Clinical Trial Details
— Status: Not yet recruiting
Administrative data
| NCT number |
NCT06162884 |
| Other study ID # |
IRB#23-001246 |
| Secondary ID |
ECR2022-3630 |
| Status |
Not yet recruiting |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
December 22, 2023 |
| Est. completion date |
December 31, 2027 |
Study information
| Verified date |
November 2023 |
| Source |
University of California, Los Angeles |
| Contact |
Grace Hyun Kim, PhD |
| Phone |
(310) 481-7594 |
| Email |
GraceKim[@]mednet.ucla.edu |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial Summary
This study is a prospective observational study for subjects with idiopathic pulmonary
fibrosis (IPF) or non-IPF interstitial lung diseases (ILD).
The purpose of this study is to compare whether imaging patterns from high-resolution
computed tomography (HRCT) at baseline can predict worsening. Single Time point Prediction
(STP) is a score derived from an artificial intelligenc/ machine learning (AI/ML) using the
radiomic features from a HRCT scan that quantifies the imaging patterns of short-term
predictive worsening.
Description:
Primary objective is to predict early for progression in both IPF and non-IPF ILD population
using an artificial intelligence (AI)/ML algorithm of STP score. The primary interest is to
validate STP score in identifying a cohort early for the candidate of anti-fibrotic
treatment. The study plans to collect clinical information such as pulmonary function tests
(PFT), symptom scores, 6-minute walk tests (6MWT), and radiological information from HRCT.
This study does not intervene with patient's standard medical care.
This proposal is a prospective study that will enroll patients from the UCLA ILD Center. STP
scores of subjects' baseline HRCT images will be grouped to one of 2 arms based on the
baseline HRCT.
- Arm A: STP>=30% in whole lung
- Arm B: STP < 30% in whole lung
A subject's allocation will be determined by the baseline HRCT scan. STP score will be
derived from the baseline HRCT to compare the early prediction of progression in ILD, STP of
30% threshold is expected to be close to the mean of overall population. In addition, a
multi-scale guided attention (MSGA) is an imaging marker from deep learning model with two
attention models to classify an IPF-likeliness using HRCT.
In IPF, progression-free survival (PFS) is defined by the reduction of 10% or more by FVC in
volume or 15% or more by DLCO (DLCO) or death from any cause, whichever came first.
In non-IPF ILD, PFS is defined by two worsening outcomes out of three elements of PFT
worsening, radiological worsening or symptom or disease-related death alone.
- Worsening in PFT is defined by 5% or more absolute decreases in the percent predicted
FVC or 10% or more absolute decrease in the percent predicted DLCO.
- Radiological evidence of disease progression is defined by visual worsening (one or more
of the following) from a radiological report or quantitative lung fibrosis (QLF) changes
>=2% in whole lung
- Symptomatic worsening can be measure by the modified Medical Research Council (mMRC)
Dyspnea scale or King's Brief Interstitial Lung Disease (K-BILD).
Secondary outcomes of this study are:
- To compare overall survival between the two arms of STP
- To compare the changes in 6-minute walk tests between the two arms of STP
- To compare PFS between two groups of MSGA marker positive and negative
- To compare overall survival between two groups of MSGA marker positive and negative
With a chronic ILD or IPF, lung function may be stable for a few years or continue to
deteriorate slowly or rapidly develop more scar tissues over time. While it is known that
age, biological sex, and lung function are factors that can impact risk of worsening lung
function, there is a great need for better methods to predict which patients are at risk of
worsening lung function. Having better methods to predict disease progression could allow
more timely treatment with anti-fibrotic treatment to prevent the disease progression.
In both IPF and non-IPF ILD, HRCT scan is required for diagnosis. Imaging patterns derived
from HRCT, called STP is designed to predict the areas in lung that may be likely to progress
in the next 6 to 12 months. High STP scores are associated with poor prognosis and worsening
the pulmonary function. The goal of this study is to test whether an AI-algorithm, the STP
score from a single CT study, can predict disease progression in subjects with IPF and non
IPF-ILD in a prospective study. This AI-algorithm was developed under NIH-sponsored study.
The purpose of prospective observational cohort study from UCLA is to test for the early sign
of progressive fibrosis using baseline HRCT. This study, Imaging Signature of Progressive
Pulmonary Fibrosis (IS-PPF) Research is a prospective study that will collect information
regarding HRCT images, pulmonary function test, 6-minute walk, symptomatic score, and
patients' clinical information to set up AI-driven imaging signature for evaluating the STP
in predicting progression in IPF and non-IPF ILD.
This is an observational study; only minimally invasive procedures will be performed with
study subjects (blood draws and nasal swabs). These biological samples will support future
research studies. The study subject's will participation in the study for up to 3 years, the
length of participation may vary. All subjects will continue to receive their usual care and
treatment.
In summary, this research will create an opportunity to test and validate the imaging score
and early prediction for IPF and non-IPF ILD that can impact current and future care
practices.