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.

BackgroundCystic echinococcosis (CE) is a well-known neglected parasitic disease. However, evidence supporting the four current treatment modalities is inadequate, and treatment options remain controversial. The aim of this work is to analyse the available data to answer clinical questions regarding medical treatment of CE.MethodsA thorough electronic search of the relevant literature without language restrictions was carried out using PubMed (Medline), Cochrane Central Register of Controlled Trials, BioMed, Database of Abstracts of Reviews of Effects, and Cochrane Plus databases up to February 1, 2017. All descriptive studies reporting an assessment of CE treatment and published in a peer-reviewed journal with available full-text were considered for a qualitative analysis. Randomized controlled trials were included in a quantitative meta-analysis. We used the standard methodological procedures established by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement.ResultsWe included 33 studies related to the pharmacological treatment of CE in humans. Of these, 22 studies with levels of evidence 2 to 4 were qualitatively analysed, and 11 randomized controlled trials were quantitatively analysed by meta-analysis.ConclusionsTreatment outcomes are better when surgery or PAIR (Puncture, Aspiration, Injection of protoscolicidal agent and Reaspiration) is combined with benzimidazole drugs given pre- and/or post-operation. Albendazole chemotherapy was found to be the primary pharmacological treatment to consider in the medical management of CE. Nevertheless, combined treatment with albendazole plus praziquantel resulted in higher scolicidal and anti-cyst activity and was more likely to result in cure or improvement relative to albendazole alone.Electronic supplementary materialThe online version of this article (10.1186/s12879-018-3201-y) contains supplementary material, which is available to authorized users.

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...

Background Efficient and early triage of hospitalized Covid-19 patients to detect those with higher risk of severe disease is essential for appropriate case management. Methods We trained, validated, and externally tested a machine-learning model to early identify patients who will die or require mechanical ventilation during hospitalization from clinical and laboratory features obtained at admission. A development cohort with 918 Covid-19 patients was used for training and internal validation, and 352 patients from another hospital were used for external testing. Performance of the model was evaluated by calculating the area under the receiver-operating-characteristic curve (AUC), sensitivity and specificity. Results A total of 363 of 918 (39.5%) and 128 of 352 (36.4%) Covid-19 patients from the development and external testing cohort, respectively, required mechanical ventilation or died during hospitalization. In the development cohort, the model obtained an AUC of 0.85 (95% confidence interval [CI], 0.82 to 0.87) for predicting severity of disease progression. Variables ranked according to their contribution to the model were the peripheral blood oxygen saturation (SpO2)/fraction of inspired oxygen (FiO2) ratio, age, estimated glomerular filtration rate, procalcitonin, C-reactive protein, updated Charlson comorbidity index and lymphocytes. In the external testing cohort, the model performed an AUC of 0.83 (95% CI, 0.81 to 0.85). This model is deployed in an open source calculator, in which Covid-19 patients at admission are individually stratified as being at high or non-high risk for severe disease progression. Conclusions This machine-learning model, applied at hospital admission, predicts risk of severe disease progression in Covid-19 patients.

BackgroundCystic echinococcosis (CE) is a chronic, complex and neglected zoonotic disease. CE occurs worldwide. In humans, it may result in a wide spectrum of clinical manifestations, ranging from asymptomatic infection to fatal disease. Clinical management procedures have evolved over decades without adequate evaluation. Despite advances in surgical techniques and the use of chemotherapy, recurrence remains one of the major problems in the management of hydatid disease. The aim of this study was to determine the frequency of CE recurrence and the risk factors involved in recurrence.MethodsA descriptive longitudinal-retrospective study was designed. We reviewed all patients diagnosed with CE according to ICD-9 (code 122–0 to 122–9) criteria admitted at Complejo Asistencial Universitario de Salamanca, Spain, between January 1998 and December 2015.ResultsAmong the 217 patients studied, 25 (11.5%) had a hydatid recurrence after curative intention treatment. Median duration of recurrence’s diagnosis was 12.35 years (SD: ±9.31). The likelihood of recurrence was higher [OR = 2.7; 95% CI, 1.1–7.1; p < 0.05] when the cyst was located in organs other than liver and lung, 22.6% (7/31) vs 14.2% (31/217) in the cohort. We detected a chance of recurrence [OR = 2.3; 95% CI, 1.4–6.5; p > 0.05] that was two times higher in those patients treated with a combination of antihelminthic treatments and surgical intervention (20/141, 14.2%) than in patients treated with surgical intervention alone (5/76, 6.6%).ConclusionsDespite advances in diagnosis and therapeutic techniques in hydatid disease, recurrence remains one of the major problems in the management of hydatid disease. The current management and treatment of recurrences is still largely based on expert opinion and moderate-to-poor quality of evidence. Consequently, large prospective and multicenter studies will be needed to provide definitive recommendations for its clinical management.

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BackgroundStevens-Johnson Syndrome (SJS) and Toxic Epidermal Necrolysis (TEN) are serious mucocutaneous reactions. In Spain, the epidemiology and resulting expenses of these diseases are not well established.MethodologyRetrospective descriptive study using the Minimum Basic Data Set (CMBD in Spanish) in patients admitted to hospitals of the National Health System between 2010 and 2015 with a diagnosis of SJS and TEN (combination of ICD-9 codes 695.13, 695.14, and 695.15, along with length of hospital stay).Principal findingsA total of 1,468 patients were recorded, 773 were men (52.7%). The mean age (± SD) was 52.25 ± 26.15 years. The mean incidence rate for all diagnoses was 5.19 cases per million person-years (2.96 in SJS, 0.31 in SJS/TEN and 1.90 in TEN). 148 patients died (10.1%), 47 due to SJS (5.6%) and 90 (16.7%) due to TEN. The estimated total medical cost of SJS, SJS/TEN, and TEN in Spain was €11.576.456,18, and the average medical cost per patient was €7.885,86 ± €11.686,26, higher medical cost in TEN (€10352.46 ± €16319,93) than in SJS (€6340,05 ± €7078,85) (p<0.001).ConclusionsOlder patients have a more severe clinical picture and higher mortality rates. The overall mortality of both diseases is approximately 10%, and clinical diagnosis and age were the variables with the greatest influence on mortality. This study describes a stable incidence and a similar prevalence to other European countries. Additionally, the data show a high cost due to hospitalizations. Finally, the CMBD could be a good system of epidemiological analysis for the study of infrequent diseases and hospital management of conditions such as SJS and TEN.

Complications of CE are one of the most common causes of mortality in CE patients, with size, location, and number of cysts, and the 'watch and wait' treatment strategy being the main factors associated with mortality.