Table 1:

Summary of Workshop Outcomes

KEY PRINCIPLE	COMPONENTS	GOALS
1. Develop novel analytical approaches to large datasets	Integration of clinical & multi-omics data Encourage development & validation of bioinformatic methods Support multidisciplinary research teams Support discovery science	Maximize high-value outputs of complex data initiatives in pulmonary vascular disease Move quickly from molecular discovery to clinical trials in pulmonary vascular disease Provide innovative analytical frameworks applicable across biomedical research
2. Incentivize dataset integration & facilitate data accessibility	Incorporation of existing data resources (e.g., PHBI, PH Biobank, PHAR, others) International partnerships to increase power and cross-validation Harmonization of present & future data creation endeavors Secure, web-based exposure of data resources	Assurance of implementation of FAIR principles Enable more advanced data analytical approaches (e.g., machine learning) in pulmonary vascular disease Extend impact of pulmonary vascular disease research across all of biomedical research
3. Organize preclinical studies of new targets and new experimental disease models	Consortia of preclinical investigators Shared core facilities Common analysis methods for physiologic & molecular data types	Streamline consensus prioritization & validation of findings Prioritize development of novel experimental models of pulmonary vascular disease Cost savings via economies of scale Improve data standardization & harmonization
4. Support acquisition of longitudinal data	Molecular measures over time Clinical measures over time	Identification of integrated pulmonary vascular disease trajectories Enhanced predictive pulmonary vascular disease modeling Informed development of precision trials & endpoints
5. Lay the foundation for a master protocol for pulmonary vascular disease trials	Define roles for biomarkers/molecular signatures Incorporation of multiple data types, including patient-centered data Consideration of adaptive designs & drug withdrawal trials Definition of consensus measures & common data elements	Harmonization of robust, clinically meaningful consensus endpoints across studies Enhance efficiency of trial execution & progression by incorporating molecular with clinical data types Facilitate impactful trials oriented toward the rarer types of pulmonary vascular disease