Is Weight Loss Associated with Less Progression of Changes in Knee Articular Cartilage among Obese and Overweight Patients as Assessed with MR Imaging over 48 Months? Data from the Osteoarthritis Initiative

Purpose To investigate the association of weight loss with progression of cartilage changes at magnetic resonance (MR) imaging over 48 months in overweight and obese participants compared with participants of stable weight. Materials and Methods The institutional review boards of the four participating centers approved this HIPAA-compliant study. Included were (a) 640 participants (mean age, 62.9 years ± 9.1 [standard deviation]; 398 women) who were overweight or obese (body mass index cutpoints of 25 and 30 kg/m2, respectively) from the Osteoarthritis Initiative, with risk factors for osteoarthritis or mild to moderate radiographic findings of osteoarthritis, categorized into groups with (a) weight loss of more than 10% (n = 82), (b) weight loss of 5%-10% (n = 238), or (c) stable weight (n = 320) over 48 months. Participants were frequency-matched for age, sex, baseline body mass index, and Kellgren-Lawrence score. Two radiologists assessed cartilage and meniscus defects on right knee 3-T MR images at baseline and 48 months by using the modified Whole-Organ Magnetic Resonance Imaging Score (WORMS). Progression of the subscores was compared between the weight loss groups by using multivariable logistic regression models. Results Over 48 months, adjusted mean increase of cartilage WORMS was significantly smaller in the 5%-10% weight loss group (1.6; 95% confidence interval [CI]: 1.3, 1.9; P = .002) and even smaller in the group with more than 10% weight loss (1.0; 95% CI: 0.6, 1.4; P = .001) when compared with the stable weight group (2.3; 95% CI: 2.0, 2.7). Moreover, percentage of weight change was significantly associated with increase in cartilage WORMS (β = 0.2; 95% CI: 0.02, 0.4; P = .007). Conclusion Participants who lost weight over 48 months showed significantly lower cartilage degeneration, as assessed with MR imaging; rates of progression were lower with greater weight loss. © RSNA, 2017

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