SARS-CoV-2 versus Influenza A Virus: Characteristics and Co-Treatments.

For three years, the novel coronavirus disease 2019 (COVID-19) pandemic, caused by infection of the SARS-CoV-2 virus, has completely changed our lifestyles and prepared us to live with this novel pneumonia for years to come. Given that pre-existing flu is caused by the influenza A virus, we have begun unprecedently co-coping with two different respiratory diseases at the same time. Hence, we draw a comparison between SARS-CoV-2 and influenza A virus based on the general characteristics, especially the main variants' history and the distribution of the two viruses. SARS-CoV-2 appeared to mutate more frequently and independently of locations than the influenza A virus. Furthermore, we reviewed present clinical trials on combined management against COVID-19 and influenza in order to explore better solutions against both at the same time.

A trial of arbidol hydrochloride in adults with COVID-19.

BACKGROUND: To date, there is no effective medicine to treat coronavirus disease 2019 (COVID-19), and the antiviral efficacy of arbidol in the treatment for COVID-19 remained equivocal and controversial. The purpose of this study was to evaluate the efficacy and safety of arbidol tablets in the treatment of COVID-19. METHODS: This was a prospective, open-label, controlled and multicenter investigator-initiated trial involving adult patients with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Patients were stratified 1:2 to either standard-of-care (SOC) or SOC plus arbidol tablets (oral administration of 200 mg per time, three times a day for 14 days). The primary endpoint was negative conversion of SARS-CoV-2 within the first week. The rates and 95% confidential intervals were calculated for each variable. RESULTS: A total of 99 patients with laboratory-confirmed SARS-CoV-2 infection were enrolled; 66 were assigned to the SOC plus arbidol tablets group, and 33 to the SOC group. The negative conversion rate of SARS-CoV-2 within the first week in patients receiving arbidol tablets was significantly higher than that of the SOC group (70.3% [45/64] vs. 42.4% [14/33]; difference of conversion rate 27.9%; 95% confidence interval [CI], 7.7%-48.1%; P  = 0.008). Compared to those in the SOC group, patients receiving arbidol tablets had a shorter duration of clinical recovery (median 7.0 days vs. 12.0 days; hazard ratio [HR]: 1.877, 95% CI: 1.151-3.060, P = 0.006), symptom of fever (median 3.0 days vs. 12.0 days; HR: 18.990, 95% CI: 5.350-67.410, P < 0.001), as well as hospitalization (median 12.5 days vs. 20.0 days; P < 0.001). Moreover, the addition of arbidol tablets to SOC led to more rapid normalization of declined blood lymphocytes (median 10.0 days vs. 14.5 days; P > 0.05). The most common adverse event in the arbidol tablets group was the elevation of transaminase (5/200, 2.5%), and no one withdrew from the study due to adverse events or disease progression. CONCLUSIONS: SOC plus arbidol tablets significantly increase the negative conversion rate of SARS-CoV-2 within the first week anas, accelerate the recovery of COVID-19 patients. During the treatment with arbidol tablets, we find no significant serious adverse events. TRIAL REGISTRATION: Chinese Clinical Trial Registry, NCT04260594, www.clinicaltrials.gov/ct2/show/NCT04260594?term=NCT04260594&draw=2&rank=1.

Pearson's patterns correlational of clinical risks at admissions with hospitalization outcomes during initial COVID-19 outbreak.

COVID-19 outbreaks have crushed our healthcare systems, which requires clinical guidance for the healthcare following the outbreaks. We conducted retrospective cohort studies with Pearson's pattern-based analysis of clinical parameters of 248 hospitalized patients with COVID-19. We found that dysregulated neutrophil densities were correlated with hospitalization duration before death (p = 0.000066, r = -0.45 for % neutrophil; p = 0.0001, r = -0.47 for neutrophil count). As such, high neutrophil densities were associated with mortality (p = 4.23 × 10-31 for % neutrophil; p = 4.14 × 10-27 for neutrophil count). These findings were further illustrated by a representative "second week crash" pattern and validated by an independent cohort (p = 5.98 × 10-11 for % neutrophil; p = 1.65 × 10-7 for neutrophil count). By contrast, low aspartate aminotransferase (AST) or lactate dehydrogenase (LDH) levels were correlated with quick recovery (p ≤ 0.00005). Collectively, these correlational at-admission findings may provide healthcare guidance for patients with COVID-19 in the absence of targeted therapy.

Effectiveness of Systemic Corticosteroids Therapy for Nonsevere Patients With COVID-19: A Multicenter, Retrospective, Longitudinal Cohort Study.

OBJECTIVES: Corticosteroids were clinically used in the treatment of nonsevere patients with COVID-19, but the efficacy of such treatment lacked sufficient clinical evidence, and the impact of dose had never been studied. This study aimed to evaluate the effect of systemic corticosteroid use (SCU) in nonsevere patients with COVID-19. METHODS: We conducted a multicenter retrospective cohort study in Hubei Province. A total of 1726 patients admitted with nonsevere type COVID-19 were included. Mixed-effect Cox model, mixed-effect Cox model with time-varying exposure, multiple linear regression, and propensity score analysis (inverse probability of treatment weight and propensity score matching) were used to explore the association between SCU and progression into severe type, all-cause mortality, and length of stay. RESULTS: During the follow-up of 30 days, 29.8% of nonsevere patients with COVID-19 received treatment with systemic corticosteroids. The use of systemic corticosteroids was associated with higher probability of developing severe type (adjusted hazard ratio 1.81; 95% confidence interval 1.47-2.21), all-cause mortality (adjusted hazard ratio 2.92; 95% confidence interval 1.39-6.15) in time-varying Cox analysis, and prolonged hospitalization (ß 4.14; P < .001) in multiple linear regression. Analysis with 2 propensity score cohorts displayed similar results. Besides, increased corticosteroid dose was significantly associated with elevated probability of developing severe type (P < .001) and prolonged hospitalization (P < .001). CONCLUSIONS: Corticosteroid treatment against nonsevere patients with COVID-19 was significantly associated with worse clinical outcomes. The higher dose was significantly associated with elevated risk of poor disease progression. We recommend that SCU should be avoided unless necessary among nonsevere patients with COVID-19.

Prevention and Control of COVID-19 after Resuming General Hospital Functions.

During the COVID-19 pandemic, many general hospitals have been transformed into designated infectious disease care facilities, where a large number of patients with COVID-19 infections have been treated and discharged. With declines in the number of hospitalizations, a major question for our healthcare systems, especially for these designated facilities, is how to safely resume hospital function after these patients have been discharged. Here, we take a designated COVID-19-care facility in Wuhan, China, as an example to share our experience in resuming hospital function while ensuring the safety of patients and medical workers. After more than 1200 patients with COVID-19 infections were discharged in late March, 2020, our hospital resumed function by setting up a three-level hospital infection management system with four grades of risk of exposure. Moreover, we also took measures to ensure the safety of medical personnel in different departments including clinics, wards, and operation rooms. After all patients with COVID-19 infections were discharged, during the five months of regular function from April to September in 2020, no positive cases have been found among more than 40,000 people in our hospital, including hospital staff and patients.

Dynamic 3D radiomics analysis using artificial intelligence to assess the stage of COVID-19 on CT images.

OBJECTIVE: To develop a dynamic 3D radiomics analysis method using artificial intelligence technique for automatically assessing four disease stages (i.e., early, progressive, peak, and absorption stages) of COVID-19 patients on CT images. METHODS: The dynamic 3D radiomics analysis method was composed of three AI algorithms (the lung segmentation, lesion segmentation, and stage-assessing AI algorithms) that were trained and tested on 313,767 CT images from 520 COVID-19 patients. This proposed method used 3D lung lesion that was segmented by the lung and lesion segmentation algorithms to extract radiomics features, and then combined with clinical metadata to assess the possible stage of COVID-19 patients using stage-assessing algorithm. Area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were used to evaluate diagnostic performance. RESULTS: Of 520 patients, 66 patients (mean age, 57 years ± 15 [standard deviation]; 35 women), including 203 CT scans, were tested. The dynamic 3D radiomics analysis method used 30 features, including 27 radiomics features and 3 clinical features to assess the possible disease stage of COVID-19 with an accuracy of 90%. For the prediction of each stage, the AUC of stage 1 was 0.965 (95% CI: 0.934, 0.997), AUC of stage 2 was 0.958 (95% CI: 0.931, 0.984), AUC of stage 3 was 0.998 (95% CI: 0.994, 1.000), and AUC of stage 4 was 0.975 (95% CI: 0.956, 0.994). CONCLUSION: With high diagnostic performance, the dynamic 3D radiomics analysis using artificial intelligence could represent a potential tool for helping hospitals make appropriate resource allocations and follow-up of treatment response. KEY POINTS: • The AI segmentation algorithms were able to accurately segment the lung and lesion of COVID-19 patients of different stages. • The dynamic 3D radiomics analysis method successfully extracted the radiomics features from the 3D lung lesion. • The stage-assessing AI algorithm combining with clinical metadata was able to assess the four stages with an accuracy of 90%, a macro-average AUC of 0.975.

Next-Generation Sequencing and Proteomics of Cerebrospinal Fluid From COVID-19 Patients With Neurological Manifestations.

The SARS-CoV-2 and its variants are still hitting the world. Ever since the outbreak, neurological involvements as headache, ageusia, and anosmia in COVID-19 patients have been emphasized and reported. But the pathogenesis of these new-onset neurological manifestations in COVID-19 patients is still obscure and controversial. As difficulty always lay in the diagnosis of neurological infection, current reports to validate the presence of SARS-CoV-2 in cerebrospinal fluid (CSF) almost relied on the basic methods and warranted improvement. Here we reported a case series of 8 patients with prominent new-onset neurological manifestations, who were screened out from a patch of 304 COVID-19 confirmed patients. Next-generation sequencing (NGS) and proteomics were conducted in the simultaneously obtained CSF and serum samples of the selected patients, with three non-COVID-19 patients with matched demographic features used as the controls for proteomic analysis. SARS-CoV-2 RNA was detected in the CSF of four COVID-19 patients and was suspicious in the rest four remaining patients by NGS, but was negative in all serum samples. Proteomic analysis revealed that 185 and 59 proteins were differentially expressed in CSF and serum samples, respectively, and that only 20 proteins were shared, indicating that the proteomic changes in CSF were highly specific. Further proteomic annotation highlighted the involvement of complement system, PI3K-Akt signaling pathway, enhanced cellular interaction, and macrophages in the CSF proteomic alterations. This study, equipped with NGS and proteomics, reported a high detection rate of SARS-CoV-2 in the CSF of COVID-19 patients and the proteomic alteration of CSF, which would provide insights into understanding the pathological mechanism of SARS-CoV-2 CNS infection.

Evaluation of early antibiotic use in patients with non-severe COVID-19 without bacterial infection.

OBJECTIVES: The use of antibiotics was common in some countries during the early phase of the coronavirus disease 2019 (COVID-19) pandemic, but adequate evaluation remains lacking. This study aimed to evaluate the effect of early antibiotic use in patients with non-severe COVID-19 admitted without bacterial infection. METHODS: This multi-centre retrospective cohort study included 1,373 inpatients with non-severe COVID-19 admitted without bacterial infection. Patients were divided into two groups according to their exposure to antibiotics within 48 h of admission. The outcomes were progression to severe COVID-19, length of stay >15 days and mortality rate. A mixed-effect Cox model and random effect logistic regression were used to explore the association between early antibiotic use and outcomes. RESULTS: During the 30-day follow-up period, the proportion of patients who progressed to severe COVID-19 in the early antibiotic use group was almost 1.4 times that of the comparison group. In the mixed-effect model, the early use of antibiotics was associated with higher probability of developing severe COVID-19 and staying in hospital for >15 days. However, there was no significant association between early use of antibiotics and mortality. Analysis with propensity-score-matched cohorts displayed similar results. In subgroup analysis, patients receiving any class of antibiotic were at increased risk of adverse health outcomes. Azithromycin did not improve disease progression and length of stay in patients with COVID-19. CONCLUSIONS: It is suggested that antibiotic use should be avoided unless absolutely necessary in patients with non-severe COVID-19, particularly in the early stages.

Inpatient use of metformin and acarbose is associated with reduced mortality of COVID-19 patients with type 2 diabetes mellitus.

AIMS: Type 2 diabetes mellitus (T2DM) is a strong risk factor for complications of coronavirus disease 2019 (COVID-19). The effect of T2DM medications on COVID-19 outcomes remains unclear. In a retrospective analysis of a cohort of 131 patients with T2DM hospitalized for COVID-19 in Wuhan, we have previously found that metformin use prior to hospitalization is associated with reduced mortality. The current study aims to investigate the effects of inpatient use of T2DM medications, including metformin, acarbose, insulin and sulfonylureas, on the mortality of COVID-19 patients with T2DM during hospitalization. METHODS: We continue to carry out a retrospective analysis of a cohort of 131 patients with T2DM hospitalized for COVID-19 and treated with different combinations of diabetes medications. RESULTS: We found that patients using metformin (p = .02) and acarbose (p = .04), alone or both together (p = .03), after admission were significantly more likely to survive than those who did not use either metformin or acarbose. 37 patients continued to take metformin after admission and 35 (94.6%) survived. Among the 57 patients who used acarbose after admission, 52 survived (91.2%). A total of 20 patients used both metformin and acarbose, while 57 used neither. Of the 20 dual-use patients, 19 (95.0%) survived. CONCLUSION: Our analyses suggest that inpatient use of metformin and acarbose together or alone during hospitalization should be studied in randomized trials.

Erratum to "Clinical Characteristics of Diabetic Patients with COVID-19".

[This corrects the article DOI: 10.1155/2020/1652403.].

Differential Diagnosis of COVID-19 Pneumonia From Influenza A (H1N1) Pneumonia Using a Model Based on Clinicoradiologic Features.

Objectives: Both coronavirus disease 2019 (COVID-19) pneumonia and influenza A (H1N1) pneumonia are highly contagious diseases. We aimed to characterize initial computed tomography (CT) and clinical features and to develop a model for differentiating COVID-19 pneumonia from H1N1 pneumonia. Methods: In total, we enrolled 291 patients with COVID-19 pneumonia from January 20 to February 13, 2020, and 97 patients with H1N1 pneumonia from May 24, 2009, to January 29, 2010 from two hospitals. Patients were randomly grouped into a primary cohort and a validation cohort using a seven-to-three ratio, and their clinicoradiologic data on admission were compared. The clinicoradiologic features were optimized by the least absolute shrinkage and selection operator (LASSO) logistic regression analysis to generate a model for differential diagnosis. Receiver operating characteristic (ROC) curves were plotted for assessing the performance of the model in the primary and validation cohorts. Results: The COVID-19 pneumonia mainly presented a peripheral distribution pattern (262/291, 90.0%); in contrast, H1N1 pneumonia most commonly presented a peribronchovascular distribution pattern (52/97, 53.6%). In LASSO logistic regression, peripheral distribution patterns, older age, low-grade fever, and slightly elevated aspartate aminotransferase (AST) were associated with COVID-19 pneumonia, whereas, a peribronchovascular distribution pattern, centrilobular nodule or tree-in-bud sign, consolidation, bronchial wall thickening or bronchiectasis, younger age, hyperpyrexia, and a higher level of AST were associated with H1N1 pneumonia. For the primary and validation cohorts, the LASSO model containing above eight clinicoradiologic features yielded an area under curve (AUC) of 0.963 and 0.943, with sensitivity of 89.7 and 86.2%, specificity of 89.7 and 89.7%, and accuracy of 89.7 and 87.1%, respectively. Conclusions: Combination of distribution pattern and category of pulmonary opacity on chest CT with clinical features facilitates the differentiation of COVID-19 pneumonia from H1N1 pneumonia.

Postpandemic Testing of Severe Acute Respiratory Syndrome Coronavirus 2 in the Huanan Seafood Market Area in Wuhan, China.

Seventy-six days after the coronavirus disease 2019 epidemic was contained in Wuhan, the Chinese government carried out a citywide severe acute respiratory syndrome coronavirus 2 nucleic acid testing initiative for all residents from 14 May to 1 June 2020. Our hospital tested 107 662 residents around Huanan seafood market, uncovering a positivity rate of 0.006%.

Sex-Disaggregated Data on Clinical Characteristics and Outcomes of Hospitalized Patients With COVID-19: A Retrospective Study.

Background: Sex and gender are crucial variables in coronavirus disease 2019 (COVID-19). We sought to provide information on differences in clinical characteristics and outcomes between male and female patients and to explore the effect of estrogen in disease outcomes in patients with COVID-19. Method: In this retrospective, multi-center study, we included all confirmed cases of COVID-19 admitted to four hospitals in Hubei province, China from Dec 31, 2019 to Mar 31, 2020. Cases were confirmed by real-time RT-PCR and were analyzed for demographic, clinical, laboratory and radiographic parameters. Random-effect logistic regression analysis was used to assess the association between sex and disease outcomes. Results: A total of 2501 hospitalized patients with COVID-19 were included in the present study. The clinical manifestations of male and female patients with COVID-19 were similar, while male patients have more comorbidities than female patients. In terms of laboratory findings, compared with female patients, male patients were more likely to have lymphopenia, thrombocytopenia, inflammatory response, hypoproteinemia, and extrapulmonary organ damage. Random-effect logistic regression analysis indicated that male patients were more likely to progress into severe type, and prone to ARDS, secondary bacterial infection, and death than females. However, there was no significant difference in disease outcomes between postmenopausal and premenopausal females after propensity score matching (PSM) by age. Conclusions: Male patients, especially those age-matched with postmenopausal females, are more likely to have poor outcomes. Sex-specific differences in clinical characteristics and outcomes do exist in patients with COVID-19, but estrogen may not be the primary cause. Further studies are needed to explore the causes of the differences in disease outcomes between the sexes.

Severe COVID-19 in Alzheimer's disease: APOE4's fault again?

Challenges have been recognized in healthcare of patients with Alzheimer's disease (AD) in the COVID-19 pandemic, given a high infection and mortality rate of COVID-19 in these patients. This situation urges the identification of underlying risks and preferably biomarkers for evidence-based, more effective healthcare. Towards this goal, current literature review and network analysis synthesize available information on the AD-related gene APOE into four lines of mechanistic evidence. At a cellular level, the risk isoform APOE4 confers high infectivity by the underlying coronavirus SARS-CoV-2; at a genetic level, APOE4 is associated with severe COVID-19; at a pathway level, networking connects APOE with COVID-19 risk factors such as ACE2, TMPRSS2, NRP1, and LZTFL1; at a behavioral level, APOE4-associated dementia may increase the exposure to coronavirus infection which causes COVID-19. Thus, APOE4 could exert multiple actions for high infection and mortality rates of the patients, or generally, with COVID-19.

Emerging Evidence for Neuropsycho-Consequences of COVID-19.

The pandemic novel coronavirus disease (COVID-19) has become a global concern in which the respiratory system is not the only one involved. Previous researches have presented the common clinical manifestations including respiratory symptoms (i.e., fever and cough), fatigue and myalgia. However, there is limited evidence for neurological and psychological influences of SARS-CoV-2. In this review, we discuss the common neurological manifestations of COVID-19 including acute cerebrovascular disease (i.e., cerebral hemorrhage) and muscle ache. Possible viral transmission to the nervous system may occur via circulation, an upper nasal transcribrial route and/or conjunctival route. Moreover, we cannot ignore the psychological influence on the public, medical staff and confirmed patients. Dealing with public psychological barriers and performing psychological crisis intervention are an important part of public health interventions.

Differences in the clinical characteristics and outcomes of COVID-19 patients in the epicenter and peripheral areas of the pandemic from China: a retrospective, large-sample, comparative analysis.

BACKGROUND: There is limited information on the difference in epidemiology, clinical characteristics and outcomes of the initial outbreak of the coronavirus disease (COVID-19) in Wuhan (the epicenter) and Sichuan (the peripheral area) in the early phase of the COVID-19 pandemic. This study was conducted to investigate the differences in the epidemiological and clinical characteristics of patients with COVID-19 between the epicenter and peripheral areas of pandemic and thereby generate information that would be potentially helpful in formulating clinical practice recommendations to tackle the COVID-19 pandemic. METHODS: The Sichuan & Wuhan Collaboration Research Group for COVID-19 established two retrospective cohorts that separately reflect the epicenter and peripheral area during the early pandemic. The epidemiology, clinical characteristics and outcomes of patients in the two groups were compared. Multivariate regression analyses were used to estimate the adjusted odds ratios (aOR) with regard to the outcomes. RESULTS: The Wuhan (epicenter) cohort included 710 randomly selected patients, and the peripheral (Sichuan) cohort included 474 consecutive patients. A higher proportion of patients from the periphery had upper airway symptoms, whereas a lower proportion of patients in the epicenter had lower airway symptoms and comorbidities. Patients in the epicenter had a higher risk of death (aOR=7.64), intensive care unit (ICU) admission (aOR=1.66), delayed time from illness onset to hospital and ICU admission (aOR=6.29 and aOR=8.03, respectively), and prolonged duration of viral shedding (aOR=1.64). CONCLUSIONS: The worse outcomes in the epicenter could be explained by the prolonged time from illness onset to hospital and ICU admission. This could potentially have been associated with elevated systemic inflammation secondary to organ dysfunction and prolonged duration of virus shedding independent of age and comorbidities. Thus, early supportive care could achieve better clinical outcomes.

Effect of a genetically engineered interferon-alpha versus traditional interferon-alpha in the treatment of moderate-to-severe COVID-19: a randomised clinical trial.

BACKGROUND: There are few effective therapies for coronavirus disease 2019 (COVID-19) upon the outbreak of the pandemic. To compare the effectiveness of a novel genetically engineered recombinant super-compound interferon (rSIFN-co) with traditional interferon-alpha added to baseline antiviral agents (lopinavir-ritonavir or umifenovir) for the treatment of moderate-to-severe COVID-19. METHOD: In this multicenter randomized (1:1) trial, patients hospitalized with moderate-to-severe COVID-19 received either rSIFN-co nebulization or interferon-alpha nebulization added to baseline antiviral agents for no more than 28 days. The primary endpoint was the time to clinical improvement. Secondary endpoints included the overall rate of clinical improvement assessed on day 28, the time to radiological improvement and virus nucleic acid negative conversion. RESULTS: A total of 94 patients were included in the safety set (46 patients assigned to rSIFN-co group, 48 to interferon-alpha group). The time to clinical improvement was 11.5 days versus 14.0 days (95% CI 1.10 to 2.81, p = .019); the overall rate of clinical improvement on day 28 was 93.5% versus 77.1% (difference, 16.4%; 95% CI 3% to 30%); the time to radiological improvement was 8.0 days versus 10.0 days (p = .002), the time to virus nucleic acid negative conversion was 7.0 days versus 10.0 days (p = .018) in the rSIFN-co and interferon alpha arms, respectively. Adverse events were balanced with no deaths among groups. CONCLUSIONS AND RELEVANCE: rSIFN-co was associated with a shorter time of clinical improvement than traditional interferon-alpha in the treatment of moderate-to-severe COVID-19 when combined with baseline antiviral agents. rSIFN-co therapy alone or combined with other antiviral therapy is worth to be further studied.Key messagesThere are few effective therapies for coronavirus disease 2019 (COVID-19) upon the outbreak of the pandemic. Interferon alphas, by inducing both innate and adaptive immune responses, have shown clinical efficacy in treating severe acute respiratory syndrome coronavirus and Middle East respiratory syndrome coronavirus.In this multicenter, head-to-head, randomized, clinical trial which included 94 participants with moderate-to-severe COVID-19, the rSIFN-co plus antiviral agents (lopinavir-ritonavir or umifenovir) was associated with a shorter time of clinical improvement than interferon-alpha plus antiviral agents.

Metformin Use in Diabetes Prior to Hospitalization: Effects on Mortality in Covid-19.

OBJECTIVE: Although type 2 diabetes mellitus (T2DM) has been reported as a risk factor for coronavirus disease 2019 (COVID-19), the effect of pharmacologic agents used to treat T2DM, such as metformin, on COVID-19 outcomes remains unclear. Metformin increases the expression of angiotensin converting enzyme 2, a known receptor for severe acute respiratory syndrome coronavirus 2. Data from people with T2DM hospitalized for COVID-19 were used to test the hypothesis that metformin use is associated with improved survival in this population. METHODS: Retrospective analyses were performed on de-identified clinical data from a major hospital in Wuhan, China, that included patients with T2DM hospitalized for COVID-19 during the recent epidemic. One hundred and thirty-one patients diagnosed with COVID-19 and T2DM were used in this study. The primary outcome was mortality. Demographic, clinical characteristics, laboratory data, diabetes medications, and respiratory therapy data were also included in the analysis. RESULTS: Of these 131 patients, 37 used metformin with or without other antidiabetes medications. Among the 37 metformin-taking patients, 35 (94.6%) survived and 2 (5.4%) did not survive. The mortality rates in the metformin-taking group versus the non-metformin group were 5.4% (2/37) versus 22.3% (21/94). Using multivariate analysis, metformin was found to be an independent predictor of survival in this cohort (P = .02). CONCLUSION: This study reveals a significant association between metformin use and survival in people with T2DM diagnosed with COVID-19. These clinical data are consistent with potential benefits of the use of metformin for COVID-19 patients with T2DM.

Efficacy of ribavirin and interferon-α therapy for hospitalized patients with COVID-19: A multicenter, retrospective cohort study.

OBJECTIVE: To assess the efficacy and safety of ribavirin and interferon-α (RBV/IFN-α) therapy in COVID-19 patients. METHODS: A multicenter, retrospective cohort study of COVID-19 patients admitted to 4 hospitals in Hubei Province, China, from 31 December 2019 to 31 March 2020. Patients were divided into 2 groups according to their exposure to RBV/IFN-α therapy within 48 h of admission. Mixed-effect Cox model and Logistic regression were used to explore the association between early treatments of RBV/IFN-α and primary outcomes. RESULTS: Of 2037 patients included, 1281 received RBV/IFN-α (RBV, IFN-α or RBV combined with IFN-α) treatments and 756 received none of these treatments. In a mixed effect model, RBV/IFN-α therapy was not associated with progression from non-severe into severe type (adjusted hazard ratio (aHR) = 1.09, 95% CI: 0.88-1.36) or with reduction in 30-day mortality (aHR = 0.89, 95% CI: 0.61-1.30). However, it was associated with a higher probability of hospital stay >15 days (adjusted odds ratio (aOR) = 2.11, 95% CI: 1.68-2.64) compared with no RBV/IFN-α therapy. The propensity score-matched cohort and subgroup analysis displayed similar results. CONCLUSION: RBV/IFN-α therapy was not observed to improve clinical outcomes in COVID-19 patients suggesting that RBV/IFN-α therapy should be avoided in COVID-19 treatment.