Dataset Information

Disease progression of 213 patients hospitalized with Covid-19 in the Czech Republic in March-October 2020: An exploratory analysis.

ABSTRACT: We collected a multi-centric retrospective dataset of patients (N = 213) who were admitted to ten hospitals in Czech Republic and tested positive for SARS-CoV-2 during the early phases of the pandemic in March-October 2020. The dataset contains baseline patient characteristics, breathing support required, pharmacological treatment received and multiple markers on daily resolution. Patients in the dataset were treated with hydroxychloroquine (N = 108), azithromycin (N = 72), favipiravir (N = 9), convalescent plasma (N = 7), dexamethasone (N = 4) and remdesivir (N = 3), often in combination. To explore association between treatments and patient outcomes we performed multiverse analysis, observing how the conclusions change between defensible choices of statistical model, predictors included in the model and other analytical degrees of freedom. Weak evidence to constrain the potential efficacy of azithromycin and favipiravir can be extracted from the data. Additionally, we performed external validation of several proposed prognostic models for Covid-19 severity showing that they mostly perform unsatisfactorily on our dataset.

SUBMITTER: Modrak M

PROVIDER: S-EPMC8494367 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Disease progression of 213 patients hospitalized with Covid-19 in the Czech Republic in March-October 2020: An exploratory analysis.

Modrák Martin M Bürkner Paul-Christian PC Sieger Tomáš T Slisz Tomáš T Vašáková Martina M Mesežnikov Grigorij G Casas-Mendez Luis Fernando LF Vajter Jaromír J Táborský Jan J Kubricht Viktor V Suk Daniel D Horejsek Jan J Jedlička Martin M Mifková Adriana A Jaroš Adam A Kubiska Miroslav M Váchalová Jana J Šín Robin R Veverková Markéta M Pospíšil Zbyšek Z Vohryzková Julie J Pokrievková Rebeka R Hrušák Kristián K Christozova Kristína K Leos-Barajas Vianey V Fišer Karel K Hyánek Tomáš T

PloS one 20211006 10

We collected a multi-centric retrospective dataset of patients (N = 213) who were admitted to ten hospitals in Czech Republic and tested positive for SARS-CoV-2 during the early phases of the pandemic in March-October 2020. The dataset contains baseline patient characteristics, breathing support required, pharmacological treatment received and multiple markers on daily resolution. Patients in the dataset were treated with hydroxychloroquine (N = 108), azithromycin (N = 72), favipiravir (N = 9), ...[more]

PMID: 34613965

Similar Datasets

Project description:BackgroundCOVID-19 is affecting the entire population of India. Understanding district level correlates of the COVID-19's infection ratio (IR) is essential for formulating policies and interventions.ObjectiveThe present study aims to investigate the district level variation in COVID-19 during March-October 2020. The present study also examines the association between India's socioeconomic and demographic characteristics and the COVID-19 infection ratio at the district level.Data and methodsWe used publicly available crowdsourced district-level data on COVID-19 from March 14, 2020, to October 31, 2020. We identified hotspot and cold spot districts for COVID-19 cases and infection ratio. We have also carried out two sets of regression analysis to highlight the district level demographic, socioeconomic, household infrastructure facilities, and health-related correlates of the COVID-19 infection ratio.ResultsThe results showed on October 31, 2020, the IR in India was 42.85 per hundred thousand population, with the highest in Kerala (259.63) and the lowest in Bihar (6.58). About 80 percent infected cases and 61 percent deaths were observed in nine states (Delhi, Gujarat, West Bengal, Uttar Pradesh, Andhra Pradesh, Maharashtra, Karnataka, Tamil Nadu, and Telangana). Moran's- I showed a positive yet poor spatial clustering in the COVID-19 IR over neighboring districts. Our regression analysis demonstrated that percent of 15-59 aged population, district population density, percent of the urban population, district-level testing ratio, and percent of stunted children were significantly and positively associated with the COVID-19 infection ratio. We also found that, with an increasing percentage of literacy, there is a lower infection ratio in Indian districts.ConclusionThe COVID-19 infection ratio was found to be more rampant in districts with a higher working-age population, higher population density, a higher urban population, a higher testing ratio, and a higher level of stunted children. The study findings provide crucial information for policy discourse, emphasizing the vulnerability of the highly urbanized and densely populated areas.

Project description:ImportanceThe reported incidence of many health care-associated infections (HAIs) increased during the COVID-19 pandemic; however, it is unclear whether this is due to increased patient risk or to increased pressure on the health care system.ObjectiveTo assess HAI occurrence among patients admitted to hospitals with and without COVID-19.Design, setting, and participantsA cross-sectional retrospective analysis of inpatients discharged both with and without laboratory-confirmed COVID-19 infection was conducted. Data were obtained between January 1, 2019, and March 31, 2022, from community hospitals affiliated with a large health care system in the US.ExposureCOVID-19 infection.Main outcomes and measuresOccurrence of central line-associated bloodstream infection (CLABSI), catheter-associated urinary tract infection (CAUTI), methicillin-resistant Staphylococcus aureus (MRSA) bacteremia, and Clostridioides difficile infection as reported to the National Healthcare Safety Network.ResultsAmong nearly 5 million hospitalizations in 182 hospitals between 2020 and 2022, the occurrence of health care-associated infections (HAIs) was high among the 313 200 COVID-19 inpatients (median [SD] age, 57 [27.3] years; 56.0% women). Incidence per 100 000 patient-days showed higher HAIs among those with COVID-19 compared with those without. For CLABSI, the incidence for the full 9 quarters of the study was nearly 4-fold higher among the COVID-19 population than the non-COVID-19 population (25.4 vs 6.9). For CAUTI, the incidence in the COVID-19 population was 2.7-fold higher in the COVID-19 population (16.5 vs 6.1), and for MRSA, 3.0-fold higher (11.2 vs 3.7). Quarterly trends were compared with the same quarter in 2019. The greatest increase in the incidence of HAI in comparison with the same quarter in 2019 for the entire population occurred in quarter 3 of 2020 for CLABSI (11.0 vs 7.3), quarter 4 of 2021 for CAUTI (7.8 vs 6.8), and quarter 3 of 2021 for MRSA (5.2 vs 3.9). When limited to the non-COVID-19 population, the increase in CLABSI incidence vs the 2019 incidence was eliminated, and the quarterly rates of MRSA and CAUTI were lower vs the prepandemic 2019 comparator quarter.Conclusions and relevanceIn this cross-sectional study of hospitals during the pandemic, HAI occurrence among inpatients without COVID-19 was similar to that during 2019 despite additional pressures for infection control and health care professionals. The findings suggest that patients with COVID-19 may be more susceptible to HAIs and may require additional prevention measures.

Project description:BackgroundThe COVID-19 pandemic caused four waves of infection in Israel until October 2021. Israel was the first country to offer vaccinations to all the adult population followed by boosters. This study addresses how mortality rates reflect the effects of the pandemic.MethodsTotal mortality rates and rates of mortality without COVID-19 deaths (non-COVID-19 mortality) between March 2020 and October 2021 were compared with the average pre-COVID-19 rates in 2017-2019 by month, population group and by age group. In addition, a cohort vaccinated at least once by 31 March 2021 was followed up for mortality in the following seven months compared to the corresponding months in 2017-2019.ResultsA small number of excess deaths was found in the first wave and a greater excess in the following waves. The monthly mortality rate ratio was highest in October 2020, 23% higher than the average in 2017-2019, followed by August 2021 (22%), September 2021 (20%) and September 2020 (19%). Excess mortality in the Arab population was greater than for Jews and Others, and they had 65% and 43% higher mortality in September and October, 2020, 20-44% higher mortality between December 2020 and April 2021 and 33%, 45% and 22% higher mortality in August, September and October 2021, respectively. In most months of the pandemic, the non-COVID-19 mortality rates were not significantly different from those in 2017-2019. However, between November 2020 and March 2021, they were significantly lower for the total population and Jews and Others. They were significantly higher for the total population only in August 2021, and particularly for the Arab population. Non-COVID-19 mortality was also lower for most sex/age groups over the total study period. In a cohort of 5.07 million Israeli citizens vaccinated at least once by 31 March, 2021, age adjusted and age specific mortality rates for the following 7 months were lower than the average rates in 2017-2019 for these months, CONCLUSION: Israel has seen significant excess mortality during the COVID-19 pandemic, particularly in the Arab sector. Following lockdowns and administration of vaccinations excess mortality was reduced, and no excess mortality was seen amongst the vaccinated in the months after the vaccination campaign. These findings highlight the importance of public health measures such as mandating mask wearing and population vaccinations to control infection and reduce mortality.

Dataset Information

Disease progression of 213 patients hospitalized with Covid-19 in the Czech Republic in March-October 2020: An exploratory analysis.

Publications

Disease progression of 213 patients hospitalized with Covid-19 in the Czech Republic in March-October 2020: An exploratory analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets