Coronary Artery Disease Reporting and Data System (CAD-RADS) Adoption: Analysis of Local Trends in a Large Academic Medical Center.

PURPOSE: To perform a retrospective review of Coronary Artery Disease Reporting and Data System (CAD-RADS) adoption at a high-volume cardiac CT service. MATERIALS AND METHODS: In this retrospective study, the adoption of CAD-RADS in 6562 coronary CT angiography (CTA) reports from January 1, 2017, to February 13, 2020, was evaluated. Reports without CAD-RADS were classified as opt-outs or exceptions to CAD-RADS. CAD-RADS classifications were retrospectively assigned to the opt-outs and the clinical indications for coronary CTA. RESULTS: CAD-RADS scores were reported in 95% (6264 of 6562) of cases. Among the 5% (n = 298) of reports not reported according to CAD-RADS, 58% (n = 172) were considered opt-outs and 42% (n = 126) were exceptions. Cases with higher degree of stenosis, stents, and coronary artery bypass grafts (CABGs) occurred more often in opt-outs versus reports with CAD-RADS (odds ratio [OR], 8.3 [95% CI: 1.6, 42.1]; P < .001). The quarterly opt-out rate decreased over consecutive quarters in the 1st year (OR, 0.77 [95% CI: 0.61, 0.96]; P = .01), then stabilized. Quarterly opt-out rate for patients with stents decreased over time (OR, 0.82 [95% CI: 0.73, 0.92]; P = .008), as did the opt-out rates in patients with CABG (OR, 0.83 [95% CI: 0.76, 0.91]; P < .001). Exceptions (n = 126) included coronary dissections (44%), anomalous coronary arteries (41%), coronary artery aneurysms or pseudoaneurysms (10%), vasculitis (2%), stent complications (2%), and extrinsic compression of grafts (2%). CONCLUSION: CAD-RADS was adopted rapidly and widely. Readers opted out of its use most often in complex cases of CAD, and the most common exceptions were coronary dissections and anomalous coronary artery.Keywords: Coronary Arteries, CT Angiography© RSNA, 2021

Long-Term Training with a Brain-Machine Interface-Based Gait Protocol Induces Partial Neurological Recovery in Paraplegic Patients

Brain-machine interfaces (BMIs) provide a new assistive strategy aimed at restoring mobility in severely paralyzed patients. Yet, no study in animals or in human subjects has indicated that long-term BMI training could induce any type of clinical recovery. Eight chronic (3-13 years) spinal cord injury (SCI) paraplegics were subjected to long-term training (12 months) with a multi-stage BMI-based gait neurorehabilitation paradigm aimed at restoring locomotion. This paradigm combined intense immersive virtual reality training, enriched visual-tactile feedback, and walking with two EEG-controlled robotic actuators, including a custom-designed lower limb exoskeleton capable of delivering tactile feedback to subjects. Following 12 months of training with this paradigm, all eight patients experienced neurological improvements in somatic sensation (pain localization, fine/crude touch, and proprioceptive sensing) in multiple dermatomes. Patients also regained voluntary motor control in key muscles below the SCI level, as measured by EMGs, resulting in marked improvement in their walking index. As a result, 50% of these patients were upgraded to an incomplete paraplegia classification. Neurological recovery was paralleled by the reemergence of lower limb motor imagery at cortical level. We hypothesize that this unprecedented neurological recovery results from both cortical and spinal cord plasticity triggered by long-term BMI usage