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...

Objective. The Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index (SDI) is a validated instrument for assessing organ damage in systemic lupus erythematosus (SLE). Trained physicians must complete it, thus limiting utility where this is impossible. Methods. We developed and pilot tested a self-assessed organ damage instrument, the Lupus Damage Index Questionnaire (LDIQ), in 37 SLE subjects and 7 physicians. After refinement, 569 English-speaking SLE subjects and 14 rheumatologists from 11 international SLE clinics participated in validation. Subjects and physicians completed the instruments separately. We calculated sensitivity, specificity, Spearman's correlations, and agreement using the SDI as the gold standard. Six hundred five SLE participants in the community-based National Data Bank for Rheumatic Diseases (NDB) study completed the LDIQ and we assessed correlations with outcome and disability measures. Results. The mean LDIQ score was 3.3 (range 0 -16) and the mean SDI score was 1.5 (range 0 -9). The LDIQ had a moderately high correlation with the SDI (Spearman's r ‫؍‬ 0.50, P < 0.001). Specificities of individual LDIQ items were >80%, except for neuropathy. Sensitivities were variable and lowest for damage, with <1% prevalence. Agreement between the SDI and LDIQ was >85% for all but neuropathy, reduced renal function, deforming arthritis, and alopecia. In the NDB, the LDIQ correlated well with the comorbidity index (r ‫؍‬ 0.45), the Short Form 36 physical component scale (r ‫؍‬ 0.43), the Medical Research Council dyspnea scale (r ‫؍‬ 0.40), disability (r ‫؍‬ 0.37), and the Systemic Lupus Erythematosus Activity Questionnaire score (r ‫؍‬ 0.37). Conclusion. The metric properties of the LDIQ are good compared with the SDI. It has construct validity and correlations with health assessments similar to the SDI. The LDIQ should allow expansion of SLE research. Its ultimate value will be determined in longitudinal studies.

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Impact of the COVID-19 pandemic on the diagnosis, management and prognosis of infective endocarditis