Lead Investigator: Gus Slotman, Inspira Health Network
Title of Proposal Research: USING PRE-RANDOMIZATION DATA TO IDENTIFY PATIENTS WITH COVID-19 AMONG WHOM FLUVOXAMINE OR IVERMECTIN IMPROVED OUTCOMES
Vivli Data Request: 8569
Funding Source: None
Potential Conflicts of Interest: None
Summary of the Proposed Research:
LAY SUMMARY: Discovering effective treatments of COVID-19 infection is vital. Randomized clinical trials (RCT) of established drugs attempt to repurpose generic medications with potential efficacy against COVID-19. Dexamethasone was the first successful repurposed drug for COVID-19 in the RECOVERY RCT. Among others, in the TOGETHER group RCT COVID-19 patients receiving fluvoxamine had a statistically significant 5% lower risk than placebo of hospitalization, defined as either retention in a COVID-19 emergency setting > 6 hours or transfer to a tertiary hospital due to COVID-19. Conversely, in the TOGETHER RCT testing the anti-parasite medication ivermectin in COVID-19, with the same end-points as the fluvoxamine RCT, adverse outcomes were reduced by only a non-significant 1.6%.
Hypothesis: The clinical definitions used as entry criteria for TOGETHER RCTs of fluvoxamine and ivermectin may have enrolled so many patients whose pathophysiology could not benefit from the study drugs that their true treatment effects were diluted to suboptimal efficacy for fluvoxamine and to invisibility for ivermectin. The present study proposes to determine whether or not it is possible to predict from pre-randomization TOGETHER RCT data which COVID-19 patients were able to benefit most from fluvoxamine and/or ivermectin. If this investigation identifies such patients within the TOGETHER RCTs of fluvoxamine and/or ivermectin in COVID-19, then the predictive models generated can be used to match COVID-19 patients predicted to respond most effectively with one of these repurposed drugs.
Statistical Analysis Plan:
Statistical Analysis: We propose to examine the TOGETHER fluvoxamine and ivermectin RCT databases using the above-described statistical approach , with the objective of determining whether or not, from pre-randomization data, cohorts of individuals among whom ivermectin reduced COVID-19 hospitalization and/or mortality among whom fluvoxamine had its greatest treatment effects can be identified and predicted. Treatment effects of fluvoxamine and of ivermectin in their own TOGETHER RCTs will be evaluated among COVID-19 patients enrolled under clinical definitions and, separately, among patients predicted by multivariate modeling on pre-randomization data to respond to . Hospitalization as defined in each RCT and mortality will be analyzed by Kaplan–Meier statistics.
All pre-randomization data on TOGETHER RCT patients with complete data sets, analyzing the fluvoxamine RCT and the ivermectin RCT separately, will be screened as possible prognostic independent variables using the statistical modeling described in our previous publication in sepsis RCT’s. (5) Using multivariate, stepwise logistic regression with all ways elimination (simultaneous forward and backward elimination of non-weighted independent variables), hospitalization and survival models will be developed separately for the placebo and active drug groups. After the modeling process is completed, pre-randomization data from every patient in that study will be entered into both equations, and lengthy explorations into the relationship between the placebo and active drug models and their interactions with treatment effects on primary and secondary endpoints will be undertaken to determine optimum cutoffs for fluvoxamine and ivermectin. Beginning with the original clinical definition patient population, this process will test each drug’s treatment effects in progressively smaller sub-populations, incrementally excluding, always at pre-randomization baseline, from efficacy analysis patients predicted to be unable to respond to or benefit from tocilizumab.
A Multicenter, Prospective, Adaptive, Double-blind, Randomized, Placebo-controlled Study to Evaluate the Effect of Fluvoxamine, Ivermectin, Doxasozin and Interferon Lambda 1A in Mild COVID-19 and High Risk of Complications
Data Contributor: Platform Life Sciences
Study ID: NCT04727424
Sponsor ID: TOGETHER_2
Summary of Results:
Unfortunately, the pre-randomization/baseline data that the sponsors uploaded to Vivli are insufficient to support the prognostic modeling we had hoped to carry out.
Update: This data request was withdrawn on 1-Mar-2023 by the researcher.