ANZCTR - Registration

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]

None

Universal Trial Number (UTN)

Trial acronym

Linked study record

Health condition

Health condition(s) or problem(s) studied:

Chronic lung allograft dysfunction

Chronic graft-versus-host disease of the lung (bronchiolitis obliterans)

Condition category

Condition code

Respiratory

Other respiratory disorders / diseases

Blood

Other blood disorders

Inflammatory and Immune System

Other inflammatory or immune system disorders

Intervention/exposure

Study type

Observational

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]

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]

Small airway resistance (resistance at 5 Hz minus resistance at 20 Hz)

Timepoint [1]

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]

Area under the reactance curve from 5 Hz to resonant frequency (AX)

Timepoint [2]

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]

Reactance at 5 Hz

Timepoint [3]

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]

Reactance at 5Hz on inspiration minus reactance at 5 Hz on expiration

Timepoint [1]

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]

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]

Entire duration of data collection period.

Secondary outcome [3]

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]

Entire duration of data collection period.

Secondary outcome [4]

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]

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

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]

The Royal Adelaide Hospital - Adelaide

Recruitment postcode(s) [1]

5000 - Adelaide

Funding & Sponsors

Funding source category [1]

Hospital

Name [1]

Department of Thoracic Medicine, Royal Adelaide Hospital

Country [1]

Australia

Primary sponsor type

Individual

Name

Dr Thomas Crowhurst

Country

Australia

Secondary sponsor category [1]

None

Name [1]

Country [1]

Other collaborator category [1]

Individual

Name [1]

A/Prof Chien-Li Holmes-Liew

Country [1]

Australia

Other collaborator category [2]

Individual

Name [2]

Dr Sonya Johnston

Country [2]

Australia

Other collaborator category [3]

Individual

Name [3]

Dr Aeneas Yeo

Country [3]

Australia

Other collaborator category [4]

Individual

Name [4]

Professor Mark Holmes

Country [4]

Australia

Other collaborator category [5]

Individual

Name [5]

Lauren Bussell

Country [5]

Australia

Other collaborator category [6]

Individual

Name [6]

Dr David Yeung

Country [6]

Australia

Other collaborator category [7]

Individual

Name [7]

Dr Jessica Butler

Country [7]

Australia

Other collaborator category [8]

Individual

Name [8]

Suzanne Edwards

Country [8]

Australia

Other collaborator category [9]

Individual

Name [9]

Prof Gregory Hodge

Country [9]

Australia

Other collaborator category [10]

Individual

Name [10]

Prof Gregory Snell

Country [10]

Australia

Ethics approval

Ethics application status

Approved

Ethics committee name [1]

Central Adelaide Local Health Network Human Research Ethics Committee

Ethics committee address [1]

Royal Adelaide Hospital
1 Port Road
Adelaide SA 5000

Ethics committee country [1]

Australia

Date submitted for ethics approval [1]

07/06/2018

Approval date [1]

19/06/2018

Ethics approval number [1]

R20180610

Summary

Brief summary

This is a prospective diagnostic study which aims to determine whether impulse oscillometry may enable the earlier detection of chronic lung allograft dysfunction and / or bronchiolitis obliterans after allogeneic haematopoietic stem cell transplantation.

Our prospective diagnostic study seeks to explore two primary and two secondary hypotheses:

Primary hypotheses:

1.	As a disease of the small airways, BOS (whether in the context of lung transplant or allogeneic HSCT) will manifest changes on IOS as follows:

1.1	Small airway resistance (represented by Rrs5-Rrs20) will be increased 
1.2	AX will be increased
1.3	Xrs5 will be more negative
1.4	In more severe disease with significant airflow limitation, there will be a significant difference between Xrs5insp and Xrs5exp in normal quiet breathing

2.	BOS will be detectable via IOS before it is evident via standard diagnostic criteria

Secondary hypotheses:

3.	RAS will also cause an increase in AX and a more negative Xrs5 but will be distinguishable from BOS on IOS due to the following:

3.1	Rrs5-Rrs20 will be normal
3.2	There will be no significant difference between Xrs5insp and Xrs5exp in normal quiet breathing

4.	RAS will be detectable via IOS before it is evident via standard diagnostic criteria

Trial website

None

Public notes

Contacts

Principal investigator

Name

Dr Thomas Crowhurst

Address

Department of Thoracic Medicine
Royal Adelaide Hospital
1 Port Road
Adelaide SA 5000

Country

Australia

Phone

+61439810678

Email

[email protected]

Contact person for public queries

Name

Thomas Crowhurst

Address

Department of Thoracic Medicine
Royal Adelaide Hospital
1 Port Road
Adelaide SA 5000

Country

Australia

Phone

+61439810678

Email

[email protected]

Contact person for scientific queries

Name

Thomas Crowhurst

Address

Department of Thoracic Medicine
Royal Adelaide Hospital
1 Port Road
Adelaide SA 5000

Country

Australia

Phone

+61439810678

Email

[email protected]

Doc. No.	Type	Email
14944	Study protocol	[email protected]
14945	Statistical analysis plan	[email protected]
14946	Informed consent form	[email protected]
14947	Clinical study report	[email protected]
14948	Ethical approval	[email protected]
14949	Analytic code	[email protected]

Trial Review

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