Differences in clinical features and mortality in very old unvaccinated patients (≥ 80 years) hospitalized with COVID-19 during the first and successive waves from the multicenter SEMI-COVID-19 Registry (Spain)

Background: Old age is one of the most important risk factors for severe COVID-19. Few studies have analyzed changes in the clinical characteristics and prognosis of COVID-19 among older adults before the availability of vaccines. This work analyzes differences in clinical features and mortality in unvaccinated very old adults during the first and successive COVID-19 waves in Spain. Methods This nationwide, multicenter, retrospective cohort study analyzes unvaccinated patients >= 80 years hospitalized for COVID-19 in 150 Spanish hospitals (SEMI-COVID-19 Registry). Patients were classified according to whether they were admitted in the first wave (March 1-June 30, 2020) or successive waves (July 1-December 31, 2020). The endpoint was all-cause in-hospital mortality, expressed as the case fatality rate (CFR). Results Of the 21,461 patients hospitalized with COVID-19, 5,953 (27.7%) were >= 80 years (mean age [IQR]: 85.6 [82.3-89.2] years). Of them, 4,545 (76.3%) were admitted during the first wave and 1,408 (23.7%) during successive waves. Patients hospitalized in successive waves were older, had a greater Charlson Comorbidity Index and dependency, less cough and fever, and met fewer severity criteria at admission (qSOFA index, PO2/FiO2 ratio, inflammatory parameters). Significant differences were observed in treatments used in the first (greater use of antimalarials, lopinavir, and macrolides) and successive waves (greater use of corticosteroids, tocilizumab and remdesivir). In-hospital complications, especially acute respiratory distress syndrome and pneumonia, were less frequent in patients hospitalized in successive waves, except for heart failure. The CFR was significantly higher in the first wave (44.1% vs. 33.3%; -10.8%; p = 95 years (54.4% vs. 38.5%; -15.9%; p < 0.001). After adjustments to the model, the probability of death was 33% lower in successive waves (OR: 0.67; 95% CI: 0.57-0.79). Conclusions Mortality declined significantly between the first and successive waves in very old unvaccinated patients hospitalized with COVID-19 in Spain. This decline could be explained by a greater availability of hospital resources and more effective treatments as the pandemic progressed, although other factors such as changes in SARS-CoV-2 virulence cannot be ruled out

Gender-Based Differences by Age Range in Patients Hospitalized with COVID-19: A Spanish Observational Cohort Study

There is some evidence that male gender could have a negative impact on the prognosis and severity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. The aim of the present study was to compare the characteristics of coronavirus disease 2019 (COVID-19) between hospitalized men and women with confirmed SARS-CoV-2 infection. This multicenter, retrospective, observational study is based on the SEMI-COVID-19 Registry. We analyzed the differences between men and women for a wide variety of demographic, clinical, and treatment variables, and the sex distribution of the reported COVID-19 deaths, as well as intensive care unit (ICU) admission by age subgroups. This work analyzed 12,063 patients (56.8% men). The women in our study were older than the men, on average (67.9 vs. 65.7 years; p < 001). Bilateral condensation was more frequent among men than women (31.8% vs. 29.9%; p = 0.007). The men needed non-invasive and invasive mechanical ventilation more frequently (5.6% vs. 3.6%, p < 0.001, and 7.9% vs. 4.8%, p < 0.001, respectively). The most prevalent complication was acute respiratory distress syndrome, with severe cases in 19.9% of men (p < 0.001). In men, intensive care unit admission was more frequent (10% vs. 6.1%; p < 0.001) and the mortality rate was higher (23.1% vs. 18.9%; p < 0.001). Regarding mortality, the differences by gender were statistically significant in the age groups from 55 years to 89 years of age. A multivariate analysis showed that female sex was significantly and independently associated with a lower risk of mortality in our study. Male sex appears to be related to worse progress in COVID-19 patients and is an independent prognostic factor for mortality. In order to fully understand its prognostic impact, other factors associated with sex must be considered

Role of age and comorbidities in mortality of patients with infective endocarditis

[Purpose]: The aim of this study was to analyse the characteristics of patients with IE in three groups of age and to assess the ability of age and the Charlson Comorbidity Index (CCI) to predict mortality. [Methods]: Prospective cohort study of all patients with IE included in the GAMES Spanish database between 2008 and 2015.Patients were stratified into three age groups:<65 years,65 to 80 years,and ≥ 80 years.The area under the receiver-operating characteristic (AUROC) curve was calculated to quantify the diagnostic accuracy of the CCI to predict mortality risk. [Results]: A total of 3120 patients with IE (1327 < 65 years;1291 65-80 years;502 ≥ 80 years) were enrolled.Fever and heart failure were the most common presentations of IE, with no differences among age groups.Patients ≥80 years who underwent surgery were significantly lower compared with other age groups (14.3%,65 years; 20.5%,65-79 years; 31.3%,≥80 years). In-hospital mortality was lower in the <65-year group (20.3%,<65 years;30.1%,65-79 years;34.7%,≥80 years;p < 0.001) as well as 1-year mortality (3.2%, <65 years; 5.5%, 65-80 years;7.6%,≥80 years; p = 0.003).Independent predictors of mortality were age ≥ 80 years (hazard ratio [HR]:2.78;95% confidence interval [CI]:2.32–3.34), CCI ≥ 3 (HR:1.62; 95% CI:1.39–1.88),and non-performed surgery (HR:1.64;95% CI:11.16–1.58).When the three age groups were compared,the AUROC curve for CCI was significantly larger for patients aged <65 years(p < 0.001) for both in-hospital and 1-year mortality. [Conclusion]: There were no differences in the clinical presentation of IE between the groups. Age ≥ 80 years, high comorbidity (measured by CCI),and non-performance of surgery were independent predictors of mortality in patients with IE.CCI could help to identify those patients with IE and surgical indication who present a lower risk of in-hospital and 1-year mortality after surgery, especially in the <65-year group

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

WHO Ordinal Scale and Inflammation Risk Categories in COVID-19

Background: The WHO ordinal severity scale has been used to predict mortality and guide trials in COVID-19. However, it has its limitations. Objective The present study aims to compare three classificatory and predictive models: the WHO ordinal severity scale, the model based on inflammation grades, and the hybrid model. Design Retrospective cohort study with patient data collected and followed up from March 1, 2020, to May 1, 2021, from the nationwide SEMI-COVID-19 Registry. The primary study outcome was in-hospital mortality. As this was a hospital-based study, the patients included corresponded to categories 3 to 7 of the WHO ordinal scale. Categories 6 and 7 were grouped in the same category. Key Results A total of 17,225 patients were included in the study. Patients classified as high risk in each of the WHO categories according to the degree of inflammation were as follows: 63.8% vs. 79.9% vs. 90.2% vs. 95.1% (p<0.001). In-hospital mortality for WHO ordinal scale categories 3 to 6/7 was as follows: 0.8% vs. 24.3% vs. 45.3% vs. 34% (p<0.001). In-hospital mortality for the combined categories of ordinal scale 3a to 5b was as follows: 0.4% vs. 1.1% vs. 11.2% vs. 27.5% vs. 35.5% vs. 41.1% (p<0.001). The predictive regression model for in-hospital mortality with our proposed combined ordinal scale reached an AUC=0.871, superior to the two models separately. Conclusions The present study proposes a new severity grading scale for COVID-19 hospitalized patients. In our opinion, it is the most informative, representative, and predictive scale in COVID-19 patients to date

Estimation of Admission D-dimer Cut-off Value to Predict Venous Thrombotic Events in Hospitalized COVID-19 Patients: Analysis of the SEMI-COVID-19 Registry.

Venous thrombotic events (VTE) are frequent in COVID-19, and elevated plasma D-dimer (pDd) and dyspnea are common in both entities. To determine the admission pDd cut-off value associated with in-hospital VTE in patients with COVID-19. Multicenter, retrospective study analyzing the at-admission pDd cut-off value to predict VTE and anticoagulation intensity along hospitalization due to COVID-19. Among 9386 patients, 2.2% had VTE: 1.6% pulmonary embolism (PE), 0.4% deep vein thrombosis (DVT), and 0.2% both. Those with VTE had a higher prevalence of tachypnea (42.9% vs. 31.1%; p = 0.0005), basal O2 saturation 1.0 μg/ml treated with prophylactic dose (p 2.0 μg/ml treated with intermediate dose (p = 0.0001), and 31.3% for those with pDd >3.0 μg/ml and full anticoagulation (p = 0.0183). In hospitalized patients with COVID-19, a pDd value greater than 3.0 μg/ml can be considered to screen VTE and to consider full-dose anticoagulation