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Trial registered on ANZCTR


Registration number
ACTRN12618001032246
Ethics application status
Approved
Date submitted
10/06/2018
Date registered
20/06/2018
Date last updated
11/01/2023
Date data sharing statement initially provided
28/05/2019
Type of registration
Prospectively registered

Titles & IDs
Public title
Impulse oscillometry for the diagnosis of bronchiolitis obliterans syndrome
Scientific title
Impulse oscillometry for the diagnosis of bronchiolitis obliterans syndrome
Secondary ID [1] 295156 0
None
Universal Trial Number (UTN)
Trial acronym
Linked study record

Health condition
Health condition(s) or problem(s) studied:
Chronic lung allograft dysfunction 308265 0
Chronic graft-versus-host disease of the lung (bronchiolitis obliterans) 308266 0
Condition category
Condition code
Respiratory 307280 307280 0 0
Other respiratory disorders / diseases
Blood 307281 307281 0 0
Other blood disorders
Inflammatory and Immune System 307368 307368 0 0
Other inflammatory or immune system disorders

Intervention/exposure
Study type
Observational
Patient registry
False
Target follow-up duration
Target follow-up type
Description of intervention(s) / exposure
This prospective diagnostic study seeks to determine whether impulse oscillometry may provide a more sensitive diagnostic tool for the diagnosis of chronic lung allograft dysfunction (both bronchiolitis obliterans and restrictive allograft syndrome subtypes) and, separately, chronic graft-versus-host disease of the lung in allogeneic haematopoietic stem cell transplant recipients (bronchiolitis obliterans).

Participants will perform impulse oscillometry (IOS) prior to performing spirometry as ordered by their treating clinicians as surveillance for chronic lung allograft dysfunction or bronchiolitis obliterans in the case of allogeneic haematopoietic stem cell transplant patients. Patients typically undergo spirometry at least three-monthly. Patients will be observed for a minimum of 12 months and a maximum of 60 months.

IOS is a simple test with no risks to the patient which involves minimal to no discomfort. It involves the subject placing their mouth over a mouthpiece and forming a seal. The subject then breathes normally and while this is occurring the machine delivers a range of impulses into the respiratory system at different frequencies. The entire procedure takes only around 2 minutes.
Intervention code [1] 301495 0
Diagnosis / Prognosis
Comparator / control treatment
The reference test is spirometry which remains the gold-standard test for the diagnosis of chronic lung allograft dysfunction and chronic graft-versus-host disease of the lung in allogeneic haematopoietic stell cell transplant recipients.
Control group
Active

Outcomes
Primary outcome [1] 306234 0
Small airway resistance (resistance at 5 Hz minus resistance at 20 Hz)
Timepoint [1] 306234 0
Paired recordings of spirometry and impulse oscillometry will be taken every 3 months for the duration of the study (nominally set at up to five years).

As chronic lung allograft dysfunction and bronchiolitis obliterans in allogeneic haematopoietic stem cell transplantation are unpredictable diseases that may develop at any point late post-transplantation, it is not considered essential that all patients are observed for the full 5 year duration of the study which has been arbitrarily determined. The minimum period of useful observation is considered to be 12 months.
Primary outcome [2] 306342 0
Area under the reactance curve from 5 Hz to resonant frequency (AX)
Timepoint [2] 306342 0
Paired recordings of spirometry and impulse oscillometry will be taken every 3 months for the duration of the study (nominally set at up to five years).

As chronic lung allograft dysfunction and bronchiolitis obliterans in allogeneic haematopoietic stem cell transplantation are unpredictable diseases that may develop at any point late post-transplantation, it is not considered essential that all patients are observed for the full 5 year duration of the study which has been arbitrarily determined. The minimum period of useful observation is considered to be 12 months.
Primary outcome [3] 306343 0
Reactance at 5 Hz
Timepoint [3] 306343 0
Paired recordings of spirometry and impulse oscillometry will be taken every 3 months for the duration of the study (nominally set at up to five years).

As chronic lung allograft dysfunction and bronchiolitis obliterans in allogeneic haematopoietic stem cell transplantation are unpredictable diseases that may develop at any point late post-transplantation, it is not considered essential that all patients are observed for the full 5 year duration of the study which has been arbitrarily determined. The minimum period of useful observation is considered to be 12 months.
Secondary outcome [1] 347933 0
Reactance at 5Hz on inspiration minus reactance at 5 Hz on expiration
Timepoint [1] 347933 0
Paired recordings of spirometry and impulse oscillometry will be taken every 3 months for the duration of the study (nominally set at up to five years).

As chronic lung allograft dysfunction and bronchiolitis obliterans in allogeneic haematopoietic stem cell transplantation are unpredictable diseases that may develop at any point late post-transplantation, it is not considered essential that all patients are observed for the full 5 year duration of the study which has been arbitrarily determined. The minimum period of useful observation is considered to be 12 months.
Secondary outcome [2] 405908 0
The following analysis will be undertaken for patients with BOS:

First, similar linear mixed-effects models will be utilised to assess associations between change in spirometric parameters (change in FEV1 and MMEF25-75 from the previous time period) versus change in IOS parameters over the same time periods, again adjusting for repeated measurements and with additional adjusted models adjusting for BMI; these models will provide insights as to whether the index and reference tests change synchronously over time.

Second, univariate binary logistic generalised estimating equation (GEE) models will be constructed for the outcome of categorical worsening of BOS at any visit (e.g. progression from CLAD0 to CLAD0p at visit X) versus the predictor of change in IOS indices occurring across the two visit intervals preceding this change (e.g. visit X-3 to X-2 and visit X-2 to X-1). In order to avoid bias arising from the comparison of continuous parameters (IOS) with a categorical variable defined by spirometry (severity of BOS), identical univariate binary logistic GEE models will be created for spirometric indices. The diagnostic value of change in spirometry versus change in IOS for predicting worsening BOS will be compared via Receiver Operating Characteristic (ROC) curves, specifically area under the curve (AUC), with separation of the 95% confidence intervals taken to indicate a statistically significant difference.

Third, patient cohorts will be constructed based on FEV1 as a percentage of predicted in brackets of 10% (e.g. 110-101%, 100-91%, and so on). Analysis will be performed separately for each patient cohort. Patients will be identified as having favourable or unfavourable key IOS parameters (Rrs5 and Xrs5 as percentages of predicted), with favourable defined as the lowest fiftieth centile for that cohort and unfavourable defined as the highest fiftieth centile for that cohort. Univariate binary logistic regression models will be created for the prediction of categorical worsening of BOS within the next 6 months for each cohort versus the variable status of each IOS parameter (favourable or unfavourable) and then separately on the status of both IOS parameters (dual favourable, mixed and dual unfavourable). The identical analysis will be repeated for the prediction of categorical worsening of BOS within the next 12 months. If there are insufficient patients within these cohorts for the models to be robust, the cohorts will be broadened to brackets of 20% of FEV1 as percentage of predicted.

Fourth, mediation analysis will be performed. This will assess change in spirometric parameters (FEV1 and MMEF25-75 as percentages of predicted) between visit X and X+1 as predictors for the categorical worsening of BOS (e.g. progression from CLADX to CLADX+1 or greater), with the mediator being change in key IOS parameters (Rrs5 and Xrs5 as percentages of predicted) over the same time intervals.
Timepoint [2] 405908 0
Entire duration of data collection period.
Secondary outcome [3] 405909 0
The following analysis will be undertaken for patients with RAS:

First, similar linear mixed-effects models will be utilised to assess associations between change in spirometric parameters (change in FEV1 and MMEF25-75 from the previous time period) versus change in IOS parameters over the same time periods, again adjusting for repeated measurements and with additional adjusted models adjusting for BMI; these models will provide insights as to whether the index and reference tests change synchronously over time.

Second, univariate binary logistic generalised estimating equation (GEE) models will be constructed for the outcome of categorical worsening of RAS at any visit (e.g. progression from CLAD0 to CLAD0p at visit X) versus the predictor of change in IOS indices occurring across the two visit intervals preceding this change (e.g. visit X-3 to X-2 and visit X-2 to X-1). In order to avoid bias arising from the comparison of continuous parameters (IOS) with a categorical variable defined by spirometry (severity of RAS), identical univariate binary logistic GEE models will be created for spirometric indices. The diagnostic value of change in spirometry versus change in IOS for predicting worsening RAS will be compared via Receiver Operating Characteristic (ROC) curves, specifically area under the curve (AUC), with separation of the 95% confidence intervals taken to indicate a statistically significant difference.

Third, patient cohorts will be constructed based on FEV1 as a percentage of predicted in brackets of 10% (e.g. 110-101%, 100-91%, and so on). Analysis will be performed separately for each patient cohort. Patients will be identified as having favourable or unfavourable key IOS parameters (Rrs5 and Xrs5 as percentages of predicted), with favourable defined as the lowest fiftieth centile for that cohort and unfavourable defined as the highest fiftieth centile for that cohort. Univariate binary logistic regression models will be created for the prediction of categorical worsening of RAS within the next 6 months for each cohort versus the variable status of each IOS parameter (favourable or unfavourable) and then separately on the status of both IOS parameters (dual favourable, mixed and dual unfavourable). The identical analysis will be repeated for the prediction of categorical worsening of RAS within the next 12 months. If there are insufficient patients within these cohorts for the models to be robust, the cohorts will be broadened to brackets of 20% of FEV1 as percentage of predicted.

Fourth, mediation analysis will be performed. This will assess change in spirometric parameters (FEV1 and MMEF25-75 as percentages of predicted) between visit X and X+1 as predictors for the categorical worsening of RAS (e.g. progression from CLADX to CLADX+1 or greater), with the mediator being change in key IOS parameters (Rrs5 and Xrs5 as percentages of predicted) over the same time intervals.
Timepoint [3] 405909 0
Entire duration of data collection period.
Secondary outcome [4] 405910 0
The following analysis will apply to patients with GvHD:

First, similar linear mixed-effects models will be utilised to assess associations between change in spirometric parameters (change in FEV1 and MMEF25-75 from the previous time period) versus change in IOS parameters over the same time periods, again adjusting for repeated measurements and with additional adjusted models adjusting for BMI; these models will provide insights as to whether the index and reference tests change synchronously over time.

Second, univariate binary logistic generalised estimating equation (GEE) models will be constructed for the outcome of categorical worsening of GvHD at any visit (e.g. progression from CLAD0 to CLAD0p at visit X) versus the predictor of change in IOS indices occurring across the two visit intervals preceding this change (e.g. visit X-3 to X-2 and visit X-2 to X-1). In order to avoid bias arising from the comparison of continuous parameters (IOS) with a categorical variable defined by spirometry (severity of GvHD), identical univariate binary logistic GEE models will be created for spirometric indices. The diagnostic value of change in spirometry versus change in IOS for predicting worsening GvHD will be compared via Receiver Operating Characteristic (ROC) curves, specifically area under the curve (AUC), with separation of the 95% confidence intervals taken to indicate a statistically significant difference.

Third, patient cohorts will be constructed based on FEV1 as a percentage of predicted in brackets of 10% (e.g. 110-101%, 100-91%, and so on). Analysis will be performed separately for each patient cohort. Patients will be identified as having favourable or unfavourable key IOS parameters (Rrs5 and Xrs5 as percentages of predicted), with favourable defined as the lowest fiftieth centile for that cohort and unfavourable defined as the highest fiftieth centile for that cohort. Univariate binary logistic regression models will be created for the prediction of categorical worsening of GvHD within the next 6 months for each cohort versus the variable status of each IOS parameter (favourable or unfavourable) and then separately on the status of both IOS parameters (dual favourable, mixed and dual unfavourable). The identical analysis will be repeated for the prediction of categorical worsening of GvHD within the next 12 months. If there are insufficient patients within these cohorts for the models to be robust, the cohorts will be broadened to brackets of 20% of FEV1 as percentage of predicted.

Fourth, mediation analysis will be performed. This will assess change in spirometric parameters (FEV1 and MMEF25-75 as percentages of predicted) between visit X and X+1 as predictors for the categorical worsening of GvHD (e.g. progression from CLADX to CLADX+1 or greater), with the mediator being change in key IOS parameters (Rrs5 and Xrs5 as percentages of predicted) over the same time intervals.
Timepoint [4] 405910 0
Entire duration of data collection period.

Eligibility
Key inclusion criteria
- Bilateral Lung transplant recipient
- 18 years old or more

OR:

- Allogeneic haematopoietic stem cell transplant recipient
- 18 years old or more
Minimum age
18 Years
Maximum age
No limit
Sex
Both males and females
Can healthy volunteers participate?
No
Key exclusion criteria
In lung transplant recipients, allograft dysfunction from causes other than BOS or RAS, including:
- BOS / RAS overlap
- Active non-BOS alloinflammatory processes (e.g. acute cellular rejection, lymphocytic bronchiolitis and antibody-mediated rejection)
- Active allograft infection
- Mechanical impediment:
- Known upper / large airway obstructive pathology (e.g. anastomotic stricture or stricture to level of segmental bronchus)
- Pleural complications such as pneumothorax, infection or pleural effusion
- Neuromuscular deficits (e.g. diaphragmatic paralysis)
- Uncontrolled cardiac failure of any cause
- Recurrent of primary disease in allograft

In allogeneic haematopoietic stem cell transplant recipients, lung dysfunction from causes other than bronchiolitis obliterans including:

- Active pulmonary infection
- Mechanical impediment:
- Known upper / large airway obstructive pathology
- Pleural complications such as pneumothorax, infection or pleural effusion
- Neuromuscular deficits (e.g. diaphragmatic paralysis)
- Uncontrolled cardiac failure of any cause
- Pre-existing lung disease that is more than mild in severity (either obstructive or restrictive)

Study design
Purpose
Natural history
Duration
Longitudinal
Selection
Defined population
Timing
Prospective
Statistical methods / analysis
Hypothesis 1 will be tested via multivariable linear mixed-effects models to assess associations between spirometric parameters as outcomes (FEV1 and MMEF25-75 as absolute values) and IOS parameters as predictors (Rrs5, Rrs5-Rrs20, Xrs5, AX and Fres as absolute values) in all patients and, separately, in those with BOS (categorised as BOS and CLAD0p or higher). Separate models will be constructed for spirometry and IOS values as percentages of predicted. All models will adjust for repeated measurements over time. Adjusted models will be performed to control for body-mass index (BMI). Assumptions of a linear model will be tested by inspection of histograms and scatter plots of residuals and predicted values. Pearson correlation coefficients will also be calculated to assess any significant correlations.

Hypothesis 3 will be tested in an identical fashion to hypothesis 1 for patients with RAS (categorised as RAS and CLAD0p or higher).

Hypotheses 1 and 3 will be further tested by comparing IOS parameters (Rrs5, Rrs5-Rrs20, Xrs5, AX and Fres both as absolute values and percentages of predicted) between patients classified as BOS and RAS in the same categories of CLAD severity. The distribution of variables will be explored by tests for normality and skewness. Comparisons between these groups will be made using the two sample t-test or non-parametric methods like the Wilcoxon test if normality assumptions are not met.

Hypothesis 2 will be tested in four ways. First, similar linear mixed-effects models will be utilised to assess associations between change in spirometric parameters (change in FEV1 and MMEF25-75 from the previous time period) versus change in IOS parameters over the same time periods, again adjusting for repeated measurements and with additional adjusted models adjusting for BMI; these models will provide insights as to whether the index and reference tests change synchronously over time.

Second, univariate binary logistic generalised estimating equation (GEE) models will be constructed for the outcome of categorical worsening of BOS at any visit (e.g. progression from CLAD0 to CLAD0p at visit X) versus the predictor of change in IOS indices occurring across the two visit intervals preceding this change (e.g. visit X-3 to X-2 and visit X-2 to X-1). In order to avoid bias arising from the comparison of continuous parameters (IOS) with a categorical variable defined by spirometry (severity of BOS), identical univariate binary logistic GEE models will be created for spirometric indices. The diagnostic value of change in spirometry versus change in IOS for predicting worsening BOS will be compared via Receiver Operating Characteristic (ROC) curves, specifically area under the curve (AUC), with separation of the 95% confidence intervals taken to indicate a statistically significant difference.

Third, patient cohorts will be constructed based on FEV1 as a percentage of predicted in brackets of 10% (e.g. 110-101%, 100-91%, and so on). Analysis will be performed separately for each patient cohort. Patients will be identified as having favourable or unfavourable key IOS parameters (Rrs5 and Xrs5 as percentages of predicted), with favourable defined as the lowest fiftieth centile for that cohort and unfavourable defined as the highest fiftieth centile for that cohort. Univariate binary logistic regression models will be created for the prediction of categorical worsening of BOS within the next 6 months for each cohort versus the variable status of each IOS parameter (favourable or unfavourable) and then separately on the status of both IOS parameters (dual favourable, mixed and dual unfavourable). The identical analysis will be repeated for the prediction of categorical worsening of BOS within the next 12 months. If there are insufficient patients within these cohorts for the models to be robust, the cohorts will be broadened to brackets of 20% of FEV1 as percentage of predicted.

Fourth, mediation analysis will be performed. This will assess change in spirometric parameters (FEV1 and MMEF25-75 as percentages of predicted) between visit X and X+1 as predictors for the categorical worsening of BOS (e.g. progression from CLADX to CLADX+1 or greater), with the mediator being change in key IOS parameters (Rrs5 and Xrs5 as percentages of predicted) over the same time intervals.

Hypothesis 4 will be tested in the same was as hypothesis 2 as outlined above, however it is acknowledged that the lower frequency of RAS may mean there are insufficient patients for these statistical models to be robust. Separate analyses of the same form will be used to assess patients with chronic GvHD.

Predicted values for both IOS and spirometry will be based on the lung transplant recipient biometrics and not those of the donor, as is standard convention in lung transplantation. Statistical significance will be defined a priori as p < 0.05. Data will be collated in Excel (Microsoft, version 1905, Redmond, Washington) and analysed using SAS 9.4 (SAS Institute Inc., Cary, North Carolina, USA).

Recruitment
Recruitment status
Active, not recruiting
Date of first participant enrolment
Anticipated
16/07/2018
Actual
8/08/2018
Date of last participant enrolment
Anticipated
16/07/2022
Actual
1/04/2022
Date of last data collection
Anticipated
16/07/2023
Actual
Sample size
Target
100
Accrual to date
Final
121
Recruitment in Australia
Recruitment state(s)
SA
Recruitment hospital [1] 11118 0
The Royal Adelaide Hospital - Adelaide
Recruitment postcode(s) [1] 22931 0
5000 - Adelaide

Funding & Sponsors
Funding source category [1] 299746 0
Hospital
Name [1] 299746 0
Department of Thoracic Medicine, Royal Adelaide Hospital
Country [1] 299746 0
Australia
Primary sponsor type
Individual
Name
Dr Thomas Crowhurst
Address
Department of Thoracic Medicine
Royal Adelaide Hospital
1 Port Road
Adelaide SA 5000
Country
Australia
Secondary sponsor category [1] 299083 0
None
Name [1] 299083 0
Address [1] 299083 0
Country [1] 299083 0
Other collaborator category [1] 280173 0
Individual
Name [1] 280173 0
A/Prof Chien-Li Holmes-Liew
Address [1] 280173 0
Department of Thoracic Medicine
Royal Adelaide Hospital
1 Port Road
Adelaide SA 5000
Country [1] 280173 0
Australia
Other collaborator category [2] 280174 0
Individual
Name [2] 280174 0
Dr Sonya Johnston
Address [2] 280174 0
Department of Thoracic Medicine
Royal Adelaide Hospital
1 Port Road
Adelaide SA 5000
Country [2] 280174 0
Australia
Other collaborator category [3] 280175 0
Individual
Name [3] 280175 0
Dr Aeneas Yeo
Address [3] 280175 0
Department of Thoracic Medicine
Royal Adelaide Hospital
1 Port Road
Adelaide SA 5000
Country [3] 280175 0
Australia
Other collaborator category [4] 280189 0
Individual
Name [4] 280189 0
Professor Mark Holmes
Address [4] 280189 0
Royal Adelaide Hospital
1 Port Road
Adelaide SA 5000
Country [4] 280189 0
Australia
Other collaborator category [5] 280190 0
Individual
Name [5] 280190 0
Lauren Bussell
Address [5] 280190 0
Royal Adelaide Hospital
1 Port Road
Adelaide SA 5000
Country [5] 280190 0
Australia
Other collaborator category [6] 280191 0
Individual
Name [6] 280191 0
Dr David Yeung
Address [6] 280191 0
Royal Adelaide Hospital
1 Port Road
Adelaide SA 5000
Country [6] 280191 0
Australia
Other collaborator category [7] 282150 0
Individual
Name [7] 282150 0
Dr Jessica Butler
Address [7] 282150 0
Department of Thoracic Medicine
Royal Adelaide Hospital
1 Port Road
Adelaide SA 5000
Country [7] 282150 0
Australia
Other collaborator category [8] 282151 0
Individual
Name [8] 282151 0
Suzanne Edwards
Address [8] 282151 0
Level 9, AHMS Building
University of Adelaide
57 North Terrace
Adelaide SA 5000
Country [8] 282151 0
Australia
Other collaborator category [9] 282152 0
Individual
Name [9] 282152 0
Prof Gregory Hodge
Address [9] 282152 0
Level 7, AHMS Building
University of Adelaide
North Terrace
Adelaide SA 5000
Country [9] 282152 0
Australia
Other collaborator category [10] 282153 0
Individual
Name [10] 282153 0
Prof Gregory Snell
Address [10] 282153 0
Lung Transplant Service
The Alfred
55 Commercial Road
Melbourne VIC 3004
Country [10] 282153 0
Australia

Ethics approval
Ethics application status
Approved
Ethics committee name [1] 300636 0
Central Adelaide Local Health Network Human Research Ethics Committee
Ethics committee address [1] 300636 0
Ethics committee country [1] 300636 0
Australia
Date submitted for ethics approval [1] 300636 0
07/06/2018
Approval date [1] 300636 0
19/06/2018
Ethics approval number [1] 300636 0
R20180610

Summary
Brief summary
Trial website
Trial related presentations / publications
Public notes

Contacts
Principal investigator
Name 84290 0
Dr Thomas Crowhurst
Address 84290 0
Department of Thoracic Medicine
Royal Adelaide Hospital
1 Port Road
Adelaide SA 5000
Country 84290 0
Australia
Phone 84290 0
+61439810678
Fax 84290 0
Email 84290 0
thomas.crowhurst@gmail.com
Contact person for public queries
Name 84291 0
Thomas Crowhurst
Address 84291 0
Department of Thoracic Medicine
Royal Adelaide Hospital
1 Port Road
Adelaide SA 5000
Country 84291 0
Australia
Phone 84291 0
+61439810678
Fax 84291 0
Email 84291 0
thomas.crowhurst@gmail.com
Contact person for scientific queries
Name 84292 0
Thomas Crowhurst
Address 84292 0
Department of Thoracic Medicine
Royal Adelaide Hospital
1 Port Road
Adelaide SA 5000
Country 84292 0
Australia
Phone 84292 0
+61439810678
Fax 84292 0
Email 84292 0
thomas.crowhurst@gmail.com

Data sharing statement
Will individual participant data (IPD) for this trial be available (including data dictionaries)?
No
No/undecided IPD sharing reason/comment
Prior ethics approval was not sought from the local committee.


What supporting documents are/will be available?

Doc. No.TypeCitationLinkEmailOther DetailsAttachment
14944Study protocol  thomas.crowhurst@gmail.com
14945Statistical analysis plan  thomas.crowhurst@gmail.com
14946Informed consent form  thomas.crowhurst@gmail.com
14947Clinical study report  thomas.crowhurst@gmail.com
14948Ethical approval  thomas.crowhurst@gmail.com
14949Analytic code  thomas.crowhurst@gmail.com



Results publications and other study-related documents

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