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Updated: May 27, 2026

Procedure for Lung Engineering
Published on: March 8, 2011
Virtual multidisciplinary discussion across borders for interstitial lung disease: a prospective, multicentre study
Asmita Anilkumar Mehta1, Sujeet Rajan2, Subin Ahmed3
1RespiratoryMedicine, Amrita Institute of Medical Sciences and Research Centre, Amrita Vishwa Vidyapeetham, Kochi, India asmitamehta@aims.amrita.edu.
Objectives:
To assess diagnostic concordance and reclassification following an India-led, multinational virtual multidisciplinary discussion (V-MDD) platform for interstitial lung disease (ILD).
Design:
Prospective, multicentre service-evaluation study.
Setting:
Twenty-four Indian referral centres connected through a secure virtual platform, with international faculty participation from the UK, Greece and Sri Lanka.
Participants:
A total of 127 anonymised ILD cases discussed across 29 V-MDD sessions (February 2024-February 2025). Each panel included ≥4 pulmonologists, two pulmonary pathologists, one of three rotating thoracic radiologists and one of two rheumatologists, along with international experts.
Results:
The cohort (mean age 52.6±16.1 years; 53.5% female (68/127)) most frequently presented with dyspnoea (82.6%) and cough (73.2%). Pre-V-MDD diagnoses included hypersensitivity pneumonitis (HP) and sarcoidosis as distinct disease entities, and usual interstitial pneumonia (UIP) and non-specific interstitial pneumonia (NSIP) as radiological patterns, along with connective tissue disease (CTD)-ILD and other ILDs. Concordance between pre- and post-V-MDD CT diagnoses was substantial (κ=0.658; 95% CI 0.562 to 0.754; p <0.001). Histopathology concordance was similarly high (κ=0.861 for HP; κ=0.650 for other ILDs). Overall, 23% of cases were reclassified following discussion. Diagnostic stability was greatest for organising pneumonia, CTD-ILD and UIP pattern, whereas HP and sarcoidosis showed the highest reclassification rates.
Conclusions:
The India-led, multinational V-MDD model demonstrated substantial diagnostic concordance and refined nearly one-quarter of ILD diagnoses. This virtual, scalable framework expands access to subspecialty expertise and offers a practical blueprint for standardising ILD care in resource-limited and cross-border settings.
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