Vaccination Against SARS-CoV-2 Is Associated With a Lower Viral Load and Likelihood of Systemic Symptoms

Favorable Antiviral Effect of Metformin on SARS-CoV-2 Viral Load in a Randomized, Placebo-Controlled Clinical Trial of COVID-19

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.

Interpreting PPV and NPV of Diagnostic Tests with Uncertain Prevalence

OBJECTIVE

Medical decision-making is often uncertain. The positive predictive value (PPV) and negative predictive value (NPV) are conditional probabilities characterizing diagnostic tests and assessing diagnostic interventions in clinical medicine and epidemiology. The PPV is the probability that a patient has a specified disease, given a positive test result for that disease. The NPV is the probability that a patient does not have the disease, given a negative test result for that disease. Both values depend on disease incidence or prevalence, which may be highly uncertain for unfamiliar diseases, epidemics, etc. Probability distributions for this uncertainty are usually unavailable. We develop a non-probabilistic method for interpreting PPV and NPV with uncertain prevalence.

METHODS

Uncertainty in PPV and NPV is managed with the non-probabilistic concept of robustness in info-gap theory. Robustness of PPV or NPV estimates is the greatest uncertainty (in prevalence) at which the estimate's error is acceptable.

RESULTS

Four properties are demonstrated. Zeroing: best estimates of PPV or NPV have no robustness to uncertain prevalence; best estimates are unreliable for interpreting diagnostic tests. Trade-off: robustness increases as error increases; this trade-off identifies robustly reliable error in PPV or NPV. Preference reversal: sometimes sub-optimal PPV or NPV estimates are more robust to uncertain incidence or prevalence than optimal estimates, motivating reversal of preference from the putative optimum to the sub-optimal estimate. Trade-off between specificity and robustness to uncertainty: the robustness increases as test-specificity decreases. These four properties underlie the interpretation of PPV and NPV.

CONCLUSIONS

The PPV and NPV assess diagnostic tests, but are sensitive to lack of knowledge that generates non-probabilistic uncertain prevalence and must be supplemented with robustness analysis. When uncertainties abound, as with unfamiliar diseases, assessing robustness is critical to avoiding erroneous decisions.

Moving Biosurveillance Beyond Coded Data Using AI for Symptom Detection From Physician Notes: Retrospective Cohort Study

BACKGROUND

Real-time surveillance of emerging infectious diseases necessitates a dynamically evolving, computable case definition, which frequently incorporates symptom-related criteria. For symptom detection, both population health monitoring platforms and research initiatives primarily depend on structured data extracted from electronic health records.

OBJECTIVE

This study sought to validate and test an artificial intelligence (AI)-based natural language processing (NLP) pipeline for detecting COVID-19 symptoms from physician notes in pediatric patients. We specifically study patients presenting to the emergency department (ED) who can be sentinel cases in an outbreak.

METHODS

Subjects in this retrospective cohort study are patients who are 21 years of age and younger, who presented to a pediatric ED at a large academic children's hospital between March 1, 2020, and May 31, 2022. The ED notes for all patients were processed with an NLP pipeline tuned to detect the mention of 11 COVID-19 symptoms based on Centers for Disease Control and Prevention (CDC) criteria. For a gold standard, 3 subject matter experts labeled 226 ED notes and had strong agreement (F1-score=0.986; positive predictive value [PPV]=0.972; and sensitivity=1.0). F1-score, PPV, and sensitivity were used to compare the performance of both NLP and the International Classification of Diseases, 10th Revision (ICD-10) coding to the gold standard chart review. As a formative use case, variations in symptom patterns were measured across SARS-CoV-2 variant eras.

RESULTS

There were 85,678 ED encounters during the study period, including 4% (n=3420) with patients with COVID-19. NLP was more accurate at identifying encounters with patients that had any of the COVID-19 symptoms (F1-score=0.796) than ICD-10 codes (F1-score =0.451). NLP accuracy was higher for positive symptoms (sensitivity=0.930) than ICD-10 (sensitivity=0.300). However, ICD-10 accuracy was higher for negative symptoms (specificity=0.994) than NLP (specificity=0.917). Congestion or runny nose showed the highest accuracy difference (NLP: F1-score=0.828 and ICD-10: F1-score=0.042). For encounters with patients with COVID-19, prevalence estimates of each NLP symptom differed across variant eras. Patients with COVID-19 were more likely to have each NLP symptom detected than patients without this disease. Effect sizes (odds ratios) varied across pandemic eras.

CONCLUSIONS

This study establishes the value of AI-based NLP as a highly effective tool for real-time COVID-19 symptom detection in pediatric patients, outperforming traditional ICD-10 methods. It also reveals the evolving nature of symptom prevalence across different virus variants, underscoring the need for dynamic, technology-driven approaches in infectious disease surveillance.

Association of Demographic, Clinical, and Vaccination Characteristics with COVID-19 Viral Load Assessed by qRT-PCR

BACKGROUND

The effect of vaccination on the SARS-CoV-2 baseline viral load and clearance during COVID-19 infection is debatable. This study aimed to assess the effects of demographic and vaccination characteristics on the viral load of SARS-CoV-2.

METHODS

We included the patients referred for outpatient SARS-CoV-2 qRT-PCR (reverse transcriptase quantitative polymerase chain reaction) test between July and September 2022. Cycle threshold (Ct) data were compared based on the demographic and vaccination characteristics. A generalized linear model was used to determine the factors associated with the SARS-CoV-2 PCR Ct value.

RESULTS

Of 657 participants, 390 (59.4%) were symptomatic and 308 (47.1%) were COVID-19 positive. Among 590 individuals with known vaccination status, 358 (60.6%) were booster vaccinated, 193 (32.6%) were fully vaccinated, 13 (2.2%) were partially vaccinated, and 26 (4.4%) were unvaccinated. Most vaccinated patients received inactivated vaccines (70.5%). The median Ct value was 20 [IQR: 18-23.75] with no significant difference between individuals with different vaccination statuses (P value = 0.182). There were significant differences in Ct value in terms of both symptom presence and onset (both P values < 0.001). Our regression model showed that inactivated vaccines (P value = 0.027), mRNA vaccines (P value = 0.037), and the presence and onset of symptoms (both P values < 0.001) were independent factors significantly associated with the viral load.

CONCLUSION

The SARS-CoV-2 baseline viral load is unaffected by vaccination status, yet vaccination might accelerate viral clearance. Furthermore, we demonstrated that the presence and onset of symptoms are independent variables substantially associated with the patient's viral load.

Variant-Specific Viral Kinetics in Acute COVID-19

Understanding variant-specific differences in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral kinetics may explain differences in transmission efficiency and provide insights on pathogenesis and prevention. We evaluated SARS-CoV-2 kinetics from nasal swabs across multiple variants (Alpha, Delta, Epsilon, Gamma) in placebo recipients of the ACTIV-2/A5401 trial. Delta variant infection led to the highest maximum viral load and shortest time from symptom onset to viral load peak. There were no significant differences in time to viral clearance across the variants. Viral decline was biphasic with first- and second-phase decays having half-lives of 11 hours and 2.5 days, respectively, with differences among variants, especially in the second phase. These results suggest that while variant-specific differences in viral kinetics exist, post-peak viral load all variants appeared to be efficiently cleared by the host. Clinical Trials Registration. NCT04518410.

Clinical features of COVID-19 in Italian outpatient children and adolescents during Parental, Delta, and Omicron waves: a prospective, observational, cohort study

Introduction

COVID-19 features changed with the Omicron variant of SARS-CoV-2 in adults. This study aims to describe COVID-19 symptoms in children and adolescents during the Parental, Delta, and Omicron eras.

Methods

A single-centre, prospective observational study was conducted on individuals aged 0-20 years attending the University Hospital of Padua (Italy) from April 2020 to December 2022. COVID-19 cases were defined by positive SARS-CoV-2 molecular detection and/or serology; patient/family symptoms and virological positivity were considered to determine the infection onset. Variables were summarized and compared using appropriate tests of descriptive statistics.

Results

A total of 509 cases [46% female, median age eight years (IQR: 4-12)] were studied. Three-hundred-eighty-seven (76%), 52 (10%), and 70 (14%) subjects experienced COVID-19 during the Parental, Delta, and Omicron waves, respectively. All subjects developed an asymptomatic/mild COVID-19. Overall, the most frequent symptoms were fever (47%) and rhinitis (21%), which showed a significant increasing incidence from the Parental to Omicron waves (p < 0.001). Conversely, diarrhea was most common during the pre-Omicron eras (p = 0.03). Stratifying symptoms according to the age group, fever, rhinitis, and skin rashes were observed more frequently among infants/toddlers; conversely, fatigue was more common in children older than five years. The duration of symptoms was similar across different SARS-CoV-2 variants of concern (VOCs); conversely, the number of symptoms varied according to the age group (p < 0.0001).

Discussion

This study showed differences in COVID-19 clinical presentation among infants, children, and adolescents and confirmed Omicron infection is more likely to be associated with upper respiratory symptoms. However, further population-based studies are needed to support these findings. In addition, active surveillance will play a crucial role in assessing the disease severity of future VOCs.

A collaborative approach to improving representation in viral genomic surveillance

The lack of routine viral genomic surveillance delayed the initial detection of SARS-CoV-2, allowing the virus to spread unfettered at the outset of the U.S. epidemic. Over subsequent months, poor surveillance enabled variants to emerge unnoticed. Against this backdrop, long-standing social and racial inequities have contributed to a greater burden of cases and deaths among minority groups. To begin to address these problems, we developed a new variant surveillance model geared toward building 'next generation' genome sequencing capacity at universities in or near rural areas and engaging the participation of their local communities. The resulting genomic surveillance network has generated more than 1,000 SARS-CoV-2 genomes to date, including the first confirmed case in northeast Louisiana of Omicron, and the first and sixth confirmed cases in Georgia of the emergent BA.2.75 and BQ.1.1 variants, respectively. In agreement with other studies, significantly higher viral gene copy numbers were observed in Delta variant samples compared to those from Omicron BA.1 variant infections, and lower copy numbers were seen in asymptomatic infections relative to symptomatic ones. Collectively, the results and outcomes from our collaborative work demonstrate that establishing genomic surveillance capacity at smaller academic institutions in rural areas and fostering relationships between academic teams and local health clinics represent a robust pathway to improve pandemic readiness.

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.

The dynamic relationship between COVID-19 cases and SARS-CoV-2 wastewater concentrations across time and space: Considerations for model training data sets

During the COVID-19 pandemic, wastewater-based surveillance has been used alongside diagnostic testing to monitor infection rates. With the decline in cases reported to public health departments due to at-home testing, wastewater data may serve as the primary input for epidemiological models, but training these models is not straightforward. We explored factors affecting noise and bias in the ratio between wastewater and case data collected in 26 sewersheds in California from October 2020 to March 2022. The strength of the relationship between wastewater and case data appeared dependent on sampling frequency and population size, but was not increased by wastewater normalization to flow rate or case count normalization to testing rates. Additionally, the lead and lag times between wastewater and case data varied over time and space, and the ratio of log-transformed individual cases to wastewater concentrations changed over time. This ratio decreased between the Epsilon/Alpha and Delta variant surges of COVID-19 and increased during the Omicron BA.1 variant surge, and was also related to the diagnostic testing rate. Based on this analysis, we present a framework of scenarios describing the dynamics of the case to wastewater ratio to aid in data handling decisions for ongoing modeling efforts.

Impact of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Vaccination and Booster on Coronavirus Disease 2019 (COVID-19) Symptom Severity Over Time in the COVID-OUT Trial

BACKGROUND

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccination has decreasing protection from acquiring any infection with emergence of new variants; however, vaccination continues to protect against progression to severe coronavirus disease 2019 (COVID-19). The impact of vaccination status on symptoms over time is less clear.

METHODS

Within a randomized trial on early outpatient COVID-19 therapy testing metformin, ivermectin, and/or fluvoxamine, participants recorded symptoms daily for 14 days. Participants were given a paper symptom diary allowing them to circle the severity of 14 symptoms as none (0), mild (1), moderate (2), or severe (3). This is a secondary analysis of clinical trial data on symptom severity over time using generalized estimating equations comparing those unvaccinated, SARS-CoV-2 vaccinated with primary vaccine series only, or vaccine-boosted.

RESULTS

The parent clinical trial prospectively enrolled 1323 participants, of whom 1062 (80%) prospectively recorded some daily symptom data. Of these, 480 (45%) were unvaccinated, 530 (50%) were vaccinated with primary series only, and 52 (5%) vaccine-boosted. Overall symptom severity was least for the vaccine-boosted group and most severe for unvaccinated at baseline and over the 14 days (P < .001). Individual symptoms were least severe in the vaccine-boosted group including cough, chills, fever, nausea, fatigue, myalgia, headache, and diarrhea, as well as smell and taste abnormalities. Results were consistent over Delta and Omicron variant time periods.

CONCLUSIONS

SARS-CoV-2 vaccine-boosted participants had the least severe symptoms during COVID-19, which abated the quickest over time. Clinical Trial Registration. NCT04510194.

Association between SARS-CoV-2 Viral Load and Patient Symptoms and Clinical Outcomes Using Droplet Digital PCR

The association between nasopharyngeal (NP) SARS-CoV-2 viral loads and clinical outcomes remains debated. Here, we examined the factors that might predict the NP viral load and the role of the viral load as a predictor of clinical outcomes. A convenience sample of 955 positive remnant NP swab eluent samples collected during routine care between 18 November 2020 and 26 September 2021 was cataloged and a chart review was performed. For non-duplicate samples with available demographic and clinical data (i.e., non-employees), an aliquot of eluent was sent for a droplet digital PCR quantification of the SARS-CoV-2 viral load. Univariate and multivariate analyses were performed to identify the clinical predictors of NP viral loads and the predictors of COVID-19-related clinical outcomes. Samples and data from 698 individuals were included in the final analysis. The sample cohort had a mean age of 50 years (range: 19-91); 86.6% were male and 76.3% were unvaccinated. The NP viral load was higher in people with respiratory symptoms (p = 0.0004) and fevers (p = 0.0006). In the predictive models for the clinical outcomes, the NP viral load approached a significance as a predictor for in-hospital mortality. In conclusion, the NP viral load did not appear to be a strong predictor of moderate-to-severe disease in the pre-Delta and Delta phases of the pandemic, but was predictive of symptomatic diseases and approached a significance for in-hospital mortality, providing support to the thesis that early viral control prevents the progression of disease.

At what frequency of vaccination do the vaccinated potentially pose an equal risk to the unvaccinated for transmission of SARS-CoV-2 inside restaurants in New York City?

From August 2021 to 7 March 2022, New York City prohibited indoor dining in restaurants selectively for persons who had not received a Coronavirus disease 2019 (COVID-19) vaccine. However, vaccinated persons may also be actively infected and potentially transmit severe acute respiratory syndrome coronavirus 2 (SARS-CoV‑2). Based on assuming a 7:1 ratio of COVID-19 cases in New York State for the unvaccinated versus the vaccinated, it can be estimated that when 87.5% of adults in New York City are vaccinated, the rate of unsuspected SARS-CoV‑2 infections (asymptomatic or minimally symptomatic) among vaccinated adults going to restaurants would be equivalent to that for the unvaccinated.

A collaborative approach to improve representation in viral genomic surveillance

The lack of routine viral genomic surveillance delayed the initial detection of SARS-CoV-2, allowing the virus to spread unfettered at the outset of the U.S. epidemic. Over subsequent months, poor surveillance enabled variants to emerge unnoticed. Against this backdrop, long-standing social and racial inequities have contributed to a greater burden of cases and deaths among minority groups. To begin to address these problems, we developed a new variant surveillance model geared toward building microbial genome sequencing capacity at universities in or near rural areas and engaging the participation of their local communities. The resulting genomic surveillance network has generated more than 1,000 SARS-CoV-2 genomes to date, including the first confirmed case in northeast Louisiana of Omicron, and the first and sixth confirmed cases in Georgia of the emergent BA.2.75 and BQ.1.1 variants, respectively. In agreement with other studies, significantly higher viral gene copy numbers were observed in Delta variant samples compared to those from Omicron BA.1 variant infections, and lower copy numbers were seen in asymptomatic infections relative to symptomatic ones. Collectively, the results and outcomes from our collaborative work demonstrate that establishing genomic surveillance capacity at smaller academic institutions in rural areas and fostering relationships between academic teams and local health clinics represent a robust pathway to improve pandemic readiness.

Author summary

Genomic surveillance involves decoding a pathogen’s genetic code to track its spread and evolution. During the pandemic, genomic surveillance programs around the world provided valuable data to scientists, doctors, and public health officials. Knowing the complete SARS-CoV-2 genome has helped detect the emergence of new variants, including ones that are more transmissible or cause more severe disease, and has supported the development of diagnostics, vaccines, and therapeutics. The impact of genomic surveillance on public health depends on representative sampling that accurately reflects the diversity and distribution of populations, as well as rapid turnaround time from sampling to data sharing. After a slow start, SARS-CoV-2 genomic surveillance in the United States grew exponentially. Despite this, many rural regions and ethnic minorities remain poorly represented, leaving significant gaps in the data that informs public health responses. To address this problem, we formed a network of universities and clinics in Louisiana, Georgia, and Mississippi with the goal of increasing SARS-CoV-2 sequencing volume, representation, and equity. Our results demonstrate the advantages of rapidly sequencing pathogens in the same communities where the cases occur and present a model that leverages existing academic and clinical infrastructure for a powerful decentralized genomic surveillance system.

SARS-CoV-2 Secondary Attack Rates in Vaccinated and Unvaccinated Household Contacts during Replacement of Delta with Omicron Variant, Spain

We performed a prospective, cross-sectional study of household contacts of symptomatic index case-patients with SARS-CoV-2 infection during the shift from Delta- to Omicron-dominant variants in Spain. We included 466 household contacts from 227 index cases. The secondary attack rate was 58.2% (95% CI 49.1%-62.6%) during the Delta-dominant period and 80.9% (95% CI 75.0%-86.9%) during the Omicron-dominant period. During the Delta-dominant period, unvaccinated contacts had higher probability of infection than vaccinated contacts (odds ratio 5.42, 95% CI 1.6-18.6), but this effect disappeared at ≈20 weeks after vaccination. Contacts showed a higher relative risk of infection (9.16, 95% CI 3.4-25.0) in the Omicron-dominant than Delta-dominant period when vaccinated within the previous 20 weeks. Our data suggest vaccine evasion might be a cause of rapid spread of the Omicron variant. We recommend a focus on developing vaccines with long-lasting protection against severe disease, rather than only against infectivity.

SARS-CoV-2 omicron variant clearance delayed in breakthrough cases with elevated fasting blood glucose

Background

Omicron variant (B.1.1.529) is a dominant variant worldwide. However, the risk factors for Omicron variant clearance are yet unknown. The present study aimed to investigate the risk factors for early viral clearance of Omicron variant in patients with a history of inactivated vaccine injection.

Methods

Demographic, clinical, and epidemiological data from 187 patients were collected retrospectively during the Omicron variant wave.

Results

73/187 and 114/187 patients were administered two and three doses of vaccine, respectively. The median duration of SARS-CoV-2 RNA positivity was 9 days, and the difference between patients with two and three vaccine injections was insignificant (P = 0.722). Fever was the most common symptom (125/187), and most patients (98.4%) had a fever for < 7 days. The RNA was undetectable in 65/187 patients on day 7. Univariable logistic analysis showed that baseline glucose, uric acid, lymphocytes count, platelet count, and CD4⁺ T lymphocyte count were associated with SARS-CoV-2 RNA-positivity on day 7. Multivariable analysis showed that glucose ≥ 6.1 mmol/L and CD4⁺T lymphocytes count were independent risk factors for RNA positivity on day 7. 163/187 patients had an undetectable RNA test on day 14, and uric acid was the only independent risk factor for RNA positivity. Moreover, baseline glucose was negatively correlated with uric acid and CD4⁺ and CD8⁺ T cell count, while uric acid was positively correlated with CD4⁺ and CD8⁺ T cell count.

Conclusions

Omicron variant clearance was delayed in breakthrough cases with elevated fasting blood glucose, irrespective of the doses of inactivated vaccine.

Randomized Trial of Metformin, Ivermectin, and Fluvoxamine for Covid-19

BACKGROUND

Early treatment to prevent severe coronavirus disease 2019 (Covid-19) is an important component of the comprehensive response to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic.

METHODS

In this phase 3, double-blind, randomized, placebo-controlled trial, we used a 2-by-3 factorial design to test the effectiveness of three repurposed drugs - metformin, ivermectin, and fluvoxamine - in preventing serious SARS-CoV-2 infection in nonhospitalized adults who had been enrolled within 3 days after a confirmed diagnosis of infection and less than 7 days after the onset of symptoms. The patients were between the ages of 30 and 85 years, and all had either overweight or obesity. The primary composite end point was hypoxemia (≤93% oxygen saturation on home oximetry), emergency department visit, hospitalization, or death. All analyses used controls who had undergone concurrent randomization and were adjusted for SARS-CoV-2 vaccination and receipt of other trial medications.

RESULTS

A total of 1431 patients underwent randomization; of these patients, 1323 were included in the primary analysis. The median age of the patients was 46 years; 56% were female (6% of whom were pregnant), and 52% had been vaccinated. The adjusted odds ratio for a primary event was 0.84 (95% confidence interval [CI], 0.66 to 1.09; P = 0.19) with metformin, 1.05 (95% CI, 0.76 to 1.45; P = 0.78) with ivermectin, and 0.94 (95% CI, 0.66 to 1.36; P = 0.75) with fluvoxamine. In prespecified secondary analyses, the adjusted odds ratio for emergency department visit, hospitalization, or death was 0.58 (95% CI, 0.35 to 0.94) with metformin, 1.39 (95% CI, 0.72 to 2.69) with ivermectin, and 1.17 (95% CI, 0.57 to 2.40) with fluvoxamine. The adjusted odds ratio for hospitalization or death was 0.47 (95% CI, 0.20 to 1.11) with metformin, 0.73 (95% CI, 0.19 to 2.77) with ivermectin, and 1.11 (95% CI, 0.33 to 3.76) with fluvoxamine.

CONCLUSIONS

None of the three medications that were evaluated prevented the occurrence of hypoxemia, an emergency department visit, hospitalization, or death associated with Covid-19. (Funded by the Parsemus Foundation and others; COVID-OUT ClinicalTrials.gov number, NCT04510194.).