Background
Effective therapies to combat COVID-19 are urgently needed. Hydroxychloroquine (HCQ) has in vitro antiviral activity against SARS-CoV-2, but the clinical benefit of HCQ in treating COVID-19 is unclear. Randomized controlled trials are needed to determine the safety and efficacy of HCQ for the treatment of hospitalized patients with COVID-19.
Methods
We conducted a multicenter, double-blind, randomized clinical trial of HCQ among patients hospitalized with laboratory confirmed COVID-19. Subjects were randomized in a 1:1 ratio to HCQ or placebo for five days and followed for 30 days. The primary efficacy outcome was a severe disease progression composite endpoint (death, ICU admission, mechanical ventilation, ECMO, and/or vasopressor use) at day 14.
Results
A total of 128 patients were included in the intention-to-treat analysis. Baseline demographic, clinical, and laboratory characteristics were similar between HCQ (N=67) and placebo (N=61) arms. At day 14, 11 (16.4%) subjects assigned to HCQ and 6 (9.8%) subjects assigned to placebo met the severe disease progression endpoint, but this did not achieve statistical significance (P=.350). There were no significant differences in COVID-19 clinical scores, number of oxygen-free days, SARS-CoV-2 clearance, or adverse events between HCQ and placebo. HCQ was associated with a slight increase in mean corrected QT interval, an increased D-dimer, and a trend towards an increased length of stay.
Conclusions
In hospitalized patients with COVID-19, our data suggest that HCQ does not prevent severe outcomes or improve clinical scores. However, our conclusions are limited by a relatively small sample size, and larger randomized controlled trials or pooled analyses are needed.
Older primary care patients with chronic pain are willing to try exercise and relaxation therapies to help manage pain but report a substantial number of barriers to participating in these programs. Addressing patient-level barriers could improve engagement in and adherence to exercise and relaxation therapies for managing pain in older persons.
The COVID-19 pandemic has challenged front-line clinical decision-making, leading to numerous published prognostic tools. However, few models have been prospectively validated and none report implementation in practice. Here, we use 3345 retrospective and 474 prospective hospitalizations to develop and validate a parsimonious model to identify patients with favorable outcomes within 96 h of a prediction, based on real-time lab values, vital signs, and oxygen support variables. In retrospective and prospective validation, the model achieves high average precision (88.6% 95% CI: [88.4–88.7] and 90.8% [90.8–90.8]) and discrimination (95.1% [95.1–95.2] and 86.8% [86.8–86.9]) respectively. We implemented and integrated the model into the EHR, achieving a positive predictive value of 93.3% with 41% sensitivity. Preliminary results suggest clinicians are adopting these scores into their clinical workflows.
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