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Background: Urothelial cells exhibit increased expression of angiotensin-converting enzyme-2 receptor, which is the binding site of severe acute respiratory syndrome coronavirus 2 to cells. The frequency and distribution of genitourinary tract symptoms in patients diagnosed with coronavirus disease 2019 (COVID-19) is unknown. Objective: We explored trends in genitourinary tract symptoms by gender and each of six pandemic waves in patients admitted for COVID-19, and related them with severity, death and length of hospitalization. Design Setting and Participants: A retrospective study took place in our institution of COVID-19 admitted patients. Only patients with RT-PCR or antigen test confirmed SARS-CoV-2 infection were included. Demographic, clinical, and genitourinary symptoms were explored. Outcome Measurements and Statistical Analysis: COVID-19 patients with genitourinary tract symptoms were compared with those without. Statistical comparisons were conducted by parametric and nonparametric tests for quantitative variables, and χ 2 test for qualitative variables. Results and limitations: Out of a total of 4,661 COVID-19 patients, genitourinary symptoms were found in 21,1%. These symptoms were more frequent in patients admitted for longer than 30 days, except for urinary incontinence (UI) and erectile dysfunction (ED). Acute kidney injury (AKI) and urinary tract infections (UTI) had a higher presence in the 5th (16.7%; 12.8% respectively) and 3rd wave (13.3%; 12.6% respectively). Genitourinary symptoms were higher for those patients admitted in critical care units. Frequency of AKI, UI, UTI and acute urinary retention (AUR) were higher for patients who were finally deceased (26.2%; 3.5%; 13.6% and 3.6% respectively). Conclusions: A high frequency of genitourinary symptoms in patients admitted for COVID-19 was observed, whose frequency and distribution varied according to pandemic waves. Specific genitourinary conditions were associated with worse outcomes and poorer prognosis.
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BackgroundInterleukin 6 (IL6) levels and SARS-CoV-2 viremia have been correlated with COVID-19 severity. The association over time between them has not been assessed in a prospective cohort. Our aim was to evaluate the relationship between SARS-CoV-2 viremia and time evolution of IL6 levels in a COVID-19 prospective cohort. MethodsSecondary analysis from a prospective cohort including COVID-19 hospitalized patients from Hospital Universitario La Princesa between November 2020 and January 2021. Serial plasma samples were collected from admission until discharge. Viral load was quantified by Real-Time Polymerase Chain Reaction and IL6 levels with an enzyme immunoassay. To represent the evolution over time of both variables we used the graphic command twoway of Stata. ResultsA total of 57 patients were recruited, with median age of 63 years (IQR [53-81]), 61.4% male and 68.4% caucasian. The peak of viremia appeared shortly after symptom onset in patients with persistent viremia (more than 1 sample with >1.3 log10 copies/ml) and also in those with at least one IL6>30 pg/ml, followed by a progressive increase in IL6 around 10 days later. Persistent viremia in the first week of hospitalization was associated with higher levels of IL6. Both IL6 and SARS-CoV-2 viral load were higher in males, with a quicker increase with age. ConclusionsIn those patients with worse outcomes, an early peak of SARS-CoV-2 viral load precedes an increase in IL6 levels. Monitoring SARS-CoV-2 viral load during the first week after symptom onset may be helpful to predict disease severity in COVID-19 patients.
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ObjectivesOn November 26, 2021, WHO designated the variant B.1.1.529 as a new SARS-CoV-2 variant of concern (VoC), named Omicron, originally identified in South Africa. Several mutations in Omicron indicate that it may have an impact on how it spreads, resistance to vaccination, or the severity of illness it causes. We used our previous modelling algorithms to forecast the spread of Omicron in England. DesignWe followed EQUATORs TRIPOD guidance for multivariable prediction models. SettingEngland. ParticipantsNot applicable. InterventionsNon-interventional, observational study with a predicted forecast of outcomes. Main outcome measuresTrends in daily COVID-19 cases with a 7-day moving average and of new hospital admissions. MethodsModelling included a third-degree polynomial curve in existing epidemiological trends on the spread of Omicron and a new Gaussian curve to estimate a downward trend after a peak in England. ResultsUp to February 15, 2022, we estimated a projection of 250,000 COVID-19 daily cases of Omicron spread in the worse scenario, and 170,000 in the "best" scenario. Omicron might represent a relative increase from the background daily rates of COVID-19 infection in England of mid December 2021 of 1.9 to 2.8-fold. With a 5-day lag-time, daily new hospital admissions would peak at around 5,063 on January 23, 2022 in the worse scenario. ConclusionThis warning of pandemic surge of COVID-19 due to Omicron is calling for further reinforcing in England and elsewhere of universal hygiene interventions (indoor ventilation, social distance, and face masks), and anticipating the need of new total or partial lockdowns in England.
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BackgroundOn November 26, 2021, WHO designated the variant B.1.1.529 as a new SARS-CoV-2 variant of concern (VoC), named Omicron, originally identified in South Africa. Several mutations in Omicron indicate that it may have an impact on how it spreads, resistance to vaccination, or the severity of illness it causes. MethodsWe used our previous modelling algorithms to forecast the spread of Omicron aggregated in the EU-27 countries, the United Kingdom and Switzerland, and report trends in daily cases with a 7-day moving average. We followed EQUATORs TRIPOD guidance for multivariable prediction models. Modelling included a third-degree polynomial curve in existing epidemiological trends on the spread of Omicron in South Africa, a five-parameter logistic (5PL) asymmetrical sigmoidal curve following a parametric growth in Europe, and a new Gaussian curve to estimate a downward trend after a peak. ResultsUp to January 15, 2022, we estimated a background rate projection in EU-27 countries, the UK and Switzerland of about 145,000 COVID-19 daily cases without Omicron, which increases up to 440,000 COVID-19 daily cases in the worst scenario of Omicron spread, and 375,000 in the "best" scenario. Therefore, Omicron might represent a relative increase from the background daily rates of COVID-19 infection in Europe of 1.03-fold or 2.03-fold, that is up to a 200% increase. ConclusionThis warning pandemic surge due to Omicron is calling for further reinforcing of COVID-19 universal hygiene interventions (indoor ventilation, social distance, and face masks), and anticipating the need of new lockdowns in Europe.
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Plitidepsin is a marine-derived cyclic-peptide that inhibits SARS-CoV-2 replication at low nanomolar concentrations by the targeting of host protein eEF1A (eukaryotic translation-elongation-factor-1A). We evaluated a model of intervention with plitidepsin in hospitalized COVID-19 adult patients where three doses were assessed (1.5, 2 and 2.5 mg/day for 3 days, as a 90-minute intravenous infusion) in 45 patients (15 per dose-cohort). Treatment was well tolerated, with only two Grade 3 treatment-related adverse events observed (hypersensitivity and diarrhea). The discharge rates by Days 8 and 15 were 56.8% and 81.8%, respectively, with data sustaining dose-effect. A mean 4.2 log10 viral load reduction was attained by Day 15. Improvement in inflammation markers was also noted in a seemingly dose-dependent manner. These results suggest that plitidepsin impacts the outcome of patients with COVID-19. One-Sentence SummaryPlitidepsin, an inhibitor of SARS-Cov-2 in vitro, is safe and positively influences the outcome of patients hospitalized with COVID-19.
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BackgroundCOVID-19 has overloaded national health services worldwide. Thus, early identification of patients at risk of poor outcomes is critical. Our objective was to analyse SARS-CoV-2 RNA detection in serum as a severity biomarker in COVID-19. Methods and FindingsRetrospective observational study including 193 patients admitted for COVID-19. Detection of SARS-CoV-2 RNA in serum (CoVemia) was performed with samples collected at 48-72 hours of admission by two techniques from Roche and Thermo Fischer Scientific (TFS). Main outcome variables were mortality and need for ICU admission during hospitalization for COVID-19. CoVemia was detected in 50-60% of patients depending on technique. The correlation of Ct in serum between both techniques was good (intraclass correlation coefficient: 0.612; p < 0.001). Patients with CoVemia were older (p = 0.006), had poorer baseline oxygenation (PaO2/FiO2; p < 0.001), more severe lymphopenia (p < 0.001) and higher LDH (p < 0.001), IL-6 (p = 0.021), C-reactive protein (CRP; p = 0.022) and procalcitonin (p = 0.002) serum levels. We defined "relevant CoVemia" when detection Ct was < 34 with Roche and < 31 for TFS. These thresholds had 95% sensitivity and 35 % specificity. Relevant CoVemia predicted death during hospitalization (OR 9.2 [3.8 - 22.6] for Roche, OR 10.3 [3.6 - 29.3] for TFS; p < 0.001). Cox regression models, adjusted by age, sex and Charlson index, identified increased LDH serum levels and relevant CoVemia (HR = 9.87 [4.13-23.57] for TFS viremia and HR = 7.09 [3.3-14.82] for Roche viremia) as the best markers to predict mortality. ConclusionsCoVemia assessment at admission is the most useful biomarker for predicting mortality in COVID-19 patients. CoVemia is highly reproducible with two different techniques (TFS and Roche), has a good consistency with other severity biomarkers for COVID-19 and better predictive accuracy. AUTHOR SUMMARYCOVID-19 shows a very heterogeneous clinical picture. In addition, it has overloaded national health services worldwide. Therefore, early identification of patients with poor prognosis is critical to improve the use of limited health resources. In this work, we evaluated whether baseline SARS-CoV2 RNA detection in blood (CoVemia) is associated with worse outcomes. We studied almost 200 patients admitted to our hospital and about 50-60% of them showed positive CoVemia. Patients with positive CoVemia were older and had more severe disease; CoVemia was also more frequent in patients requiring admission to the ICU. Moreover, we defined "relevant CoVemia", as the amount of viral load that better predicted mortality obtaining 95% sensitivity and 35% specificity. In addition, relevant CoVemia was a better predictor than other biomarkers such as LDH, lymphocyte count, interleukin-6, and indexes used in ICU such as qSOFA and CURB65. In summary, detection of CoVemia is the best biomarker to predict death in COVID-19 patients. Furthermore, it is easy to be implemented and is reproducible with two techniques (Roche and Thermo Fisher Scientific) that are currently used for diagnosis in nasopharyngeal swabs samples.
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BACKGROUND@#In-hospital mortality in patients with coronavirus disease 2019 (COVID-19) is high. Simple prognostic indices are needed to identify patients at high-risk of COVID-19 health outcomes. We aimed to determine the usefulness of the CONtrolling NUTritional status (CONUT) index as a potential prognostic indicator of mortality in COVID-19 patients upon hospital admission.@*METHODS@#Our study design is of a retrospective observational study in a large cohort of COVID-19 patients. In addition to descriptive statistics, a Kaplan-Meier mortality analysis and a Cox regression were performed, as well as receiver operating curve (ROC).@*RESULTS@#From February 5, 2020 to January 21, 2021, there was a total of 2969 admissions for COVID-19 at our hospital, corresponding to 2844 patients. Overall, baseline (within 4 days of admission) CONUT index could be scored for 1627 (57.2%) patients. Patients' age was 67.3 ± 16.5 years and 44.9% were women. The CONUT severity distribution was: 194 (11.9%) normal (0-1); 769 (47.2%) light (2-4); 585 (35.9%) moderate (5-8); and 79 (4.9%) severe (9-12). Mortality of 30 days after admission was 3.1% in patients with normal risk CONUT, 9.0% light, 22.7% moderate, and 40.5% in those with severe CONUT (P < 0.05). An increased risk of death associated with a greater baseline CONUT stage was sustained in a multivariable Cox regression model (P < 0.05). An increasing baseline CONUT stage was associated with a longer duration of admission, a greater requirement for the use of non-invasive and invasive mechanical ventilation, and other clinical outcomes (all P < 0.05). The ROC of CONUT for mortality had an area under the curve (AUC) and 95% confidence interval of 0.711 (0.676-0746).@*CONCLUSION@#The CONUT index upon admission is potentially a reliable and independent prognostic indicator of mortality and length of hospitalization in COVID-19 patients.
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Aged , Aged, 80 and over , Female , Humans , Middle Aged , COVID-19 , Hospitalization , Hospitals , Nutrition Assessment , Nutritional Status , Outcome Assessment, Health Care , Prognosis , Retrospective Studies , SARS-CoV-2ABSTRACT
BackgroundIt remains unknown whether the frequency and severity of COVID-19 affect women differently than men. Here, we aim to describe the characteristics of COVID-19 patients at disease onset, with special focus on the diagnosis and management of female patients with COVID-19. MethodsWe explored the unstructured free text in the electronic health records (EHRs) within the SESCAM Healthcare Network (Castilla La-Mancha, Spain). The study sample comprised the entire population with available EHRs (1,446,452 patients) from January 1st to May 1st, 2020. We extracted patients clinical information upon diagnosis, progression, and outcome for all COVID-19 cases. ResultsA total of 4,780 patients with a test-confirmed diagnosis of COVID-19 were identified. Of these, 2,443 (51%) were female, who were on average 1.5 years younger than males (61.7{+/-}19.4 vs. 63.3{+/-}18.3, p=0.0025). There were more female COVID-19 cases in the 15-59 yr.-old interval, with the greatest sex ratio (SR; 95% CI) observed in the 30-39 yr.-old interval (1.69; 1.35-2.11). Upon diagnosis, headache, anosmia, and ageusia were significantly more frequent in females than males. Imaging by chest X-ray or blood tests were performed less frequently in females (65.5% vs. 78.3% and 49.5% vs. 63.7%, respectively), all p<0.001. Regarding hospital resource use, females showed less frequency of hospitalization (44.3% vs. 62.0%) and ICU admission (2.8% vs. 6.3%) than males, all p<0.001. ConclusionOur results indicate important sex-dependent differences in the diagnosis, clinical manifestation, and treatment of patients with COVID-19. These results warrant further research to identify and close the gender gap in the ongoing pandemic.
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BackgroundFrom the onset of the COVID-19 pandemic, an association between the severity of COVID-19 and the presence of certain medical chronic conditions has been suggested. However, unlike influenza and other viruses, the burden of the disease in patients with asthma has been less evident. ObjectiveThis study aims at a better understanding of the burden of COVID-19 in patients with asthma and the impact of asthma, its related comorbidities, and treatment on the prognosis of COVID-19. MethodsWe analyzed clinical data from patients with asthma from January 1st to May 10th, 2020 using big data analytics and artificial intelligence through the SAVANA Manager(R) clinical platform. ResultsOut of 71,192 patients with asthma, 1,006 (1.41%) suffered from COVID-19. Compared to asthmatic individuals without COVID-19, patients with asthma and COVID-19 were significantly older (55 vs. 42 years), predominantly female (66% vs. 59%), had higher prevalence of hypertension, dyslipidemias, diabetes, and obesity, and smoked more frequently. Contrarily, allergy-related factors such as rhinitis and eczema were less frequent in asthmatic patients with COVID-19 (P < .001). Higher prevalence of hypertension, dyslipidemia, diabetes, and obesity was also confirmed in those patients with asthma and COVID-19 who required hospital admission. The percentage of individuals using inhaled corticosteroids (ICS) was lower in patients who required hospitalization due to COVID-19, as compared to non-hospitalized patients (48.3% vs. 61.5%; OR: 0.58: 95% CI 0.44 - 0.77). During the study period, 865 (1.21%) patients with asthma were being treated with biologics. Although these patients showed increased severity and more comorbidities at the ear, nose, and throat (ENT) level, their hospital admission rates due to COVID-19 were relatively low (0.23%). COVID-19 increased inpatient mortality in asthmatic patients (2.29% vs 0.54%; OR 2.29: 95% CI 4.35 - 6.66). ConclusionOur results indicate that the number of COVID-19 cases in patients with asthma has been low, although higher than the observed in the general population. Patients with asthma and COVID-19 were older and were at increased risk due to comorbidity-related factors. ICS and biologics are generally safe and may be associated with a protective effect against severe COVID-19 infection.
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There remain many unknowns regarding the onset and clinical course of the ongoing COVID-19 pandemic. We used a combination of classic epidemiological methods, natural language processing (NLP), and machine learning (for predictive modeling), to analyse the electronic health records (EHRs) of patients with COVID-19. We explored the unstructured free text in the EHRs within the SESCAM Healthcare Network (Castilla La-Mancha, Spain) from the entire population with available EHRs (1,364,924 patients) from January 1st to March 29th, 2020. We extracted related clinical information upon diagnosis, progression and outcome for all COVID-19 cases, focusing in those requiring ICU admission. A total of 10,504 patients with a clinical or PCR-confirmed diagnosis of COVID-19 were identified, 52.5% males, with age of 58.2{+/-}19.7 years. Upon admission, the most common symptoms were cough, fever, and dyspnoea, but all in less than half of cases. Overall, 6% of hospitalized patients required ICU admission. Using a machine-learning, data-driven algorithm we identified that a combination of age, fever, and tachypnoea was the most parsimonious predictor of ICU admission: those younger than 56 years, without tachypnoea, and temperature <39{degrees}C, (or >39{degrees}C without respiratory crackles), were free of ICU admission. On the contrary, COVID-19 patients aged 40 to 79 years were likely to be admitted to the ICU if they had tachypnoea and delayed their visit to the ER after being seen in primary care. Our results show that a combination of easily obtainable clinical variables (age, fever, and tachypnoea with/without respiratory crackles) predicts which COVID-19 patients require ICU admission.
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The SARS-CoV-2 is responsible for the pandemic COVID-19 in infected individuals, who can either exhibit mild symptoms or progress towards a life-threatening acute respiratory distress syndrome (ARDS). It is known that exacerbated inflammation and dysregulated immune responses involving T and myeloid cells occur in COVID-19 patients with severe clinical progression. However, the differential contribution of specific subsets of dendritic cells and monocytes to ARDS is still poorly understood. In addition, the role of CD8+ T cells present in the lung of COVID-19 patients and relevant for viral control has not been characterized. With the aim to improve the knowledge in this area, we developed a cross-sectional study, in which we have studied the frequencies and activation profiles of dendritic cells and monocytes present in the blood of COVID-19 patients with different clinical severity in comparison with healthy control individuals. Furthermore, these subpopulations and their association with antiviral effector CD8+ T cell subsets were also characterized in lung infiltrates from critical COVID-19 patients. Collectively, our results suggest that inflammatory transitional and non-classical monocytes preferentially migrate from blood to lungs in patients with severe COVID-19. CD1c+ conventional dendritic cells also followed this pattern, whereas CD141+ conventional and CD123hi plasmacytoid dendritic cells were depleted from blood but were absent in the lungs. Thus, this study increases the knowledge on the pathogenesis of COVID-19 disease and could be useful for the design of therapeutic strategies to fight SARS-CoV-2 infection. Single-sentence summaryDepletion from the blood and differential activation patterns of inflammatory monocytes and CD1c+ conventional dendritic cells associate with development of ARDS in COVID-19 patients.
