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BackgroundCrimean-Congo haemorrhagic fever (CCHF) is a widespread tick-borne viral disease caused by the Crimean-Congo haemorrhagic fever virus (CCHFV). CCHFV has been implicated in severe viral haemorrhagic fever outbreaks. During the summer of 2016, the first two cases with genotype III (Africa 3) were reported in Spain. The first objective of our study was to determine the presence of CCHFV among patients with febrile illness during the spring and summer periods in 2017 and 2018. Finally, we perform a phylogenetic analysis to determine the genotype of the virus.MethodologyWe prospectively evaluated patients aged 18 years and older who came to the emergency department at the Salamanca's University Hospital (HUS) with fever. Specific IgM and IgG antibodies against CCHFV by ELISA and one immunofluorescence assay against two different proteins (nucleoprotein and glycoprotein C) was done. Moreover, molecular detection by Real Time PCR was performed in all collected samples. A phylogenetic analysis was carried out to genetically characterize CCHFV detected in this study.Principal findingsA total of 133 patients were selected. The mean age was 67.63 years and 60.9% were male. One-third of the patients presented an acute undifferentiated febrile illness. Three patients had anti-CCHFV IgG antibodies, suggesting a previous infection. One patient had anti-CCHFV IgM antibodies and a confirmatory RT-PCR. Phylogenetic analysis indicated that the virus corresponds to the European genotype V. This patient came to the emergency department at HUS in August 2018 presenting an acute febrile syndrome with thrombopenia and liver impairment.ConclusionsWe describe a new circulation of European genotype V CCHFV in Spain. Moreover, this study suggests that CCHFV is an identifiable cause of febrile illness of unknown origin in Spain. Thus, CCHF could be suspected in patients with fever, liver damage, and/or haemorrhagic disorders, particularly in people with risk activities who present in the spring or summer.

[This corrects the article DOI: 10.1371/journal.pone.0240200.].

BackgroundBabesiosis is a zoonosis caused by an intraerythrocytic protozoan of the genus Babesia and transmitted mainly by ticks of the Ixodes spp. complex. There is no comprehensive global incidence in the literature, although the United States, Europe and Asia are considered to be endemic areas. In Europe, the percentage of ticks infected with Babesia spp. ranges from 0.78% to 51.78%. The incidence of babesiosis in hospitalized patients in Spain is 2.35 cases per 10,000,000 inhabitants/year. The mortality rate is estimated to be approximately 9% in hospitalized patients but can reach 20% if the disease is transmitted by transfusion.ObjectiveTo analyze the epidemiological impact of inpatients diagnosed with babesiosis on the National Health System (NHS) of Spain between 1997 and 2019.MethodologyA retrospective longitudinal descriptive study that included inpatients diagnosed with babesiosis [ICD-9-CM code 088.82, ICD-10 code B60.0, cases ap2016-2019] in public Spanish NHS hospitals between 1 January 1997 and 31 December 2019 was developed. Data were obtained from the minimum basic dataset (CMBD in Spanish), which was provided by the Ministerio de Sanidad, Servicios Sociales e Igualdad after the receipt of a duly substantiated request and the signing of a confidentiality agreement.Main findingsTwenty-nine inpatients diagnosed with babesiosis were identified in Spain between 1997 and 2019 (IR: 0.28 cases/10,000,000 person-years). A total of 82.8% of the cases were men from urban areas who were approximately 46 years old. The rate of primary diagnoses was 55.2% and the number of readmissions was 79.3%. The mean hospital stay was 20.3±19.2 days, with an estimated cost of €186,925.66. Two patients, both with secondary diagnoses of babesiosis, died in our study.ConclusionsHuman babesiosis is still a rare zoonosis in Spain, with an incidence rate that has been increasing over the years. Most cases occurred in middle-aged men from urban areas between summer and autumn. The Castilla-La-Mancha and Extremadura regions recorded the highest number of cases. Given the low rate of primary diagnoses (55.2%) and the high number of readmissions (79.3%), a low clinical suspicion is likely. There was a 6.9% mortality in our study. Both patients who died were patients with secondary diagnoses of the disease.

BackgroundEfficient and early triage of hospitalized Covid-19 patients to detect those with higher risk of severe disease is essential for appropriate case management.MethodsWe 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.ResultsA 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.ConclusionsThis machine-learning model, applied at hospital admission, predicts risk of severe disease progression in Covid-19 patients.