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...

Objectives: Primary objective: to evaluate the efficacy of melatonin as a prophylactic treatment on prevention of symptomatic SARS-CoV-2 infection among healthcare workers at high risk of SARS-CoV-2 exposure. Secondary objectives:-To evaluate the efficacy of melatonin as a prophylactic treatment on prevention of asymptomatic SARS-CoV-2 infection.-To evaluate the efficacy of melatonin to prevent the development of severe COVID-19 in the participants enrolled in this study who develop SARS-CoV-2 infection along the trial.-To evaluate the duration of COVID-19 symptoms in participants receiving melatonin before the infection.-To evaluate seroconversion timing post-symptom onset.

Objectives: • Primary objective: to evaluate the effect of intravenous melatonin (IVM) on mortality in adult patients admitted to the intensive care unit (ICU) with COVID-19. • Secondary objectives: • To evaluate the effect of IVM on ICU length of stay. • To evaluate the effect of IVM on the length of mechanical ventilation (MV). • To evaluate if the use of IVM is associated with an increase in the number of ventilator-free days. • To evaluate if the use of IVM is associated with a reduced number of failing organs as determined by the sequential organ failure assessment (SOFA) scale. • To evaluate if the use of IVM is associated with a reduction of the frequency and severity of COVID-19-associated thromboembolic phenomena. • To evaluate if the use of IVM is associated with a decreased systemic inflammatory response assessed by plasma

Children with chronic critical illness (CCI) are hypothesized to be a high-risk patient population with persistent multiple organ dysfunction and functional morbidities resulting in recurrent or prolonged critical care; however, it is unclear how CCI should be defined. The aim of this scoping review was to evaluate the existing literature for case definitions of pediatric CCI and case definitions of prolonged PICU admission and to explore the methodologies used to derive these definitions.DATA SOURCES: Four electronic databases (Ovid Medline, Embase, CINAHL, and Web of Science) from inception to March 3, 2021. STUDY SELECTION:We included studies that provided a specific case definition for CCI or prolonged PICU admission. Crowdsourcing was used to screen citations independently and in duplicate. A machine-learning algorithm was developed and validated using 6,284 citations assessed in duplicate by trained crowd reviewers. A hybrid of crowdsourcing and machine-learning methods was used to complete the remaining citation screening. DATA EXTRACTION:We extracted details of case definitions, study demographics, participant characteristics, and outcomes assessed.DATA SYNTHESIS: Sixty-seven studies were included. Twelve studies (18%) provided a definition for CCI that included concepts of PICU length of stay (n = 12), medical complexity or chronic conditions (n = 9), recurrent admissions (n = 9), technology dependence (n = 5), and uncertain prognosis (n = 1). Definitions were commonly referenced from another source (n = 6) or opinion-based (n = 5). The remaining 55 studies (82%) provided a definition for prolonged PICU admission, most frequently greater than or equal to 14 (n = 11) or greater than or equal to 28 days (n = 10). Most of these definitions were derived by investigator opinion (n = 24) or statistical method (n = 18). CONCLUSIONS: Pediatric CCI has been variably defined with regard to the concepts of patient complexity and chronicity of critical illness. A consensus definition is needed to advance this emerging and important area of pediatric critical care research.

We evaluated in this randomised, double-blind clinical trial the efficacy of melatonin as a prophylactic treatment for prevention of SARS-CoV-2 infection among healthcare workers at high risk of SARS-CoV-2 exposure. Healthcare workers fulfilling inclusion criteria were recruited in five hospitals in Spain and were randomised 1:1 to receive melatonin 2 mg administered orally for 12 weeks or placebo. The main outcome was the number of SARS-CoV-2 infections. A total of 344 volunteers were screened, and 314 were randomised: 151 to placebo and 163 to melatonin; 308 received the study treatment (148 placebo; 160 melatonin). We detected 13 SARS-CoV-2 infections, 2.6% in the placebo arm and 5.5% in the melatonin arm (p = 0.200). A total of 294 adverse events were detected in 127 participants (139 in placebo; 155 in melatonin). We found a statistically significant difference in the incidence of adverse events related to treatment: 43 in the placebo arm and 67 in the melatonin arm (p = 0.040), and in the number of participants suffering from somnolence related to treatment: 8.8% (n = 14) in the melatonin versus 1.4% (n = 2) in the placebo arm (p = 0.008). No severe adverse events related to treatment were reported. We cannot confirm our hypothesis that administration of melatonin prevents the development of SARS-CoV-2 infection in healthcare workers.

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