BACKGROUND: Computed tomography (CT) imaging is widely used for diagnosing and determining the severity of coronavirus disease 2019 (COVID-19). Chest CT imaging can be used to calculate the epicardial adipose tissue (EAT) and upper abdominal visceral adipose tissue (Abd-VAT) areas. The EAT is the main source of inflammatory cytokines involved in chest inflammatory diseases; thus, the EAT area might be a more useful severity predictor than the Abd-VAT area for COVID-19. However, to the best of our knowledge, there are no large-scale reports that sufficiently consider this issue. In addition, there are no reports on the characteristics of patients with normal body mass index (BMI) and high adipose tissue.
OBJECTIVE: The purpose of this study was to analyze whether the EAT area, among various adipose tissues, was the most associated factor with COVID-19 severity. Using a multicenter COVID-19 patient database, we analyzed the associations of chest subcutaneous, chest visceral, abdominal subcutaneous, and Abd-VAT areas with COVID-19 outcomes. In addition, the clinical significance of central obesity, commonly disregarded by BMI, was examined.
METHODS: This retrospective cohort study evaluated patients with COVID-19 aged ≥18 years In Japan. Data including from chest CT images collected between February 2020 and October 2022 in four hospitals of the Japan COVID-19 Task Force were analyzed. Patient characteristics and COVID-19 severity were compared according to the adipose tissue areas (chest and abdominal subcutaneous adipose tissue [Chest-SAT and Abd-SAT], EAT, and Abd-VAT) calculated from chest CT images.
RESULTS: We included 1077 patients in the analysis. Patients with risk factors of severe COVID-19 such as old age, male sex, and comorbidities had significantly higher areas of EAT and Abd-VAT. High EAT area but not high Abd-VAT area was significantly associated with COVID-19 severity (adjusted odds ratio (aOR): 2.66, 95 % confidence interval [CI]: 1.19-5.93). There was no strong correlation between BMI and VAT. Patients with high VAT area accounted for 40.7 % of the non-obesity population (BMI < 25 kg/m2). High EAT area was also significantly associated with COVID-19 severity in the non-obesity population (aOR: 2.50, 95 % CI: 1.17-5.34).
CONCLUSIONS: Our study indicated that VAT is significantly associated with COVID-19 severity and that EAT is the best potential predictor for risk stratification in COVID-19 among adipose tissue areas. Body composition assessment using EAT is an appropriate marker for identifying obesity patients overlooked by BMI. Considering the next pandemic of the global health crisis, our findings open new avenues for implementing appropriate body composition assessments based on CT imaging.

Patients suffering from neuro-inflammatory diseases such as multiple sclerosis (MS) and neuromyelitis optica spectrum disorders (NMOSD) remain vulnerable to COVID-19. We investigated the risk of COVID-19 in MS and NMOSD patients over time, considering the impact of disease-modifying treatments (DMTs), vaccinations, and the spread of new SARS-CoV-2 variants. We retrospectively collected clinical information regarding all MS and NMOSD consecutive patients seen at the Neurocenter of Southern Switzerland. Logistic regression was used to test variables (age, sex, vaccination status, DMT at vaccination, DMT at infection, disease course, disability scores, prevalent SARS-CoV-2 variant) for association with COVID-19 risk and severe outcome (hospitalization or death). We included 352 individuals in this study; 315 (89.5%) received ≥1 dose of SARS-CoV-2 mRNA-vaccine, and 134 (38.1%) experienced COVID-19 between March 2020 and August 2022. COVID-19 risk decreased in vaccinated patients (OR = 0.10, 95% CI = 0.05-0.20, p < 0.001) and increased in anti-CD20 therapies (OR = 2.26, 95% CI = 1.28-4.00, p = 0.005). Anti-CD20 treatment was associated with severe COVID-19 (OR = 27.41, 95% CI = 3.68-204.25, p = 0.001), whereas Omicron infections were milder compared to Alpha infections (OR = 0.03, 95% CI = 0.01-0.35, p = 0.006). We confirmed a protective effect of mRNA vaccines on COVID-19 risk, which is impaired by anti-CD20 treatment. We provided evidence for milder COVID-19 with the Omicron SARS-CoV-2 variant, which should not, however, discourage vaccinations.

[This corrects the article DOI: 10.3389/fimmu.2022.850846.].

Adults and children exhibit a broad range of clinical outcomes from SARS-CoV-2 infection, with minimal to mild symptoms, especially in the pediatric age. However, some children present with a severe hyperinflammatory post-infectious complication named multisystem inflammatory syndrome in children (MIS-C), mainly affecting previously healthy subjects. Understanding these differences is still an ongoing challenge, that can lead to new therapeutic strategies and avoid unfavorable outcomes. In this review, we discuss the different roles of T lymphocyte subsets and interferon-γ (IFN-γ) in the immune responses of adults and children. Lymphopenia can influence these responses and represent a good predictor for the outcome, as reported by most authors. The increased IFN-γ response exhibited by children could be the starting point for the activation of a broad response that leads to MIS-C, with a significantly higher risk than in adults, although a single IFN signature has not been identified. Multicenter studies with large cohorts in both age groups are still needed to study SARS-CoV-2 pathogenesis with new tools and to understand how is possible to better modulate immune responses.

UNASSIGNED: To evaluate the demographic profile, clinical severity, and outcome of Covid-19 infection in hospitalised vaccinated individuals.
UNASSIGNED: An observational, cross-sectional study was conducted among Covid-19 infected hospitalised patients. Clinicodemographic profile, severity, and outcome of Covid-19 infection among the vaccinated group (VG) were recorded. These patients were also compared with unvaccinated group (UVG) with Covid-19 infection admitted during the study period. Cox proportional hazards models was used to estimate hazard ratios for mortality risk in both groups.
UNASSIGNED: Out of 580 participants, 48.2% were vaccinated with either one (71%) or two doses (28.9%). In both, VG and UVG, majority 55.8% belonged to 51-75 years. Males were predominant with 62.9% in both VG and UVGs. Day of illness at admission from symptom onset (DOI), progression of disease, ICU stay, oxygen requirement, mortality was significantly higher in UVG than in VG (p < 0.05). Steroid duration (p < 0.001) and anti-coagulation time (p < 0.001) were significantly higher in UVG than in VG. D dimer levels were significantly higher in UVG than in VG (p < 0.05). Increased age, (p < 0.0004), severity of disease, (p < 0.0052), increased oxygen requirement (p < 0.001), elevated C-reactive protein levels (Moderate: P < 0.0013; Severe P < 0.0082), and elevated IL-6 levels (p < 0.001) were the significant determinants of Covid-19-related mortality in both VG and UVGs.
UNASSIGNED: Vaccinated individuals have shown milder severity, had reduced hospital stay and better outcomes as compared to unvaccinated individuals suggesting a potential vaccine efficacy against Covid-19.

Background: COVID-19 outcomes display multiple unexpected varieties, ranging from unnoticed symptomless infection to death, without any previous alarm or known aggravating factors. Aim: To appraise the impact of ACErs4291(A/T) and ERAP1rs26618(T/C) human polymorphisms on the outcome of COVID-19. Subjects and methods: In total, 240 individuals were enrolled in the study (80 with severe manifestations, 80 with mild manifestations, and 80 healthy persons). ACErs4291(A/T) and ERAP1rs26618(T/C) genotyping was performed using RT-PCR. Results: The frequency of the ACErs4291AA genotype was higher among the severe COVID-19 group than others (p < 0.001). The ERAP1rs26618TT genotype frequency was higher among the severe COVID-19 group in comparison with the mild group (p < 0.001) and non-infected controls (p = 0.0006). The frequency of the ACErs4291A allele was higher among severe COVID-19 than mild and non-infected groups (64.4% vs. 37.5%, and 34.4%, respectively), and the ERAP1rs26618T allele was also higher in the severe group (67.5% vs. 39.4%, and 49.4%). There was a statistically significant association between severe COVID-19 and ACErs4291A or ERAP1rs26618T alleles. The coexistence of ACErs4291A and ERAP1rs26618T alleles in the same individual increase the severity of the COVID-19 risk by seven times [OR (95%CI) (LL−UL) = 7.058 (3.752−13.277), p < 0.001). A logistic regression analysis revealed that age, male gender, non-vaccination, ACErs4291A, and ERAP1rs26618T alleles are independent risk factors for severe COVID-19. Conclusions: Persons carrying ACErs4291A and/or ERAP1rs26618T alleles are at higher risk of developing severe COVID-19.

The COVID-19 pandemic has exacerbated health inequities in the United States. People with unhealthy opioid use (UOU) may face disproportionate challenges with COVID-19 precautions, and the pandemic has disrupted access to opioids and UOU treatments. UOU impairs the immunological, cardiovascular, pulmonary, renal, and neurological systems and may increase severity of outcomes for COVID-19.
We applied machine learning techniques to explore clinical presentations of hospitalized patients with UOU and COVID-19 and to test the association between UOU and COVID-19 disease severity.
This retrospective, cross-sectional cohort study was conducted based on data from 4110 electronic health record patient encounters at an academic health center in Chicago between January 1, 2020, and December 31, 2020. The inclusion criterion was an unplanned admission of a patient aged ≥18 years; encounters were counted as COVID-19-positive if there was a positive test for COVID-19 or 2 COVID-19 International Classification of Disease, Tenth Revision codes. Using a predefined cutoff with optimal sensitivity and specificity to identify UOU, we ran a machine learning UOU classifier on the data for patients with COVID-19 to estimate the subcohort of patients with UOU. Topic modeling was used to explore and compare the clinical presentations documented for 2 subgroups: encounters with UOU and COVID-19 and those with no UOU and COVID-19. Mixed effects logistic regression accounted for multiple encounters for some patients and tested the association between UOU and COVID-19 outcome severity. Severity was measured with 3 utilization metrics: low-severity unplanned admission, medium-severity unplanned admission and receiving mechanical ventilation, and high-severity unplanned admission with in-hospital death. All models controlled for age, sex, race/ethnicity, insurance status, and BMI.
Topic modeling yielded 10 topics per subgroup and highlighted unique comorbidities associated with UOU and COVID-19 (eg, HIV) and no UOU and COVID-19 (eg, diabetes). In the regression analysis, each incremental increase in the classifier\'s predicted probability of UOU was associated with 1.16 higher odds of COVID-19 outcome severity (odds ratio 1.16, 95% CI 1.04-1.29; P=.009).
Among patients hospitalized with COVID-19, UOU is an independent risk factor associated with greater outcome severity, including in-hospital death. Social determinants of health and opioid-related overdose are unique comorbidities in the clinical presentation of the UOU patient subgroup. Additional research is needed on the role of COVID-19 therapeutics and inpatient management of acute COVID-19 pneumonia for patients with UOU. Further research is needed to test associations between expanded evidence-based harm reduction strategies for UOU and vaccination rates, hospitalizations, and risks for overdose and death among people with UOU and COVID-19. Machine learning techniques may offer more exhaustive means for cohort discovery and a novel mixed methods approach to population health.

A relevant portion of patients with disease caused by the severe acute respiratory syndrome coronavirus 2 (COVID-19) experience negative outcome, and several laboratory tests have been proposed to predict disease severity. Among others, dramatic changes in peripheral blood cells have been described. We developed and validated a laboratory score solely based on blood cell parameters to predict survival in hospitalized COVID-19 patients. We retrospectively analyzed 1,619 blood cell count from 226 consecutively hospitalized COVID-19 patients to select parameters for inclusion in a laboratory score predicting severity of disease and survival. The score was derived from lymphocyte- and granulocyte-associated parameters and validated on a separate cohort of 140 consecutive COVID-19 patients. Using ROC curve analysis, a best cutoff for score of 30.6 was derived, which was associated to an overall 82.0% sensitivity (95% CI: 78-84) and 82.5% specificity (95% CI: 80-84) for detecting outcome. The scoring trend effectively separated survivor and non-survivor groups, starting 2 weeks before the end of the hospitalization period. Patients\' score time points were also classified into mild, moderate, severe, and critical according to the symptomatic oxygen therapy administered. Fluctuations of the score should be recorded to highlight a favorable or unfortunate trend of the disease. The predictive score was found to reflect and anticipate the disease gravity, defined by the type of the oxygen support used, giving a proof of its clinical relevance. It offers a fast and reliable tool for supporting clinical decisions and, most important, triage in terms of not only prioritization but also allocation of limited medical resources, especially in the period when therapies are still symptomatic and many are under development. In fact, a prolonged and progressive increase of the score can suggest impaired chances of survival and/or an urgent need for intensive care unit admission.

Vaccine breakthrough SARS-CoV-2 infections necessitating hospitalization have emerged as a relevant problem with longer time interval since vaccination and the predominance of the Delta variant. The aim of this study was to evaluate the association between primary vaccination with four SARS-CoV-2 vaccines authorized for use in the European Union-BNT162b2, ChAdOx-1S, mRNA-1273 or Ad.26.COV2.S-and progression to critically severe disease (mechanical ventilation or death) and duration of hospitalization among adult patients with PCR-confirmed acute COVID-19 hospitalized during the Delta variant predominance (October-November 2021) in Slovenia. Among the 529 enrolled patients hospitalized with COVID-19 (median age, 65 years; 58.2% men), 175 (33.1%) were fully vaccinated at the time of symptom onset. Compared with 345 unvaccinated patients, fully vaccinated patients with breakthrough infections were older, more often immunocompromised, and had higher Charlson comorbidity index scores. After adjusting for sex, age, and comorbidities, fully vaccinated patients had lower odds for progressing to critically severe disease and were discharged from the hospital earlier than unvaccinated patients. Vaccination against SARS-CoV-2 remains an extremely effective intervention to alleviate morbidity and mortality in COVID-19 patients.

Old age is a crucial risk factor for severe coronavirus disease 2019 (COVID-19), with serious or fatal outcomes disproportionately affecting older adults compared with the rest of the population. We proposed that the physiological health status and biological age, beyond the chronological age itself, could be the driving trends affecting COVID-19 severity and mortality. A total of 155 participants hospitalized with confirmed COVID-19 aged 26-94 years were recruited for the study. Four different physiological summary indices were calculated: Klemera and Doubal\'s biological age, PhenoAge, physiological dysregulation (PD; globally and in specific systems), and integrated albunemia. All of these indices significantly predicted the risk of death (p < 0.01) after adjusting for chronological age and sex. In all models, men were 2.4-4.4-times more likely to die than women. The global PD was shown to be a good predictor of deterioration, with the odds of deterioration increasing by 41.7% per 0.5-unit increase in the global PD. As for death, the odds also increased by 68.3% per 0.5-unit increase in the global PD. Our results are partly attributed to common chronic diseases that aggravate COVID-19, but they also suggest that the underlying physiological state could capture vulnerability to severe COVID-19 and serve as a tool for prognosis that would, in turn, help inpatient management.