The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers. MATERIALS AND METHODS: The Clinical and Translational Science Award Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics. RESULTS: Organized in inclusive workstreams, we created legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access. CONCLUSIONS: The N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19.

Longitudinal migration and inducible displacement of the Mobility Total Ankle System.

BACKGROUND AND PURPOSE: RSA can be used for early detection of unstable implants. We assessed the micromotion of the Mobility Total Ankle System over 2 years, to evaluate the stability of the bone-implant interface using radiostereometric analysis measurements of longitudinal migration and inducible displacement. PATIENTS AND METHODS: 23 patients were implanted with the Mobility system. Median age was 62 (28-75) years and median BMI was 28.8 (26.0-34.5). Supine radiostereometric analysis examinations were done from postoperatively to the 2-year follow-up. Standing examinations were taken from the 3-month to the 2-year follow-up. Migrations and displacements were assessed using model-based RSA software (v. 3.2). RESULTS: The median maximum total point motion (MTPM) for the implants at 2 years was 1.19 (0.39-1.95) mm for the talar component and 0.90 (0.17-2.28) mm for the spherical tip of the tibial component. The general pattern for all patients was that the slope of the migration curves decreased over time. The main direction of motion for both components was that of subsidence. The median 2-year MTPM inducible displacement for the talar component was 0.49 (0.27-1.15) mm, and it was 0.07 (0.03-0.68) mm for the tibial component tip. INTERPRETATION: The implants subside into the bone over time and under load. This corresponds to the direction of primary loading during standing or walking. This statistically significant motion may become a clinically significant finding that would correspond with premature implant failure.