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Drug Saf ; 43(7): 657-660, 2020 07.
Article in English | MEDLINE | ID: covidwho-1482335


INTRODUCTION: Hydroxychloroquine was recently promoted in patients infected with COVID-19 infection. A recent experimental study has suggested an increased toxicity of hydroxychloroquine in association with metformin in mice. OBJECTIVE: The present study was undertaken to investigate the reality of this putative drug-drug interaction between hydroxychloroquine and metformin using pharmacovigilance data. METHODS: Using VigiBase®, the WHO pharmacovigilance database, we performed a disproportionality analysis (case/non-case study). Cases were reports of fatal outcomes with the drugs of interest and non-cases were all other reports for these drugs registered between 1 January 2000 and 31 December 2019. Data with hydroxychloroquine (or metformin) alone were compared with the association hydroxychloroquine + metformin. Results are reported as ROR (reporting odds ratio) with their 95% confidence interval. RESULTS: Of the 10,771 Individual Case Safety Reports (ICSR) involving hydroxychloroquine, 52 were recorded as 'fatal outcomes'. In comparison with hydroxychloroquine alone, hydroxychloroquine + metformin was associated with an ROR value of 57.7 (23.9-139.3). In comparison with metformin alone, hydroxychloroquine + metformin was associated with an ROR value of 6.0 (2.6-13.8). CONCLUSION: Our study identified a signal for the association hydroxychloroquine + metformin that appears to be more at risk of fatal outcomes (particularly by completed suicides) than one of the two drugs when given alone.

Coronavirus Infections , Drug Interactions , Drug Therapy, Combination , Hydroxychloroquine , Metformin , Pandemics , Pneumonia, Viral , Adult , Adverse Drug Reaction Reporting Systems/statistics & numerical data , Betacoronavirus/isolation & purification , COVID-19 , Coronavirus Infections/diagnosis , Coronavirus Infections/drug therapy , Coronavirus Infections/epidemiology , Drug Therapy, Combination/adverse effects , Drug Therapy, Combination/mortality , Female , Humans , Hydroxychloroquine/pharmacokinetics , Hydroxychloroquine/therapeutic use , Hypoglycemic Agents/pharmacokinetics , Hypoglycemic Agents/therapeutic use , Male , Metformin/pharmacokinetics , Metformin/therapeutic use , Middle Aged , Pharmacovigilance , Pneumonia, Viral/diagnosis , Pneumonia, Viral/drug therapy , Pneumonia, Viral/epidemiology , SARS-CoV-2
Viruses ; 13(10)2021 10 06.
Article in English | MEDLINE | ID: covidwho-1460085


According to various estimates, only a small percentage of existing viruses have been discovered, naturally much less being represented in the genomic databases. High-throughput sequencing technologies develop rapidly, empowering large-scale screening of various biological samples for the presence of pathogen-associated nucleotide sequences, but many organisms are yet to be attributed specific loci for identification. This problem particularly impedes viral screening, due to vast heterogeneity in viral genomes. In this paper, we present a new bioinformatic pipeline, VirIdAl, for detecting and identifying viral pathogens in sequencing data. We also demonstrate the utility of the new software by applying it to viral screening of the feces of bats collected in the Moscow region, which revealed a significant variety of viruses associated with bats, insects, plants, and protozoa. The presence of alpha and beta coronavirus reads, including the MERS-like bat virus, deserves a special mention, as it once again indicates that bats are indeed reservoirs for many viral pathogens. In addition, it was shown that alignment-based methods were unable to identify the taxon for a large proportion of reads, and we additionally applied other approaches, showing that they can further reveal the presence of viral agents in sequencing data. However, the incompleteness of viral databases remains a significant problem in the studies of viral diversity, and therefore necessitates the use of combined approaches, including those based on machine learning methods.

Alphacoronavirus/isolation & purification , Betacoronavirus/isolation & purification , Chiroptera/virology , Genome, Viral/genetics , Metagenome/genetics , Alphacoronavirus/classification , Alphacoronavirus/genetics , Animals , Betacoronavirus/classification , Betacoronavirus/genetics , Chiroptera/genetics , Computational Biology/methods , Feces/virology , High-Throughput Nucleotide Sequencing , Metagenomics/methods , Moscow , Phycodnaviridae/classification , Phycodnaviridae/genetics , Phycodnaviridae/isolation & purification , Sequence Analysis, DNA
Anaesthesist ; 69(10): 717-725, 2020 10.
Article in German | MEDLINE | ID: covidwho-1453673


BACKGROUND: Following the regional outbreak in China, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread all over the world, presenting the healthcare systems with huge challenges worldwide. In Germany the coronavirus diseases 2019 (COVID-19) pandemic has resulted in a slowly growing demand for health care with a sudden occurrence of regional hotspots. This leads to an unpredictable situation for many hospitals, leaving the question of how many bed resources are needed to cope with the surge of COVID-19 patients. OBJECTIVE: In this study we created a simulation-based prognostic tool that provides the management of the University Hospital of Augsburg and the civil protection services with the necessary information to plan and guide the disaster response to the ongoing pandemic. Especially the number of beds needed on isolation wards and intensive care units (ICU) are the biggest concerns. The focus should lie not only on the confirmed cases as the patients with suspected COVID-19 are in need of the same resources. MATERIAL AND METHODS: For the input we used the latest information provided by governmental institutions about the spreading of the disease, with a special focus on the growth rate of the cumulative number of cases. Due to the dynamics of the current situation, these data can be highly variable. To minimize the influence of this variance, we designed distribution functions for the parameters growth rate, length of stay in hospital and the proportion of infected people who need to be hospitalized in our area of responsibility. Using this input, we started a Monte Carlo simulation with 10,000 runs to predict the range of the number of hospital beds needed within the coming days and compared it with the available resources. RESULTS: Since 2 February 2020 a total of 306 patients were treated with suspected or confirmed COVID-19 at this university hospital. Of these 84 needed treatment on the ICU. With the help of several simulation-based forecasts, the required ICU and normal bed capacity at Augsburg University Hospital and the Augsburg ambulance service in the period from 28 March 2020 to 8 June 2020 could be predicted with a high degree of reliability. Simulations that were run before the impact of the restrictions in daily life showed that we would have run out of ICU bed capacity within approximately 1 month. CONCLUSION: Our simulation-based prognosis of the health care capacities needed helps the management of the hospital and the civil protection service to make reasonable decisions and adapt the disaster response to the realistic needs. At the same time the forecasts create the possibility to plan the strategic response days and weeks in advance. The tool presented in this study is, as far as we know, the only one accounting not only for confirmed COVID-19 cases but also for suspected COVID-19 patients. Additionally, the few input parameters used are easy to access and can be easily adapted to other healthcare systems.

Coronavirus Infections/therapy , Critical Care/organization & administration , Hospital Bed Capacity , Hospitals, University/organization & administration , Intensive Care Units/organization & administration , Pneumonia, Viral/therapy , Betacoronavirus/isolation & purification , COVID-19 , Coronavirus Infections/epidemiology , Critical Care/statistics & numerical data , Germany , Hospitals, University/statistics & numerical data , Humans , Intensive Care Units/statistics & numerical data , Pandemics , Pneumonia, Viral/epidemiology , Prognosis , SARS-CoV-2