Objectives: Angiotensin-converting enzyme inhibitors (ACEi), angiotensin II receptor blockers (ARBs), and HMG-CoA reductase inhibitors ("statins") have been hypothesized to affect COVID-19 severity. However, up to now, no studies investigating this association have been conducted in the most vulnerable and affected population groups (ie, older adults residing in nursing homes). The objective of this study was to explore the association of ACEi/ARB and/or statins with clinical manifestations in COVID-19einfected older adults residing in nursing homes. Design: We undertook a retrospective multicenter cohort study to analyze the association between ACEi/ ARB and/or statin use with clinical outcome of COVID-19. The outcomes were (1) serious COVID-19 defined as long-stay hospital admission or death within 14 days of disease onset, and (2) asymptomatic (ie, no disease symptoms in the whole study period while still being diagnosed by polymerase chain reaction). Setting and participants: A total of 154 COVID-19epositive subjects were identified, residing in 1 of 2 Belgian nursing homes that experienced similar COVID-19 outbreaks. Measures: Logistic regression models were applied with age, sex, functional status, diabetes, and hypertension as covariates. Results: We found a statistically significant association between statin intake and the absence of symptoms during COVID-19 (odds ratio [OR] 2.91; confidence interval [CI] 1.27e6.71), which remained statistically significant after adjusting for covariates (OR 2.65; CI 1.13e6.68). Although the effects of statin intake on serious clinical outcome were in the same beneficial direction, these were not statistically significant (OR 0.75; CI 0.24e1.87). There was also no statistically significant association between ACEi/ ARB and asymptomatic status (OR 2.72; CI 0.59e25.1) or serious clinical outcome (OR 0.48; CI 0.10e1.97).

Background and Purpose Because robotic devices record the kinematics and kinetics of human movements with high resolution, we hypothesized that robotic measures collected longitudinally in patients after stroke would bear a significant relationship to standard clinical outcome measures and, therefore, might provide superior biomarkers. Methods In patients with moderate-to-severe acute ischemic stroke, we used clinical scales and robotic devices to measure arm movement 7, 14, 21, 30, and 90 days after the event at 2 clinical sites. The robots are interactive devices that measure speed, position, and force so that calculated kinematic and kinetic parameters could be compared with clinical assessments. Results Among 208 patients, robotic measures predicted well the clinical measures (cross-validated R2 of modified Rankin scale=0.60; National Institutes of Health Stroke Scale=0.63; Fugl-Meyer=0.73; Motor Power=0.75). When suitably scaled and combined by an artificial neural network, the robotic measures demonstrated greater sensitivity in measuring the recovery of patients from day 7 to day 90 (increased standardized effect=1.47). Conclusions These results demonstrate that robotic measures of motor performance will more than adequately capture outcome, and the altered effect size will reduce the required sample size. Reducing sample size will likely improve study efficiency.