Impact of Age and Sex on COVID-19 Severity Assessed From Radiologic and Clinical Findings

Age, gender, and race differences in nasal morphology: Linking air conditioning and filtration efficiency to disparities in air pollution health outcomes and COVID-19 mortality

COVID-19 mortality disparities underscore the critical role of environmental factors, age, sex, and racial demographics. This study investigates how individual variations in nasal morphology - specifically its air conditioning (temperature and humidity regulation) and filtration functions - may influence respiratory health and contribute to differential COVID-19 outcomes. Analysis reveals significant differences in nasal structure and function across racial, sex, and age groups, demonstrating associations with disparities in respiratory vulnerability to environmental stressors such as air pollution, infectious aerosols, and climatic conditions. Specifically, wider nasal cavities (more common in certain populations), larger male nasal passages, and age-related changes like mucosal atrophy and increased endonasal volume impair air conditioning and filtration efficiency. These morphological variations influence the nose's protective capacity, which is critical for shielding the middle and lower airways from environmental exposures. Populations with inherently reduced nasal filtration and conditioning efficiency demonstrate higher vulnerability, aligning with U.S. mortality patterns for both COVID-19 and air pollution across demographic groups. This suggests a direct link between nasal anatomy and population-level health disparities. These findings provide novel insights into the role of nasal anatomy in mediating respiratory health disparities by modulating individual responses to environmental exposures, air pollution, and pathogens. They highlight the need to address critical gaps in understanding how airway characteristics influence susceptibility to environmental stressors and to develop targeted interventions aimed at reducing health disparities.

Predicting coronavirus disease 2019 severity using explainable artificial intelligence techniques

Predictive models for determining coronavirus disease 2019 (COVID-19) severity have been established; however, the complexity of the interactions among factors limits the use of conventional statistical methods. This study aimed to establish a simple and accurate predictive model for COVID-19 severity using an explainable machine learning approach. A total of 3,301 patients ≥ 18 years diagnosed with COVID-19 between February 2020 and October 2022 were included. The discovery cohort comprised patients whose disease onset fell before October 1, 2020 (N = 1,023), and the validation cohort comprised the remaining patients (N = 2,278). Pointwise linear and logistic regression models were used to extract 41 features. Reinforcement learning was used to generate a simple model with high predictive accuracy. The primary evaluation was the area under the receiver operating characteristic curve (AUC). The predictive model achieved an AUC of ≥ 0.905 using four features: serum albumin levels, lactate dehydrogenase levels, age, and neutrophil count. The highest AUC value was 0.906 (sensitivity, 0.842; specificity, 0.811) in the discovery cohort and 0.861 (sensitivity, 0.804; specificity, 0.675) in the validation cohort. Simple and well-structured predictive models were established, which may aid in patient management and the selection of therapeutic interventions.

Mathematical modeling of impacts of patient differences on renin-angiotensin system and applications to COVID-19 lung fibrosis outcomes

Patient-specific premorbidity, age, and sex are significant heterogeneous factors that influence the severe manifestation of lung diseases, including COVID-19 fibrosis. The renin-angiotensin system (RAS) plays a prominent role in regulating the effects of these factors. Recent evidence shows patient-specific alterations of RAS peptide homeostasis concentrations with premorbidity and the expression level of angiotensin-converting enzyme 2 (ACE2) during COVID-19. However, conflicting evidence suggests decreases, increases, or no changes in RAS peptides after SARS-CoV-2 infection. A multiscale computational model was developed to quantify the systemic contribution of heterogeneous factors of RAS during COVID-19. Three submodels were connected-an agent-based model for in-host COVID-19 response in the lung tissue, a RAS dynamics model, and a fibrosis dynamics model to investigate the effects of patient-group-specific factors in the systemic alteration of RAS and collagen deposition in the lung. The model results indicated cell death due to inflammatory response as a major contributor to the reduction of ACE and ACE2. The model explained possible mechanisms for conflicting evidence of patient-group-specific changes in RAS peptides in previously published studies. RAS peptides decreased for all virtual patient groups with aging in both sexes. In contrast, large variations in the magnitude of reduction were observed between male and female virtual patients in the older and middle-aged groups. The patient-specific variations in homeostasis RAS peptide concentrations of SARS-CoV-2-negative patients affected the dynamics of RAS during infection. This model may find further applications in patient-specific calibrations of tissue models for acute and chronic lung diseases to develop personalized treatments.

Factors Associated with Self-reported COVID-19 Infection and Hospitalization among Patients Seeking Care at a Comprehensive Cancer Center

BACKGROUND

COVID-19 infection severity differs by race and ethnicity, but its long-term effect on cancer-related outcomes is unknown. Therefore, information on COVID-19 history is critical to ascertain among new cancer patients in order to advance research on its impact on cancer outcomes and potentially related health disparities.

METHODS

A cross-sectional study was conducted among 16,025 new patients seeking care at Moffitt Cancer Center (MCC) between 2021 and 2022. Patient self-reported histories of COVID-19 infection and other pre-existing health conditions were obtained from electronic questionnaires administered to all new MCC patients. Associations between demographics and COVID-19 infection and hospitalization were examined.

RESULTS

A total of 1,971 patients (12.3%) reported ever having COVID-19. Self-reported COVID-19 history was significantly more prevalent in Hispanic vs. non-Hispanic patients (OR = 1.24, 1.05-1.45) and less prevalent in Asian versus White patients (OR = 0.49, 95% 0.33-0.70). Among patients who ever had COVID-19, 10.6% reported a COVID-19-related hospitalization. Males had higher odds of a COVID-19 related hospitalization than females (OR = 1.50, 95% CI = 1.09-2.05), as did Black/African American patients (OR = 2.11, 95% CI = 1.18-3.60) and patients of races other than Black/African American and Asian (OR = 2.61, 95% CI = 1.43-4.54) compared to White patients. Hispanic patients also experienced higher odds of hospitalization (OR = 2.06, 95% CI-1.29- 3.23) compared with non-Hispanic patients of all races in a sensitivity analysis that combined race/ethnicity. Pre-existing lung and breathing problems were associated with higher odds of being hospitalized with COVID-19 (OR = 2.38, 95% CI = 1.61-3.48), but these and other health conditions did not explain the observed associations between race and COVID-19 hospitalization.

CONCLUSIONS

Higher rates of COVID-19 hospitalization were observed among patients identifying as Black/African American or Hispanic independent of pre-existing health conditions. Future studies evaluating long-term effects of COVID-19 should carefully examine potential racial/ethnic disparities in cancer outcomes.

Long-term increase in soluble interleukin-6 receptor levels in convalescents after mild COVID-19 infection

Introduction

Serum levels of interleukin-6 (IL-6) are increased in COVID-19 patients. IL-6 is an effective therapeutic target in inflammatory diseases and tocilizumab, a monoclonal antibody that blocks signaling via the IL-6 receptor (IL-6R), is used to treat patients with severe COVID-19. However, the IL-6R exists in membrane-bound and soluble forms (sIL-6R), and the sIL-6R in combination with soluble glycoprotein 130 (sgp130) forms an IL-6-neutralizing buffer system capable of neutralizing small amounts of IL-6.

Methods

In this study, we analyzed serum levels of IL-6, sIL-6R and sgp130 in the serum of COVID-19 convalescent individuals with a history of mild COVID-19 disease and in acute severely ill COVID-19 patients compared to uninfected control subjects. Furthermore, we used single cell RNA sequencing data in order to determine which immune cell types are sources and targets of the individual cytokines and whether their expression is altered in severe COVID-19 patients.

Results

We find that sIL-6R levels are not only increased in acute severely ill patients, but also in convalescents after a mild COVID-19 infection. We show that this increase in sIL-6R results in an enhanced capacity of the sIL-6R/sgp130 buffer system, but that significantly enhanced free IL-6 is still present due to an overload of the buffer. Further, we identify IL-6 serum levels, age and the number of known pre-existing medical conditions as crucial determinants of disease outcome for the patients. We also show that IL-11 has no major systemic role in COVID-19 patients and that sCD25 is only increased in acute severely ill COVID-19 patients, but not in mild convalescent individuals.

Discussion

In conclusion, our study shows long-lasting alterations of the IL-6 system after COVID-19 disease, which might be relevant when applying anti-IL-6 or anti-IL-6R therapy.

COVID-19 vaccine uptake inequality among older adults: A multidimensional demographic analysis

BACKGROUND

Age, race, ethnicity, and sex are important determinants of coronavirus disease of 2019 (COVID-19) outcomes. Older adults (65 years and older) are at the highest risk of COVID-19 morbidity and mortality. Analyzing their vaccine uptake by subclassifying demographics is rare and can assist vaccination policies. This study investigates COVID-19 dose 1 and 2 vaccine uptakes among them by race, ethnicity, and sex.

METHODS

Immunization registry data were used to calculate temporal changes in older adults' COVID-19 vaccine uptake by race, ethnicity, race-sex, and ethnicity-sex in Kentucky's most populous county, Jefferson County, during the first 6 quarters of the COVID-19 vaccination program.

RESULTS

By May 2022, the county's Asian residents had the highest dose 1 and 2 vaccination rates (97.0% and 80.4%), then White residents (90.0% and 80.2%). Black residents had one of the lowest COVID-19 vaccination rates (87.3% and 77.3%). The rate among Hispanic residents (82.0% and 66.4%) was considerably lower than non-Hispanic residents (90.2% and 80.1%). The rates were consistently lower in males.

CONCLUSIONS

Racial, ethnic, and sex-based COVID-19 vaccine inequalities were largely maintained during the study period. Vaccine rollout practices and promotional programs should aim to boost the uptake of the COVID-19 vaccination among racial minority and male older adults.

The Impact of Vaccination on COVID-19 Outcomes in Vietnam

Objectives: This study aimed to assess the effectiveness of the COVID-19 vaccine on the outcomes of patients in three hospitals in Vietnam. Methods: An observational study involved 3102 confirmed COVID-19 patients from Vietnam. Participants were classified into unvaccinated, partially vaccinated (one dose) (PV), fully vaccinated (two doses) (FV), and boosted (three doses) groups. We used a regression model to assess the relationship between vaccine status and disease outcome, including mortality, persistent symptoms after treatment, and hospital duration. Results: The proportions of unvaccinated, PV, FV, and boosted groups were 43.39%, 4.63%, 43.93%, and 8.05%, respectively, and 48% of the participants had at least one comorbidity. The proportion of severe clinical disease was significantly higher in the unvaccinated compared with the vaccinated. Biomarkers of cellular injury and organ failure, e.g., aspartate aminotransferase (AST), ferritin, troponin T, proBNP, D-dimer, and urea plasma concentration were significantly higher in unvaccinated and PV patients compared with FV and boosted patients. Age was the most crucial predictor of critical illness, followed by vaccine status, hypertension, diabetes, heart disease, and chronic kidney disease. The unvaccinated group had the highest proportion of deaths (5.2% vs. 1.4% and 0.3% in FV and boosted groups, respectively). Conclusions: Vaccination reduced mortality and both hospitalization length and disease severity in COVID-19 survivors, especially the older and patients with chronic comorbidities.

Surviving COVID-19 and Battling Fibrosis: A Retrospective Cohort Study Across Three Pandemic Waves

BACKGROUND/OBJECTIVES

We aimed to characterize the fibrosis following COVID-19 pneumonia, using quantitative analysis, after three months and subsequently, after two years of patients' release from the hospital, and to identify the risk factors for pulmonary fibrosis.

METHODS

We performed a retrospective, observational cohort study on 420 patients with severe forms of COVID-19. For all patients, we registered demographic, inflammatory and biochemical parameters, complete blood count and D-dimers; all patients underwent three computed tomography scans (at admittance, at 3 months and at 2 years).

RESULTS

We found fibrosis in 67.9% of patients at the 3-month evaluation and in 42.4% of patients at the 2-year evaluation, registering a significant decrease in the severe and moderate fibrosis cases, with a slight increase in the mild fibrosis cases. The risk of fibrosis was found to be proportional to the values of age, duration of hospital stay, inflammatory markers (ESR, fibrinogen), cytolytic markers (LDH, AST) and D-dimers. The highest correlations with lung fibrosis were registered for interstitial pulmonary involvement (for the 3-month evaluation) and total pulmonary involvement (for the 2-year evaluation).

CONCLUSIONS

Lung fibrosis represents a significant post-COVID-19 complication found in 42% of patients with severe forms of pneumonia at the 2-year evaluation. A significant overall decrease in the severity of lung fibrosis was registered at the 2-year evaluation compared to the 3-month evaluation. We consider that the amount of interstitial pulmonary involvement represents the optimal parameter to estimate the risk of lung fibrosis following SARS-CoV-2 pneumonia.

Combined association between dietary antioxidant quality score and leisure-time physical activity on sleep pattern in cancer survivors: a cross-sectional study of National Health and Nutrition Examination Surveys database

This study aimed to explore the combined association between the dietary antioxidant quality score (DAQS) and leisure-time physical activity on sleep patterns in cancer survivors. Data of cancer survivors were extracted from the National Health and Nutrition Examination Surveys database in 2007-2014 in this cross-sectional study. Weighted multivariable logistic regression models were used to estimate OR and 95 % CI for the association of DAQS and leisure-time physical activity on sleep patterns. The combined association was also assessed in subgroups of participants based on age and use of painkillers and antidepressants. Among the eligible participants, 1133 had unhealthy sleep patterns. After adjusting for covariates, compared with low DAQS level combined with leisure-time physical activity level < 600 MET·min/week, high DAQS level combined with leisure-time physical activity ≥ 600 MET·min/week was associated with lower odds of unhealthy sleep patterns (OR = 0·41, 95 % CI: 0·23, 0·72). Additionally, the association of high DAQS level combined with high leisure-time physical activity with low odds of unhealthy sleep patterns was also significant in < 65 years old (OR = 0·30, 95 % CI: 0·13, 0·70), non-painkiller (OR = 0·39, 95 % CI: 0·22, 0·71), non-antidepressant (OR = 0·49, 95 % CI: 0·26, 0·91) and antidepressant (OR = 0·11, 95 % CI: 0·02, 0·50) subgroups. DAQS and leisure-time physical activity had a combined association on sleep patterns in cancer survivors. However, the causal associations of dietary nutrient intake and physical activity with sleep patterns in cancer survivors need further clarification.

Streamlining management in thoracic trauma: radiomics- and AI-based assessment of patient risks

BACKGROUND

In blunt chest trauma, patient management is challenging because clinical guidelines miss tools for risk assessment. No clinical scale reliably measures the severity of cases and the chance of complications.

AIM

The objective of the study was to optimize the management of patients with blunt chest trauma by creating models prognosticating the transfer to the intensive care unit and in-hospital length of stay (LOS).

METHODS

The study cohort consisted of 212 cases. We retrieved information on the cases from the hospital's trauma registry. After segmenting the lungs with Lung CT Analyzer, we performed volumetric feature extraction with data-characterization algorithms in PyRadiomics.

RESULTS

To predict whether the patient will require intensive care, we used the three groups of findings: ambulance, admission, and radiomics data. When trained on the ambulance data, the models exhibited a borderline performance. The metrics improved after we retrained the models on a combination of ambulance, laboratory, radiologic, and physical examination data (81.5% vs. 94.4% Sn). Radiomics data were the top-accurate predictors (96.3% Sn). Age, vital signs, anthropometrics, and first aid time were the best-performing features collected by the ambulance service. Laboratory findings, AIS scores for the lower extremity, abdomen, head, and thorax constituted the top-rank predictors received on admission to the hospital. The original first-order kurtosis had the highest predictive value among radiomics data. Top-informative radiomics features were derived from the right hemithorax because the right lung is larger. We constructed regression models that can adequately reflect the in-hospital LOS. When trained on different groups of data, the machine-learning regression models showed similar performance (MAE/ROV ≈ 8%). Anatomic scores for the body parts other than thorax and laboratory markers of hemorrhage had the highest predictive value. Hence, the number of injured body parts correlated with the case severity.

CONCLUSION

The study findings can be used to optimize the management of patients with a chest blunt injury as a specific case of monotrauma. The models we built may help physicians to stratify patients by risk of worsening and overcome the limitations of existing tools for risk assessment. High-quality AI models trained on radiomics data demonstrate superior performance.

Neopterin production in relation to COVID-19 in the Haut-Ogooué Province, Gabon

BACKGROUND

In sub-Saharan Africa, understanding of the immune process associated with the COVID-19 pandemic remains scarce. This study aimed to investigate the relationship between plasma neopterin concentrations and COVID-19 infection, focusing on changes over time and age-related changes in immune response.

METHODS

A retrospective case study was conducted during the first wave of COVID-19 from March to August 2020. Whole blood and associated symptoms and comorbidities were collected from patients of all ages and sexes. Concentrations of plasma neopterin were measured using a commercial competitive neopterin ELISA (Neopterin ELISA, IBL International GmbH, Germany).

RESULTS

We analyzed data for 325 patients: 38% (n = 124) with COVID-19, and 62% (n = 201) without COVID-19, as a control group. We found that plasma neopterin concentrations were significantly higher in the COVID-19 group (mean value 45.1 nmol/L (SD 19)) than in the control group (mean value 33.8 nmol/L (SD 13)) (p = 0.004). In addition, neopterin levels decreased gradually over time in patients with COVID-19 (p < 0.001). Moreover, ROC analysis found that the best cut-off value for diagnosing COVID-19 patients based on plasma neopterin levels was 38.85 nmol/L with 70% sensitivity and 82% specificity (AUC, 0.74 [0.69-0.82], p < 0.05). We also found an increase in neopterin production with increasing age (p < 0.001).

CONCLUSION

Our findings contribute to our growing understanding of neopterin levels as a promising biomarker for the detection of COVID-19 cases in sub-Saharan Africa.

Feature Identification Using Interpretability Machine Learning Predicting Risk Factors for Disease Severity of In-Patients with COVID-19 in South Florida

Objective: The objective of the study was to establish an AI-driven decision support system by identifying the most important features in the severity of disease for Intensive Care Unit (ICU) with Mechanical Ventilation (MV) requirement, ICU, and InterMediate Care Unit (IMCU) admission for hospitalized patients with COVID-19 in South Florida. The features implicated in the risk factors identified by the model interpretability can be used to forecast treatment plans faster before critical conditions exacerbate. Methods: We analyzed eHR data from 5371 patients diagnosed with COVID-19 from South Florida Memorial Healthcare Systems admitted between March 2020 and January 2021 to predict the need for ICU with MV, ICU, and IMCU admission. A Random Forest classifier was trained on patients' data augmented by SMOTE, collected at hospital admission. We then compared the importance of features utilizing different model interpretability analyses, such as SHAP, MDI, and Permutation Importance. Results: The models for ICU with MV, ICU, and IMCU admission identified the following factors overlapping as the most important predictors among the three outcomes: age, race, sex, BMI, diarrhea, diabetes, hypertension, early stages of kidney disease, and pneumonia. It was observed that individuals over 65 years ('older adults'), males, current smokers, and BMI classified as 'overweight' and 'obese' were at greater risk of severity of illness. The severity was intensified by the co-occurrence of two interacting features (e.g., diarrhea and diabetes). Conclusions: The top features identified by the models' interpretability were from the 'sociodemographic characteristics', 'pre-hospital comorbidities', and 'medications' categories. However, 'pre-hospital comorbidities' played a vital role in different critical conditions. In addition to individual feature importance, the feature interactions also provide crucial information for predicting the most likely outcome of patients' conditions when urgent treatment plans are needed during the surge of patients during the pandemic.

A Retrospective Study: Evaluating the Impact of the COVID-19 Pandemic on Inflammatory Markers in Hospitalized Patients

BACKGROUND

The COVID-19 pandemic has had a significant impact globally, and understanding the relationship between inflammatory markers and disease progression is crucial for effective management. This retrospective study aimed to examine the association between various inflammatory markers, such as C-reactive protein (CRP), the erythrocyte sedimentation rate (ESR), lactate dehydrogenase (LDH), D-dimer, ferritin, and procalcitonin (PCT), and the characteristics of disease progression and outcomes in individuals affected by COVID-19.

METHODS

This study collected raw data from 470 patients who tested positive for SARS-CoV-2 using RT-PCR.

RESULTS

The logistic regression analysis revealed that elevated LDH levels were associated with male gender, ICU admission, low oxygen saturation (O2 < 93%), the need for mechanical ventilation, death, and the presence of lung infiltrates. Higher D-dimer levels were associated with older age, diabetes mellitus, cardiac disease, and low oxygen saturation. Ferritin levels were significantly associated with older age, ICU admission, low oxygen saturation, mechanical ventilation, and lung infiltrates. In contrast, CRP was only significant regarding lung infiltrates and procalcitonin levels were not significantly associated with any of the examined factors.

CONCLUSION

This study highlights the importance of monitoring key inflammatory markers, such as LDH, D-dimer, and ferritin, as they are significantly associated with the severity of COVID-19 illness. These findings can inform clinical decision-making and guide the development of targeted interventions to improve patient outcomes.

Investigating the Potential Impact of CCR5-Δ32 Variant on COVID-19 Outcome: A Case-Control Study in Iranian Population

Background

The impact of CCR5-Δ32 on COVID-19 outcomes has been the focus of much research. This genetic variant may protect against SARS-CoV-2 infection, while others have produced conflicting results. Given the controversial results of previous research on different populations, we aimed to investigate the possible association between the CCR5-Δ32 variant and COVID-19 severity in an Iranian population.

Methods

This case-control study was conducted between 25th of April till 10th of October 2021 at Rasoul Akram Hospital of Iran University of Medical Sciences, Tehran, Iran. We investigated the association between CCR5-Δ32 genotype and COVID-19 severity in 200 unrelated Iranian patients. The patients were divided into 2 groups: 100 patients with severe COVID-19 (case group) and 100 patients with mild COVID-19 (control group). Genotyping of CCR5-Δ32 was performed using the polymerase chain reaction (PCR) technique.

Results

The frequency of CCR5-Δ32 allele was 11 in the case group and 16 in the control group. However, no significant association was found between this genetic variant and the clinical outcomes of COVID-19.

Conclusion

The CCR5-Δ32 variant cannot serve as a reliable predictive factor for identifying individuals prone to developing severe COVID-19 in Iranian population. Additionally, targeting CCR5 would not be a viable treatment approach for COVID-19 in Iranians.

Dynamic changes of hematological and hemostatic parameters in COVID-19 hospitalized patients: Potential role as severity biomarkers for the Chilean population

Background

COVID-19 is still a global health issue, there is limited evidence in South America regarding laboratory biomarkers associated with severe disease. The objective of our study was to identify hematological and hemostatic changes associated with severe COVID-19.

Methods

A total of 170 hospitalized patients with COVID19 were included in the study, defining their severity according to established criteria. Demographic, clinical, and laboratory (days 1, 3, 7, 15) data were obtained. We performed a statistical analysis, assuming significance with a value of p < 0.05. We analyzed the correlation between severity and biomarkers and established cut-off values for severe patients through ROC curves, estimating Odds Ratio associated with severe disease.

Results

Day 1 was observed significant differences between moderate vs severe patients for leukocytes (WBC), Neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR) and D-dimer, establishing cut-off points for each of them. The markers we found associated to risk of severe disease were WBC (OR=3.2396; p = 0.0003), NLR (OR=5.7084; p < 0.0001), PLR (OR=4.4094; p < 0.0001), Neutrophil (OR=4.1193; p < 0.0001), D-dimer (OR=2.7827; p = 0.0124).

Conclusions

The results allow to establish basic laboratory biomarkers associated to severe disease, which could be used as prognostic markers.

Detection and Characterisation of SARS-CoV-2 in Eastern Province of Zambia: A Retrospective Genomic Surveillance Study

Mutations have driven the evolution and development of new variants of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with potential implications for increased transmissibility, disease severity and vaccine escape among others. Genome sequencing is a technique that allows scientists to read the genetic code of an organism and has become a powerful tool for studying emerging infectious diseases. Here, we conducted a cross-sectional study in selected districts of the Eastern Province of Zambia, from November 2021 to February 2022. We analyzed SARS-CoV-2 samples (n = 76) using high-throughput sequencing. A total of 4097 mutations were identified in 69 SARS-CoV-2 genomes with 47% (1925/4097) of the mutations occurring in the spike protein. We identified 83 unique amino acid mutations in the spike protein of the seven Omicron sublineages (BA.1, BA.1.1, BA.1.14, BA.1.18, BA.1.21, BA.2, BA.2.23 and XT). Of these, 43.4% (36/83) were present in the receptor binding domain, while 14.5% (12/83) were in the receptor binding motif. While we identified a potential recombinant XT strain, the highly transmissible BA.2 sublineage was more predominant (40.8%). We observed the substitution of other variants with the Omicron strain in the Eastern Province. This work shows the importance of pandemic preparedness and the need to monitor disease in the general population.

Association of the @ctivehip tele-rehabilitation with the fear of falling of older adults with hip fracture and their family caregivers: Secondary analysis of a non-randomised controlled trial

INTRODUCTION

Osteoporotic hip fracture is a major health problem. Falls, the primary cause, might lead to a persistent fear of falling (FoF) among older adults, affecting their daily activities and rehabilitation. While in-person interventions exist, limited research is available on the effects of tele-rehabilitation on the FoF after a hip fracture. Thus, this study aims to test the association of the @ctivehip tele-rehabilitation programme on reducing the levels of FoF experienced by both older adults with hip fracture and their family caregivers.

METHODS

A non-randomised controlled trial (ClinicalTrials.gov; Identifier: NCT02968589) that compared a webpage-based tele-rehabilitation (@ctivehip) against usual care. Fear of falling was assessed using the Short Falls Efficacy Scale-International. Patients' functional status was evaluated using the Functional Independence Measure. Physical performance was assessed by the Timed Up and Go test and Short Physical Performance Battery. We conducted a per-protocol analysis as the primary outcome, and an intention-to-treat approach as secondary analysis.

RESULTS

A total of 71 patients with hip fracture (78.75 ± 6.12 years, 75% women) and their family caregivers participated. Participants in the intervention showed a higher decrease in FoF in comparison to those in the usual care (0.5 Cohen's d; p = 0.042). The reduction in FoF resulting from participation in the tele-rehabilitation programme was mediated by improvements in functional status by 79%. The @ctivehip programme did not decrease FoF of family caregivers.

DISCUSSION

@ctivehip is associated with a reduction of the FoF in older adults with hip fractures, but not in their family caregivers, with the reduction being mostly explained by improvements in the patients' functional status. Although the intervention seems promising, it should not be applied in clinical settings until confirmed by appropriate-designed randomised clinical trials.

Metabolic profiling during COVID-19 infection in humans: Identification of potential biomarkers for occurrence, severity and outcomes using machine learning

BACKGROUND

After its emergence in China, the coronavirus SARS-CoV-2 has swept the world, leading to global health crises with millions of deaths. COVID-19 clinical manifestations differ in severity, ranging from mild symptoms to severe disease. Although perturbation of metabolism has been reported as a part of the host response to COVID-19 infection, scarce data exist that describe stage-specific changes in host metabolites during the infection and how this could stratify patients based on severity.

METHODS

Given this knowledge gap, we performed targeted metabolomics profiling and then used machine learning models and biostatistics to characterize the alteration patterns of 50 metabolites and 17 blood parameters measured in a cohort of 295 human subjects. They were categorized into healthy controls, non-severe, severe and critical groups with their outcomes. Subject's demographic and clinical data were also used in the analyses to provide more robust predictive models.

RESULTS

The non-severe and severe COVID-19 patients experienced the strongest changes in metabolite repertoire, whereas less intense changes occur during the critical phase. Panels of 15, 14, 2 and 2 key metabolites were identified as predictors for non-severe, severe, critical and dead patients, respectively. Specifically, arginine and malonyl methylmalonyl succinylcarnitine were significant biomarkers for the onset of COVID-19 infection and tauroursodeoxycholic acid were potential biomarkers for disease progression. Measuring blood parameters enhanced the predictive power of metabolic signatures during critical illness.

CONCLUSIONS

Metabolomic signatures are distinctive for each stage of COVID-19 infection. This has great translation potential as it opens new therapeutic and diagnostic prospective based on key metabolites.

Hemostatic and liver function parameters as COVID-19 severity markers

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a disease newly discovered in December 2019 which affects coagulation cascade and liver functions. The aim of this study was to investigate the potential of hemostatic and liver function parameters as severity markers in COVID-19 patients. This study was an observational analytic with cohort retrospective design using total sampling method. Data were retrieved from medical record of COVID-19 patients admitted to provincial hospital in Banda Aceh, Indonesia from March 2020 to March 2022. There were 1208 data eligible for the study after applying certain criteria. Mann-Whitney, logistic regression, and receiving operating characteristic (ROC) analyses were used to analysis the data. Thrombocyte count (p<0.001), prothrombin time (p<0.001), activated partial thromboplastin time (p<0.001), D-dimer (p<0.001), fibrinogen (p<0.001), aspartate aminotransferase (p<0.001), and alanine transaminase (p<0.001) significantly increased in severe compared to mild COVID-19 patients. After being adjusted, age (odds ratio (OR); 1.026 (95% confidence interval (CI): 1.016-1.037) was the most significant factor in predicting COVID-19 severity. Fibrinogen (cut-off 526.5 mg/L) was the best parameter associated with COVID-19 severity with 70% sensitivity and 66.4% specificity. Meanwhile, D-dimer (cut-off 805 ng/mL) had a sensitivity of 72.3% and specificity of 66.4%. Combining the parameters resulted in improved sensitivity to 82.0% with a slight decline of specificity to 65.5%. In conclusion, fibrinogen and D-dimer level on admission could be used as biomarkers in predicting COVID-19 prognosis. Routine monitoring and evaluation of laboratory testing especially D-dimer and fibrinogen could be implemented in order to reduce morbidity and mortality rate of COVID-19.

Clinical Manifestations and Outcomes of Older Patients with COVID-19: A Comprehensive Review

The consequences of coronavirus disease 2019 (COVID-19) are particularly severe in older adults with a disproportionate number of severe and fatal outcomes. Therefore, this integrative review aimed to provide a comprehensive overview of the clinical characteristics, management approaches, and prognosis of older patients diagnosed with COVID-19. Common clinical presentations in older patients include fever, cough, and dyspnea. Additionally, preexisting comorbidities, especially diabetes and pulmonary and cardiovascular diseases, were frequently observed and associated with adverse outcomes. Management strategies varied, however, early diagnosis, vigilant monitoring, and multidisciplinary care were identified as key factors for enhancing patient outcomes. Nonetheless, the prognosis remains guarded for older patients, with increased rates of hospitalization, mechanical ventilation, and mortality. However, timely therapeutic interventions, especially antiviral and supportive treatments, have demonstrated some efficacy in mitigating the severe consequences in this age group. In conclusion, while older adults remain highly susceptible to severe outcomes from COVID-19, early intervention, rigorous monitoring, and comprehensive care can play a pivotal role in improving their clinical outcomes.

Utility of the Xpert Xpress CoV-2/Flu/RSV Plus Kit: A Glance at RSV Infection in Adults and Coinfection Rate

Background

Viral infection is responsible for the majority of respiratory tract infections (RTI). This retrospective study evaluates the advantages of using the multiplex Xpert Xpress CoV-2/Flu/RSV plus kit in laboratory diagnosis of RTI caused by the most related viruses.

Materials and Methods

Data were obtained from medical records between November 2021 and May 2023 for any sample tested using either the Xpert Xpress Flu/RSV kit, Xpert Xpress Flu kit, Xpert Xpress SARS-CoV-2 kit, and Xpert Xpress CoV-2/Flu/RSV plus kit.

Results

Influenza virus A was detected in 8.5% (55/649) of the samples using the Flu PCR kit and in 4.9% (123/2538) using CoV-2/Flu/RSV plus kit, while influenza virus B detection rates were 3.7% (24/649) using the Flu PCR kit and 1.7% (43/2538) using the CoV-2/Flu/RSV plus kit. However, the detection rates using the two kits were comparable when evaluated for the same time period of the year. SARS-CoV-2 infections were detected in 16.9% (1545/9153) and 10.5% (266/2538) of the cases using the SARS-CoV-2 kit and CoV-2/Flu/RSV plus kits, respectively. Respiratory syncytial virus (RSV) was identified in 17.7% (66/372) of children using Flu/RSV kit; this number dropped significantly when age-targeted testing of RSV was performed due to involvements of adults. With the CoV-2/Flu/RSV plus kit, about 34% (35/103) of RSV infections detected were in patients aged >20 years; these cases would have previously been overlooked because adults are not routinely tested for RSV using the Flu/RSV kit. All coinfection cases (n = 16) were only detected with the CoV-2/Flu/RSV plus kit.

Conclusion

The use of Xpert Xpress CoV-2/Flu/RSV plus kit not only results in shorter turnaround times through accurate detection of all four viruses, but also provides information on RSV infection in adults and coinfection rates.

Trends of the COVID-19 dynamics in 2022 and 2023 vs. the population age, testing and vaccination levels

Introduction

The population, governments, and researchers show much less interest in the COVID-19 pandemic. However, many questions still need to be answered: why the much less vaccinated African continent has accumulated 15 times less deaths per capita than Europe? or why in 2023 the global value of the case fatality risk is almost twice higher than in 2022 and the UK figure is four times higher than the global one?

Methods

The averaged daily numbers of cases DCC and death DDC per million, case fatality risks DDC/DCC were calculated for 34 countries and regions with the use of John Hopkins University (JHU) datasets. Possible linear and non-linear correlations with the averaged daily numbers of tests per thousand DTC, median age of population A, and percentages of vaccinations VC and boosters BC were investigated.

Results

Strong correlations between age and DCC and DDC values were revealed. One-year increment in the median age yielded 39.8 increase in DCC values and 0.0799 DDC increase in 2022 (in 2023 these figures are 5.8 and 0.0263, respectively). With decreasing of testing level DTC, the case fatality risk can increase drastically. DCC and DDC values increase with increasing the percentages of fully vaccinated people and boosters, which definitely increase for greater A. After removing the influence of age, no correlations between vaccinations and DCC and DDC values were revealed.

Discussion

The presented analysis demonstrates that age is a pivot factor of visible (registered) part of the COVID-19 pandemic dynamics. Much younger Africa has registered less numbers of cases and death per capita due to many unregistered asymptomatic patients. Of great concern is the fact that COVID-19 mortality in 2023 in the UK is still at least 4 times higher than the global value caused by seasonal flu.

Baseline and early changes in laboratory parameters predict disease severity and fatal outcomes in COVID-19 patients

Introduction

Coronavirus disease 2019 (COVID-19) has become the worst catastrophe of the twenty-first century and has led to the death of more than 6.9 million individuals across the globe. Despite the growing knowledge of the clinicopathological features of COVID-19, the correlation between baseline and early changes in the laboratory parameters and the clinical outcomes of patients is not entirely understood.

Methods

Here, we conducted a time series cross-sectional study aimed at assessing different measured parameters and socio-demographic factors that are associated with disease severity and the outcome of the disease in 268 PCR-confirmed COVID-19 Patients.

Results

We found COVID-19 patients who died had a median age of 61 years (IQR, 50 y - 70 y), which is significantly higher (p < 0.05) compared to those who survived and had a median age of 54 years (IQR, 42y - 65y). The median RBC count of COVID-19 survivors was 4.9 × 106/μL (IQR 4.3 × 106/μL - 5.2 × 106/μL) which is higher (p < 0.05) compared to those who died 4.4 × 106/μL (3.82 × 106/μL - 5.02 × 106/μL). Similarly, COVID-19 survivors had significantly (p < 0.05) higher lymphocyte and monocyte percentages compared to those who died. One important result we found was that COVID-19 patients who presented with severe/critical cases at the time of first admission but managed to survive had a lower percentage of neutrophil, neutrophil to lymphocyte ratio, higher lymphocyte and monocyte percentages, and RBC count compared to those who died.

Conclusion

To conclude here, we showed that simple laboratory parameters can be used to predict severity and outcome in COVID-19 patients. As these parameters are simple, inexpensive, and radially available in most resource-limited countries, they can be extrapolated to future viral epidemics or pandemics to allocate resources to particular patients.

VEGFR and DPP-IV as Markers of Severe COVID-19 and Predictors of ICU Admission

The pathophysiology of the severe course of COVID-19 is multifactorial and not entirely elucidated. However, it is well known that the hyperinflammatory response and cytokine storm are paramount events leading to further complications. In this paper, we investigated the vascular response in the pathophysiology of severe COVID-19 and aimed to identify novel biomarkers predictive of ICU admission. The study group consisted of 210 patients diagnosed with COVID-19 (age range: 18-93; mean ± SD: 57.78 ± 14.16), while the control group consisted of 80 healthy individuals. We assessed the plasma concentrations of various vascular factors using the Luminex technique. Then, we isolated RNA from blood mononuclear cells and performed a bioinformatics analysis investigating various processes related to vascular response, inflammation and angiogenesis. Our results confirmed that severe COVID-19 is associated with vWF/ADAMTS 13 imbalance. High plasma concentrations of VEGFR and low DPP-IV may be potential predictors of ICU admission. SARS-CoV-2 infection impairs angiogenesis, hinders the generation of nitric oxide, and thus impedes vasodilation. The hypercoagulable state develops mainly in the early stages of the disease, which may contribute to the well-established complications of COVID-19.

Interleukin-6 and the determinants of severe COVID-19: A retrospective cohort study

Cytokines, notably interleukin-6 (IL-6), increase considerably in patients with severe corona virus disease 2019 (COVID-19). This vigorous immune response may cause end-organ failure or death; hence, measuring IL-6 in the context of patient characteristics may help predict outcomes and encourage early comprehensive therapy. This study investigated the association between serum IL-6 levels, COVID-19 severity, and demographic, clinical, and biochemical characteristics. COVID-19 inpatients in NMC hospitals were investigated between November 2020 and November 2021. Several patient variables related to serum IL-6 and COVID-19 severity have been examined. The study included 374 COVID-19 inpatients, 235 of whom had severe disease with a median age of 51. The elderly had an increased risk of severe COVID-19 (73.8%) compared with young adults (71%), with higher white blood cells, D-dimer, Lactate dehydrogenase, creatinine, ferritin, prothrombin time, Procalcitonin, and fibrinogen levels (P < .001). C-reactive protein, troponin, intensive care unit admission, disease severity score, and mortality were significantly associated with higher serum IL-6 levels (P = .05) in the univariate analysis, but this significance disappeared in the multivariate analysis. IL-6, along with other demographic and clinical variables affected COVID-19 severity. These characteristics may predict patients at risk of severe disease and assist in establishing early comprehensive disease outcome strategies. Large-scale clinical research is needed to emphasize IL-6 and COVID-19.

Adverse effect of VEGFR-2 (rs1870377) polymorphism on the clinical course of COVID-19 in females and males in an age-dependent manner

The COVID-19 pandemic has affected people worldwide with varying clinical presentations ranging from mild to severe or fatal, and studies have found that age, gender, and some comorbidities can influence the severity of the disease. It would be valuable to have genetic markers that might help predict the likely outcome of infection. For this objective, genes encoding VEGFR-2 (rs1870377), CCR5Δ32 (rs333), and TLR3 (rs5743313) were analyzed for polymorphisms in the peripheral blood of 160 COVID-19 patients before COVID-19 vaccine was available in Türkiye. We observed that possession of the VEGFR-2 rs1870377 mutant allele increased the risk of severe/moderate disease in females and subjects ≥65 years of age, but was protective in males <65 years of age. Other significant results were that the CCR5Δ32 allele was protective against severe disease in subjects ≥65 years of age, while TLR3 rs5743313 polymorphism was found to be protective against severe/moderate illness in males <65 years of age. The VEGFR-2 rs1870377 mutant allele was a risk factor for severe/moderate disease, particularly in females over the age of 65. These findings suggest that genetic polymorphisms have an age- and sex-dependent influence on the severity of COVID-19, and the VEGFR-2 rs1870377 mutant allele could be a potential predictor of disease severity.

Artificial Intelligence Models in Health Information Exchange: A Systematic Review of Clinical Implications

Electronic health record (EHR) systems collate patient data, and the integration and standardization of documents through Health Information Exchange (HIE) play a pivotal role in refining patient management. Although the clinical implications of AI in EHR systems have been extensively analyzed, its application in HIE as a crucial source of patient data is less explored. Addressing this gap, our systematic review delves into utilizing AI models in HIE, gauging their predictive prowess and potential limitations. Employing databases such as Scopus, CINAHL, Google Scholar, PubMed/Medline, and Web of Science and adhering to the PRISMA guidelines, we unearthed 1021 publications. Of these, 11 were shortlisted for the final analysis. A noticeable preference for machine learning models in prognosticating clinical results, notably in oncology and cardiac failures, was evident. The metrics displayed AUC values ranging between 61% and 99.91%. Sensitivity metrics spanned from 12% to 96.50%, specificity from 76.30% to 98.80%, positive predictive values varied from 83.70% to 94.10%, and negative predictive values between 94.10% and 99.10%. Despite variations in specific metrics, AI models drawing on HIE data unfailingly showcased commendable predictive proficiency in clinical verdicts, emphasizing the transformative potential of melding AI with HIE. However, variations in sensitivity highlight underlying challenges. As healthcare's path becomes more enmeshed with AI, a well-rounded, enlightened approach is pivotal to guarantee the delivery of trustworthy and effective AI-augmented healthcare solutions.

The Risk Factors of COVID-19 Infection and Mortality among Older Adults in South Korea

BACKGROUND

This study aimed to identify the risk factors associated with coronavirus disease 2019 (COVID-19) infection and mortality among older adults in South Korea.

METHODS

Using Korean National Health Insurance data from January 1, 2020, to March 31, 2022, we analyzed the impact of various factors, including age, comorbidity burden, and insurance type, on COVID-19 infection and mortality rates.

RESULTS

Age was the most significant risk factor for mortality in older adults. A higher comorbidity burden was also associated with increased infection (odds ratio [OR]=1.33 for Charlson Comorbidity Index [CCI] ≥2, 95% confidence interval [CI] 1.321-1.339) and mortality (OR=1.537 for CCI ≥2, 95% CI 1.459-1.618) rates. While Medical Aid recipients exhibited lower infection rates (OR=0.898, 95% CI 0.89-0.906) than National Health Insurance beneficiaries, they had higher mortality rates (OR=1.692, 95% CI 1.623-1.763).

CONCLUSION

These results emphasized the need to prioritize vaccination and allocate healthcare resources for older adults, particularly those with multiple comorbidities. Addressing socioeconomic disparities and ensuring equitable access to testing and healthcare services are crucial for mitigating the impact of COVID-19 on older adults.

Post-COVID-19 illness and associations with sex and gender

BACKGROUND

Post-COVID-19 syndromes have associated with female sex, but the pathophysiological basis is uncertain.

AIM

There are sex differences in myocardial inflammation identified using cardiac magnetic resonance (CMR) in post-COVID-19 patients, and in patient reported health outcomes following COVID-19 infection.

DESIGN

This prospective study investigated the time-course of multiorgan injury in survivors of COVID-19 during convalescence.

METHODS

Clinical information, blood biomarkers, and patient reported outcome measures were prospectively acquired at enrolment (visit 1) and 28-60 days post-discharge (visit 2). Chest computed tomography (CT) and CMR were performed at visit 2. Follow-up was carried out for serious adverse events, including death and rehospitalization.

RESULTS

Sixty-nine (43%) of 159 patients recruited were female. During the index admission, females had a lower peak C-reactive protein (74 mg/l (21,163) versus 123 mg/l (70, 192) p = 0.008) and peak ferritin (229 μg/l (103, 551) versus 514 μg/l (228, 1122) p < 0.001). Using the Modified Lake-Louise criteria, females were more likely to have definite evidence of myocardial inflammation (54% (37/68) versus 33% (30/90) p = 0.003). At enrolment and 28-60 days post-discharge, enhanced illness perception, higher levels of anxiety and depression and lower predicted maximal oxygen utilization occurred more commonly in women. The mean (SD, range) duration of follow-up after hospital discharge was 450 (88) days (range 290, 627 days). Compared to men, women had lower rates of cardiovascular hospitalization (0% versus 8% (7/90); p = 0.018).

CONCLUSIONS

Women demonstrated worse patient reported outcome measures at index admission and 28-60 days follow-up though cardiovascular hospitalization was lower.

Comorbidities of COVID-19 Patients

The novel Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) responsible for the coronavirus disease outbreak initiated in 2019 (COVID-19) has been shown to affect the health of infected patients in a manner at times dependent on pre-existing comorbidities. Reported here is an overview of the correlation between comorbidities and the exacerbation of the disease in patients with COVID-19, which may lead to poor clinical outcomes or mortality. General medical issues are also reviewed, such as the types of symptoms present in people infected with SARS-CoV-2, the long-term effects of COVID-19 disease, and the types of treatment that are currently used.

Using machine learning to identify patient characteristics to predict mortality of in-patients with COVID-19 in south Florida

Introduction

The SARS-CoV-2 (COVID-19) pandemic has created substantial health and economic burdens in the US and worldwide. As new variants continuously emerge, predicting critical clinical events in the context of relevant individual risks is a promising option for reducing the overall burden of COVID-19. This study aims to train an AI-driven decision support system that helps build a model to understand the most important features that predict the "mortality" of patients hospitalized with COVID-19.

Methods

We conducted a retrospective analysis of "5,371" patients hospitalized for COVID-19-related symptoms from the South Florida Memorial Health Care System between March 14th, 2020, and January 16th, 2021. A data set comprising patients' sociodemographic characteristics, pre-existing health information, and medication was analyzed. We trained Random Forest classifier to predict "mortality" for patients hospitalized with COVID-19.

Results

Based on the interpretability of the model, age emerged as the primary predictor of "mortality", followed by diarrhea, diabetes, hypertension, BMI, early stages of kidney disease, smoking status, sex, pneumonia, and race in descending order of importance. Notably, individuals aged over 65 years (referred to as "older adults"), males, Whites, Hispanics, and current smokers were identified as being at higher risk of death. Additionally, BMI, specifically in the overweight and obese categories, significantly predicted "mortality". These findings indicated that the model effectively learned from various categories, such as patients' sociodemographic characteristics, pre-hospital comorbidities, and medications, with a predominant focus on characterizing pre-hospital comorbidities. Consequently, the model demonstrated the ability to predict "mortality" with transparency and reliability.

Conclusion

AI can potentially provide healthcare workers with the ability to stratify patients and streamline optimal care solutions when time is of the essence and resources are limited. This work sets the platform for future work that forecasts patient responses to treatments at various levels of disease severity and assesses health disparities and patient conditions that promote improved health care in a broader context. This study contributed to one of the first predictive analyses applying AI/ML techniques to COVID-19 data using a vast sample from South Florida.

Association between dietary antioxidant quality score and severity of coronavirus infection: a case-control study

The purpose of this study was to examine the association between the dietary antioxidant quality score (DAQS) and the severity of Coronavirus disease 2019 (COVID-19). The present case-control study was carried out on 295 patients diagnosed with COVID-19 (≥18 years old), including 104 critical patients (Intensive care unit [ICU] admission) and 191 COVID-19 patients without severe complications (Non-intensive care unit [Non-ICU] patients) as cases. Dietary intake was assessed by a 147-item, semi-quantitative food frequency questionnaire (FFQ). Logistic regression was performed to calculate the odds ratio (OR) and 95% confidence interval (CI) for the considered risk factors. Our outcomes (after multivariate adjustment) suggested that higher adherence to DAQS was significantly associated with a decreased risk of COVID-19 infection severity (OR = 0.12; 95% CI: 0.04-0.29, p < 0.001). Similar results were seen when analyzed by sex [men (OR = 0.02; 95% CI: 0.002-0.15, p < 0.001) and women (OR = 0.21; 95% CI: 0.06-0.68, p = 0.012)]. A significant association between vitamin D3 intake and decreased risk of COVID-19 severity (OR = 0.91; 95% CI: 0.89-0.94, p < 0.001) was also observed. Moreover, multivariate results revealed that there were no significant associations between vitamin C (OR = 1.00; 95% CI: 0.99-1.00, p = 0.067), vitamin E (OR = 0.98; 95% CI: 0.86-1.11, p = 0.798), zinc (OR = 1.02; 95% CI: 0.86-1.20, p = 0.805), and selenium (OR = 0.99; 95% CI: 0.99-1.00, p = 0.077) intakes with the risk of COVID-19 severity. However, subgroup analyses by sex suggested a significant association between vitamin C intake and the risk of COVID-19 infection severity in women (OR = 1.00; 95% CI: 1.00-1.00, p = 0.028). Our findings showed a negative association between DAQS adherence and the risk of COVID-19 infection severity. Our results may be used to develop potential dietary therapies to decrease COVID-19 severity.

Towards Predicting Length of Stay and Identification of Cohort Risk Factors Using Self-Attention-Based Transformers and Association Mining: COVID-19 as a Phenotype

Predicting length of stay (LoS) and understanding its underlying factors is essential to minimizing the risk of hospital-acquired conditions, improving financial, operational, and clinical outcomes, and better managing future pandemics. The purpose of this study was to forecast patients' LoS using a deep learning model and to analyze cohorts of risk factors reducing or prolonging LoS. We employed various preprocessing techniques, SMOTE-N to balance data, and a TabTransformer model to forecast LoS. Finally, the Apriori algorithm was applied to analyze cohorts of risk factors influencing hospital LoS. The TabTransformer outperformed the base machine learning models in terms of F1 score (0.92), precision (0.83), recall (0.93), and accuracy (0.73) for the discharged dataset and F1 score (0.84), precision (0.75), recall (0.98), and accuracy (0.77) for the deceased dataset. The association mining algorithm was able to identify significant risk factors/indicators belonging to laboratory, X-ray, and clinical data, such as elevated LDH and D-dimer levels, lymphocyte count, and comorbidities such as hypertension and diabetes. It also reveals what treatments have reduced the symptoms of COVID-19 patients, leading to a reduction in LoS, particularly when no vaccines or medication, such as Paxlovid, were available.

SARS-CoV-2 susceptibility and COVID-19 illness course and outcome in people with pre-existing neurodegenerative disorders: systematic review with frequentist and Bayesian meta-analyses

BACKGROUND

People with neurodegenerative disease and mild cognitive impairment (MCI) may have an elevated risk of acquiring severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and may be disproportionally affected by coronavirus disease 2019 (COVID-19) once infected.

AIMS

To review all eligible studies and quantify the strength of associations between various pre-existing neurodegenerative disorders and both SARS-CoV-2 susceptibility and COVID-19 illness course and outcome.

METHOD

Pre-registered systematic review with frequentist and Bayesian meta-analyses. Systematic searches were executed in PubMed, Web of Science and preprint servers. The final search date was 9 January 2023. Odds ratios (ORs) were used as measures of effect.

RESULTS

In total, 136 primary studies (total sample size n = 97 643 494), reporting on 268 effect-size estimates, met the inclusion criteria. The odds for a positive SARS-CoV-2 test result were increased for people with pre-existing dementia (OR = 1.83, 95% CI 1.16-2.87), Alzheimer's disease (OR = 2.86, 95% CI 1.44-5.66) and Parkinson's disease (OR = 1.65, 95% CI 1.34-2.04). People with pre-existing dementia were more likely to experience a relatively severe COVID-19 course, once infected (OR = 1.43, 95% CI 1.00-2.03). People with pre-existing dementia or Alzheimer's disease were at increased risk for COVID-19-related hospital admission (pooled OR range: 1.60-3.72). Intensive care unit admission rates were relatively low for people with dementia (OR = 0.54, 95% CI 0.40-0.74). All neurodegenerative disorders, including MCI, were at higher risk for COVID-19-related mortality (pooled OR range: 1.56-2.27).

CONCLUSIONS

Our findings confirm that, in general, people with neurodegenerative disease and MCI are at a disproportionally high risk of contracting COVID-19 and have a poor outcome once infected.

Thirty-Day Outcomes of Young and Middle-Aged Adults Admitted with Severe COVID-19 in Uganda: A Retrospective Cohort Study

Background

There is scarcity of data regarding young and middle-aged adults hospitalized with severe Corona Virus Disease 2019 (COVID-19) in Africa. In this study, we describe the clinical characteristics and 30-day survival among adults aged 18 to 49 years admitted with severe COVID-19 in Uganda.

Methods

We reviewed treatment records of patients admitted with severe COVID-19 across five COVID-19 treatment units (CTU) in Uganda. We included individuals aged 18 to 49 years, who had a positive test or met the clinical criteria for COVID-19. We defined severe COVID-19 as having an oxygen saturation <94%, lung infiltrates >50% on imaging and presence of a co-morbidity that required admission in the CTU. Our main outcome was the 30-day survival from the time of admission. We used a Cox proportional hazards model to determine the factors associated with 30-day survival at a 5% level of significance.

Results

Of the 246 patient files reviewed, 50.8% (n = 125) were male, the mean ± (standard deviation) age was 39 ± 8 years, majority presented with cough, 85.8% (n = 211) and median C-reactive protein (interquartile range) was 48 (47.5, 178.8) mg/L. The 30-day mortality was 23.9% (59/246). At admission, anemia (hazard ratio (HR): 3.00, 95% confidence interval (CI), 1.32-6.82; p = 0.009) and altered mental state (GCS <15) (HR: 6.89, 95% CI: 1.48-32.08, p = 0.014) were significant predictors of 30-day mortality.

Conclusion

There was a high 30-day mortality among young and middle-aged adults with severe COVID-19 in Uganda. Early recognition and targeted management of anemia and altered consciousness are needed to improve clinical outcomes.

Post-COVID-19 Brain [18F] FDG-PET Findings: A Retrospective Single-Center Study in the United States

BACKGROUND AND PURPOSE

The pathophysiology of neurologic manifestations of postacute sequelae of Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) infection is not clearly understood. Our aim was to investigate brain metabolic activity on [18F] FDG-PET/CT scans in patients with a history of coronavirus disease 2019 (COVID-19) infection before imaging.

MATERIALS AND METHODS

This retrospective study included 45 patients who underwent [18F] FDG-PET/CT imaging for any reason and had, at least once, tested positive for COVID-19 at any time before imaging. Fifteen patients had available [18F] FDG-PET scans obtained under identical conditions before the infection. A group of 52 patients with melanoma or multiple myeloma who underwent [18F] FDG-PET/CT were used as controls. Whole-brain 2-sample t test analysis was performed using SPM software to identify clusters of hypo- and hypermetabolism and compare brain metabolic activity between patients with COVID-19 and controls. Paired sample t test comparison was also performed for 15 patients, and correlations between metabolic values of clusters and clinical data were measured.

RESULTS

Compared with the control group, patients with a history of COVID-19 infection exhibited focal areas of hypometabolism in the bilateral frontal, parietal, occipital, and posterior temporal lobes and cerebellum (P = .05 uncorrected at the voxel level, family-wise error-corrected at the cluster level) that peaked during the first 2 months, improved to near-complete recovery around 6 months, and disappeared at 12 months. Hypermetabolism involving the brainstem, cerebellum, limbic structures, frontal cortex, and periventricular white matter was observed only at 2-6 months after infection. Older age, neurologic symptoms, and worse disease severity scores positively correlated with the metabolic changes.

CONCLUSIONS

This study demonstrates a profile of time-dependent brain PET hypo- and hypermetabolism in patients with confirmed SARS-CoV-2 infection.

Self-perception of Voice Handicap in COVID 19 Patients

Voice abnormalities were reported in patients during the course of COVID-19 infection. This study aims to evaluate the effect of COVID 19 infection on the self-perception of voice handicap in positive COVID 19 patients in addition to investigating the factors that might correlate with voice handicap if present. Voice handicap index-10 was filled in by 200 patients that were confirmed to be COVID 19 positive based on the RT-qPCR and symptomatology of the disease. The result showed that about 65.5% had mild degree of COVID 19 and 27.5% had moderate degree. Dysphonia was reported by 19% of the patients when questioned about voice symptoms. Dysphonia was detected in 35% of them by auditory perceptual assessment. Symptoms of Dyspnea, dysphonia, headache were significantly correlated with total and subtotal scores of Voice handicap index. COVID 19 infection has a negative impact on some of the patients? self- perception of voice handicap on the functional, physical and emotional domains. Age and degree of COVID 19 severity were correlating with the patients? self -perception of voice handicap.

Prevalence and clinical implications of abnormal body composition phenotypes in patients with COVID-19: a systematic review

BACKGROUND

The impact of body composition (BC) abnormalities on coronavirus disease 2019 (COVID-19) outcomes remains to be determined.

OBJECTIVE

We summarized the evidence on BC abnormalities and their relationship with adverse clinical outcomes in patients with COVID-19.

METHODS

A systematic search was conducted up until September 26, 2022 for observational studies using BC techniques to quantify skeletal muscle mass (or related compartments), muscle radiodensity or echo intensity, adipose tissue (AT, or related compartments), and phase angle (PhA) in adults with COVID-19. Methodological quality of studies was assessed using the Newcastle-Ottawa Scale. A synthesis without meta-analysis was conducted to summarize the prevalence of BC abnormalities and their significant associations with clinical outcomes.

RESULTS

We included 62 studies (69.4% low risk of bias) with 12 to 1,138 participants, except three with up to 490,301 participants. Using computed tomography and different cut-offs, prevalence ranged approximately from 22-90% for low muscle mass, 12-85% for low muscle radiodensity, 16-70% for high visceral AT. Using bioelectrical impedance analysis (BIA), prevalence of high fat mass was 51% and low PhA was 22-88%. Mortality was inversely related to PhA (3/4 studies) and positively related to intramuscular AT (4/5 studies), muscle echo intensity (2/2 studies), and BIA-estimated fat mass (2/2 studies). Intensive care unit admission was positively related to visceral AT (6/7 studies) and total AT (2/3 studies). Disease severity and hospitalization outcomes were positively related to intramuscular AT (2/2 studies). Inconsistent associations were found for the rest of BC measures and hospitalization outcomes.

CONCLUSIONS

Abnormalities in BC were prevalent in patients with COVID-19. Although conflicting associations were observed among certain BC abnormalities and clinical outcomes, higher muscle echo intensity (reflective of myosteatosis) and lower PhA were more consistently associated with greater mortality risk. Likewise, high IMAT and VAT were associated with mortality and ICU admission, respectively.

Host–Pathogen Interactions Influencing Zoonotic Spillover Potential and Transmission in Humans

Emerging infectious diseases of zoonotic origin are an ever-increasing public health risk and economic burden. The factors that determine if and when an animal virus is able to spill over into the human population with sufficient success to achieve ongoing transmission in humans are complex and dynamic. We are currently unable to fully predict which pathogens may appear in humans, where and with what impact. In this review, we highlight current knowledge of the key host–pathogen interactions known to influence zoonotic spillover potential and transmission in humans, with a particular focus on two important human viruses of zoonotic origin, the Nipah virus and the Ebola virus. Namely, key factors determining spillover potential include cellular and tissue tropism, as well as the virulence and pathogenic characteristics of the pathogen and the capacity of the pathogen to adapt and evolve within a novel host environment. We also detail our emerging understanding of the importance of steric hindrance of host cell factors by viral proteins using a “flytrap”-type mechanism of protein amyloidogenesis that could be crucial in developing future antiviral therapies against emerging pathogens. Finally, we discuss strategies to prepare for and to reduce the frequency of zoonotic spillover occurrences in order to minimize the risk of new outbreaks.

Three Outbreaks of COVID-19 in a Single Nursing Home over Two Years of the SARS-CoV-2 Pandemic

Older people in nursing homes (NH) have been hit particularly hard by the COVID-19 pandemic. We conducted a retrospective study of three outbreaks of COVID-19, occurring during the waves of the initial pre-Alpha, Delta and Omicron SARS-CoV-2 variants, in one NH in suburban Belgrade, Serbia. All staff and 95% residents were vaccinated in February 2021, mostly with BBIBP-CorV, and two thirds were boosted with a third dose in August 2021. COVID-19 was diagnosed by positive PCR and/or antigen test. After the first outbreak, 80 affected individuals were tested for SARS-CoV-2 specific antibodies. The first outbreak involved 64/126 (50.8%) residents and 45/64 (70.3%) staff, the second 22/75 (29.3%) residents and 3/40 (7.5%) staff, and the third involved 36/110 (32.7%) residents and 19/56 (33.9%) staff. Clinical presentation ranged from asymptomatic to severe, with severe cases referred to hospital ICUs. Deaths occurred only in residents, and the case fatality rate was 31.2%, 9.1% and 0%, respectively in outbreaks 1, 2 and 3. Specific IgG antibodies were detected in all 35 residents and 44 of the 45 staff, and higher IgG levels were detected in the residents (417.3±273.5) than in the staff (201.9±192.9, p<0.0001) despite a double difference in age (79.0±7.4 vs. 40.1±11.5 years). Outbreaks 2 and 3 involved four and 23 breakthrough infections, respectively. Older individuals mounted a good immunological response to SARS-CoV-2 infection and vaccination, which prevented significant mortality and severe morbidity in the subsequent outbreaks, despite a significant number of breakthrough infections.

The Influence of Sex on Characteristics and Outcomes of Coronavirus-19 Patients: A Retrospective Cohort Study

BACKGROUND

The influence of sex on the clinical characteristics and prognosis of coronavirus disease (COVID-19) patients is variable. This study aimed to evaluate COVID-19 management based on sex differences.

METHODS

We retrospectively reviewed COVID-19 patients who were admitted to the tertiary hospital between January 2020 and March 2021. Logistic regression analysis was used to evaluate the factors associated with in-hospital mortality.

RESULTS

During the study period, 584 patients were admitted to our hospital. Among them, 305 patients (52.2%) were female, and 279 patients (47.8%) were male. Males were younger than females, and frailty scale was lower in males than in females. Fever was more common in males, and there was no difference in other initial symptoms. Among the underlying comorbidities, chronic obstructive disease was more common in males, and there were no significant differences in other comorbidities. Moreover, treatment, severity, and outcome did not significantly differ between the groups. The risk factors for in-hospital mortality were age, high white blood cell count, and c-reactive protein level.

CONCLUSIONS

We found no definite sex differences in the clinical characteristics and outcomes of COVID-19 patients. However, a better understanding of sex-dependent differences in COVID-19 patients could help in understanding and treating patients.

Galectin-3 as an important prognostic marker for COVID-19 severity

Galectin-3 (Gal-3), multifunctional protein plays important roles in inflammatory response, infection and fibrosis. The goal of study was to determine the association of Gal-3, immune response, clinical, biochemical, and radiographic findings with COVID-19 severity. Study included 280 COVID-19 patients classified according to disease severity into mild, moderate, severe and critical group. Cytokines, clinical, biochemical, radiographic data and peripheral blood immune cell make up were analyzed. Patients in critical group had significantly higher serum level of Gal-3, IL-1β, TNF-α, IL-12, IL-10 compared to the patients in less severe stages of disease. Strong positive correlation was detected between Gal-3 and IL-1β, moderate positive correlation between Gal-3, TNF-α and IL-12, moderate negative correlation between Gal-3, IL-10/IL-1β and IL-10/TNF-α. Moderate positive correlation noted between Gal-3 and urea, D dimer, CXR findings. Strong negative correlation detected between Gal-3 and p0₂, Sa0_2, and moderate negative correlation between Gal-3, lymphocyte and monocyte percentage. In the peripheral blood of patients with more severe stages of COVID-19 we detected significantly increased percentages of CD56^- CD3⁺TNF-α⁺T cells and CD56^- CD3⁺Gal-3⁺T cells and increased expression of CCR5 in PBMCs. Our results predict Gal-3 as an important marker for critical stage of COVID-19. Higher expression of Gal-3, TNF-α and CCR5 on T cells implicate on promoting inflammation and more severe form of disease.

Gastrointestinal manifestations in children with COVID-19 infection: Retrospective tertiary center experience

OBJECTIVE

The majority of pediatric severe acute respiratory syndrome coronavirus 2 (COVID-19) cases demonstrate asymptomatic, mild or moderate disease. The main symptoms in children with COVID-19 are respiratory symptoms but some patients develop gastrointestinal symptoms and liver injury. We aim to review gastrointestinal symptoms and liver injury in children with confirmed COVID-19 infection.

METHOD

This is a retrospective case note review of children with positive COVID-19 nasal Polymerase Chain Reaction aged 0-18 years admitted to a tertiary pediatric hospital from March 1st till June 1st 2020.

RESULTS

180 children were identified. Mean age was 5 years (Range: 0.01-17), the majority of patients were school aged (30%). Patients were mainly from East Asia 81 (45%) and Arabs 67 (37%). Gastrointestinal symptoms were encountered in 48 (27%) patients and 8 (4%) patients had only Gastrointestinal symptoms with no associated fever or respiratory symptoms. Liver injury was seen in 57 (32%) patients. Patients with fever and cough were more likely to have gastrointestinal symptoms (P = <0.001 and 0.004 respectively). Fever was more likely to be associated with liver injury (P = 0.021). Children with abdominal pain were more likely to have elevated C-Reactive Protein (P = 0.037). Patients with diarrhea and vomiting were more likely to have elevated procalcitonin (P = 0.034 and 0.002 respectively). Children with Gastrointestinal symptoms were not more likely to be admitted to Pediatric Intensive Care Unit (P = 0.57).

CONCLUSION

COVID-19 infection in children can display gastrointestinal symptoms at initial presentation. Additionally, gastrointestinal symptoms can be the only symptoms patients display. We demonstrated that children with gastrointestinal symptoms and liver injury can develop more severe COVID-19 disease and are more likely to have fever, cough, and raised inflammatory markers. Identifying children with gastrointestinal manifestations needs to be part of the initial screening assessment of children.What is known?• Pediatric COVID-19 cases mostly demonstrate asymptomatic, mild or moderate disease.• The symptoms in children are mainly respiratory but some display gastrointestinal symptoms.• Children with COVID-19 display increased gastrointestinal symptoms when compared to adults.What is new?• Children with COVID-19 displaying gastrointestinal symptoms are more likely to have fever, cough and elevated inflammatory markers.• Children with liver injury are more likely to develop fever.• Children with gastrointestinal involvement in COVID-19 are more likely to demonstrate more severe disease but are not more likely to be admitted to PICU.

The Effect of Preventive Measures and Vaccination against SARS-CoV-2 on the Infection Risk, Treatment, and Hospitalization: A Cross-Sectional Study of Algeria

Coronavirus disease (COVID-19) caused by the SARS-CoV-2 virus continues to afflict many countries around the world. The resurgence of COVID-19 cases and deaths in many countries shows a complacency in adhering to preventive guidelines. Consequently, vaccination continues to be a crucial intervention to reduce the effects of this pandemic. This study investigated the impact of preventive measures and COVID-19 vaccination on the infection, medication, and hospitalization. A cross-sectional online survey was conducted between 23 December 2021 and 12 March 2022 in Algeria. To evaluate the effectiveness of strategies aimed at avoiding and minimizing SARS-CoV-2 infection and severity, a questionnaire was created and validated. Descriptive statistics and logistic regression analyses were computed to identify associations between dependent and independent variables. Variables with a p-value of < 0.05 were considered statistically significant. Our results indicated that out of 2294 answers received, only 16% of our sample was vaccinated, and more than 60% did not apply preventive guidelines. As a result, 45% were infected with SARS-CoV-2, 75% took treatment (even preventive), and 9% were hospitalized. The logistic regression showed that the impact of preventive measures on the unvaccinated is statistically not significant (OR: 0.764, 95% CI = 0. 555-1.052; p = 0.09). However, this relationship changes significantly for people who are vaccinated (OR: 0.108, 95% CI = 0.047-0.248; p < 0.0001). Our results also demonstrated that the impact of protective measures on non-vaccinated individuals is statistically significant in reducing the need to receive anti-COVID-19 treatments (OR: 0.447, 95% CI = 0.321-0.623; p < 0.0001). Furthermore, the results showed that the impact of preventive measures on the non-vaccinated population is also statistically significant in reducing the risk of hospitalization (OR: 0.211, 95% CI = 0.081-0.548; p < 0.0001). Moreover, vaccinated individuals who neglect preventive measures must take the COVID-19 medication at a rate of 3.77 times (OR: 3.77) higher than those who follow preventive measures and are vaccinated. In short, our findings demonstrate the importance of combining preventive measures and vaccination in order to fight against the pandemic. Therefore, we advise the Ministry of Health and relevant authorities to put more effort into enhancing public knowledge about the COVID-19 infection and vaccination through education and awareness initiatives. Parallel to implementing vaccination as additional preventive strategy, behavioral change initiatives must be improved to encourage adherence to COVID-19 prevention recommendations.

The Inhibition of SARS-CoV-2 and the Modulation of Inflammatory Responses by the Extract of Lactobacillus sakei Probio65

In the three years since the first outbreak of COVID-19 in 2019, the SARS-CoV-2 virus has continued to be prevalent in our community. It is believed that the virus will remain present, and be transmitted at a predictable rate, turning endemic. A major challenge that leads to this is the constant yet rapid mutation of the virus, which has rendered vaccination and current treatments less effective. In this study, the Lactobacillus sakei Probio65 extract (P65-CFS) was tested for its safety and efficacy in inhibiting SARS-CoV-2 replication. Viral load quantification by RT-PCR showed that the P65-CFS inhibited SARS-CoV-2 replication in human embryonic kidney (HEK) 293 cells in a dose-dependent manner, with 150 mg/mL being the most effective concentration (60.16% replication inhibition) (p < 0.05). No cytotoxicity was inflicted on the HEK 293 cells, human corneal epithelial (HCE) cells, or human cervical (HeLa) cells, as confirmed by the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) assay. The P65-CFS (150 mg/mL) also reduced 83.40% of reactive oxidizing species (ROS) and extracellular signal-regulated kinases (ERK) phosphorylation in virus-infected cells, both of which function as important biomarkers for the pathogenesis of SARS-CoV-2. Furthermore, inflammatory markers, including interferon-α (IFN-α), IFN-ß, and interleukin-6 (IL-6), were all downregulated by P65-CFS in virus-infected cells as compared to the untreated control (p < 0.05). It was conclusively found that L. sakei Probio65 showed notable therapeutic efficacy in vitro by controlling not only viral multiplication but also pathogenicity; this finding suggests its potential to prevent severe COVID-19 and shorten the duration of infectiousness, thus proving useful as an adjuvant along with the currently available treatments.

IgG anti-RBD levels during 8-month follow-up post-vaccination with BNT162b2 and mRNA-1273 vaccines in healthcare workers: A one-center study

Introduction

Since the COVID-19 outbreak, specific mRNA-based anti-SARS-CoV-2 vaccines have been developed and distributed worldwide. Because this is the first time that mRNA vaccines have been used, there are several questions regarding their capacity to confer immunity and the durability of the specific anti-SARS-CoV-2 response. Therefore, the objective of this study was to recruit a large cohort of healthcare workers from the Gregorio Marañón Hospital vaccinated with the mRNA-1273 or BNT126b2 vaccines and to follow-up on IgG anti-RBD levels at 8 months post-vaccination.

Methods

We recruited 4,970 volunteers and measured IgG anti-RBD antibodies on days 30 and 240 post-vaccination.

Results

We observed that both vaccines induced high levels of antibodies on day 30, while a drastic wane was observed on day 240, where mRNA-1273 vaccinated induced higher levels than BNT162b2. Stratifying by vaccine type, age, gender, and comorbidities, we identified that older mRNA-1273-vaccinated volunteers had higher antibody levels than the younger volunteers, contrary to what was observed in the BNT162b2-vaccinated volunteers.

Discussion

In conclusion, we observed that mRNA-1273 has a higher capacity to induce a humoral response than BNT162b2 and that age is a factor in the specific response.

The Impact of Demographic, Clinical Characteristics and the Various COVID-19 Variant Types on All-Cause Mortality: A Case-Series Retrospective Study

(1) Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly evolved into a pandemic affecting virtually every country in the world. We evaluated the demographic, clinical, laboratory, and all-cause mortality of moderate and severe COVID-19 patients admitted to a tertiary care hospital in Oman during the different COVID-19 waves and variant types. (2) Methods: A case-series retrospective study was carried out between 12 March 2020 and 30 June 2022. All adults over the age of 18 with laboratory-confirmed COVID-19 were enrolled. Analyses were performed using univariate and multivariate statistics. (3) Results: A total of 1462 confirmed cases enrolled with the mean age of the cohort was 55 ± 17 years with significant differences among the groups (p = 0.006). A total of 63% and 80% of the patients were males and citizens of Oman, respectively. Patients infected with the Alpha COVID-19 variant type were more likely to have acute respiratory distress syndrome (ARDS) (p < 0.001), stay longer in the hospital (p < 0.001), and get admitted to the intensive care unit (ICU) (p < 0.001). At the same time, those who had the Omicron COVID-19 type were more likely to have renal impairment (p < 0.001) and less likely to be associated with non-invasive ventilation (NIV) (p = 0.001) compared with other COVID-19 variant types. The Delta (adjusted odds ratio (aOR), 1.8; 95% confidence interval (CI): 1.22−2.66; p = 0.003) and Omicron (aOR, 1.88; 95% CI: 1.09−3.22; p = 0.022) COVID-19 variant types were associated with higher all-cause mortality when compared to the initial COVID-19 variant. Old age (aOR, 1.05; 95% CI: 1.04−1.06; p < 0.001), the presence of respiratory disease (aOR, 1.58; 95% CI: 1.02−2.44; p = 0.04), ICU admission (aOR, 3.41; 95% CI: 2.16−5.39; p < 0.001), lower eGFR (aOR, 1.61; 95% CI: 1.17−2.23; p = 0.004), and ARDS (aOR, 5.75; 95% CI: 3.69−8.98; p < 0.001) were also associated with higher mortality while NIV requirements were associated with lower odds of dying (aOR, 0.65; 95% CI: 0.46−0.91; p = 0.012). (4) Conclusions: Alpha and Delta variants were associated with a longer hospital stay, need for intensive care, mechanical ventilation, and increased mortality. Old age, cardiac renal dysfunction were commonly associated with Omicron variants. Large-scale national studies to further assess the risk factors for mortality related to COVID-19 waves are warranted.

Coagulation parameters predict COVID-19-related thrombosis in a neural network with a positive predictive value of 98

Thrombosis is a major clinical complication of COVID-19 infection. COVID-19 patients show changes in coagulation factors that indicate an important role for the coagulation system in the pathogenesis of COVID-19. However, the multifactorial nature of thrombosis complicates the prediction of thrombotic events based on a single hemostatic variable. We developed and validated a neural net for the prediction of COVID-19-related thrombosis. The neural net was developed based on the hemostatic and general (laboratory) variables of 149 confirmed COVID-19 patients from two cohorts: at the time of hospital admission (cohort 1 including 133 patients) and at ICU admission (cohort 2 including 16 patients). Twenty-six patients suffered from thrombosis during their hospital stay: 19 patients in cohort 1 and 7 patients in cohort 2. The neural net predicts COVID-19 related thrombosis based on C-reactive protein (relative importance 14%), sex (10%), thrombin generation (TG) time-to-tail (10%), α2-Macroglobulin (9%), TG curve width (9%), thrombin-α2-Macroglobulin complexes (9%), plasmin generation lag time (8%), serum IgM (8%), TG lag time (7%), TG time-to-peak (7%), thrombin-antithrombin complexes (5%), and age (5%). This neural net can predict COVID-19-thrombosis at the time of hospital admission with a positive predictive value of 98%-100%.

Biomarkers of Inflammation among Patients with COVID-19: A Single-Centre Prospective Study from Prishtina, Kosovo

SARS-CoV-2 infection involves the phase of viral replication and inflammatory response predicting the severity of COVID-19. The aim of the study was to analyze the association between IL-6 and hematological and inflammatory parameters and outcomes of patients with COVID-19. Plasma interleukin 6 (IL-6) levels and other inflammatory and hematological parameters were analyzed in 86 adult patients diagnosed with SARS-CoV-2 infection in Kosovo. The median age of patients was 61.50 (49.75–67.25) years. Over half of patients were categorised as severe (58%) and had comorbidities (69%) with hypertension being the most common. The overall mortality rate was 4.7%. The distribution of biochemical parameters across disease severity groups was significantly different for C-reactive protein (CRP), lactate dehydrogenase (LDH), erythrocyte sedimentation rate (ESR), white blood cells (WBC), and granulocytes with higher median values in more severe and critically ill patients whereas lower percentage of lymphocytes, monocytes, and platelet count in severe and critically ill patients. IL-6 levels were increased in 63% of patients with significant differences in the distribution across the following groups; age, disease severity, hospitalisation status, pulmonary infiltrates, oxygen therapy, and hypertension status. IL-6 significantly correlated with CRP, LDH, CK, ESR, and percentages of granulocytes. IL-6 and other inflammatory and hematological parameters were strongly associated with disease severity and may predict the outcome of the SARS-CoV-2 infection.