Characterizing healthcare personnel attitudes toward receipt of a voluntary bivalent COVID-19 booster vaccine during a COVID-19 outbreak at a behavioral health hospital in Connecticut.

COVID-19 vaccine uptake in healthcare personnel (HCP) is poor. A cross-sectional survey study of behavioral health HCP was performed. Commonly identified reasons for vaccination were protecting others and oneself. Reasons against were a lack of perceived protection, dosing intervals, and side effects. Assessing vaccination attitudes can assist in uptake strategy.

Favorable Antiviral Effect of Metformin on Severe Acute Respiratory Syndrome Coronavirus 2 Viral Load in a Randomized, Placebo-Controlled Clinical Trial of Coronavirus Disease 2019.

BACKGROUND: Metformin has antiviral activity against RNA viruses including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The mechanism appears to be suppression of protein translation via targeting the host mechanistic target of rapamycin pathway. In the COVID-OUT randomized trial for outpatient coronavirus disease 2019 (COVID-19), metformin reduced the odds of hospitalizations/death through 28 days by 58%, of emergency department visits/hospitalizations/death through 14 days by 42%, and of long COVID through 10 months by 42%. METHODS: COVID-OUT was a 2 × 3 randomized, placebo-controlled, double-blind trial that assessed metformin, fluvoxamine, and ivermectin; 999 participants self-collected anterior nasal swabs on day 1 (n = 945), day 5 (n = 871), and day 10 (n = 775). Viral load was quantified using reverse-transcription quantitative polymerase chain reaction. RESULTS: The mean SARS-CoV-2 viral load was reduced 3.6-fold with metformin relative to placebo (-0.56 log10 copies/mL; 95% confidence interval [CI], -1.05 to -.06; P = .027). Those who received metformin were less likely to have a detectable viral load than placebo at day 5 or day 10 (odds ratio [OR], 0.72; 95% CI, .55 to .94). Viral rebound, defined as a higher viral load at day 10 than day 5, was less frequent with metformin (3.28%) than placebo (5.95%; OR, 0.68; 95% CI, .36 to 1.29). The metformin effect was consistent across subgroups and increased over time. Neither ivermectin nor fluvoxamine showed effect over placebo. CONCLUSIONS: In this randomized, placebo-controlled trial of outpatient treatment of SARS-CoV-2, metformin significantly reduced SARS-CoV-2 viral load, which may explain the clinical benefits in this trial. Metformin is pleiotropic with other actions that are relevant to COVID-19 pathophysiology. CLINICAL TRIALS REGISTRATION: NCT04510194.

Early trajectories of virological and immunological biomarkers and clinical outcomes in patients admitted to hospital for COVID-19: an international, prospective cohort study.

BACKGROUND: Serial measurement of virological and immunological biomarkers in patients admitted to hospital with COVID-19 can give valuable insight into the pathogenic roles of viral replication and immune dysregulation. We aimed to characterise biomarker trajectories and their associations with clinical outcomes. METHODS: In this international, prospective cohort study, patients admitted to hospital with COVID-19 and enrolled in the Therapeutics for Inpatients with COVID-19 platform trial within the Accelerating COVID-19 Therapeutic Interventions and Vaccines programme between Aug 5, 2020 and Sept 30, 2021 were included. Participants were included from 108 sites in Denmark, Greece, Poland, Singapore, Spain, Switzerland, Uganda, the UK, and the USA, and randomised to placebo or one of four neutralising monoclonal antibodies: bamlanivimab (Aug 5 to Oct 13, 2020), sotrovimab (Dec 16, 2020, to March 1, 2021), amubarvimab-romlusevimab (Dec 16, 2020, to March 1, 2021), and tixagevimab-cilgavimab (Feb 10 to Sept 30, 2021). This trial included an analysis of 2149 participants with plasma nucleocapsid antigen, anti-nucleocapsid antibody, C-reactive protein (CRP), IL-6, and D-dimer measured at baseline and day 1, day 3, and day 5 of enrolment. Day-90 follow-up status was available for 1790 participants. Biomarker trajectories were evaluated for associations with baseline characteristics, a 7-day pulmonary ordinal outcome, 90-day mortality, and 90-day rate of sustained recovery. FINDINGS: The study included 2149 participants. Participant median age was 57 years (IQR 46-68), 1246 (58·0%) of 2149 participants were male and 903 (42·0%) were female; 1792 (83·4%) had at least one comorbidity, and 1764 (82·1%) were unvaccinated. Mortality to day 90 was 172 (8·0%) of 2149 and 189 (8·8%) participants had sustained recovery. A pattern of less favourable trajectories of low anti-nucleocapsid antibody, high plasma nucleocapsid antigen, and high inflammatory markers over the first 5 days was observed for high-risk baseline clinical characteristics or factors related to SARS-CoV-2 infection. For example, participants with chronic kidney disease demonstrated plasma nucleocapsid antigen 424% higher (95% CI 319-559), CRP 174% higher (150-202), IL-6 173% higher (144-208), D-dimer 149% higher (134-165), and anti-nucleocapsid antibody 39% lower (60-18) to day 5 than those without chronic kidney disease. Participants in the highest quartile for plasma nucleocapsid antigen, CRP, and IL-6 at baseline and day 5 had worse clinical outcomes, including 90-day all-cause mortality (plasma nucleocapsid antigen hazard ratio (HR) 4·50 (95% CI 3·29-6·15), CRP HR 3·37 (2·30-4·94), and IL-6 HR 5·67 (4·12-7·80). This risk persisted for plasma nucleocapsid antigen and CRP after adjustment for baseline biomarker values and other baseline factors. INTERPRETATION: Patients admitted to hospital with less favourable 5-day biomarker trajectories had worse prognosis, suggesting that persistent viral burden might drive inflammation in the pathogenesis of COVID-19, identifying patients that might benefit from escalation of antiviral or anti-inflammatory treatment. FUNDING: US National Institutes of Health.

Optimal timing for an accelerated interim futility analysis incorporating real world data.

BACKGROUND: Randomized controlled trials include interim monitoring guidelines to stop early for safety, efficacy, or futility. Futility monitoring facilitates re-allocation of limited resources. However, conventional methods for interim futility monitoring require a trial to accrue nearly half of the outcome data to make a reliable early stopping decision, limiting its benefit. As early stopping for futility will not inflate type-I error, these analyses are an appealing venue for incorporating external data to improve efficiency. METHODS: We propose a Bayesian approach to futility monitoring leveraging real world data using Semi-Supervised MIXture Multi-source Exchangeability Models, which accounts for both measured and unmeasured differences between data sources. We implement futility monitoring using predictive probabilities and investigate the optimal timing with respect to the expected sample size under the null hypothesis. Because we only incorporate external data during the interim futility analysis the proposed design is not limited by type-I error inflation. RESULTS: When the external and trial data are exchangeable, the proposed method provides a roughly 70 person reduction in expected sample size under the null. Under scenarios where exchangeability does not hold, our approach still provides a 10-20 person reduction in expected sample size under the null with about 80% power. CONCLUSIONS: External data borrowing in interim futility monitoring is a promising venue to improve trial efficiency without type-I error inflation. Approaches that are acceptable to regulatory authorities and leverage the complementary strengths of real world and trial data are vital to more efficiently allocate limited resources amongst clinical trials.

Novel Approaches to Multidrug-Resistant Infections in Cystic Fibrosis.

Patients with cystic fibrosis (CF) often develop respiratory tract infections with pathogenic multidrug-resistant organisms (MDROs) such as methicillin-resistant Staphylococcus aureus, and a variety of gram-negative organisms that include Pseudomonas aeruginosa, Burkholderia sp., Stenotrophomonas maltophilia, Achromobacter xylosoxidans, and nontuberculous mycobacteria (NTM). Despite the introduction of new therapies to address underlying cystic fibrosis transmembrane conductance regulator (CFTR) dysfunction, MDRO infections remain a problem and novel antimicrobial interventions are still needed. Therapeutic approaches include improving the efficacy of existing drugs by adjusting the dose based on differences in CF patient pharmacokinetics/pharmacodynamics, the development of inhaled formulations to reduce systemic adverse events, and the use of newer beta-lactam/beta-lactamase combinations. Alternative innovative therapeutic approaches include the use of gallium and bacteriophages to treat MDRO pulmonary infections including those with extreme antibiotic resistance. However, additional clinical trials are required to determine the optimal dosing and efficacy of these different strategies and to identify patients with CF most likely to benefit from these new treatment options.

Genetic algorithms reveal identity independent representation of emotional expressions.

People readily and automatically process facial emotion and identity, and it has been reported that these cues are processed both dependently and independently. However, this question of identity independent encoding of emotions has only been examined using posed, often exaggerated expressions of emotion, that do not account for the substantial individual differences in emotion recognition. In this study, we ask whether people's unique beliefs of how emotions should be reflected in facial expressions depend on the identity of the face. To do this, we employed a genetic algorithm where participants created facial expressions to represent different emotions. Participants generated facial expressions of anger, fear, happiness, and sadness, on two different identities. Facial features were controlled by manipulating a set of weights, allowing us to probe the exact positions of faces in high-dimensional expression space. We found that participants created facial expressions belonging to each identity in a similar space that was unique to the participant, for angry, fearful, and happy expressions, but not sad. However, using a machine learning algorithm that examined the positions of faces in expression space, we also found systematic differences between the two identities' expressions across participants. This suggests that participants' beliefs of how an emotion should be reflected in a facial expression are unique to them and identity independent, although there are also some systematic differences in the facial expressions between two identities that are common across all individuals. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Effect of Neutralizing Monoclonal Antibody Treatment on Early Trajectories of Virologic and Immunologic Biomarkers in Patients Hospitalized With COVID-19.

BACKGROUND: Neutralizing monoclonal antibodies (nmAbs) failed to show clear benefit for hospitalized patients with coronavirus disease 2019 (COVID-19). Dynamics of virologic and immunologic biomarkers remain poorly understood. METHODS: Participants enrolled in the Therapeutics for Inpatients with COVID-19 trials were randomized to nmAb versus placebo. Longitudinal differences between treatment and placebo groups in levels of plasma nucleocapsid antigen (N-Ag), anti-nucleocapsid antibody, C-reactive protein, interleukin-6, and D-dimer at enrollment, day 1, 3, and 5 were estimated using linear mixed models. A 7-point pulmonary ordinal scale assessed at day 5 was compared using proportional odds models. RESULTS: Analysis included 2149 participants enrolled between August 2020 and September 2021. Treatment resulted in 20% lower levels of plasma N-Ag compared with placebo (95% confidence interval, 12%-27%; P < .001), and a steeper rate of decline through the first 5 days (P < .001). The treatment difference did not vary between subgroups, and no difference was observed in trajectories of other biomarkers or the day 5 pulmonary ordinal scale. CONCLUSIONS: Our study suggests that nmAb has an antiviral effect assessed by plasma N-Ag among hospitalized patients with COVID-19, with no blunting of the endogenous anti-nucleocapsid antibody response. No effect on systemic inflammation or day 5 clinical status was observed. CLINICAL TRIALS REGISTRATION: NCT04501978.

Network meta analysis to predict the efficacy of an approved treatment in a new indication.

Drug repurposing refers to the process of discovering new therapeutic uses for existing medicines. Compared to traditional drug discovery, drug repurposing is attractive for its speed, cost, and reduced risk of failure. However, existing approaches for drug repurposing involve complex, computationally-intensive analytical methods that are not widely used in practice. Instead, repurposing decisions are often based on subjective judgments from limited empirical evidence. In this article, we develop a novel Bayesian network meta-analysis (NMA) framework that can predict the efficacy of an approved treatment in a new indication and thereby identify candidate treatments for repurposing. We obtain predictions using two main steps: first, we use standard NMA modeling to estimate average relative effects from a network comprised of treatments studied in both indications in addition to one treatment studied in only one indication. Then, we model the correlation between relative effects using various strategies that differ in how they model treatments across indications and within the same drug class. We evaluate the predictive performance of each model using a simulation study and find that the model minimizing root mean squared error of the posterior median for the candidate treatment depends on the amount of available data, the level of correlation between indications, and whether treatment effects differ, on average, by drug class. We conclude by discussing an illustrative example in psoriasis and psoriatic arthritis and find that the candidate treatment has a high probability of success in a future trial.

Strategies used for the COVID-OUT decentralized trial of outpatient treatment of SARS-CoV-2.

The COVID-19 pandemic accelerated the development of decentralized clinical trials (DCT). DCT's are an important and pragmatic method for assessing health outcomes yet comprise only a minority of clinical trials, and few published methodologies exist. In this report, we detail the operational components of COVID-OUT, a decentralized, multicenter, quadruple-blinded, randomized trial that rapidly delivered study drugs nation-wide. The trial examined three medications (metformin, ivermectin, and fluvoxamine) as outpatient treatment of SARS-CoV-2 for their effectiveness in preventing severe or long COVID-19. Decentralized strategies included HIPAA-compliant electronic screening and consenting, prepacking investigational product to accelerate delivery after randomization, and remotely confirming participant-reported outcomes. Of the 1417 individuals with the intention-to-treat sample, the remote nature of the study caused an additional 94 participants to not take any doses of study drug. Therefore, 1323 participants were in the modified intention-to-treat sample, which was the a priori primary study sample. Only 1.4% of participants were lost to follow-up. Decentralized strategies facilitated the successful completion of the COVID-OUT trial without any in-person contact by expediting intervention delivery, expanding trial access geographically, limiting contagion exposure, and making it easy for participants to complete follow-up visits. Remotely completed consent and follow-up facilitated enrollment.

Modified interactive Q-learning for attenuating the impact of model misspecification with treatment effect heterogeneity.

A sequential multiple assignment randomized trial, which incorporates multiple stages of randomization, is a popular approach for collecting data to inform personalized and adaptive treatments. There is an extensive literature on statistical methods to analyze data collected in sequential multiple assignment randomized trials and estimate the optimal dynamic treatment regime. Q-learning with linear regression is widely used for this purpose due to its ease of implementation. However, model misspecification is a common problem with this approach, and little attention has been given to the impact of model misspecification when treatment effects are heterogeneous across subjects. This article describes the integrative impact of two possible types of model misspecification related to treatment effect heterogeneity: omitted early-stage treatment effects in late-stage main effect model, and violated linearity assumption between pseudo-outcomes and predictors despite non-linearity arising from the optimization operation. The proposed method, aiming to deal with both types of misspecification concomitantly, builds interactive models into modified parametric Q-learning with Murphy's regret function. Simulations show that the proposed method is robust to both sources of model misspecification. The proposed method is applied to a two-stage sequential multiple assignment randomized trial with embedded tailoring aimed at reducing binge drinking in first-year college students.

Undergraduate teaching of surgical skills in the UK: systematic review.

BACKGROUND: Students must be proficient in surgical skills according to General Medical Council and Royal College of Surgeons of England guidelines. If these skills are not appropriately taught, there is a risk of an incoming junior workforce with inadequate surgical skills. This paper aimed to review the literature relating to undergraduate teaching of surgical skills in the UK and summarize future suggested training methods. METHODS: The databases MEDLINE, Embase and SCOPUS were searched, and the existing literature relating to methodology of undergraduate teaching of surgical skills in the UK over the past 10 years was summarized. The Medical Education Research Quality Instrument was used to assess research quality. RESULTS: A total of 19 papers were included. Cross-sectional evaluations and survey-based studies highlight a clear deficit in surgical skills teaching in the UK. Medical students are currently unable to fulfil their own learning needs and meet requirements set out by the General Medical Council. This lack of surgical teaching appears to negatively affect student desire to pursue a surgical career. The three main themes for improvement are extracurricular surgical skills days, near-peer teaching and simulation. Each method appeared to improve learning, although no studies utilized medium- to long-term follow-up to demonstrate efficacy and there lacks a clear consensus as to the 'standard' of undergraduate surgical skill education. There was also potential for selection bias and response shift bias in many of the studies assessing pre- and postintervention confidence and opinions. CONCLUSION: There is a concerning lack of surgical skills teaching that has resulted in medical students and junior doctors not having the necessary surgical skills as per General Medical Council guidance and students feel that their own learning needs are not met. This failure to address the learning deficit may be responsible for the fall in surgical competition ratios. While surgical skills teaching must be improved urgently, more robust evidence is required to evaluate the optimal ways of approaching this issue.

Bayesian hierarchical models incorporating study-level covariates for multivariate meta-analysis of diagnostic tests without a gold standard with application to COVID-19.

When evaluating a diagnostic test, it is common that a gold standard may not be available. One example is the diagnosis of SARS-CoV-2 infection using saliva sampling or nasopharyngeal swabs. Without a gold standard, a pragmatic approach is to postulate a "reference standard," defined as positive if either test is positive, or negative if both are negative. However, this pragmatic approach may overestimate sensitivities because subjects infected with SARS-CoV-2 may still have double-negative test results even when both tests exhibit perfect specificity. To address this limitation, we propose a Bayesian hierarchical model for simultaneously estimating sensitivity, specificity, and disease prevalence in the absence of a gold standard. The proposed model allows adjusting for study-level covariates. We evaluate the model performance using an example based on a recently published meta-analysis on the diagnosis of SARS-CoV-2 infection and extensive simulations. Compared with the pragmatic reference standard approach, we demonstrate that the proposed Bayesian method provides a more accurate evaluation of prevalence, specificity, and sensitivity in a meta-analytic framework.

Outcome trajectory estimation for optimal dynamic treatment regimes with repeated measures.

In recent sequential multiple assignment randomized trials, outcomes were assessed multiple times to evaluate longer-term impacts of the dynamic treatment regimes (DTRs). Q-learning requires a scalar response to identify the optimal DTR. Inverse probability weighting may be used to estimate the optimal outcome trajectory, but it is inefficient, susceptible to model mis-specification, and unable to characterize how treatment effects manifest over time. We propose modified Q-learning with generalized estimating equations to address these limitations and apply it to the M-bridge trial, which evaluates adaptive interventions to prevent problematic drinking among college freshmen. Simulation studies demonstrate our proposed method improves efficiency and robustness.

Tryptophan Substitution in CJ-15,208 (cyclo[Phe-D-Pro-Phe-Trp]) Introduces δ-Opioid Receptor Antagonism, Preventing Antinociceptive Tolerance and Stress-Induced Reinstatement of Extinguished Cocaine-Conditioned Place Preference.

The macrocyclic tetrapeptide CJ-15,208 (cyclo[Phe-D-Pro-Phe-Trp]) and its D-Trp isomer exhibit kappa opioid receptor (KOR) antagonism which prevents stress-induced reinstatement of extinguished cocaine-conditioned place preference. Here, we evaluated the effects of substitution of Trp and D-Trp on the peptides' opioid activity, antinociceptive tolerance, and the ability to prevent relapse to extinguished drug-CPP. Six analogs were synthesized using a combination of solid-phase peptide synthesis and cyclization in solution. The analogs were evaluated in vitro for opioid receptor affinity in radioligand competition binding assays, efficacy in the [35S]GTPγS assay, metabolic stability in mouse liver microsomes, and for opioid activity and selectivity in vivo in the mouse 55 °C warm-water tail-withdrawal assay. Potential liabilities of locomotor impairment, respiratory depression, acute tolerance, and conditioned place preference (CPP) were also assessed in vivo, and the ameliorating effect of analogs on the reinstatement of extinguished cocaine-place preference was assessed. Substitutions of other D-amino acids for D-Trp did not affect (or in one case increased) KOR affinity, while two of the three substitutions of an L-amino acid for Trp decreased KOR affinity. In contrast, all but one substitution increased mu opioid receptor (MOR) affinity in vitro. The metabolic stabilities of the analogs were similar to those of their respective parent peptides, with analogs containing a D-amino acid being much more rapidly metabolized than those containing an L-amino acid in this position. In vivo, CJ-15,208 analogs demonstrated antinociception, although potencies varied over an 80-fold range and the mediating opioid receptors differed by substitution. KOR antagonism was lost for all but the D-benzothienylalanine analog, and the 2'-naphthylalanine analog instead demonstrated significant delta opioid receptor (DOR) antagonism. Introduction of DOR antagonism coincided with reduced acute opioid antinociceptive tolerance and prevented stress-induced reinstatement of extinguished cocaine-CPP.

Randomized controlled dose-escalation design to evaluate the safety of a novel pharmacological cardiopulmonary resuscitation strategy.

BACKGROUND/AIMS: The motivating randomized controlled phase I trial evaluates three sodium nitroprusside doses in a novel sodium nitroprusside-enhanced cardiopulmonary resuscitation strategy for improved end-organ perfusion relative to local standard of care. Sodium nitroprusside is a vasodilator with an established safety profile in other indications, whereas the local standard of care uses vasoconstrictors, typically epinephrine. The purpose of the proposed trial is to identify the highest safe dose of sodium nitroprusside in this new context as excessive doses may cause severe hypotension with compromised end-organ perfusion. METHODS: The proposed phase I trial design expands upon traditional dose-finding designs to include a randomized control arm, which is needed to assess safety through the relative increase in serum lactate on hospital admission. For guiding dose escalation, we propose and compare six Bayesian models which characterize expected serum lactate as a function of sodium nitroprusside dose and randomization group. Each model makes a different assumption about the expected change in serum lactate across control cohorts concurrently randomized with each dose. Model selection aims to minimize the expected number of times that a dose is incorrectly classified as safe or unsafe while sample size selection targets an expected number of incorrectly classified doses. Randomization is 1:1 for the initial cohort, and for subsequent cohorts is chosen to maximize the lower confidence bound. RESULTS: The spike-and-slab model minimizes the expected number of times that a dose is incorrectly classified as safe or unsafe under the most scenarios in the motivating three-dose trial, but all six models exhibit relatively similar performance. A 2:1 randomization ratio for the second and third cohorts maximizes the lower confidence bound when using the spike-and-slab model. With the optimal design, on average, 70 individuals will ensure 1 incorrectly classified dose in 6 opportunities. CONCLUSION: We recommend that the motivating trial use the spike-and-slab model with a 1:1 randomization ratio for the initial cohort and 2:1 randomization ratio for subsequent cohorts; however, the simpler fixed effects approaches performed similarly well.

Metformin reduces SARS-CoV-2 in a Phase 3 Randomized Placebo Controlled Clinical Trial.

Current antiviral treatment options for SARS-CoV-2 infections are not available globally, cannot be used with many medications, and are limited to virus-specific targets.1-3 Biophysical modeling of SARS-CoV-2 replication predicted that protein translation is an especially attractive target for antiviral therapy.4 Literature review identified metformin, widely known as a treatment for diabetes, as a potential suppressor of protein translation via targeting of the host mTor pathway.5 In vitro, metformin has antiviral activity against RNA viruses including SARS-CoV-2.6,7 In the COVID-OUT phase 3, randomized, placebo-controlled trial of outpatient treatment of COVID-19, metformin had a 42% reduction in ER visits/hospitalizations/death through 14 days; a 58% reduction in hospitalizations/death through 28 days, and a 42% reduction in Long COVID through 10 months.8,9 Here we show viral load analysis of specimens collected in the COVID-OUT trial that the mean SARS-CoV-2 viral load was reduced 3.6-fold with metformin relative to placebo (-0.56 log10 copies/mL; 95%CI, -1.05 to -0.06, p=0.027) while there was no virologic effect for ivermectin or fluvoxamine vs placebo. The metformin effect was consistent across subgroups and with emerging data.10,11 Our results demonstrate, consistent with model predictions, that a safe, widely available,12 well-tolerated, and inexpensive oral medication, metformin, can be repurposed to significantly reduce SARS-CoV-2 viral load.

Outpatient treatment of COVID-19 and incidence of post-COVID-19 condition over 10 months (COVID-OUT): a multicentre, randomised, quadruple-blind, parallel-group, phase 3 trial.

BACKGROUND: Post-COVID-19 condition (also known as long COVID) is an emerging chronic illness potentially affecting millions of people. We aimed to evaluate whether outpatient COVID-19 treatment with metformin, ivermectin, or fluvoxamine soon after SARS-CoV-2 infection could reduce the risk of long COVID. METHODS: We conducted a decentralised, randomised, quadruple-blind, parallel-group, phase 3 trial (COVID-OUT) at six sites in the USA. We included adults aged 30-85 years with overweight or obesity who had COVID-19 symptoms for fewer than 7 days and a documented SARS-CoV-2 positive PCR or antigen test within 3 days before enrolment. Participants were randomly assigned via 2 × 3 parallel factorial randomisation (1:1:1:1:1:1) to receive metformin plus ivermectin, metformin plus fluvoxamine, metformin plus placebo, ivermectin plus placebo, fluvoxamine plus placebo, or placebo plus placebo. Participants, investigators, care providers, and outcomes assessors were masked to study group assignment. The primary outcome was severe COVID-19 by day 14, and those data have been published previously. Because the trial was delivered remotely nationwide, the a priori primary sample was a modified intention-to-treat sample, meaning that participants who did not receive any dose of study treatment were excluded. Long COVID diagnosis by a medical provider was a prespecified, long-term secondary outcome. This trial is complete and is registered with ClinicalTrials.gov, NCT04510194. FINDINGS: Between Dec 30, 2020, and Jan 28, 2022, 6602 people were assessed for eligibility and 1431 were enrolled and randomly assigned. Of 1323 participants who received a dose of study treatment and were included in the modified intention-to-treat population, 1126 consented for long-term follow-up and completed at least one survey after the assessment for long COVID at day 180 (564 received metformin and 562 received matched placebo; a subset of participants in the metformin vs placebo trial were also randomly assigned to receive ivermectin or fluvoxamine). 1074 (95%) of 1126 participants completed at least 9 months of follow-up. 632 (56·1%) of 1126 participants were female and 494 (43·9%) were male; 44 (7·0%) of 632 women were pregnant. The median age was 45 years (IQR 37-54) and median BMI was 29·8 kg/m2 (IQR 27·0-34·2). Overall, 93 (8·3%) of 1126 participants reported receipt of a long COVID diagnosis by day 300. The cumulative incidence of long COVID by day 300 was 6·3% (95% CI 4·2-8·2) in participants who received metformin and 10·4% (7·8-12·9) in those who received identical metformin placebo (hazard ratio [HR] 0·59, 95% CI 0·39-0·89; p=0·012). The metformin beneficial effect was consistent across prespecified subgroups. When metformin was started within 3 days of symptom onset, the HR was 0·37 (95% CI 0·15-0·95). There was no effect on cumulative incidence of long COVID with ivermectin (HR 0·99, 95% CI 0·59-1·64) or fluvoxamine (1·36, 0·78-2·34) compared with placebo. INTERPRETATION: Outpatient treatment with metformin reduced long COVID incidence by about 41%, with an absolute reduction of 4·1%, compared with placebo. Metformin has clinical benefits when used as outpatient treatment for COVID-19 and is globally available, low-cost, and safe. FUNDING: Parsemus Foundation; Rainwater Charitable Foundation; Fast Grants; UnitedHealth Group Foundation; National Institute of Diabetes, Digestive and Kidney Diseases; National Institutes of Health; and National Center for Advancing Translational Sciences.