Associations of Combustible Cigarette, Electronic Cigarette, and Dual Use With COVID Infection and Severity in the U.S.: A Cross-sectional Analysis of the 2021 National Health Information Survey.

Given the potential respiratory health risks, the association of COVID infection and the use of combustible cigarettes, electronic nicotine delivery systems (ENDS), and concurrent dual use is a priority for public health. Many published reports have not accounted for known covarying factors. This study sought to calculate adjusted odds ratios for self-reported COVID infection and disease severity as a function of smoking and ENDS use, while accounting for factors known to influence COVID infection and disease severity (i.e., age, sex, race and ethnicity, socioeconomic status and educational attainment, rural or urban environment, self-reported diabetes, COPD, coronary heart disease, and obesity status). Data from the 2021 U.S. National Health Interview Survey, a cross-sectional questionnaire design, were used to calculate both unadjusted and adjusted odds ratios for self-reported COVID infection and severity of symptoms. Results indicate that combustible cigarette use is associated with a lower likelihood of self-reported COVID infection relative to non-use of tobacco products (AOR = .64; 95% CI [.55, .74]), whereas ENDS use is associated with a higher likelihood of self-reported COVID infection (AOR = 1.30; 95% CI [1.04, 1.63]). There was no significant difference in COVID infection among dual users (ENDS and combustible use) when compared with non-users. Adjusting for covarying factors did not substantially change the results. There were no significant differences in COVID disease severity between those of varying smoking status. Future research should examine the relationship between smoking status and COVID infection and disease severity utilizing longitudinal study designs and non-self-report measures of smoking status (e.g., the biomarker cotinine), COVID infection (e.g., positive tests), and disease severity (e.g., hospitalizations, ventilator assistance, mortality, and ongoing symptoms of long COVID).

Medication regimen complexity (MRC-ICU) for in-hospital mortality prediction in COVID-19 patients

Purpose: The medication regimen complexity-intensive care unit (MRC-ICU) score was developed prior to the existence of COVID-19. The purpose of this study was to assess if MRC-ICU could predict in-hospital mortality in patients with COVID-19. Method(s): A single-center, observational study was conducted from August 2020 to January 2021. The primary outcome of this study was the area under the receiver operating characteristic (AUROC) for in-hospital mortality for the 48-hour MRC-ICU. Age, sequential organ failure assessment (SOFA), and World Health Organization (WHO) COVID-19 Severity Classification were assessed. Logistic regression was performed to predict in-hospital mortality as well as WHO Severity Classification at 7 days. Result(s): A total of 149 patients were included. The median SOFA score was 8 (IQR 5-11) and median MRC-ICU score at 48 hours was 15 (IQR 7-21). The in-hospital mortality rate was 36% (n = 54). The AUROC for MRC-ICU was 0.71 (95% Confidence Interval (CI), 0.62-0.78) compared to 0.66 for age, 0.81 SOFA, and 0.72 for the WHO Severity Classification. In univariate analysis, age, SOFA, MRC-ICU, and WHO Severity Classification all demonstrated significant association with in-hospital mortality, while SOFA, MRC-ICU, and WHO Severity Classification demonstrated significant association with WHO Severity Classification at 7 days. In univariate analysis, all 4 characteristics showed significant association with mortality;however, only age and SOFA remained significant following multivariate analysis. Conclusion(s): In the first analysis of medication-related variables as a predictor of severity and in-hospital mortality in COVID-19, MRC-ICU demonstrated acceptable predictive ability as represented by AUROC;however, SOFA was the strongest predictor in both AUROC and regression analysis.Copyright © The Author(s) 2023.

A large cohort study of the effects of Lewis, ABO, 13 other blood groups, and secretor status on COVID-19 susceptibility, severity, and long COVID-19.

BACKGROUND: Previous studies have reported Blood type O to confer a lower risk of SARS-CoV-2 infection, while secretor status and other blood groups have been suspected to have a similar effect as well. STUDY DESIGN AND METHODS: To determine whether any other blood groups influence testing positive for SARS-CoV-2, COVID-19 severity, or prolonged COVID-19, we used a large cohort of 650,156 Danish blood donors with varying available data for secretor status and blood groups ABO, Rh, Colton, Duffy, Diego, Dombrock, Kell, Kidd, Knops, Lewis, Lutheran, MNS, P1PK, Vel, and Yt. Of these, 36,068 tested positive for SARS-CoV-2 whereas 614,088 tested negative between 2020-02-17 and 2021-08-04. Associations between infection and blood groups were assessed using logistic regression models with sex and age as covariates. RESULTS: The Lewis blood group antigen Lea displayed strongly reduced SARS-CoV-2 susceptibility OR 0.85 CI[0.79-0.93] p < .001. Compared to blood type O, the blood types B, A, and AB were found more susceptible toward infection with ORs 1.1 CI[1.06-1.14] p < .001, 1.17 CI[1.14-1.2] p < .001, and 1.2 CI[1.14-1.26] p < .001, respectively. No susceptibility associations were found for the other 13 blood groups investigated. There was no association between any blood groups and COVID-19 hospitalization or long COVID-19. No secretor status associations were found. DISCUSSION: This study uncovers a new association to reduced SARS-CoV-2 susceptibility for Lewis type Lea and confirms the previous link to blood group O. The new association to Lea could be explained by a link between mucosal microbiome and SARS-CoV-2.

Altered hematological profile: The forerunner of fatalities caused by COVID-19

Background: Being a systemic infection, coronavirus disease 2019 (COVID-19) affects hematologic system, along with cardiovascular, pulmonary, gastrointestinal, and neurological systems, resulting in altered hematological parameters. These altered hematological findings are thought to have a role in early risk stratification and prognostication of COVID-19 patients. However, the data on hematological abnormalities associated with the disease among Eastern Indian COVID-19 patients, particularly the Bengalis, are limited. Aim: The aim is to study the association, if any, between various hematological parameters and disease severity of COVID-19. Materials and Methods: The study was a cross-sectional study involving 145 laboratory-confirmed cases of SARS-CoV-2 infection. Based on the disease severity, the patients were divided into three groups: mild, moderate, and severe. Various hematological parameters were analyzed. Results: Of the 145 patients, 82.8%, 9.6%, and 7.5% of the cases were in the mild, moderate, and severe groups, respectively. The mean age was 48 years. The result of our study showed that the age of the patients is directly proportional to the severity of the illness. About 62.1% of the patients were male, whereas the rest (37.9%) were female. Our study showed an independent association of Covid severity with male gender. Although mean total leukocyte count (TLC), absolute count of neutrophil, lymphocyte, eosinophil, neutrophil-lymphocyte ratio (NLR), neutrophil-monocyte ratio (NMR), lymphocyte-monocyte ratio, platelet-lymphocyte ratio, and systemic inflammatory index among mild, moderate, and severe COVID-19 cases were statistically significant (P 0.05), basophil, monocyte, and platelet count were statistically insignificant among the three groups. Nearly all of the hematological parameters could be used as potential diagnostic biomarkers for subsequent analysis because their area under the curve was higher than 0.50. Conclusion: Severity of COVID-19 is associated with older age, male sex, higher TLC, neutrophilia, lymphopenia, eosinopenia, high NLR, and high NMR. As complete blood count is an inexpensive routine blood investigation, it can be very useful in a resource-poor healthcare facility, which is unable to provide high-end investigations. © 2022 Medical Journal of Babylon ;Published by Wolters Kluwer-Medknow.

COVID-19 Infection May Not Produce Lasting Immunity in Non-vaccinated Young Adults: A Retrospective Review of COVID-19 Antibody Data

BackgroundDespite progress in achieving herd immunity through recovery from previous infection and vaccination efforts, the COVID-19 pandemic continues to be an imminent health concern. Exposure to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral antigen through infection or vaccination facilitates immune system efficacy against future infection, but it is currently unclear how long this immunity lasts. Therefore, understanding the necessary exposures to produce adequate antibody levels and the duration of this humoral response to prevent infection is imperative in updating guidelines for vaccination and ultimately ending this public health crisis. AimsThis study aimed to compare the presence of serum antibodies in younger and older age groups to determine how vaccination and previous infection compare as indicators of immunity against COVID-19. We also evaluated age to determine its role in antibody presence. We hope that this information will be helpful to the public to develop the best recommendations for vaccination guidelines concerning distinct demographics. ​Materials and methodsIn this retrospective data analysis, we evaluated saliva SARS-CoV-2 test results taken from 309 subjects (192F/117M;median age=53.4) during a community fair in Crawford County, PA. We sorted the subjects into groups based on age, reported infection with the COVID-19 virus, and vaccination status. We then performed a Chi-square analysis to compare the frequency of positive SARS-CoV-2 antibody tests within these groups.ResultsThe vaccinated but not previously-infected cohort (n=146, 81.5%) was significantly more likely to have antibodies than the unvaccinated infected cohort (n=55, 65.5%;p<0.0001). In the previously-infected, unvaccinated cohort, individuals who were 55 and older were more likely to have antibodies than younger individuals (p<0.0157), but no age-dependent difference was observed among vaccinated individuals.ConclusionsThe results suggest that vaccination provides a more durable immune response than recovery from infection, and there is an age-dependent humoral response following previous infection but not vaccination. Practically speaking, this information implies that despite popular misconception, individuals under the age of 55 must receive a COVID-19 vaccine despite the previous infection as they are significantly less likely to have antibodies following infection than their counterparts who are over the age of 55.

Clinical management of COVID-19 in hospitals and the community: A snapshot from a medical insurance database in South Africa.

BACKGROUND: Little is known about the clinical care, use of medicines, and risk factors associated with mortality among the population with private health insurance with COVID-19 in South Africa. METHODS: This was a retrospective cross-sectional study using claims data of patients with confirmed COVID-19. Sociodemographics, comorbidities, severity, concurrent/progressive comorbidity, drug treatment, and outcomes were extracted from administrative data. Univariate and multivariate logistic regression models were used to explore the risk factors associated with in-hospital death. RESULTS: This study included 154,519 patients with COVID-19; only 24% were categorized as severe because they received in-hospital care. Antibiotic (42.8%) and steroid (30%) use was high in this population. After adjusting for known comorbidities, concurrent/progressive diagnosis of the following conditions were associated with higher in-hospital death odds: acute respiratory distress syndrome (aOR = 1.55; 95% CI = 1.44-1.68), septic shock (aOR = 1.55; 95% CI = 2.00-4.12), pneumonia (aOR = 1.35; 95% CI = 1.24-1.47), acute renal failure (aOR = 2.30; 95% CI = 2.09-2.5), and stroke (aOR = 2.09; 95% CI = 1.75-2.49). The use of antivirals (aOR = 0.47; 95% CI= 0.40-0.54), and/or steroids (aOR = 0.46; 95% CI = 0.43-0.50) were associated with decreased death odds. The use of antibiotics in-hospital was not associated with increased survival (aOR = 0.97; 95% CI = 0.91-1.04). CONCLUSIONS: Comorbidities remain significant risk factors for death mediated by organ failure. The use of antibiotics did not change the odds of death, suggesting inappropriate use.

Trends in Clinical Severity of Hospitalized Patients With Coronavirus Disease 2019-Premier Hospital Dataset, April 2020-April 2021.

BACKGROUND: Clinical severity of coronavirus disease 2019 (COVID-19) may vary over time; trends in clinical severity at admission during the pandemic among hospitalized patients in the United States have been incompletely described, so a historical record of severity over time is lacking. METHODS: We classified 466677 hospital admissions for COVID-19 from April 2020 to April 2021 into 4 mutually exclusive severity grades based on indicators present on admission (from most to least severe): Grade 4 included intensive care unit (ICU) admission and invasive mechanical ventilation (IMV); grade 3 included non-IMV ICU and/or noninvasive positive pressure ventilation; grade 2 included diagnosis of acute respiratory failure; and grade 1 included none of the above indicators. Trends were stratified by sex, age, race/ethnicity, and comorbid conditions. We also examined severity in states with high vs low Alpha (B.1.1.7) variant burden. RESULTS: Severity tended to be lower among women, younger adults, and those with fewer comorbidities compared to their counterparts. The proportion of admissions classified as grade 1 or 2 fluctuated over time, but these less-severe grades comprised a majority (75%-85%) of admissions every month. Grades 3 and 4 consistently made up a minority of admissions (15%-25%), and grade 4 showed consistent decreases in all subgroups, including states with high Alpha variant burden. CONCLUSIONS: Clinical severity among hospitalized patients with COVID-19 has varied over time but has not consistently or markedly worsened over time. The proportion of admissions classified as grade 4 decreased in all subgroups. There was no consistent evidence of worsening severity in states with higher vs lower Alpha prevalence.

Psychological and Clinical Predictors of COVID-19 Severity and Outcomes.

Objective The present study aimed to assess the psychological and clinical determinants of coronavirus disease 2019 (COVID-19) and their association with the disease severity and outcomes. Methods This prospective study was conducted at Hayatabad Medical Complex, Peshawar-Pakistan. Admitted patients were screened for COVID-19 with reverse transcription-polymerase chain reaction (RT-PCR) and subsequently, 250 COVID positive patients were included in the final analysis. Data were obtained from the patient's medical chart; demographic and clinical characteristics were recorded using a structured questionnaire. Psychological determinants, including anxiety and depression, were measured using the Hospital Anxiety and Depression Scale (HADS). The predictors of disease severity and outcomes (recovery vs. mortality) were also studied. Results A total of 250 patients were included in this study; out of which, 193 patients recovered from this deadly virus and 57 died. Based on psychological assessment, 58.4% of the enrolled COVID-19 patients had poor HADS scores. Most of the patients who died (70.2%) had severe symptoms (poor HADS scores). Similarly, 49.6% of the total cases were observed with poor HADS, and 50.9% of those who died had severe depression. Conclusion It is concluded from the study results that psychological distress is frequent in COVID-19 patients. Age, hypertension, fatigue, abnormal respiratory rate, oxygen saturation, ferritin, and poor HADS sore were determined as the significant predictors of COVID-19 severity and outcomes.

A Novel Block Imaging Technique Using Nine Artificial Intelligence Models for COVID-19 Disease Classification, Characterization and Severity Measurement in Lung Computed Tomography Scans on an Italian Cohort.

Computer Tomography (CT) is currently being adapted for visualization of COVID-19 lung damage. Manual classification and characterization of COVID-19 may be biased depending on the expert's opinion. Artificial Intelligence has recently penetrated COVID-19, especially deep learning paradigms. There are nine kinds of classification systems in this study, namely one deep learning-based CNN, five kinds of transfer learning (TL) systems namely VGG16, DenseNet121, DenseNet169, DenseNet201 and MobileNet, three kinds of machine-learning (ML) systems, namely artificial neural network (ANN), decision tree (DT), and random forest (RF) that have been designed for classification of COVID-19 segmented CT lung against Controls. Three kinds of characterization systems were developed namely (a) Block imaging for COVID-19 severity index (CSI); (b) Bispectrum analysis; and (c) Block Entropy. A cohort of Italian patients with 30 controls (990 slices) and 30 COVID-19 patients (705 slices) was used to test the performance of three types of classifiers. Using K10 protocol (90% training and 10% testing), the best accuracy and AUC was for DCNN and RF pairs were 99.41 ± 5.12%, 0.991 (p < 0.0001), and 99.41 ± 0.62%, 0.988 (p < 0.0001), respectively, followed by other ML and TL classifiers. We show that diagnostics odds ratio (DOR) was higher for DL compared to ML, and both, Bispecturm and Block Entropy shows higher values for COVID-19 patients. CSI shows an association with Ground Glass Opacities (0.9146, p < 0.0001). Our hypothesis holds true that deep learning shows superior performance compared to machine learning models. Block imaging is a powerful novel approach for pinpointing COVID-19 severity and is clinically validated.