Background The clinical presentation of COVID-19 in patients admitted to hospital is heterogeneous. We aimed to determine whether clinical phenotypes of patients with COVID-19 can be derived from clinical data, to assess the reproducibility of these phenotypes and correlation with prognosis, and to derive and validate a simplified probabilistic model for phenotype assignment. Phenotype identification was not primarily intended as a predictive tool for mortality. MethodsIn this study, we used data from two cohorts: the COVID-19@Spain cohort, a retrospective cohort including 4035 consecutive adult patients admitted to 127 hospitals in Spain with COVID-19 between Feb 2 and March 17, 2020, and the COVID-19@HULP cohort, including 2226 consecutive adult patients admitted to a teaching hospital in Madrid between Feb 25 and April 19, 2020. The COVID-19@Spain cohort was divided into a derivation cohort, comprising 2667 randomly selected patients, and an internal validation cohort, comprising the remaining 1368 patients. The COVID-19@HULP cohort was used as an external validation cohort. A probabilistic model for phenotype assignment was derived in the derivation cohort using multinomial logistic regression and validated in the internal validation cohort. The model was also applied to the external validation cohort. 30-day mortality and other prognostic variables were assessed in the derived phenotypes and in the phenotypes assigned by the probabilistic model. Findings Three distinct phenotypes were derived in the derivation cohort (n=2667)-phenotype A (516 [19%] patients), phenotype B (1955 [73%]) and phenotype C (196 [7%])-and reproduced in the internal validation cohort (n=1368)phenotype A (233 [17%] patients), phenotype B (1019 [74%]), and phenotype C (116 [8%]). Patients with phenotype A were younger, were less frequently male, had mild viral symptoms, and had normal inflammatory parameters. Patients with phenotype B included more patients with obesity, lymphocytopenia, and moderately elevated inflammatory parameters. Patients with phenotype C included older patients with more comorbidities and even higher inflammatory parameters than phenotype B. We developed a simplified probabilistic model (validated in the internal validation cohort) for phenotype assignment, including 16 variables. In the derivation cohort, 30-day mortality rates were 2•5% (95% CI 1•4-4•3) for patients with phenotype A, 30•5% (28•5-32•6) for patients with phenotype B, and 60•7% (53•7-67•2) for patients with phenotype C (log-rank test p<0•0001). The predicted phenotypes in the internal validation cohort and external validation cohort showed similar mortality rates to the assigned phenotypes (internal validation cohort: 5•3% [95% CI 3•4-8•1] for phenotype A, 31•3% [28•5-34•2] for phenotype B, and 59•5% [48•8-69•3] for phenotype C; external validation cohort: 3•7% [2•0-6•4] for phenotype A, 23•7% [21•8-25•7] for phenotype B, and 51•4% [41•9-60•7] for phenotype C).Interpretation Patients admitted to hospital with COVID-19 can be classified into three...
2. Dominguez SR, Briese T, Palacios G, et al. Multiplex MassTag-PCR for respiratory pathogens in pediatric nasopharyngeal washes negative by conventional diagnostic testing shows a high prevalence of viruses belonging to a newly recognized rhinovirus clade.
Background In April 2020, two independent clinical trials to assess SARS-CoV-2 prophylaxis strategies among health care workers were initiated at our hospital: MeCOVID (melatonin vs placebo) and EPICOS (tenofovir disoproxil/emtricitabine vs hydroxychloroquine vs combination therapy vs placebo). Objective This study aimed to evaluate the reasons why health care workers chose to participate in the MeCOVID and EPICOS trials, as well as why they chose one over the other. Methods Both trials were offered to health care workers through an internal news bulletin. After an initial screening visit, all subjects were asked to respond to a web-based survey. Results In the first month, 206 health care workers were screened and 160 were randomized. The survey participation was high at 73.3%. Health care workers cited “to contribute to scientific knowledge” (n=80, 53.0%), followed by “to avoid SARS-CoV-2 infection” (n=33, 21.9%) and “the interest to be tested for SARS-CoV-2” (n=28, 18.5%), as their primary reasons to participate in the trials. We observed significant differences in the expected personal benefits across physicians and nurses (P=.01). The vast majority of volunteers (n=202, 98.0%) selected the MeCOVID trial, their primary reason being their concern regarding adverse reactions to treatments in the EPICOS trial (n=102, 69.4%). Conclusions Health care workers’ reasons to participate in prophylaxis trials in an acute pandemic context appear to be driven largely by their desire to contribute to science and to gain health benefits. Safety outweighed efficacy when choosing between the two clinical trials.
BACKGROUND In April 2020, two independent clinical trials (CT) to assess SARS-Cov-2 prophylaxis in healthcare workers were started in our Hospital: MeCOVID (melatonin vs placebo) and EPICOS (tenofovir disoproxil/emtricitabine vs hydroxychloroquine vs the combination of both vs placebo). OBJECTIVE We aim to evaluate the motivations to participate and the reasons to choose one or another. METHODS Both CTs were offered to personnel through the internal news bulletin. After the screening visit, all subjects were asked to answer a web-based survey. RESULTS In the first month, 206 healthcare workers were screened and 160 randomized. Survey participation was quite high, 73.3%. The main motivation to participate was “to contribute to scientific knowledge” 53.0%, followed by “to avoid SARS-Cov-2 infection” and “the interest to be tested for SARS-CoV-2”. We found differences between physicians and nurses in the expected personal benefits (P<0.05). The vast majority of the volunteers selected the MeCOVID study, the main reason was the fear of adverse reactions associated with treatments in EPICOS, 69.4%. CONCLUSIONS Healthcare workers´ motivations to participate in prophylaxis trials in a pandemic context appear to be driven mostly by their desire to contribute to science and getting some health benefits. Safety seems to weight way more than efficacy to choose between CTs.
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