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.

Objectives The objective of this study was to estimate the association between tocilizumab or corticosteroids and the risk of intubation or death in patients with coronavirus disease 19 (COVID-19) with a hyperinflammatory state according to clinical and laboratory parameters. Methods A cohort study was performed in 60 Spanish hospitals including 778 patients with COVID-19 and clinical and laboratory data indicative of a hyperinflammatory state. Treatment was mainly with tocilizumab, an intermediate-high dose of corticosteroids (IHDC), a pulse dose of corticosteroids (PDC), combination therapy, or no treatment. Primary outcome was intubation or death; follow-up was 21 days. Propensity score-adjusted estimations using Cox regression (logistic regression if needed) were calculated. Propensity scores were used as confounders, matching variables and for the inverse probability of treatment weights (IPTWs). Results In all, 88, 117, 78 and 151 patients treated with tocilizumab, IHDC, PDC, and combination therapy, respectively, were compared with 344 untreated patients. The primary endpoint occurred in 10 (11.4%), 27 (23.1%), 12 (15.4%), 40 (25.6%) and 69 (21.1%), respectively. The IPTW-based hazard ratios (odds ratio for combination therapy) for the primary endpoint were 0.32 (95%CI 0.22–0.47; p < 0.001) for tocilizumab, 0.82 (0.71–1.30; p 0.82) for IHDC, 0.61 (0.43–0.86; p 0.006) for PDC, and 1.17 (0.86–1.58; p 0.30) for combination therapy. Other applications of the propensity score provided similar results, but were not significant for PDC. Tocilizumab was also associated with lower hazard of death alone in IPTW analysis (0.07; 0.02–0.17; p < 0.001). Conclusions Tocilizumab might be useful in COVID-19 patients with a hyperinflammatory state and should be prioritized for randomized trials in this situation.

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...

Effect estimates from randomized trials and observational studies might not be directly comparable because of differences in study design, other than randomization, and in data analysis. We propose a 3-step procedure to facilitate meaningful comparisons of effect estimates from randomized trials and observational studies: 1) harmonization of the study protocols (eligibility criteria, treatment strategies, outcome, start and end of follow-up, causal contrast) so that the studies target the same causal effect, 2) harmonization of the data analysis to estimate the causal effect, and 3) sensitivity analyses to investigate the impact of discrepancies that could not be accounted for in the harmonization process. To illustrate our approach, we compared estimates of the effect of immediate with deferred initiation of antiretroviral therapy in individuals positive for the human immunodeficiency virus from the Strategic Timing of Antiretroviral Therapy (START) randomized trial and the observational HIV-CAUSAL Collaboration.

ObjectiveTo develop and validate a prediction model of mortality in patients with COVID-19 attending hospital emergency rooms.DesignMultivariable prognostic prediction model.Setting127 Spanish hospitals.ParticipantsDerivation (DC) and external validation (VC) cohorts were obtained from multicentre and single-centre databases, including 4035 and 2126 patients with confirmed COVID-19, respectively.InterventionsPrognostic variables were identified using multivariable logistic regression.Main outcome measures30-day mortality.ResultsPatients’ characteristics in the DC and VC were median age 70 and 61 years, male sex 61.0% and 47.9%, median time from onset of symptoms to admission 5 and 8 days, and 30-day mortality 26.6% and 15.5%, respectively. Age, low age-adjusted saturation of oxygen, neutrophil-to-lymphocyte ratio, estimated glomerular filtration rate by the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation, dyspnoea and sex were the strongest predictors of mortality. Calibration and discrimination were satisfactory with an area under the receiver operating characteristic curve with a 95% CI for prediction of 30-day mortality of 0.822 (0.806–0.837) in the DC and 0.845 (0.819–0.870) in the VC. A simplified score system ranging from 0 to 30 to predict 30-day mortality was also developed. The risk was considered to be low with 0–2 points (0%–2.1%), moderate with 3–5 (4.7%–6.3%), high with 6–8 (10.6%–19.5%) and very high with 9–30 (27.7%–100%).ConclusionsA simple prediction score, based on readily available clinical and laboratory data, provides a useful tool to predict 30-day mortality probability with a high degree of accuracy among hospitalised patients with COVID-19.

We evaluated treatment outcomes in a prospective registry of human immunodeficiency virus/hepatitis C virus (HCV)–coinfected patients treated with interferon‐free direct‐acting antiviral agent–based therapy in hospitals from the region of Madrid between November 2014 and August 2016. We assessed sustained viral response at 12 weeks after completion of treatment and used multivariable logistic regression to identify predictors of treatment failure. We evaluated 2,369 patients, of whom 59.5% did not have cirrhosis, 33.9% had compensated cirrhosis, and 6.6% had decompensated cirrhosis. The predominant HCV genotypes were 1a (40.9%), 4 (22.4%), 1b (15.1%), and 3 (15.0%). Treatment regimens included sofosbuvir (SOF)/ledipasvir (61.9%), SOF plus daclatasvir (14.6%), dasabuvir plus ombitasvir/paritaprevir/ritonavir (13.2%), and other regimens (10.3%). Ribavirin was used in 30.6% of patients. Less than 1% of patients discontinued therapy owing to adverse events. The frequency of sustained viral response by intention‐to‐treat analysis was 92.0% (95% confidence interval, 90.9%‐93.1%) overall, 93.8% (92.4%‐95.0%) for no cirrhosis, 91.0% (88.8%‐92.9%) for compensated cirrhosis, and 80.8% (73.7%‐86.6%) for decompensated cirrhosis. The factors associated with treatment failure were male sex (adjusted odds ratio, 1.75; 95% confidence interval, 1.14‐2.69), Centers for Diseases Control and Prevention category C (adjusted odds ratio, 1.65; 95% confidence interval, 1.12‐2.41), a baseline cluster of differentiation 4–positive (CD4+) T‐cell count <200/mm3 (adjusted odds ratio, 2.30; 95% confidence interval, 1.35‐3.92), an HCV RNA load ≥800,000 IU/mL (adjusted odds ratio, 1.63; 95% confidence interval, 1.14‐2.36), compensated cirrhosis (adjusted odds ratio, 1.35; 95% confidence interval, 0.96‐1.89), decompensated cirrhosis (adjusted odds ratio, 2.92; 95% confidence interval, 1.76‐4.87), and the use of SOF plus simeprevir, SOF plus ribavirin, and simeprevir plus daclatasvir. Conclusion: In this large real‐world study, direct‐acting antiviral agent–based therapy was safe and highly effective in coinfected patients; predictors of failure included gender, human immunodeficiency virus–related immunosuppression, HCV RNA load, severity of liver disease, and the use of suboptimal direct‐acting antiviral agent–based regimens. (Hepatology 2018;68:32‐47).

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