Beneficial ex vivo immunomodulatory and clinical effects of clarithromycin in COVID-19.

INTRODUCTION: Macrolide antibiotics have immunomodulatory properties which may be beneficial in viral infections. However, the precise effects of macrolides on T cell responses to COVID, differences between different macrolides, and synergistic effects with other antibiotics have not been explored. METHODS: We investigated the effect of antibiotics (amoxicillin, azithromycin, clarithromycin, and combined amoxicillin with clarithromycin) on lymphocyte intracellular cytokine levels and monocyte phagocytosis in healthy volunteer PBMCs stimulated ex vivo with SARS-CoV-2 S1+2 spike protein. A retrospective cohort study was performed on intensive care COVID-19 patients. RESULTS: Co-incubation of clarithromycin with spike protein-stimulated healthy volunteer PBMCs ex vivo resulted in an increase in CD8+ (p = 0.004) and CD4+ (p = 0.007) IL-2, with a decrease in CD8+ (p = 0.032) and CD4+ (p = 0.007) IL-10. The addition of amoxicillin to clarithromycin resulted in an increase in CD8+ IL-6 (p = 0.010), decrease in CD8+ (p = 0.014) and CD4+ (p = 0.022) TNF-alpha, and decrease in CD8+ IFN-alpha (p = 0.038). Amoxicillin alone had no effect on CD4+ or CD8+ cytokines. Co-incubation of azithromycin resulted in increased CD8+ (p = 0.007) and CD4+ (p = 0.011) IL-2. There were no effects on monocyte phagocytosis. 102 COVID-19 ICU patients received antibiotics on hospital admission; 62 (61%) received clarithromycin. Clarithromycin use was associated with reduction in mortality on univariate analysis (p = 0.023), but not following adjustment for confounders (HR = 0.540; p = 0.076). CONCLUSIONS: Clarithromycin has immunomodulatory properties over and above azithromycin. Amoxicillin in addition to clarithromycin is associated with synergistic ex vivo immunomodulatory properties. The potential benefit of clarithromycin in critically ill patients with COVID-19 and other viral pneumonitis merits further exploration.

Bacteraemia variation during the COVID-19 pandemic; a multi-centre UK secondary care ecological analysis.

BACKGROUND: We investigated for change in blood stream infections (BSI) with Enterobacterales, coagulase negative staphylococci (CoNS), Streptococcus pneumoniae, and Staphylococcus aureus during the first UK wave of SARS-CoV-2 across five London hospitals. METHODS: A retrospective multicentre ecological analysis was undertaken evaluating all blood cultures taken from adults from 01 April 2017 to 30 April 2020 across five acute hospitals in London. Linear trend analysis and ARIMA models allowing for seasonality were used to look for significant variation. RESULTS: One hundred nineteen thousand five hundred eighty-four blood cultures were included. At the height of the UK SARS-CoV-2 first wave in April 2020, Enterobacterales bacteraemias were at an historic low across two London trusts (63/3814, 1.65%), whilst all CoNS BSI were at an historic high (173/3814, 4.25%). This differed significantly for both Enterobacterales (p = 0.013), CoNS central line associated BSIs (CLABSI) (p < 0.01) and CoNS non-CLABSI (p < 0.01), when compared with prior periods, even allowing for seasonal variation. S. pneumoniae (p = 0.631) and S. aureus (p = 0.617) BSI did not vary significant throughout the study period. CONCLUSIONS: Significantly fewer than expected Enterobacterales BSI occurred during the UK peak of the COVID-19 pandemic; identifying potential causes, including potential unintended consequences of national self-isolation public health messaging, is essential. High rates of CoNS BSI, with evidence of increased CLABSI, but also likely contamination associated with increased use of personal protective equipment, may result in inappropriate antimicrobial use and indicates a clear area for intervention during further waves.

The utility of a local multidisciplinary working group to oversee the establishment of rapidly evolving standards of care and to support trial recruitment during the COVID-19 pandemic.

Coronavirus disease 2019 (COVID-19) was first identified in December 2019 in Wuhan, China. The first analyses of cases described high numbers of critically ill patients requiring intensive care admission with significant late inflammatory features. By the time the first cases of SARS-CoV-2 infection were diagnosed in the UK, a wide range of drugs were under consideration and it became clear that the input of clinicians covering all organ systems (in particular, infectious diseases, haematology, rheumatology, renal medicine and intensive care) and of expert specialist pharmacists was necessary at the local level. Thus, an expert multidisciplinary (MDT) group within our organisation was convened to offer a standardised approach and robust clinical governance for the treatment of COVID-19 patients admitted to our hospitals and rapidly develop standards of care as evidence evolved. This commentary explores the methods and mechanisms for creating an MDT COVID-19 treatment working group which are applicable to any hospital likely to admit and care for high numbers of COVID-19 patients and demonstrates how the structure and governance of the group allowed for rapid adoption of both dexamethasone and tocilizumab into standard of care as data became available.

Supervised machine learning to support the diagnosis of bacterial infection in the context of COVID-19.

BACKGROUND: Bacterial infection has been challenging to diagnose in patients with COVID-19. We developed and evaluated supervised machine learning algorithms to support the diagnosis of secondary bacterial infection in hospitalized patients during the COVID-19 pandemic. METHODS: Inpatient data at three London hospitals for the first COVD-19 wave in March and April 2020 were extracted. Demographic, blood test and microbiology data for individuals with and without SARS-CoV-2-positive PCR were obtained. A Gaussian Naive Bayes, Support Vector Machine (SVM) and Artificial Neural Network were trained and compared using the area under the receiver operating characteristic curve (AUCROC). The best performing algorithm (SVM with 21 blood test variables) was prospectively piloted in July 2020. AUCROC was calculated for the prediction of a positive microbiological sample within 48 h of admission. RESULTS: A total of 15 599 daily blood profiles for 1186 individual patients were identified to train the algorithms; 771/1186 (65%) individuals were SARS-CoV-2 PCR positive. Clinically significant microbiology results were present for 166/1186 (14%) patients during admission. An SVM algorithm trained with 21 routine blood test variables and over 8000 individual profiles had the best performance. AUCROC was 0.913, sensitivity 0.801 and specificity 0.890. Prospective testing on 54 patients on admission (28/54, 52% SARS-CoV-2 PCR positive) demonstrated an AUCROC of 0.960 (95% CI: 0.90-1.00). CONCLUSIONS: An SVM using 21 routine blood test variables had excellent performance at inferring the likelihood of positive microbiology. Further prospective evaluation of the algorithms ability to support decision making for the diagnosis of bacterial infection in COVID-19 cohorts is underway.

Handheld Point-of-Care System for Rapid Detection of SARS-CoV-2 Extracted RNA in under 20 min.

The COVID-19 pandemic is a global health emergency characterized by the high rate of transmission and ongoing increase of cases globally. Rapid point-of-care (PoC) diagnostics to detect the causative virus, SARS-CoV-2, are urgently needed to identify and isolate patients, contain its spread and guide clinical management. In this work, we report the development of a rapid PoC diagnostic test (<20 min) based on reverse transcriptase loop-mediated isothermal amplification (RT-LAMP) and semiconductor technology for the detection of SARS-CoV-2 from extracted RNA samples. The developed LAMP assay was tested on a real-time benchtop instrument (RT-qLAMP) showing a lower limit of detection of 10 RNA copies per reaction. It was validated against extracted RNA from 183 clinical samples including 127 positive samples (screened by the CDC RT-qPCR assay). Results showed 91% sensitivity and 100% specificity when compared to RT-qPCR and average positive detection times of 15.45 ± 4.43 min. For validating the incorporation of the RT-LAMP assay onto our PoC platform (RT-eLAMP), a subset of samples was tested (n = 52), showing average detection times of 12.68 ± 2.56 min for positive samples (n = 34), demonstrating a comparable performance to a benchtop commercial instrument. Paired with a smartphone for results visualization and geolocalization, this portable diagnostic platform with secure cloud connectivity will enable real-time case identification and epidemiological surveillance.

Bacterial and Fungal Coinfection in Individuals With Coronavirus: A Rapid Review To Support COVID-19 Antimicrobial Prescribing.

BACKGROUND: To explore and describe the current literature surrounding bacterial/fungal coinfection in patients with coronavirus infection. METHODS: MEDLINE, EMBASE, and Web of Science were searched using broad-based search criteria relating to coronavirus and bacterial coinfection. Articles presenting clinical data for patients with coronavirus infection (defined as SARS-1, MERS, SARS-CoV-2, and other coronavirus) and bacterial/fungal coinfection reported in English, Mandarin, or Italian were included. Data describing bacterial/fungal coinfections, treatments, and outcomes were extracted. Secondary analysis of studies reporting antimicrobial prescribing in SARS-CoV-2 even in absence of coinfection was performed. RESULTS: 1007 abstracts were identified. Eighteen full texts reporting bacterial/fungal coinfection were included. Most studies did not identify or report bacterial/fungal coinfection (85/140; 61%). Nine of 18 (50%) studies reported on COVID-19, 5/18 (28%) on SARS-1, 1/18 (6%) on MERS, and 3/18 (17%) on other coronaviruses. For COVID-19, 62/806 (8%) patients were reported as experiencing bacterial/fungal coinfection during hospital admission. Secondary analysis demonstrated wide use of broad-spectrum antibacterials, despite a paucity of evidence for bacterial coinfection. On secondary analysis, 1450/2010 (72%) of patients reported received antimicrobial therapy. No antimicrobial stewardship interventions were described. For non-COVID-19 cases, bacterial/fungal coinfection was reported in 89/815 (11%) of patients. Broad-spectrum antibiotic use was reported. CONCLUSIONS: Despite frequent prescription of broad-spectrum empirical antimicrobials in patients with coronavirus-associated respiratory infections, there is a paucity of data to support the association with respiratory bacterial/fungal coinfection. Generation of prospective evidence to support development of antimicrobial policy and appropriate stewardship interventions specific for the COVID-19 pandemic is urgently required.