Predicting Outcomes using DIGital TechnologY in patients with Interstitial Lung Disease (PRODIGY-ILD): Protocol for a...

Area of Science:

• Pulmonary Medicine
• Digital Health Technologies
• Biomedical Data Science

Background:

• Interstitial lung disease (ILD) is a progressive, life-limiting condition with unpredictable clinical courses.
• Accurate prediction of disease progression, exacerbations, and mortality in ILD is challenging.
• Current methods lack the precision needed for effective treatment planning and clinical trial design.

Purpose of the Study:

• To develop novel outcome predictors for interstitial lung disease (ILD).
• To leverage comprehensive, real-time data from digital health technologies for deep patient profiling.
• To evaluate the accuracy of developed prediction models against observed patient outcomes.

Main Methods:

• Prospective cohort study involving 50 participants diagnosed with ILD.
• Utilized smartwatches for passive collection of biometric data.
• Integrated smartwatch data with clinical history, disease course, and patient-reported outcomes.

Main Results:

• The study successfully collected comprehensive real-time data from ILD patients.
• Initial data were used to develop clinical prediction models for ILD outcomes.
• Models are undergoing evaluation for accuracy using longitudinal follow-up data.

Conclusions:

• Digital health technologies offer a promising avenue for deep phenotyping and outcome prediction in ILD.
• The developed prediction models have the potential to improve treatment planning and clinical trial efficiency.
• Further validation is required to establish the clinical utility of these novel ILD predictors.