Clinical trial NCT06472362: Chest CT Biomarkers as Prognostic Predictors in SSc-ILD
Official title: Deep-learning Derived Chest Computed Tomography (CT) Biomarkers as Prognostic Predictors in Systemic Sclerosis Associated Interstitial Lung Disease (SSc-ILD)
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Systemic Sclerosis
Lead sponsor: Royal Brompton & Harefield NHS Foundation Trust
Sponsor class: OTHER
Collaborators: name: Imperial College London
name: Royal Free and University College Medical School
name: The Leeds Teaching Hospitals NHS Trust
name: Hannover Medical School
name: University of Siena
name: Università Politecnica delle Marche
name: Azienda Ospedaliero Universitaria di Sassari
name: Bichat Hospital
First posted: 2024-06-25
Enrollment: 1000
Sex: ALL
Minimum age (years): 18
Maximum age (years): 99
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: Secondary/tertiary care patients with SSc

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 fibrosis=stiffening 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 1,000 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.

Eligibility criteria: Inclusion Criteria:

* diagnosed with SSc
* ≥18 years old
* HRCT between 01/01/1990 and 31/12/2019

Exclusion Criteria:

* Patients who do not have SSc
* \<18 years old
* lack of availability of HRCT imaging data

Primary outcomes:
measure: Survival
timeFrame: 15 years
measure: Pulmonary hypertension
timeFrame: 15 years
measure: Decline in FVC
timeFrame: 15 years
measure: Decline in DLCO
timeFrame: 15 years
measure: Decline in KCO
timeFrame: 15 years
Secondary outcomes:
Other outcomes:
Locations:
city: Paris
country: France
facility: Bichat-Claude Bernard hospital
city: Hannover
country: Germany
facility: Hanover Medical School
city: Ancona
country: Italy
facility: Marche Polytechnic University
city: Sassari
country: Italy
facility: Sassari University
city: Siena
country: Italy
facility: Siena University Hospital
city: Leeds
country: United Kingdom
facility: Leeds Hospital/University of Leeds
city: London
country: United Kingdom
facility: Royal Brompton Hospital
Study officials:
affiliation: Royal Brompton and Harefield Hospitals
name: Elisabetta A Renzoni
role: PRINCIPAL_INVESTIGATOR
References: