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...

No abstract

Background Mortality due to COVID-19 is high, especially in patients requiring mechanical ventilation. The purpose of the study is to investigate associations between mortality and variables measured during the first three days of mechanical ventilation in patients with COVID-19 intubated at ICU admission. Methods Multicenter, observational, cohort study includes consecutive patients with COVID-19 admitted to 44 Spanish ICUs between February 25 and July 31, 2020, who required intubation at ICU admission and mechanical ventilation for more than three days. We collected demographic and clinical data prior to admission; information about clinical evolution at days 1 and 3 of mechanical ventilation; and outcomes. Results Of the 2,095 patients with COVID-19 admitted to the ICU, 1,118 (53.3%) were intubated at day 1 and remained under mechanical ventilation at day three. From days 1 to 3, PaO2/FiO2 increased from 115.6 [80.0–171.2] to 180.0 [135.4–227.9] mmHg and the ventilatory ratio from 1.73 [1.33–2.25] to 1.96 [1.61–2.40]. In-hospital mortality was 38.7%. A higher increase between ICU admission and day 3 in the ventilatory ratio (OR 1.04 [CI 1.01–1.07], p = 0.030) and creatinine levels (OR 1.05 [CI 1.01–1.09], p = 0.005) and a lower increase in platelet counts (OR 0.96 [CI 0.93–1.00], p = 0.037) were independently associated with a higher risk of death. No association between mortality and the PaO2/FiO2 variation was observed (OR 0.99 [CI 0.95 to 1.02], p = 0.47). Conclusions Higher ventilatory ratio and its increase at day 3 is associated with mortality in patients with COVID-19 receiving mechanical ventilation at ICU admission. No association was found in the PaO2/FiO2 variation.

No abstract

g Severely burned patients have altered drug pharmacokinetics (PKs), but it is unclear how different they are from those in other critically ill patient groups. The aim of the present study was to compare the population pharmacokinetics of micafungin in the plasma and burn eschar of severely burned patients with those of micafungin in the plasma and peritoneal fluid of postsurgical critically ill patients with intra-abdominal infection. Fifteen burn patients were compared with 10 patients with intra-abdominal infection; all patients were treated with 100 to 150 mg/day of micafungin. Micafungin concentrations in serial blood, peritoneal fluid, and burn tissue samples were determined and were subjected to a population pharmacokinetic analysis. The probability of target attainment was calculated using area under the concentration-time curve from 0 to 24 h/MIC cutoffs of 285 for Candida parapsilosis and 3,000 for non-parapsilosis Candida spp. by Monte Carlo simulations. Twenty-five patients (18 males; median age, 50 years; age range, 38 to 67 years; median total body surface area burned, 50%; range of total body surface area burned, 35 to 65%) were included. A three-compartment model described the data, and only the rate constant for the drug distribution from the tissue fluid to the central compartment was statistically significantly different between the burn and intra-abdominal infection patients (0.47 ؎ 0.47 versus 0.15 ؎ 0.06 h ؊1 , respectively; P < 0.05). Most patients would achieve plasma PK/pharmacodynamic (PD) targets of 90% for non-parapsilosis Candida spp. and C. parapsilosis with MICs of 0.008 and 0.064 mg/liter, respectively, for doses of 100 mg daily and 150 mg daily. The PKs of micafungin were not significantly different between burn patients and intra-abdominal infection patients. After the first dose, micafungin at 100 mg/day achieved the PK/PD targets in plasma for MIC values of <0.008 mg/liter and <0.064 mg/liter for non-parapsilosis Candida spp. and Candida parapsilosis species, respectively.