Lopinavir/ritonavir for treatment of non-hospitalized patients with COVID-19: a randomized clinical trial

Kaizer, Alexander; Shapiro, Nathan I.; Wild, Jessica; Brown, Samuel M.; Cwik, B Jessica; Hart, Kimberly W.; Jones, Alan E.; Pulia, Michael S.; Self, Wesley H.; Smith, Clay; Smith, Stephanie A; Ng, Patrick; Thompson, B. Taylor; Rice, Todd W.; Lindsell, Christopher J.; Ginde, Adit A.

doi:10.1016/j.ijid.2022.12.028

Cited by 10 publications

(5 citation statements)

References 33 publications

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“…conducted a 6-month trial collecting AEs via monthly televisits [ 8 ]. Kaizer et al used safety triggers sent to staff for telephone follow-up when daily surveys were not completed for 2 consecutive days on their short-duration trial [ 9 ]. Naggie et al ran a short-duration trial and asked participants to complete assessments daily through a study portal [ 10 ].…”

Section: Discussionmentioning

confidence: 99%

“…Decentralized clinical trials of assorted designs were conducted during the pandemic and offered various strategies for safety data collection [8][9][10]. Josan et al conducted a 6-month trial collecting AEs via monthly televisits [8].…”

Section: Principal Findingsmentioning

confidence: 99%

“…As with clinical care, there was an urgent need to leverage technological advances to conduct prospective clinical trials remotely [4,7]. Decentralized trials, while not a new concept, were conducted in response to the pandemic [8][9][10]. Research Electronic Data Capture (REDCap) was also utilized by several groups to conduct their trials both prior to [11][12][13][14] and during the pandemic [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

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Remote and semi-automated methods to conduct a decentralized randomized clinical trial

Cafaro¹,

LaRiccia²,

Bandomer³

et al. 2023

J. Clin. Trans. Sci.

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Introduction: Designing and conducting clinical trials is challenging for some institutions and researchers due to associated time and personnel requirements. We conducted recruitment, screening, informed consent, study product distribution, and data collection remotely. Our objective is to describe how to conduct a randomized clinical trial using remote and automated methods. Methods: A randomized clinical trial in healthcare workers is used as a model. A random group of workers were invited to participate in the study through email. Following an automated process, interested individuals scheduled consent/screening interviews. Enrollees received study product by mail and surveys via email. Adherence to study product and safety were monitored with survey data review and via real-time safety alerts to study staff. Results: A staff of 10 remotely screened 406 subjects and enrolled 299 over a 3-month period. Adherence to study product was 87%, and survey data completeness was 98.5% over 9 months. Participants and study staff scored the System Usability Scale 93.8% and 90%, respectively. The automated and remote methods allowed the study maintenance period to be managed by a small study team of two members, while safety monitoring was conducted by three to four team members. Conception of the trial to study completion was 21 months. Conclusions: The remote and automated methods produced efficient subject recruitment with excellent study product adherence and data completeness. These methods can improve efficiency without sacrificing safety or quality. We share our XML file for researchers to use as a template for learning purposes or designing their own clinical trials.

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Section: Discussionmentioning

confidence: 99%

Section: Principal Findingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Remote and semi-automated methods to conduct a decentralized randomized clinical trial

Cafaro¹,

LaRiccia²,

Bandomer³

et al. 2023

J. Clin. Trans. Sci.

View full text Add to dashboard Cite

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“…This was done to develop an understanding of Bayesian approaches in common settings, which we hope will translate to confidence in adopting Bayesian approaches in complex settings where traditional/classical estimation approaches may have significant limitations or be impossible. Examples include high dimensional problems [ 36 ], variable selection [ 37 , 38 ], clustering [ 39 ], and incorporating historical data in clinical trials [ 40 ].…”

Section: Discussionmentioning

confidence: 99%

A practical guide to adopting Bayesian analyses in clinical research

Gunn-Sandell,

Bedrick,

Hutchins

et al. 2023

J. Clin. Trans. Sci.

Self Cite

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Background: Bayesian statistical approaches are extensively used in new statistical methods but have not been adopted at the same rate in clinical and translational (C&T) research. The goal of this paper is to accelerate the transition of new methods into practice by improving the C&T researcher's ability to gain confidence in interpreting and implementing Bayesian analyses. Methods: We developed a Bayesian data analysis plan and implemented that plan for a two-arm clinical trial comparing the effectiveness of a new opioid in reducing time to discharge from the post-operative anesthesia unit and nerve block usage in surgery. Through this application, we offer a brief tutorial on Bayesian methods and exhibit how to apply four Bayesian statistical packages from STATA, SAS, and RStan to conduct linear and logistic regression analyses in clinical research. Results: The analysis results in our application were robust to statistical package and consistent across a wide range of prior distributions. STATA was the most approachable package for linear regression but was more limited in the models that could be fitted and easily summarized. SAS and R offered more straightforward documentation and data management for the posteriors. They also offered direct programming of the likelihood making them more easily extendable to complex problems. Conclusion: Bayesian analysis is now accessible to a broad range of data analysts and should be considered in more C&T research analyses. This will allow C&T research teams the ability to adopt and interpret Bayesian methodology in more complex problems where Bayesian approaches are often needed.

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“…The following therapies were deemed to not have significant clinical efficacy based on evidence from RCTs, published systematic reviews and meta-analyses: hydroxychloroquine, 34 ivermectin, 35 favipiravir, 36 lopinavir/ritonavir, 37 nitazoxanide 38 and fluvoxamine. 39 Therapies were included in the ‘uncertain clinical efficacy’ category, where evidence of clinical efficacy in early symptomatic COVID-19 was sparse, based on small trials underpowered to detect clinical treatment effects, or evidence was conflicting (metformin 43 ).…”

Section: Methodsmentioning

confidence: 99%

The relationship between viral clearance rates and disease progression in early symptomatic COVID-19: a systematic review and meta-regression analysis

Singh,

Boyd,

Schilling

et al. 2024

Journal of Antimicrobial Chemotherapy

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Background Effective antiviral drugs accelerate viral clearance in acute COVID-19 infections; the relationship between accelerating viral clearance and reducing severe clinical outcomes is unclear. Methods A systematic review was conducted of randomized controlled trials (RCTs) of antiviral therapies in early symptomatic COVID-19, where viral clearance data were available. Treatment benefit was defined clinically as the relative risk of hospitalization/death during follow-up (≥14 days), and virologically as the SARS-CoV-2 viral clearance rate ratio (VCRR). The VCRR is the ratio of viral clearance rates between the intervention and control arms. The relationship between the clinical and virological treatment effects was assessed by mixed-effects meta-regression. Results From 57 potentially eligible RCTs, VCRRs were derived for 44 (52 384 participants); 32 had ≥1 clinical endpoint in each arm. Overall, 9.7% (R2) of the variation in clinical benefit was explained by variation in VCRRs with an estimated linear coefficient of −0.92 (95% CI: −1.99 to 0.13; P = 0.08). However, this estimate was highly sensitive to the inclusion of the recent very large PANORAMIC trial. Omitting this outlier, half the variation in clinical benefit (R2 = 50.4%) was explained by variation in VCRRs [slope −1.47 (95% CI −2.43 to −0.51); P = 0.003], i.e. higher VCRRs were associated with an increased clinical benefit. Conclusion Methods of determining viral clearance in COVID-19 studies and the relationship to clinical outcomes vary greatly. As prohibitively large sample sizes are now required to show clinical treatment benefit in antiviral therapeutic assessments, viral clearance is a reasonable surrogate endpoint.

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Lopinavir/ritonavir for treatment of non-hospitalized patients with COVID-19: a randomized clinical trial

Cited by 10 publications

References 33 publications

Remote and semi-automated methods to conduct a decentralized randomized clinical trial

Remote and semi-automated methods to conduct a decentralized randomized clinical trial

A practical guide to adopting Bayesian analyses in clinical research

The relationship between viral clearance rates and disease progression in early symptomatic COVID-19: a systematic review and meta-regression analysis

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