Important considerations for trials for peripheral arterial disease: Lessons learned from the paclitaxel mortality signal: A report on behalf of the registry assessment for peripheral interventional Devices (RAPID) Paclitaxel Pathways Program.

The Registry Assessment of Peripheral Devices (RAPID) convened a multidisciplinary group of stakeholders including clinicians, academicians, regulators and industry representatives to conduct an in-depth review of limitations associated with the data available to assess the paclitaxel mortality signal. Available studies were evaluated to identify strengths and limitations in the study design and data quality, which were translated to lessons learned to help guide the design, execution, and analyses of future studies. We suggest numerous actionable responses, such as the development and use of harmonized data points and outcomes in a consensus lean case report form. We advocate for reduction in missing data and efficient means for accrual of larger sample sizes in Peripheral arterial disease studies or use of supplemental datasets. Efforts to share lessons learned and working collaboratively to address such issues may improve future data in this device area and ultimately benefit patients. Condensed Abstract: Data sources evaluating paclitaxel-coated devices were evaluated to identify strengths and limitations in the study design and data quality, which were translated to lessons learned to help guide the design, execution, and analyses of future studies. We suggest numerous actionable responses, which we believe may improve future data in this device area and ultimately benefit patients.

Ontologizing health systems data at scale: making translational discovery a reality.

Common data models solve many challenges of standardizing electronic health record (EHR) data but are unable to semantically integrate all of the resources needed for deep phenotyping. Open Biological and Biomedical Ontology (OBO) Foundry ontologies provide computable representations of biological knowledge and enable the integration of heterogeneous data. However, mapping EHR data to OBO ontologies requires significant manual curation and domain expertise. We introduce OMOP2OBO, an algorithm for mapping Observational Medical Outcomes Partnership (OMOP) vocabularies to OBO ontologies. Using OMOP2OBO, we produced mappings for 92,367 conditions, 8611 drug ingredients, and 10,673 measurement results, which covered 68-99% of concepts used in clinical practice when examined across 24 hospitals. When used to phenotype rare disease patients, the mappings helped systematically identify undiagnosed patients who might benefit from genetic testing. By aligning OMOP vocabularies to OBO ontologies our algorithm presents new opportunities to advance EHR-based deep phenotyping.