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Clinical Trials/NCT07404423
NCT07404423
Not yet recruiting
Not Applicable

Observational Prediction Model for Clinical Outcomes in Idiopathic Pulmonary Fibrosis: a Multicentre, ML-driven Study (OPEN-IPF)

University of Modena and Reggio Emilia0 sites1,000 target enrollmentStarted: June 1, 2026Last updated:

Overview

Phase
Not Applicable
Status
Not yet recruiting
Sponsor
University of Modena and Reggio Emilia
Enrollment
1,000
Primary Endpoint
Disease progression (guideline-based functional/composite criteria)

Overview

Brief Summary

Idiopathic pulmonary fibrosis (IPF) is a progressive fibrotic lung disease with marked inter-individual heterogeneity in trajectories and outcomes. Despite antifibrotic therapies, reliable risk stratification in routine practice remains suboptimal. OPEN-IPF is a multicentre retrospective observational cohort study designed to build a harmonised real-world dataset across Italian IPF referral centres to enable the development and external validation of machine-learning (ML) models predicting clinically relevant outcomes.

Detailed Description

OPEN-IPF addresses the current limitation of AI/ML research in IPF-namely, the lack of large multicentre real-world datasets with harmonised variables and robust external validation. The study will retrospectively include adult patients with IPF followed in routine practice in participating Italian referral centres from 1 January 2015 to 31 December 2025 (data lock). No study-specific procedures will be performed. De-identified/pseudonymised data will be collected using a common data model, including demographics, smoking history, comorbidities, pulmonary function (FVC, DLCO), oxygen requirement, 6-minute walk test (where available), antifibrotic treatment exposure, HRCT features routinely reported, basic laboratory parameters, and clinical outcomes. The primary modelling targets are disease progression, acute exacerbations of IPF (AE-IPF), and real-world response to antifibrotic treatment. Model development will be performed using multicentre data with explicit external validation across centres

Study Design

Study Type
Observational
Observational Model
Cohort
Time Perspective
Retrospective

Eligibility Criteria

Ages
18 Years to 90 Years (Adult, Older Adult)
Sex
All
Accepts Healthy Volunteers
No

Inclusion Criteria

  • Age ≥18 years
  • Diagnosis of idiopathic pulmonary fibrosis established according to international guidelines and local multidisciplinary team (MDT) assessment
  • Availability of baseline clinical and functional data
  • Availability of follow-up data for at least 12 months, or until a clinically relevant event (e.g., death, lung transplantation)

Exclusion Criteria

  • Interstitial lung disease other than IPF
  • Lung transplantation performed before the baseline (index) date
  • Absence of any follow-up information after baseline

Outcomes

Primary Outcomes

Disease progression (guideline-based functional/composite criteria)

Time Frame: From baseline (index date) up to 12 months and up to end of available follow-up (maximum: 31 December 2025)

Disease progression defined using guideline-based criteria derived from routinely collected clinical data (e.g., decline in lung function and/or composite progression definitions as per the shared operational document).

Secondary Outcomes

  • Acute exacerbation of IPF (AE-IPF)(From baseline to end of follow-up (maximum: 31 December 2025))
  • Real-world response to antifibrotic therapy(From treatment initiation (or baseline if already treated) up to 12 months and end of follow-up (maximum: 31 December 2025))
  • Overall survival(From baseline to end of follow-up (maximum: 31 December 2025))
  • Transplant-free survival(From baseline to end of follow-up (maximum: 31 December 2025))
  • Time to first progression or AE-IPF event(From baseline to end of follow-up (maximum: 31 December 2025))

Investigators

Sponsor
University of Modena and Reggio Emilia
Sponsor Class
Other
Responsible Party
Principal Investigator
Principal Investigator

Roberto Tonelli

MD, PhD

University of Modena and Reggio Emilia

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