsubscript{2}. Elevated N\textsubscript{2} concentrations cause a
significant overestimation of the main MBW outcomes FRC and LCI as well
as of the N\textsubscript{2} back-diffusion from the
lungs\textsuperscript{1}. An updated Spiroware analysis software
(v3.3.1, Eco Medics AG, Duernten, Switzerland), will correct
automatically for this error. Previously reported results, however, need
to be reanalyzed. In this study, we reanalyzed raw data (A-Files) using
LungSim 1.01, a custom Python script developed by our group replicating
the signal processing and outcome calculation used in Spiroware analysis
software (v3.2.1 and 3.3.1) \textsuperscript{1}.

\textbf{2.4 Re-assessment of clinimetric properties} \textbf{and
statistical analysis}

We present non-parametric summaries for skewed data and parametric
summaries for normally distributed characteristics. Statistical analyses
were performed using Stata 16.1 (StataCorp 2019, College Station; TX).
Figures were created using Stata 16.1 or Graph Pad Prism 8 (Prism G
2018, La Jolla; California). The following clinimetric properties of LCI
were examined and compared between the Spiroware software versions 3.2.1
and 3.3.1:

\textbf{2.4.1 feasibility (success rates of measurements)}

Feasibility was defined by the number of study participants with
acceptable MBW measurements, defined as at least two acceptable trials
according to guidelines \textsuperscript{13,14}.

\textbf{2.4.2 short- and mid-term repeatability (within 15 minutes and
24 hours)}

As previously described, for short- and mid-term repeatability of LCI,
MBW measurements were performed in triplicates 15 minutes and 24 hours
apart respectively, with unchanged measurement conditions in school-aged
children with CF \textsuperscript{17,18}. We used mixed-effects linear
regression models to calculate the variability in LCI between
measurements allowing for correlation of repeated measurements within
individuals. We used mean difference, intercept, and residual standard
deviation to calculate the coefficient of variation (CV\%), intraclass
correlation coefficient (ICC), and coefficient of repeatability (CR).
Based on a log-linear model, we calculated upper limit of normal (ULN)
for relative differences (95\% quantile of a normal distribution)
indicating a threshold below which 95\% of the relative differences are
expected to fall.

\textbf{2.4.3 validity}

\textbf{discrimination between health and disease}

For discrimination between health and disease, we used the ULN of LCI,
calculated as mean LCI of healthy study participants + 1.96*Standard
deviation (SD) from a previously described dataset \textsuperscript{1}.

\textbf{b) correlation with functional MRI outcomes}

Functional MRI data were acquired using matrix pencil decomposition MRI
as previously described \textsuperscript{18}. Main outcomes are
fractional ventilation defect percentage (VDP) and relative perfusion
defect percentage (QDP) expressed as the impaired fraction relative to
whole lung volume. VDP and QDP were recalculated due to updated
post-processing \textsuperscript{19}. Correlations between VDP or QDP
and LCI were assessed by Spearman's rank
correlation\textsuperscript{20}.

\textbf{c) longitudinal changes}

Longitudinal changes in LCI were evaluated from a subset of the
previously reported cohort including three monthly clinical routine MBW
measurements in children with CF aged 3 - 18 years attending routine
care between 2014-2018 in our center along with matching clinical
information \textsuperscript{5}. We used a mixed-effects linear
regression model to assess the mean rate of change in LCI with age
included as a linear term, a participant-specific random intercept and
random slope to account for between-participant variability, different
observation periods for each participant, and unequal numbers of study
visits \textsuperscript{21}. As previously described\textsuperscript{5},
we used a baseline model and a final model adjusted for predefined
clinically most relevant covariates. Next, we assessed all potentially
influencing covariates on LCI course first in a univariate analysis and
second in the fully adjusted model.

\textbf{d) response to intervention}

The response to intervention by modulator therapy was assessed by
evaluating the change in LCI between baseline, i.e. before treatment
start and after at least two weeks of treatment in three different
treatment groups: In (i) either Orkambi(r) (Lumacaftor/Ivacaftor) or
Symdeko(r) (Tezacaftor/Ivacaftor)-treated patients (double modulator
therapy), in (ii) Trikafta(r) (Elexacaftor/Tezacaftor/Ivacaftor)--treated
patients (triple modulator therapy), and (iii) patients who received
first Orkambi(r) or Symdeko(r) followed by Trikafta(r) (combined modulator
therapy). Response to intervention was defined as within-group changes
from baseline to under treatment and analyzed by paired t-tests.

\textbf{Results}

\subsubsection*{3.1 Study population}

{\label{study-population}}

Summaries for demographical characteristics and number of measurements
included are presented in Table 1.

\textbf{3.2.1 feasibility (success rates of measurements)}

Fortunately, N\textsubscript{2} concentration was overestimated rather
than underestimated due to the cross-sensitivity error, thus allowing
the re-analysis of all previously collected measurements and therefore
unchanged feasibility. Following re-analysis, we found a reduction in
CEV of 19.6\% which results in a comparably shorter washout duration for
the participant.

\textbf{3.2.2 short- and mid-term repeatability}

Short-term repeatability of LCI and FRC was assessed in sixteen children
with CF and is summarized in Supplemental Table 1. Average LCI in
children with CF was lower when analyzed in Spiroware 3.3.1 (LCI 9.4 (SD
1.8) vs. 10.8 (2.2)). In general, repeatability of MBW measurements was
similar when analyzed in Spiroware 3.2.1 and 3.3.1. Variability between
measurements expressed as CV\% increased slightly in Spiroware 3.3.1
(LCI: 4.2 vs 3.7; FRC 3.7 vs 3.6). Also when expressing relative
differences between measurements, the upper limit of normal (95\%
quantile) increased marginally (9.1 vs 8.4\%).

Mid-term repeatability of LCI measurements was assessed in 12 healthy
children and 23 children with CF, results are summarized in Supplemental
Table 1. Mean (SD) LCI in healthy children was 6.8 (0.4) when assessed
in Spiroware 3.2.1 and 6.1 (0.3) in Spiroware 3.3.1, in children with CF
mean (SD) LCI was 11.6 (2.6) and 9.7 (2.2) in Spiroware 3.2.1 and 3.3.1,
respectively. Repeatability indices were similar when assessed in
Spiroware 3.2.1 and 3.3.1. While variability tended to be lower in
healthy children when analyzed in Spiroware 3.3.1 (CV\% 3.4 vs 4.4) this
was the opposite in children with CF (CV\% 9.6 (3.3.1) vs 8.1 (3.2.1).
Similarly, when assessing relative changes between visits, the upper
limit of normal (95\% quantile) was lower in healthy children in
Spiroware 3.3.1 compared to 3.2.1 but higher in children with CF in
3.3.1 (Supplemental Table 1). While we found within-subject between test
standard deviation to be independent of the magnitude of LCI for both
Spiroware settings (Supplemental Figure 1), within-subject within-test
standard deviation was associated with the magnitude of LCI for both
settings (Spiroware 3.2.1: R\textsuperscript{2}= 0.3
(p\textless{}0.001); 3.3.1: R\textsuperscript{2}= 0.2
(p\textless{}0.001).

\textbf{3.2.3} \textbf{validity}

\textbf{(a) discrimination between health and disease}

ULN based on 75 healthy controls from a retrospective
dataset\textsuperscript{1} was lower in Spiroware 3.3.1 with LCI 7.1
compared to 8.1 in 3.2.1. Sensitivity was comparable (77.8\% (3.2.1) and
76.2\% (3.3.1)) and specificity identical (98.7\%) between 3.2.1 and
3.3.1 with some scatter in the critical area (Figure 1, Supplemental
Figure 2) based on natural variability. In Spiroware 3.3.1, two children
with CF had abnormal LCI values compared to normal values in 3.2.1,
while one healthy child was falsely categorized as abnormal based on
slightly elevated LCI. In both Spiroware versions LCI values of all the
other healthy controls were within normal range and 15 children with CF
had values below the ULN.

\textbf{b) correlation with functional MRI outcomes}

We reanalyzed the correlation of functional MRI and LCI in 14 children
with CF. The extent of QDP ranged between 15\% and 35\% (Figure 2) The
correlation between QDP and LCI remained consistently strong in both
Spiroware versions (3.2.1: r\textsubscript{S}=0.66, p=0.03; 3.3.1:
r\textsubscript{S}=0.69 p=0.02). The increase in LCI per increase in
ventilation defect was comparable for both Spiroware versions (Spiroware
3.2.1: 1.28 LCI units/percent perfusion defect (95\% CI 0.67; 1.89);
Spiroware 3.3.1: 1.32 LCI units/percent perfusion defect (95\% CI 0.55;
2.10); Supplemental Figure 3).

\textbf{c) longitudinal tracking}

To assess the differences in the longitudinal course of LCI between
Spiroware 3.2.1 and 3.3.1, we reanalyzed 796 measurements from 72
children with CF (Table 1). Without adjustment for risk factors, LCI
increase was comparable in both settings, however less pronounced in
Spiroware 3.3.1 (0.16 LCI units/year (95\% CI 0.08; 0.24) vs. 0.30 LCI
units/year (95\% CI 0.21; 0.38) in Spiroware 3.2.1). The pattern of
increase in LCI was similar in both settings, remaining stable during
preschool years and school-age and then starting to increase in
adolescence (Figure 3 and Supplemental Table 2). Similar to our previous
findings, Aspergillus and P. aeruginosa colonization, severe
exacerbations, and experiencing ABPA during the study period remained
individually associated with a steeper increase in LCI also in Spiroware
3.3.1, even though with a smaller magnitude (Supplemental Table 3). The
effect on covariates associated with acute changes in LCI (acute
exacerbations, CF-related diabetes, BMI z-score) remained similar for
both Spiroware settings (Supplemental Table 4). With adjustment for
previously defined risk factors (sex, BMI, PsA- and
Aspergillus-colonization, CF-related diabetes, acute and severe
exacerbations) the pattern of increase in LCI was similar for both
Spiroware settings, again, less pronounced for 3.3.1 (0.08 LCI
units/year (95\% CI 0.01; 0.14) (Spiroware 3.3.1) vs. 0.19 LCI
units/year (95\% CI 0.12; 0.27) in Spiroware 3.2.1) (Supplemental Figure
4).

\textbf{d) Response to intervention} To characterize differences in the
response to intervention with double or triple modulator therapy between
Spiroware 3.2.1 and 3.3.1, we reanalyzed 212 visits from 28 patients
(Table 1) and compared mean LCI values at baseline, under double, under
triple, and combined therapy. There was a statistically significant
improvement (reduction) in LCI in all three treatment groups when
compared to baseline in both Spiroware algorithms (Figure 4). In
Spiroware 3.2.1, within-group mean (95\% CI) absolute change from
baseline was -1.7 LCI units (-2.8 to -0.5, p=0.012) under double
therapy, -1.7 LCI units (-2.5 to -0.9; p\selectlanguage{english}[?]0.001) under triple therapy,
and -2.5 LCI units (-4.1 to -1.0; p=0.007) under combined modulator
therapy (Figure 4 and supplemental Table 5). In Spiroware 3.3.1, LCI was
substantially lower but the change from baseline remained statistically
significant in all groups (within-group mean (95\% CI) absolute change
from baseline was -1.5 LCI units (-2.5 to -0.4, p=0.013) under double
therapy, -1.3 LCI units (-1.9 to -0.7; p\selectlanguage{english}[?]0.001) under triple therapy,
and -1.7 LCI units (-2.9 to -0.5; p=0.014) under combined modulator
therapy). Overall, within-group mean values differed substantially
between Spiroware 3.2.1 and 3.3.1 (mean (95\% CI) difference between
software versions over all groups was -1.5 (-2.0 to -1.1) LCI units).

\textbf{Discussion}

\textbf{4.1 Summary}

In this study, we can confirm that clinimetric properties of LCI are
still valid after correction for the recently described measurement
error in the widely used MBW Spiroware software \textsuperscript{1}. As
expected, the correction led to lower LCI values and thus lower
thresholds, implicating that we need to re-define existing thresholds
and accordingly adjust clinical interpretation of LCI and its changes.

\textbf{4.2 Comparison to literature}

\textbf{4.2.1 Clinical impact of measurement error}

The initial concerns that the findings of Wyler et
al.\textsuperscript{1} about the measurement error in the widely used
MBW software Spiroware 3.2.1 could tremendously change utility of LCI in
clinical routine, can be rejected by our findings. We could show, that
clinimetric properties of LCI still hold, even though with a slightly
smaller magnitude based on overall lower LCI values. This is due to the
measurement principle of the test, the indirect
N\textsubscript{2}-calculation was found to be overestimated by the
cross-sensitivity error in the O\textsubscript{2} and
CO\textsubscript{2}- signals, leading to a prolongation of the washout
with falsely high LCI values. Our results suggest that clinical
interpretation of LCI change is not affected by the correction of this
error. As such we can support recently published results from clinical
trials and extent the interpretation into the clinical
application\textsuperscript{2}.

\textbf{4.2.2} \textbf{Impact on feasibility (success rates of
measurements)}

Since the correction of the gas measurement error results in shorter
washouts, we were able to reanalyze all our trials with the corrected
software version \textsuperscript{22}. Thus, also in clinical practice,
overall N\textsubscript{2}-MBW will become shorter with the corrected
software which might increase the feasibility of the test, especially in
the younger and possibly less cooperative age range but also in patients
with more advanced disease and longer washout
times\textsuperscript{13,22}.

\textbf{4.2.3} \textbf{Impact on Repeatability}

Overall, we found short-and midterm repeatability\textsuperscript{17,18}
to be similar after cross-sensitivity correction, and thus limits for
intervisit changes and variability to be unaffected. Within-subject
between test standard deviation remained independent of the magnitude of
LCI, which preserves the property of LCI to guide individual clinical
decisions.

\textbf{4.2.4 Validity}

The discriminatory ability of LCI holds after correction with a linear
trend to lower LCI values for healthy but also children with CF.
Thereby, the higher the LCI value, the higher the downward correction,
which needs closer reevaluation of patients with highly elevated LCI.
Potentially, the overestimation of back-diffusion of
N\textsubscript{2}might have overrated LCI values of patients with more
severe disease and longer washouts. However, correlation with functional
defects detected by MRI scans \textsuperscript{17,18,20} remained very
similar before and after measurement correction, reassuring that the
pathophysiological understanding of LCI is still valid. Correspondingly,
the pattern of increase in LCI with age remained similar with a smaller
magnitude after measurement correction (Figure 3, Supplemental Figure
4). A very similar effect was seen by Robinson et al. reanalyzing change
in LCI in preschool CF children over 12 months \textsuperscript{2}. In
line with Robinson et al., treatment effects of CFTR modulators remained
statistically significant whereas the magnitude of the change was
smaller.

\textbf{4.3 Strengths and weaknesses of this study}

We performed a thorough reanalysis of clinical and research MBW data
using the corrected algorithm incorporated in Spiroware 3.3.1, with data
originally collected or reloaded in Spiroware 3.2.1\textsuperscript{23}.
We performed rigorous quality control with only perfect data being used
\textsuperscript{15} which minimizes bias due to sampling variation.
Further, we were able to use a wide range of different datasets obtained
in a clinical routine setting or from clinical studies to assess the
impact of measurement correction on various clinimetric properties of
LCI. By nature, the main limitation lies in the retrospective
application of the correction.

\textbf{4.4 Clinical relevance and outlook}

Although there is a significant change with the corrected algorithm, we
show that the clinimetric properties of LCI still hold. This means that
clinical utility and our understanding of LCI and its changes remain
valid. This also suggests that previously published papers do not need
to be withdrawn. However, this implies also that for absolute correct
estimation of effect sizes, previously published datasets need to be
reanalyzed to enable comparison with results from Spiroware 3.3.1.
Besides, we will need to closely evaluate changes within individuals
applying the new software, to adjust individual therapeutic goals but
also to capture individual disease progression at this new level. The
option of retrospective correction has the great advantage that all
previously collected data in Spiroware can be reanalyzed and corrected.

\textbf{Conclusion}

Our study confirms that clinimetric properties of LCI remain unaffected
after correction for the recently detected measurement error in the
widely used Spiroware software. However, the correction results in lower
LCI values, with the concurrent need to redefine existing thresholds and
adjust clinical understanding of LCI and its changes on this new level.

\textbf{Acknowledgements}

We thank all our patients and study participants as well as their
families for allowing their MBW data to be used for research. In
addition, we thank the lab technicians and study nurses for their
contribution by performing MBW measurements. Furthermore, we thank the
physicians from our department, especially Insa Korten and Elisabeth
Kieninger.

\textbf{Funding}

This project was funded by the Swiss National Science Foundation, Grant
Nos. 182719 (to P. Latzin) and 179905 (to S. Yammine).

\textbf{Author contributions}

Bettina Sarah Frauchiger: Conceptualization, Methodology, Formal
analysis, Investigation, Resources, Writing - Original Draft, Review \&
Editing, Visualization.

Marc-Alexander Oestreich: Conceptualization, Data Curation, Methodology,
Formal analysis, Investigation, Resources, Writing - Original Draft,
Review \& Editing, Visualization.

Florian Wyler: Software.

Nathalie Monney: Resources.

Corin Willers: Resources.

Sophie Yammine: Writing - Original Draft, Review \& Editing.

Philipp Latzin: Conceptualization, Supervision, Project administration,
Funding acquisition.

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\begin{enumerate}
\tightlist
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\textbf{Hosted file}

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