Sex, obesity, diabetes, and exposure to particulate matter among patients with severe asthma: Scientific insights from a comparative analysis of open clinical data sources during a five-day hackathon

Abstract

This special communication describes activities, products, and lessons learned from a recent hackathon that was funded by the National Center for Advancing Translational Sciences via the Biomedical Data Translator program ('Translator'). Specifically, Translator team members self-organized and worked together to conceptualize and execute, over a five-day period, a multi-institutional clinical research study that aimed to examine, using open clinical data sources, relationships between sex, obesity, diabetes, and exposure to airborne fine particulate matter among patients with severe asthma. The goal was to develop a proof of concept that this new model of collaboration and data sharing could effectively produce meaningful scientific results and generate new scientific hypotheses. Three Translator Clinical Knowledge Sources, each of which provides open access (via Application Programming Interfaces) to data derived from the electronic health record systems of major academic institutions, served as the source of study data. Jupyter Python notebooks, shared in GitHub repositories, were used to call the knowledge sources and analyze and integrate the results. The results replicated established or suspected relationships between sex, obesity, diabetes, exposure to airborne fine particulate matter, and severe asthma. In addition, the results demonstrated specific differences across the three Translator Clinical Knowledge Sources, suggesting cohort- and/or environment-specific factors related to the services themselves or the catchment area from which each service derives patient data. Collectively, this special communication demonstrates the power and utility of intense, team-oriented hackathons and offers general technical, organizational, and scientific lessons learned.

Keywords: Application programming interface; Clinical data; Hackathon; Multi-institutional collaboration; Open data; Team science.

Figure 1.

Hackathon flowchart, showing the three major operational components of the event and highlighting key example activities associated with each component. Pre-hackathon planning helped to guide group discussions during the first day or two of the hackathon. Hackathon implementation and management was aided by the pre-hackathon planning, although it did involve a nimble approach to respond to unexpected emergent issues. Post-hackathon activities focused largely on scientific outcomes.

Figure 2.

Scientific flowchart, showing key hackathon aspects of clinical workflow development and implementation. (A) Pre-hackathon planning focused on the development of two generic clinical workflows; this planning helped to guide group discussions during the first one and a half days of the hackathon. (B) Initial hackathon activities focused on evaluating the three open Translator Clinical Knowledge Sources that were developed as part of the Translator program, in terms of the clinical workflows and the capabilities and data available from each knowledge source. (C) Subsequent brainstorming in the context of the generic workflows and the capabilities of the clinical knowledge sources led to the development of a specific instance of the generic clinical workflows. (D) The workflow was successfully implemented and executed over days three and four of the hackathon, and a first-pass analysis of the results was conducted by day five.