Objectives To analyse the characteristics and predictors of death in hospitalized patients with coronavirus disease 2019 (COVID-19) in Spain. Methods A retrospective observational study was performed of the first consecutive patients hospitalized with COVID-19 confirmed by real-time PCR assay in 127 Spanish centres until 17 March 2020. The follow-up censoring date was 17 April 2020. We collected demographic, clinical, laboratory, treatment and complications data. The primary endpoint was all-cause mortality. Univariable and multivariable Cox regression analyses were performed to identify factors associated with death. Results Of the 4035 patients, male subjects accounted for 2433 (61.0%) of 3987, the median age was 70 years and 2539 (73.8%) of 3439 had one or more comorbidity. The most common symptoms were a history of fever, cough, malaise and dyspnoea. During hospitalization, 1255 (31.5%) of 3979 patients developed acute respiratory distress syndrome, 736 (18.5%) of 3988 were admitted to intensive care units and 619 (15.5%) of 3992 underwent mechanical ventilation. Virus- or host-targeted medications included lopinavir/ritonavir (2820/4005, 70.4%), hydroxychloroquine (2618/3995, 65.5%), interferon beta (1153/3950, 29.2%), corticosteroids (1109/3965, 28.0%) and tocilizumab (373/3951, 9.4%). Overall, 1131 (28%) of 4035 patients died. Mortality increased with age (85.6% occurring in older than 65 years). Seventeen factors were independently associated with an increased hazard of death, the strongest among them including advanced age, liver cirrhosis, low age-adjusted oxygen saturation, higher concentrations of C-reactive protein and lower estimated glomerular filtration rate. Conclusions Our findings provide comprehensive information about characteristics and complications of severe COVID-19, and may help clinicians identify patients at a higher risk of death.

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...

on behalf of the PENUT Trial Consortium* Objective To evaluate whether extremely low gestational age neonates (ELGANs) randomized to erythropoietin have better or worse kidney-related outcomes during hospitalization and at 22-26 months of corrected gestational age (cGA) compared with those randomized to placebo. Study designWe performed an ancillary study to a multicenter double-blind, placebo-controlled randomized clinical trial of erythropoietin in ELGANs. ResultsThe prevalence of severe (stage 2 or 3) acute kidney injury (AKI) was 18.2%. We did not find a statistically significant difference between those randomized to erythropoietin vs placebo for in-hospital primary (severe AKI) or secondary outcomes (any AKI and serum creatinine/cystatin C values at days 0, 7, 9, and 14). At 22-26 months of cGA, 16% of the cohort had an estimated glomerular filtration rate (eGFR) <90 mL/min/1.73 m 2 , 35.8% had urine albumin/creatinine ratio >30 mg/g, 23% had a systolic blood pressure (SBP) >95th percentile for age, and 40% had a diastolic blood pressure (DBP) >95th percentile for age. SBP >90th percentile occurred less often among recipients of erythropoietin (P < .04). This association remained even after controlling for gestational age, site, and sibship (aOR 0.6; 95% CI 0.39-0.92). We did not find statistically significant differences between treatment groups in eGFR, albumin/creatinine ratio, rates of SBP >95th percentile, or DBP >90th or >95th percentiles at the 2 year follow-up visit.Conclusions ELGANs have high rates of in-hospital AKI and kidney-related problems at 22-26 months of cGA.Recombinant erythropoietin may protect ELGANs against long-term elevated SBP but does not appear to protect from AKI, low eGFR, albuminuria, or elevated DBP at 22-26 months of cGA.

and for the PENUT Consortium BACKGROUND: Outcomes of extremely low gestational age neonates (ELGANs) may be adversely impacted by packed red blood cell (pRBC) transfusions. We investigated the impact of transfusions on neurodevelopmental outcome in the Preterm Erythropoietin (Epo) Neuroprotection (PENUT) Trial population. METHODS: This is a post hoc analysis of 936 infants 24-0/6 to 27-6/7 weeks' gestation enrolled in the PENUT Trial. Epo 1000 U/kg or placebo was given every 48 h × 6 doses, followed by 400 U/kg or sham injections 3 times a week through 32 weeks postmenstrual age. Six hundred and twenty-eight (315 placebo, 313 Epo) survived and were assessed at 2 years of age. We evaluated associations between BSID-III scores and the number and volume of pRBC transfusions. RESULTS: Each transfusion was associated with a decrease in mean cognitive score of 0.96 (95% CI of [−1.34, −0.57]), a decrease in mean motor score of 1.51 (−1.91, −1.12), and a decrease in mean language score of 1.10 (−1.54, −0.66). Significant negative associations between BSID-III score and transfusion volume and donor exposure were observed in the placebo group but not in the Epo group. CONCLUSIONS: Transfusions in ELGANs were associated with worse outcomes. We speculate that strategies to minimize the need for transfusions may improve outcomes.

ÔØ Å ÒÙ× Ö ÔØThis is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Keywords Thermometry · Fever · Temperature monitoring · Infectious diseases · Entropy · ApEn A C C E P T E D M A N U S C R I P T ACCEPTED MANUSCRIPT A C C E P T E D M A N U S C R I P T ACCEPTED MANUSCRIPTAbstract Body temperature monitoring provides healthcarers with key clinical information about the physiological status of patients. Temperature readings are taken periodically to detect febrile episodes and consequently implement the appropriate medical countermeasures. However, fever is often difficult to assess at early stages, or remains undetected until the next reading, probably a few hours later. The objective of this paper is to develop a statistical model to forecast fever before a temperature threshold is exceeded to improve the therapeutic approach to the subjects involved. To this end, temperature series of nine patients admitted to a general Internal Medicine ward were obtained with a continuous monitoring holter device, collecting measurements of peripheral and core temperature once per minute. These series were used to develop different statistical models that could quantify the probability of having a fever spike in the following 60 minutes. A validation series was collected to assess the accuracy of the models. Finally, the results were compared with the analysis of some series by experienced clinicians. Two different models were developed: a logistic regression model and a linear discrimination analysis model. Both of them exhibited a fever peak forecasting accuracy above 84%. When compared with experts assessment, both models identified 35 out of 36 fever spikes (97.2%). The models proposed are highly accurate in forecasting the appearance of fever spikes within a short period of time in patients with suspected or confirmed febrile related illnesses.