Ignite Creation Date:
2024-07-17 @ 10:41 AM
Last Modification Date:
2024-10-26 @ 3:32 PM
Study NCT ID:
NCT06472362
Status:
NOT_YET_RECRUITING
Last Update Posted:
2024-06-25
First Post:
2024-06-11
Brief Title:
Chest CT Biomarkers as Prognostic Predictors in SSc-ILD
Sponsor:
Royal Brompton Harefield NHS Foundation Trust
Organization:
Royal Brompton Harefield NHS Foundation Trust
Study Overview
Official Title:
Deep-learning Derived Chest Computed Tomography CT Biomarkers as Prognostic Predictors in Systemic Sclerosis Associated Interstitial Lung Disease SSc-ILD
Status:
NOT_YET_RECRUITING
Status Verified Date:
2024-06
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
The goal of this retrospective observational study is to investigate whether novel imaging biomarkers of airways vessels and overall extent of fibrosis at baseline predict ILD progression vasculopathy development and survival in SSc-ILD
Detailed Description:
Interstitial lung disease ILD or lung fibrosisstiffening of the lungs by scar tissue develops in over half of patients with systemic sclerosis SSc Whilst ILD remains stable in some patients at least a third have progressively increasing fibrosis There is a pressing need for accurate indicators that identify a patients at higher risk of progression needing immediate treatment to prevent further irreversible ILD and b patients at lower risk not needing treatment
In this study the prognostic potential and accuracy of machine-learning derived biomarkers to evaluate abnormalities that are difficult to quantify visually will be investigated Whether novel high resolution computed tomography HRCT imaging biomarkers of airways vessels and overall extent of fibrosis at baseline can predict ILD progression vasculopathy development and survival will be investigated in a cohort of approximately 1000 SSc-ILD patients
The algorithm scores will be evaluated against survival using Cox proportional hazards modelling while mixed effects model analysis will be used to assess links with change in lung function forced vital capacity FVC diffusing capacity for carbon monoxide DLco and carbon monoxide transfer coefficient Kco The airway algorithm measuring traction bronchiectasis dilatation of the airways due to surrounding fibrosis may predict worsening of FVC reflective of ILD progression The vessel algorithm may predict decline in KCO a marker of pulmonary vascular involvement Exploratory analyses evaluating change in HRCT fibrosis extent over time for patients with repeat HRCTs will also be performed and whether composite outcomes of change in HRCT and lung function variables improve long term outcome prediction and pave the way to their use in clinical trials and routine clinical use Patients with trivial changes on CT will also be included to assess for very early changes that could be predictive of future decline These algorithms will be combined with the findings of our previous study which suggest that a certain type of pattern on CT called UIP predicts shorter survival
In this study the prognostic potential and accuracy of machine-learning derived biomarkers to evaluate abnormalities that are difficult to quantify visually will be investigated Whether novel high resolution computed tomography HRCT imaging biomarkers of airways vessels and overall extent of fibrosis at baseline can predict ILD progression vasculopathy development and survival will be investigated in a cohort of approximately 1000 SSc-ILD patients
The algorithm scores will be evaluated against survival using Cox proportional hazards modelling while mixed effects model analysis will be used to assess links with change in lung function forced vital capacity FVC diffusing capacity for carbon monoxide DLco and carbon monoxide transfer coefficient Kco The airway algorithm measuring traction bronchiectasis dilatation of the airways due to surrounding fibrosis may predict worsening of FVC reflective of ILD progression The vessel algorithm may predict decline in KCO a marker of pulmonary vascular involvement Exploratory analyses evaluating change in HRCT fibrosis extent over time for patients with repeat HRCTs will also be performed and whether composite outcomes of change in HRCT and lung function variables improve long term outcome prediction and pave the way to their use in clinical trials and routine clinical use Patients with trivial changes on CT will also be included to assess for very early changes that could be predictive of future decline These algorithms will be combined with the findings of our previous study which suggest that a certain type of pattern on CT called UIP predicts shorter survival
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None