Retrospective Analysis of Vaccination Status and Predominant Viral Variants in Patients Hospitalized with COVID-19 in Reus, Spain.

SARS-CoV-2 infection in already-vaccinated individuals is still possible and may require hospitalization. The aim of the present study was to evaluate the clinical evolution of patients with COVID-19 admitted to a public hospital. The outcomes were assessed in relation to the predominant viral variant and the vaccination status. This retrospective study was performed on 1295 COVID-19-positive patients who attended a 352-bed university hospital between 2021 and 2022. Clinical variables and vaccination status were recorded. Of the patients, 799 had not been vaccinated (NV, 61.7%), 449 were partially vaccinated (PV, 34.7%), and 47 were completely vaccinated (CV, 3.6%). The mean age of the CV patients was significantly higher than that of PV and NV. Additionally, they had higher percentages of chronic diseases. The outcomes depended on age but not on vaccination status. There were 209 patients admitted during the Omicron-infection period, of whom 70 (33.5%) were NV, 135 (64.6%) were PV, and 4 (1.9%) were CV. In conclusion, correct vaccination greatly reduces the risk of acquiring severe COVID-19. Partial vaccination does not guarantee protection of the population. This highlights the need for continuous vaccination promotion with all recommended doses, while also investigating alternative treatments for those patients who do not respond to the vaccines.

Low HDL-c levels at admission are associated with greater severity and worse clinical outcomes in patients with COVID-19 disease.

Background and aims: HDL particles may act to buffer host cells from excessive inflammatory mediators. The aim of this study is to investigate if the lipid profile provides a prognostic biomarker for COVID-19 outcomes. Methods: This was a prospective study of the characteristics of 125 adult COVID-19 patients with a lipid profile performed on the day of admission analyzed with regard to clinical outcomes. Results: Seventy-seven patients (61.2%) were men, with a mean age of 66.3 (15.6) years. 54.1% had bilateral pneumonia. The all-cause mortality rate during hospitalization was 20.8%. We found a direct association between more severe disease assessed by the WHO classification, admission to the ICU and death with more pronounced lymphopenia, higher levels of CRP, ferritin (p < 0.001), D-dímer and lactate dehydrogenase (LDH) all statistically significant. Lower leves of HDL-c and LDL-c were also associated with a worse WHO classification, ICU admission, and death,. HDL-c levels were inversely correlated with inflammatory markers CRP (r = -0.333; p < 0.001), ferritin (r = -0.354; p < 0.001), D-dímer (r = -0.214; p < 0.001), LDH (r = -0.209; p < 0.001. LDL-c levels were significantly associated with CRP (r = -0.320; p < 0.001) and LDH (r = -0.269; p < 0.001). ROC curves showed that HDL [AUC = 0.737(0.586-0.887), p = 0.005] and lymphocytes [AUC = 0.672(0.497-0.847], p < 0.043] had the best prognostic accuracy to predict death. In a multivariate analysis, HDL-c (ß = -0.146(0.770-0.971), p = 0.014) and urea (ß = 0.029(1.003-1.057), p = 0.027) predicted mortality. Conclusion: Hypolipidemia including HDL levels at admission identifies patients with a higher risk of death and worse clinical manifestations who may require more intensive care.

Combining Semi-Targeted Metabolomics and Machine Learning to Identify Metabolic Alterations in the Serum and Urine of Hospitalized Patients with COVID-19.

Viral infections cause metabolic dysregulation in the infected organism. The present study used metabolomics techniques and machine learning algorithms to retrospectively analyze the alterations of a broad panel of metabolites in the serum and urine of a cohort of 126 patients hospitalized with COVID-19. Results were compared with those of 50 healthy subjects and 45 COVID-19-negative patients but with bacterial infectious diseases. Metabolites were analyzed by gas chromatography coupled to quadrupole time-of-flight mass spectrometry. The main metabolites altered in the sera of COVID-19 patients were those of pentose glucuronate interconversion, ascorbate and fructose metabolism, nucleotide sugars, and nucleotide and amino acid metabolism. Alterations in serum maltose, mannonic acid, xylitol, or glyceric acid metabolites segregated positive patients from the control group with high diagnostic accuracy, while succinic acid segregated positive patients from those with other disparate infectious diseases. Increased lauric acid concentrations were associated with the severity of infection and death. Urine analyses could not discriminate between groups. Targeted metabolomics and machine learning algorithms facilitated the exploration of the metabolic alterations underlying COVID-19 infection, and to identify the potential biomarkers for the diagnosis and prognosis of the disease.

Characteristics of hospitalized patients with SARS-CoV-2 infection during successive waves of the COVID-19 pandemic in a reference hospital in Spain.

Since the beginning of the COVID-19 pandemic and until September 2021, Spain suffered five waves of infection, the latter being related to the expansion of the Delta variant and with a high incidence. A vaccination campaign began in December 2020 and by the end of the fifth wave 77.3% of people had been fully vaccinated. Examining the changing dynamics of COVID-19 pandemic and its impact on outcomes among those hospitalized is essential. Our objective was to ascertain any differences in the characteristics and outcomes of hospitalized patients during that period compared to previous waves. We prospectively enrolled 200 consecutively admitted hospital patients from each wave and collected their clinical and demographic data from the medical records, including symptoms, comorbidities, deaths and whether they needed to be admitted to the Intensive Care Unit to receive assisted ventilation. We found that patients in the fifth wave were considerably younger than before, and the mortality rate fell from 22.5 to 2.0%. Admissions to the Intensive Care Unit decreased from 10 to 2%. Patients in the fifth wave had fewer comorbidities, and the age of the patients who died was higher than those who survived. Our results show a marked improvement in patient outcomes in the fifth wave, suggesting success of the vaccination campaign despite the explosion in cases due to the Delta variant.

Usefulness of the Measurement of Serum Paraoxonase-1 Arylesterase Activity in the Diagnoses of COVID-19.

The development of inexpensive, fast, and reliable screening tests for COVID-19 is, as yet, an unmet need. The present study was aimed at evaluating the usefulness of serum arylesterase activity of paraoxonase-1 (PON1) measurement as a screening test in patients with different severity levels of COVID-19 infection. We included 615 COVID-19-positive patients who were classified as asymptomatic, mildly symptomatic, severely symptomatic, or fatally symptomatic. Results were compared with 50 healthy volunteers, 330 patients with cancer, and 343 with morbid obesity. Results showed PON1 activity greatly decreased in COVID-19 compared to healthy volunteers; a receiver operating characteristics plot showed a high diagnostic accuracy. The degree of COVID-19 severity did not influence PON1 levels. Our results indicated that PON1 determination was efficient for disease diagnosis, but not for prognosis. Furthermore, patients with obesity or cancer presented alterations similar to those of COVID-19 patients. As such, elevated levels of PON1 indicate the absence of COVID-19, but low levels may be present in various other chronic diseases. The assay is fast and inexpensive. We suggest that PON1 measurement could be used as an initial, high cut-off point screening method, while lower values should be confirmed with the more expensive nucleic acid amplification test.

Machine learning and semi-targeted lipidomics identify distinct serum lipid signatures in hospitalized COVID-19-positive and COVID-19-negative patients.

BACKGROUND: Lipids are involved in the interaction between viral infection and the host metabolic and immunological responses. Several studies comparing the lipidome of COVID-19-positive hospitalized patients vs. healthy subjects have already been reported. It is largely unknown, however, whether these differences are specific to this disease. The present study compared the lipidomic signature of hospitalized COVID-19-positive patients with that of healthy subjects, as well as with COVID-19-negative patients hospitalized for other infectious/inflammatory diseases. METHODS: We analyzed the lipidomic signature of 126 COVID-19-positive patients, 45 COVID-19-negative patients hospitalized with other infectious/inflammatory diseases and 50 healthy volunteers. A semi-targeted lipidomics analysis was performed using liquid chromatography coupled to mass spectrometry. Two-hundred and eighty-three lipid species were identified and quantified. Results were interpreted by machine learning tools. RESULTS: We identified acylcarnitines, lysophosphatidylethanolamines, arachidonic acid and oxylipins as the most altered species in COVID-19-positive patients compared to healthy volunteers. However, we found similar alterations in COVID-19-negative patients who had other causes of inflammation. Conversely, lysophosphatidylcholine 22:6-sn2, phosphatidylcholine 36:1 and secondary bile acids were the parameters that had the greatest capacity to discriminate between COVID-19-positive and COVID-19-negative patients. CONCLUSION: This study shows that COVID-19 infection shares many lipid alterations with other infectious/inflammatory diseases, and which differentiate them from the healthy population. The most notable alterations were observed in oxylipins, while alterations in bile acids and glycerophospholipis best distinguished between COVID-19-positive and COVID-19-negative patients. Our results highlight the value of integrating lipidomics with machine learning algorithms to explore the pathophysiology of COVID-19 and, consequently, improve clinical decision making.

First and second waves of coronavirus disease-19: A comparative study in hospitalized patients in Reus, Spain.

Many countries have seen a two-wave pattern in reported cases of coronavirus disease-19 during the 2020 pandemic, with a first wave during spring followed by the current second wave in late summer and autumn. Empirical data show that the characteristics of the effects of the virus do vary between the two periods. Differences in age range and severity of the disease have been reported, although the comparative characteristics of the two waves still remain largely unknown. Those characteristics are compared in this study using data from two equal periods of 3 and a half months. The first period, between 15th March and 30th June, corresponding to the entire first wave, and the second, between 1st July and 15th October, corresponding to part of the second wave, still present at the time of writing this article. Two hundred and four patients were hospitalized during the first period, and 264 during the second period. Patients in the second wave were younger and the duration of hospitalization and case fatality rate were lower than those in the first wave. In the second wave, there were more children, and pregnant and post-partum women. The most frequent signs and symptoms in both waves were fever, dyspnea, pneumonia, and cough, and the most relevant comorbidities were cardiovascular diseases, type 2 diabetes mellitus, and chronic neurological diseases. Patients from the second wave more frequently presented renal and gastrointestinal symptoms, were more often treated with non-invasive mechanical ventilation and corticoids, and less often with invasive mechanical ventilation, conventional oxygen therapy and anticoagulants. Several differences in mortality risk factors were also observed. These results might help to understand the characteristics of the second wave and the behaviour and danger of SARS-CoV-2 in the Mediterranean area and in Western Europe. Further studies are needed to confirm our findings.

Clinical performance of the Elecsys® anti-SARS-CoV-2 combined in an algorithm with two specific anti-IgG immunoassays for the evaluation of the serological response of patients with COVID-19 in a population with a high prevalence of infection.

BACKGROUND: Anti-SARS-CoV-2 antibodies have been used in the study of the immune response in infected patients. However, differences in sensitivity and specificity have been reported, depending on the method of analysis. The aim of the present study was to evaluate the diagnostic accuracy of an algorithm in which a high-throughput automated assay for total antibodies was used for screening and two semi-automated IgG-specific methods were used to confirm the results, and also to correlate the analytical results with the clinical data and the time elapsed since infection. METHODS: We studied 306 patients, some hospitalized and some outpatients, belonging to a population with a high prevalence of COVID-19. One-hundred and ten patients were classified as SARS-CoV-2 negative and 196 as positive by polymerase chain reaction. RESULTS: The algorithm and automated assay alone had a specificity and a positive predictive value of 100%, although the sensitivity and negative predictive value of the algorithm was higher. Both methods showed a good sensitivity from day 11 of the onset of symptoms in asymptomatic and symptomatic patients. The absorbance of the total antibodies was significantly higher in severely symptomatic than in asymptomatic or mildly symptomatic patients, which suggests the antibody level was higher. We found 15 patients who did not present seroconversion at 12 days from the onset of symptoms or the first polymerase chain reaction test. CONCLUSION: This study highlights the proper functioning of algorithms in the diagnosis of the immune response to COVID-19, which can help to define testing strategies against this disease.

Study of the Plasma and Buffy Coat in Patients with SARS-CoV-2 Infection-A Preliminary Report.

The pandemic caused by the SARS-CoV-2 infection affects many aspects of public health knowledge, science, and practice around the world. Several studies have shown that SARS-CoV-2 RNA in plasma seems to be associated with a worse prognosis of COVID-19. In the present study, we investigated plasma and buffy RNA in patients with COVID-19 to determine its prognostic value. A prospective study was carried out in patients hospitalized for COVID-19, in which RNA was analyzed in plasma and the buffy coat. Morphological and immunohistochemical studies were used to detect the presence of SARS-CoV-2 in the buffy coat. In COVID-19 patients, the obtained RNA concentration in plasma was 448.3 ± 31.30 ng/mL. Of all the patients with positive plasma tests for SARS-CoV-2, 46.15% died from COVID-19. In four cases, tests revealed that SARS-CoV-2 was present in the buffy coat. Abnormal morphology of monocytes, lymphocytes and neutrophils was found. An immunohistochemical study showed positivity in mononuclear cells and platelets. Our results suggest that SARS-CoV-2 is present in the plasma. This facilitates viral dissemination and migration to specific organs, where SARS-CoV-2 infects target cells by binding to their receptors. In our study, the presence of plasma SARS-CoV-2 RNA was correlated with worse prognoses.

Clinical Performance of Paraoxonase-1-Related Variables and Novel Markers of Inflammation in Coronavirus Disease-19. A Machine Learning Approach.

SARS-CoV-2 infection produces a response of the innate immune system causing oxidative stress and a strong inflammatory reaction termed 'cytokine storm' that is one of the leading causes of death. Paraoxonase-1 (PON1) protects against oxidative stress by hydrolyzing lipoperoxides. Alterations in PON1 activity have been associated with pro-inflammatory mediators such as the chemokine (C-C motif) ligand 2 (CCL2), and the glycoprotein galectin-3. We aimed to investigate the alterations in the circulating levels of PON1, CCL2, and galectin-3 in 126 patients with COVID-19 and their interactions with clinical variables and analytical parameters. A machine learning approach was used to identify predictive markers of the disease. For comparisons, we recruited 45 COVID-19 negative patients and 50 healthy individuals. Our approach identified a synergy between oxidative stress, inflammation, and fibrogenesis in positive patients that is not observed in negative patients. PON1 activity was the parameter with the greatest power to discriminate between patients who were either positive or negative for COVID-19, while their levels of CCL2 and galectin-3 were similar. We suggest that the measurement of serum PON1 activity may be a useful marker for the diagnosis of COVID-19.

Risk factors associated with mortality in hospitalized patients with SARS-CoV-2 infection. A prospective, longitudinal, unicenter study in Reus, Spain.

Spain is one of the countries that has suffered the most from the impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the strain that causes coronavirus disease 2019 (COVID-19). However, there is a lack of information on the characteristics of this disease in the Spanish population. The objective of this study has been to characterize our patients from an epidemiological point of view and to identify the risk factors associated with mortality in our geographical area. We performed a prospective, longitudinal study on 188 hospitalized cases of SARS-Cov-2 infection in Hospital Universitari de Sant Joan, in Reus, Spain, admitted between 15th March 2020 and 30th April 2020. We recorded demographic data, signs and symptoms and comorbidities. We also calculated the Charlson and McCabe indices. A total of 43 deaths occurred during the study period. Deceased patients were older than the survivors (77.7 ± 13.1 vs. 62.8 ± 18.4 years; p < 0.001). Logistic regression analyses showed that fever, pneumonia, acute respiratory distress syndrome, diabetes mellitus and cancer were the variables that showed independent and statistically significant associations with mortality. The Charlson index was more efficient than the McCabe index in discriminating between deceased and survivors. This is one of the first studies to describe the factors associated with mortality in patients infected with SARS-CoV-2 in Spain, and one of the few in the Mediterranean area. We identified the main factors independently associated with mortality in our population. Further studies are needed to complete and confirm our findings.