Predictors of COVID-19 severity: a systematic review and meta-analysis

ACCREDIT: Validation of clinical score for progression of COVID-19 while hospitalized

COVID-19 is no longer a global health emergency, but it remains challenging to predict its prognosis.

Objective

To develop and validate an instrument to predict COVID-19 progression for critically ill hospitalized patients in a Brazilian population.

Methodology

Observational study with retrospective follow-up. Participants were consecutively enrolled for treatment in non-critical units between January 1, 2021, to February 28, 2022. They were included if they were adults, with a positive RT-PCR result, history of exposure, or clinical or radiological image findings compatible with COVID-19. The outcome was characterized as either transfer to critical care or death. Predictors such as demographic, clinical, comorbidities, laboratory, and imaging data were collected at hospitalization. A logistic model with lasso or elastic net regularization, a random forest classification model, and a random forest regression model were developed and validated to estimate the risk of disease progression.

Results

Out of 301 individuals, the outcome was 41.8 %. The majority of the patients in the study lacked a COVID-19 vaccination. Diabetes mellitus and systemic arterial hypertension were the most common comorbidities. After model development and cross-validation, the Random Forest regression was considered the best approach, and the following eight predictors were retained: D-dimer, Urea, Charlson comorbidity index, pulse oximetry, respiratory frequency, Lactic Dehydrogenase, RDW, and Radiologic RALE score. The model's bias-corrected intercept and slope were - 0.0004 and 1.079 respectively, the average prediction error was 0.028. The ROC AUC curve was 0.795, and the variance explained was 0.289.

Conclusion

The prognostic model was considered good enough to be recommended for clinical use in patients during hospitalization (https://pedrobrasil.shinyapps.io/INDWELL/). The clinical benefit and the performance in different scenarios are yet to be known.

Efficient and scalable construction of clinical variable networks for complex diseases with RAMEN

Understanding the interplay among clinical variables-such as demographics, symptoms, and laboratory results-and their relationships with disease outcomes is critical for advancing diagnostics and understanding mechanisms in complex diseases. Existing methods fail to capture indirect or directional relationships, while existing Bayesian network learning methods are computationally expensive and only infer general associations without focusing on disease outcomes. Here we introduce random walk- and genetic algorithm-based network inference (RAMEN), a method for Bayesian network inference that uses absorbing random walks to prioritize outcome-relevant variables and a genetic algorithm for efficient network refinement. Applied to COVID-19 (Biobanque québécoise de la COVID-19), intensive care unit (ICU) septicemia (MIMIC-III), and COPD (CanCOLD) datasets, RAMEN reconstructs networks linking clinical markers to disease outcomes, such as elevated lactate levels in ICU patients. RAMEN demonstrates advantages in computational efficiency and scalability compared to existing methods. By modeling outcome-specific relationships, RAMEN provides a robust tool for uncovering critical disease mechanisms, advancing diagnostics, and enabling personalized treatment strategies.

Prevalence of Self-reported COVID-19 Infection in Persons With Complicated Mild to Severe Traumatic Brain Injury: A Traumatic Brain Injury Model Systems Study

OBJECTIVES

The aims of the study are to describe the incidence of self-reported COVID-19 history in a longitudinal cohort of individuals with complicated mild to severe traumatic brain injury and describe demographic, injury, and functional differences based on history of COVID-19 infection.

DESIGN

Individuals with complicated mild to severe traumatic brain injury aged 16 or older at time of injury who were enrolled in the Traumatic Brain Injury Model Systems longitudinal cohort study, completed a baseline or follow-up interview between October 1, 2021-March 31, 2023, and provided information about COVID-19 history and timing of COVID-19 infection was collected.

RESULTS

Of the 3627 individuals included in the analysis, 29.5% reported a history of COVID-19 infection. Those with reported COVID-19 history tended to be younger, not of a racial/ethnic minority background, and greater functional status at follow-up based on the Glasgow Outcome Scale-Extended Scale compared with those with no reported COVID-19 history ( P < 0.05). Among those with COVID-19 history, 61.8% did not receive medical care, 27.6% received medical care but no hospitalization, and 10.5% were hospitalized. Of those hospitalized, 21.4% required ventilator use.

CONCLUSIONS

Incidence of COVID-19 diagnosis and related hospitalization characteristics in persons with complicated mild to severe traumatic brain injury was similar to national incidence between March 2020-2023. Secondary effects of the COVID-19 pandemic on persons with traumatic brain injury require investigation.

Insertion/deletion (I/D) polymorphisms of angiotensin-converting enzyme gene and their implications for susceptibility and severity of COVID-19: A systematic review and meta-analysis

The insertion or deletion polymorphisms of the angiotensin-converting enzyme gene (ACE I/D) have been the subject of significant research related to coronavirus disease 2019 (COVID-19). Despite this, the findings have remained uncertain and debatable. The aim of this study was to determine the associations between the ACE I/D polymorphisms and the susceptibility as well as the severity of COVID-19. A meta-analysis study (PROSPERO: CRD42022384562) was conducted by searching the articles published on PubMed, Scopus, and Embase as of May 15, 2023. Information regarding the impact of ACE I/D variant on the susceptibility to COVID-19 and its severity was collected and analyzed utilizing the Mantel-Haenszel method with a random effects model or fixed effects model, depending on the presence or absence of heterogeneity. Out of 3,335 articles, 21 articles were included, of which 13 investigated the association between ACE I/D and the risk of COVID-19 infection and 18 of them examined its influence on disease severity. The D allele of ACE increased risk of COVID-19 infection (OR: 1.41; 95%CI: 1.08-1.85; p-Egger: 0.0676; p-Heterogeneity: <0.001; p=0.0120), while ACE I allele (OR: 0.71; 95%CI: 0.54-0.93; p-Egger: 0.0676; p-Heterogeneity: <0.001; p=0.012) and II genotype (OR: 0.55; 95%CI: 0.34-0.87; p-Egger: 0.200; p-Heterogeneity: <0.001; p=0.011) decreased the risk of infection. Additionally, there was a notable association between the ACE ID genotype and an elevated likelihood of experiencing severe COVID-19 within the Asian population (OR: 1.46; 95%CI: 1.15-1.84; p-Egger: 0.092; p-Heterogeneity: 0.116; p=0.002). The presence of ACE I/D polymorphisms significantly influences the likelihood of being susceptible to and experiencing the severity of COVID-19.

Prospective Variation of Cytokine Trends during COVID-19: A Progressive Approach from Disease Onset until Outcome

COVID-19 is characterized by pronounced hypercytokinemia. The cytokine switch, marked by an imbalance between pro-inflammatory and anti-inflammatory cytokines, emerged as a focal point of investigation throughout the COVID-19 pandemic. However, the kinetics and temporal dynamics of cytokine release remain contradictory, making the development of new therapeutics difficult, especially in severe cases. This study collected serum samples from SARS-CoV-2 infected patients at 72 h intervals and monitored them for various cytokines at each timepoint until hospital discharge or death. Cytokine levels were analyzed based on time since symptom onset and patient outcomes. All cytokines studied prospectively were strong predictors of mortality, particularly IL-4 (AUC = 0.98) and IL-1β (AUC = 0.96). First-timepoint evaluations showed elevated cytokine levels in the mortality group (p < 0.001). Interestingly, IFN-γ levels decreased over time in the death group but increased in the survival group. Patients who died exhibited sustained levels of IL-1β and IL-4 and increased IL-6 levels over time. These findings suggest cytokine elevation is crucial in predicting COVID-19 mortality. The dynamic interplay between IFN-γ and IL-4 highlights the balance between Th1/Th2 immune responses and underscores IFN-γ as a powerful indicator of immune dysregulation throughout the infection.

Peripheral biomarkers as a predictor of poor prognosis in severe cases of COVID-19

We evaluated glycemia and triglyceride, hepatic, muscular, and renal damage markers, redox profile, and leptin and ghrelin hormone levels in COVID-19 patients. We also conducted statistical analysis to verify the potential of biomarkers to predict poor prognosis and the correlation between them in severe cases. We assessed glycemia and the levels of triglycerides, hepatic, muscular, and renal markers in automatized biochemical analyzer. The leptin and ghrelin hormones were assessed by the ELISA assay. Severe cases presented high glycemia and triglyceride levels. Hepatic, muscular, and renal biomarkers were altered in severe patients. Oxidative stress status was found in severe COVID-19 patients. Severe cases also had increased levels of leptin. The ROC curves indicated many biomarkers as poor prognosis predictors in severe cases. The Spearman analysis showed that biomarkers correlate between themselves. Patients with COVID-19 showed significant dysregulation in the levels of several peripheral biomarkers. We bring to light that a robust panel of peripheral biomarkers and hormones predict poor prognosis in severe cases of COVID-19 and biomarkers correlate with each other. Early monitoring of these biomarkers may lead to appropriate clinical interventions in patients infected by SARS-CoV2.

Comparison of ELISA with automated ECLIA for IL-6 determination in COVID-19 patients: An Italian real-life experience

OBJECTIVES

Coronavirus disease 2019 (COVID-19) has a wide spectrum of clinical severity. A cytokine storm is associated with COVID-19 severity. Of these, IL-6 is significantly associated with higher mortality and is also a marker for predicting disease prognosis. IL-6 may act as a target for therapeutics and, a blockade of IL-6 function by Tocilizumab has been described as a treatment of the inflammatory process COVID-19-related. This study aims to describe our experience comparing two different methods, in detail Human IL-6 Instant ELISA and the Elecsys IL-6 based on ECLIA, for the IL-6 assessment.

DESIGN AND METHODS

IL-6 levels from serum samples of 104 COVID-19 patients, admitted to the AOU Careggi (Hospital in Florence -Italy), were assessed by using the two above-mentioned methods, and the results were analysed through Passing-Bablok regression fit and Bland-Altman plot.

RESULTS

The regression exhibited a linear relation between the methods with a regression equation (y = - 0.13 + 0.63 x; 95 % C.I. intercept = - 0.13 to 4.55; 95 % C.I. slope = 1.03 to 1.26 with R2 = 0.89, p > 0.05), showing a positive slope. The agreement of the two methods reported a bias of -25.0 pg/mL. Thus, the two methods correlate but do not agree in terms of numeric results.

CONCLUSIONS

The two assays showed good comparability. However, because of the extremely wide linear range of the ECLIA, its throughput and its capacity for immune profiling, it represents an interesting emerging technology in the immunology field.

Quantification of hs-Troponin Levels and Global Longitudinal Strain among Critical COVID-19 Patients with Myocardial Involvement

Background. Myocardial involvement among critically ill patients with coronavirus disease 2019 (COVID-19) often has worse outcomes. An imbalance in the oxygen supply causes the excessive release of pro-inflammatory cytokines, which results in increased ventilation requirements and the risk of death in COVID-19 patients. Purpose. We evaluated the association between the hs-troponin I levels and global longitudinal strain (GLS) as evidence of myocardial involvement among critical COVID-19 patients. Methods. We conducted a prospective cohort study from 1 February to 31 July 2021 at RSUD Dr. Soetomo, Surabaya, as a COVID-19 referral center. Of the 65 critical COVID-19 patients included, 41 (63.1%) were men, with a median age (interquartile range) of 51.0 years (20.0-75.0). Subjects were recruited based on WHO criteria for severe COVID-19, and myocardial involvement in the form of myocarditis was assessed using CDC criteria. Subjects were examined using echocardiography to measure the GLS, and blood samples were taken to measure the hs-troponin. Subjects were then followed for their need for mechanical ventilation and in-hospital mortality. Results. Severe COVID-19 patients with cardiac injury were associated with an increased need for intubation (78.5%) and an increased incidence of myocarditis (50.8%). There was a relationship between the use of intubation and the risk of death in patients (66.7% vs. 33.3%, p-value < 0.001). Decreased GLS and increased hs-troponin were associated with increased myocarditis (p values < 0.001 and 0.004, respectively). Decreased GLS was associated with a higher need for mechanical ventilation (12.17 + 4.79 vs. 15.65 + 4.90, p-value = 0.02) and higher mortality (11.36 + 4.64 vs. 14.74 + 4.82; p-value = 0.005). Elevated hs-troponin was associated with a higher need for mechanical ventilation (25.33% vs. 3.56%, p-value = 0.002) and higher mortality (34.57% vs. 5.76%, p-value = 0.002). Conclusions. Critically ill COVID-19 patients with myocardial involvement and elevated cardiac troponin levels are associated with a higher need for mechanical ventilation and higher mortality.

Artificial intelligence for predicting mortality in hospitalized COVID-19 patients

BACKGROUND

The global demographic situation has been significantly impacted by the COVID-19 pandemic. The objective of this study was to develop a model that predicts the risk of COVID-associated mortality using clinical and laboratory data collected within 72 h of hospital admission.

MATERIALS AND METHODS

A total of 3024 subjects with PCR-confirmed COVID-19 were admitted to Almazov National Research Medical Center between May 2020 and August 2021. Among them, 6.25% (n = 189) of patients had a fatal outcome. Five machine learning models and the Boruta-SHAP feature selection method were utilized to assess the risk of mortality during COVID-19 hospitalization.

RESULTS

All methods demonstrated high efficacy, with ROC AUC (Receiver Operating Characteristic Area Under the Curve) values exceeding 80%. The selected Boruta-SHAP features, when incorporated into the random forest model, achieved an ROC AUC of 93.1% in the validation.

CONCLUSION

Throughout the study, close collaboration with healthcare professionals ensured that the developed tool met their practical needs. The success of our model validates the potential of machine learning techniques as decision support systems in clinical practice.

A Study on Mortality Predictors in Hemodialysis Patients Infected with COVID-19: Impact of Vaccination Status

The global outbreak of COVID-19, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has prompted significant public health concerns. This study focuses on 442 chronic hemodialysis patients diagnosed with COVID-19, emphasizing the impact of vaccination status on clinical outcomes. The study investigates the correlation between vaccination status and laboratory findings, aiming to identify predictive factors for mortality. Results indicate that vaccination status plays a crucial role in outcomes. Full vaccination, evidenced by two or three doses, is associated with better outcomes, including reduced incidence of bilateral pneumonia and lower risks of complications such as hemorrhage and thrombosis. Laboratory analyses reveal significant differences between vaccinated and unvaccinated patients in parameters like C-reactive protein, ferritin, and white blood cell counts. Univariate and multivariate Cox proportional hazards regression analyses identify several factors influencing mortality, including comorbidities, pneumonia development, and various inflammatory markers. In conclusion among hemodialysis patients affected by COVID-19 infection, vaccination with at least three doses emerges as a protective factor against fatal outcomes. Independent predictors of mortality are CRP levels upon admission, maximum CRP values during the illness and cardiovascular comorbidities. Noteworthy lymphocytopenia during infection exhibits a notable level of specificity and sensitivity in predicting mortality.

Machine learning-driven development of a disease risk score for COVID-19 hospitalization and mortality: a Swedish and Norwegian register-based study

Aims

To develop a disease risk score for COVID-19-related hospitalization and mortality in Sweden and externally validate it in Norway.

Method

We employed linked data from the national health registries of Sweden and Norway to conduct our study. We focused on individuals in Sweden with confirmed SARS-CoV-2 infection through RT-PCR testing up to August 2022 as our study cohort. Within this group, we identified hospitalized cases as those who were admitted to the hospital within 14 days of testing positive for SARS-CoV-2 and matched them with five controls from the same cohort who were not hospitalized due to SARS-CoV-2. Additionally, we identified individuals who died within 30 days after being hospitalized for COVID-19. To develop our disease risk scores, we considered various factors, including demographics, infectious, somatic, and mental health conditions, recorded diagnoses, and pharmacological treatments. We also conducted age-specific analyses and assessed model performance through 5-fold cross-validation. Finally, we performed external validation using data from the Norwegian population with COVID-19 up to December 2021.

Results

During the study period, a total of 124,560 individuals in Sweden were hospitalized, and 15,877 individuals died within 30 days following COVID-19 hospitalization. Disease risk scores for both hospitalization and mortality demonstrated predictive capabilities with ROC-AUC values of 0.70 and 0.72, respectively, across the entire study period. Notably, these scores exhibited a positive correlation with the likelihood of hospitalization or death. In the external validation using data from the Norwegian COVID-19 population (consisting of 53,744 individuals), the disease risk score predicted hospitalization with an AUC of 0.47 and death with an AUC of 0.74.

Conclusion

The disease risk score showed moderately good performance to predict COVID-19-related mortality but performed poorly in predicting hospitalization when externally validated.

Exploring the relationship between SARS-CoV-2 variants, illness severity at presentation, in-hospital mortality and COVID-19 vaccination in a low middle-income country: A retrospective cross-sectional study

Background and Aims

COVID-19 morbidity and mortality varied globally through the pandemic. We studied the relationship of SARS-CoV-2 variants of concern (VOC) with COVID-19 severity and mortality among hospitalized patients in Pakistan.

Methods

A retrospective review of clinical, laboratory, and vaccination data of 197 COVID-19 adult patients at the Aga Khan University Hospital, Karachi between April 2021, and February 2022 was performed. SARS-CoV-2 VOC identified in respiratory samples were analyzed. Univariate and multivariate analysis was conducted to identify factors associated with COVID-19 outcomes.

Results

The median age of cases was 55 years and 51.8% were males. Twenty-four percent of females were pregnant. Of COVID-19 cases, 48.2% had nonsevere disease, while 52.8% had severe/critical disease. Hypertension (48%) and diabetes mellitus (41%) were common comorbids. SARS-CoV-2 VOC identified comprised; Omicron (55.3%), Beta (14.7%), Alpha (13.7%), Delta (12.7%), and Gamma (3.6%) variants. Most (59.7%) study subjects were unvaccinated. Of vaccines, 88% had received inactivated virus COVID-19 vaccines. Increased risk of severe disease was associated with age ≥50 years (odds ratio [OR]: 5.73; 95% confidence interval [CI]: [2.45-13.7]), as well as with diabetes mellitus (OR: 4.24; 95% CI: [1.82-9.85]). Full vaccination (OR: 0.25; 95% CI: [0.11-0.58]) or infection with Omicron (OR: 0.42; 95% CI: [0.23-0.74]) was associated with reduced disease severity. The risk of mortality increased with age ≥50 years (OR: 5.07; 95% CI: [1.92-13.42]) and a history of myocardial infarction (OR: 5.11; 95% CI: [1.45-17.93]) whilst, infection with Omicron was found to reduce the risk (OR: 0.22; 95% CI: [0.10-0.53]).

Conclusion

Our study describes the relationship between the severity of COVID-19, in-hospital mortality in relation to SARS-CoV-2 variants, and the impact of COVID-19 vaccination in Pakistan. Outcomes were more favorable in younger individuals, after vaccinations and with Omicron variant infections. Most cases received inactivated virus vaccines therefore these data highlight the protection provided against severe COVID-19.

Remdesivir and SARS-CoV-2 monoclonal antibodies to prevent COVID-19 progression in hematological patients: an observational study

BACKGROUND

Patients with hematological malignancies (HM) are at high risk of COVID-19 progression. Hence, early treatments to prevent progression are needed. The aim of our work was to evaluate the effectiveness and safety of remdesivir (RDV) and SARS-CoV-2 monoclonal antibodies (mAb) in patients with HM and mild-to-moderate disease in real clinical practice.

METHODS

We conducted a prospective study in a tertiary hospital in 55 HM patients with mild-to-moderate SARS-CoV-2 disease diagnosed between August 2021 and July 2022 and who received RDV or mAb to prevent COVID-19 progression (related death or hospitalization). The primary endpoint was COVID-19 progression on day 28. Other outcomes were COVID-19 progression beyond day 28 and viral load evolution.

RESULTS

RDV was administered to 44 (80.0%) patients and mAb to 11 (20.0%) patients. Death occurred in 1 (1.8%) patient and hospitalization in 9 (16.4%) patients by day 28, respectively; 3 patients (5.5%) required intensive care and 8 (14.5%), oxygen support. Of note, 5 additional patients [15, (27.3%) in total] died or required hospitalization after day 28. Two hazard Cox regression models yielded the absence of anti-SARS-CoV-2 antibodies, age over 65 years, and ECOG-performance status ≥ 2 as the main risk factors for COVID-19-related death or hospitalization.

CONCLUSION

Our results from clinical practice suggest that RDV and SARS-CoV-2 mAb therapies elicit worse outcomes in hematological patients than those reported for high-risk population in clinical trials.

Epidemic-Prevention Measures and Health Management in a Nursing Home during the Coronavirus Disease 2019 Pandemic

This study aimed to investigate the impact of epidemic prevention and isolation policies on residents' health and well-being and assess the effectiveness of implementing intervention measures to maintain their quality of life. This mixed-methods research study involved a retrospective record review of residents' daily life diaries and descriptive statistical analysis. Data were collected between March 2021 and June 2022, and epidemic-prevention measures were implemented using Taiwan's Centers for Disease Control guidelines. Three interventions were developed to address residents' health, social, and rehabilitation needs. Despite an overall infection rate of 10% at various times between 2021 and 2022, there were no reported outbreaks of nosocomial infections. The concept of reablement proved effective in helping residents maintain their independence and physical function, with a maintenance rate of 66.6%, thereby improving their quality of life. By implementing epidemic-prevention measures, we found that proper hand washing and the use of surgical masks were effective in controlling infections. Furthermore, the decline in physical function is a continuous and gradual process for older adults. Even under the restriction of social interaction, it is essential to incorporate rehabilitation plans into residents' daily activities and encourage their active participation, as this promotes improved physical function and enhances their overall quality of life.

Training and testing of a gradient boosted machine learning model to predict adverse outcome in patients presenting to emergency departments with suspected covid-19 infection in a middle-income setting

COVID-19 infection rates remain high in South Africa. Clinical prediction models may be helpful for rapid triage, and supporting clinical decision making, for patients with suspected COVID-19 infection. The Western Cape, South Africa, has integrated electronic health care data facilitating large-scale linked routine datasets. The aim of this study was to develop a machine learning model to predict adverse outcome in patients presenting with suspected COVID-19 suitable for use in a middle-income setting. A retrospective cohort study was conducted using linked, routine data, from patients presenting with suspected COVID-19 infection to public-sector emergency departments (EDs) in the Western Cape, South Africa between 27th August 2020 and 31st October 2021. The primary outcome was death or critical care admission at 30 days. An XGBoost machine learning model was trained and internally tested using split-sample validation. External validation was performed in 3 test cohorts: Western Cape patients presenting during the Omicron COVID-19 wave, a UK cohort during the ancestral COVID-19 wave, and a Sudanese cohort during ancestral and Eta waves. A total of 282,051 cases were included in a complete case training dataset. The prevalence of 30-day adverse outcome was 4.0%. The most important features for predicting adverse outcome were the requirement for supplemental oxygen, peripheral oxygen saturations, level of consciousness and age. Internal validation using split-sample test data revealed excellent discrimination (C-statistic 0.91, 95% CI 0.90 to 0.91) and calibration (CITL of 1.05). The model achieved C-statistics of 0.84 (95% CI 0.84 to 0.85), 0.72 (95% CI 0.71 to 0.73), and 0.62, (95% CI 0.59 to 0.65) in the Omicron, UK, and Sudanese test cohorts. Results were materially unchanged in sensitivity analyses examining missing data. An XGBoost machine learning model achieved good discrimination and calibration in prediction of adverse outcome in patients presenting with suspected COVID19 to Western Cape EDs. Performance was reduced in temporal and geographical external validation.

Intensive care unit admission and associated factors in patients hospitalised for COVID-19: A national retrospective cohort study in Iran

OBJECTIVES

This study aimed to determine factors associated with intensive care unit (ICU) admission in patients hospitalised due to COVID-19.

DESIGN

Retrospective cohort.

SETTING

Confirmed hospitalised patients from all over Iran were considered for the study.

PARTICIPANTS

All patients with COVID-19 admitted to the hospital from March 2020 to May 2021 were included by census. ICU admission was defined by the following criteria: (1) admission to the ICU ward; (2) level of consciousness (loss of consciousness); and (3) use of invasive ventilation.

METHODS

This is a secondary data analysis from the Medical Care Monitoring Center. The association between different variables and ICU admission was assessed by forward Logistic regression and restricted cubic spline method.

RESULTS

The mean age of the 1 469 620 patients with COVID-19 was 54.49±20.58 years old, and 51.32% of the patients were male. The prevalence of ICU admission was 19.19%. The mean age of patients admitted to the ICU was higher than that of other hospitalised patients (62.49±19.73 vs 52.59±20.31 years). The prevalence of ICU admission was 17.17% in the first, 21.52% in the second, 19.72% in the third, 21.43 in the fourth and 17.4% in the fifth wave. In the multivariable model, age groups, sex, waves of the epidemic, comorbidities and saturation of peripheral oxygen (SpO2) <93% and acute respiratory distress syndrome (ARDS) were associated with an increased odds of ICU admission. The OR for ICU admission indicates a significant protective effect at a young age and then a significant risk factor for admission to the ICU ward at an old age.

CONCLUSIONS

Men, older adults, people who suffer from ARDS, patients with SpO2 levels of less than 93% and cases with comorbidities had the highest odds of ICU admission. Therefore, these groups should take all necessary precautions to avoid contracting COVID-19.

Tools and factors predictive of the severity of COVID-19

The outbreak of the novel coronavirus infection caused worldwide confusion. The problem with this infection is that it causes severe illness in some patients, resulting in a high rate of death if appropriate treatment is not given. If patients with severe illness that requires treatment are appropriately identified, treatment can be focused on these patients. However, in the early days of the COVID-19 outbreak, the inability to predict and diagnose the disease led to hospitals being overwhelmed. Therefore, various methods for the diagnosis of severe disease were developed early on, and various methods are still being investigated to predict high-risk patients. The currently available prediction methods are divided into those that predict the onset of severe disease and those used to determine the severity of the disease. Specifically, the main methods include genetic factors, serum humoral factors, laboratory tests, and diagnostic imaging. Since each of these factors has different features, using them in combination is likely to be advantageous.

Random Blood Glucose, but Not HbA1c, Was Associated with Mortality in COVID-19 Patients with Type 2 Diabetes Mellitus-A Retrospective Study

Previous studies have yielded inconsistent results on whether glycated hemoglobin (HbA1c) and random blood glucose (RBG) are associated with mortality of coronavirus disease 2019 (COVID-19) patients with type 2 diabetes mellitus (T2DM). This study aimed to assess the association of HbA1c and RBG with mortality among COVID-19 patients with T2DM. A retrospective study was conducted on 237 patients with COVID-19 and T2DM (survival (n = 169) and non-survival groups (n = 68)). Data on socio-demography, comorbidities, clinical symptoms, laboratory examination, and mortality were collected. Patients in the non-survival group had an older age range as compared with those in the survival group (60 (52.3-65.0) vs. 56.0 (48.5-61.5) years, p = 0.009). There was no statistical gender difference between the two groups. After matching was done, chronic kidney disease, NLR, d-dimer, procalcitonin, and random blood glucose were higher in the non-survival group compared to the survival group (p < 0.05). HbA1c levels were similar in survivors and non-survivors (8.7% vs. 8.9%, p=0.549). The level of RBG was independently associated with mortality of COVID-19 patients with T2DM (p = 0.003, adjusted OR per 1-SD increment 2.55, 95% CI: 1.36-4.76). In conclusion, RBG was associated with the mortality of COVID-19 patients with T2DM, but HbA1c was not.

Age, comorbidities, c-reactive protein and procalcitonin as predictors of severity in confirmed COVID-19 patients in the Philippines

Background

The Coronavirus Disease 2019 (COVID-19) pandemic has been affecting people globally, and the Philippines is one of the countries greatly struck by the virus. The continued rise of new positive cases has drawn attention to the urgent need for healthcare management to cope with this challenge. Severity prediction could help improve medical decision-making and optimise the patient's treatment plan with a good clinical outcome. This study aimed to identify the determinants of COVID-19 disease severity.

Methods

Demographic characteristics and laboratory findings were collected from electronic medical records and paper forms of all confirmed COVID-19 cases reported by the University of Perpetual Help DALTA Medical Center between the September 1, 2020 and the October 31, 2021. We performed statistical analyses and interpretation of data to compare severe and non-severe groups.

Results

5,396 confirmed cases were examined. Most of the severe cases were elderly, male, had blood type A, and with comorbidities. Cycle threshold (Ct) values were lower in the severe group. Most patients had higher-than-normal levels of all blood parameters except platelet, white blood cell (WBC), neutrophil, and lymphocyte counts. Age, sex, ABO blood groups, comorbidities, open reading frame 1 ab (ORF1ab) and nucleocapsid (N) gene Ct values, ferritin, C-reactive protein (CRP), procalcitonin (PCT), D-dimer, white blood cell (WBC) count, neutrophil count, and lymphocyte count were significantly associated with disease severity. In multivariate analysis, age groups >60 and 30-59 years, presence of comorbidities, CRP level >5 ng/mL, and PCT >0.05 ng/mL were identified as disease severity predictors.

Conclusions

Based on our results, age, comorbidities, CRP, and PCT level may be utilised as primary assessment factors for possible hospital admission and close monitoring upon testing. Early detection of these risk factors may provide strategic interventions that help reduce mortality, hospital admissions, and more expensive and extensive treatments.

Disease severity in hospitalized COVID-19 patients: comparing routine surveillance with cohort data from the LEOSS study in 2020 in Germany

INTRODUCTION

Studies investigating risk factors for severe COVID-19 often lack information on the representativeness of the study population. Here, we investigate factors associated with severe COVID-19 and compare the representativeness of the dataset to the general population.

METHODS

We used data from the Lean European Open Survey on SARS-CoV-2 infected patients (LEOSS) of hospitalized COVID-19 patients diagnosed in 2020 in Germany to identify associated factors for severe COVID-19, defined as progressing to a critical disease stage or death. To assess the representativeness, we compared the LEOSS cohort to cases of hospitalized patients in the German statutory notification data of the same time period. Descriptive methods and Poisson regression models were used.

RESULTS

Overall, 6672 hospitalized patients from LEOSS and 132,943 hospitalized cases from the German statutory notification data were included. In LEOSS, patients above 76 years were less likely represented (34.3% vs. 44.1%). Moreover, mortality was lower (14.3% vs. 21.5%) especially among age groups above 66 years. Factors associated with a severe COVID-19 disease course in LEOSS included increasing age, male sex (adjusted risk ratio (aRR) 1.69, 95% confidence interval (CI) 1.53-1.86), prior stem cell transplantation (aRR 2.27, 95% CI 1.53-3.38), and an elevated C-reactive protein at day of diagnosis (aRR 2.30, 95% CI 2.03-2.62).

CONCLUSION

We identified a broad range of factors associated with severe COVID-19 progression. However, the results may be less applicable for persons above 66 years since they experienced lower mortality in the LEOSS dataset compared to the statutory notification data.

Should We Interfere with the Interleukin-6 Receptor During COVID-19: What Do We Know So Far?

Severe manifestations of COVID-19 consist of acute respiratory distress syndrome due to an initially local reaction leading to a systemic inflammatory response that results in hypoxia. Many therapeutic approaches have been attempted to reduce the clinical consequences of an excessive immune response to viral infection. To date, systemic corticosteroid therapy is still the most effective intervention. More recently, new hope has emerged with the use of interleukin (IL)-6 receptor inhibitors (tocilizumab and sarilumab). However, the great heterogeneity of the methodology and results of published studies obfuscate the true value of this treatment, leading to a confusing synthesis in recent meta-analyses, and the persistence of doubts in terms of patient groups and the appropriate time to treat. Moreover, their effects on the anti-infectious or pro-healing response are still poorly studied. This review aims to clarify the potential role of IL-6 receptor inhibitors in the treatment of severe forms of COVID-19.

Prevalence of hypertension and associated risks in hospitalized patients with COVID-19: a meta-analysis of meta-analyses with 1468 studies and 1,281,510 patients

BACKGROUND

Since the COVID-19 outbreak, preliminary research has shown that some risk-associated conditions increase death and severe complications of the disease, hypertension being one of them. Thus, numerous meta-analyses have been conducted to explore this issue. Therefore, this umbrella review aims to perform a meta-analysis of the meta-analyses to estimate the prevalence and associated risks of hypertension in patients with COVID-19.

METHODS

PubMed, Scopus, Web of Knowledge, Embase, and Cochrane databases were searched for the published meta-analyses up to January 1, 2022. Google Scholar, citation check, reference check, and Grey literature were also manually searched. A random-effect model approach was used for analysis.

RESULTS

The overall death rate was estimated at 12%. Hypertension was present in 25% of the patients as a comorbid disease. The overall RR for death, disease severity, and the possibility of ICU admission were estimated at 1.79 [1.68-1.89 with 95% CI], 1.74 [1.66-1.83 with 95% CI], and 1.91 [1.48-2.34 with 95% CI], respectively. The meta-regression results showed that being "male" significantly increases the risk of disease severity and ICU admission.

CONCLUSIONS

The results indicated that hypertension is a common comorbid disease in hospitalized patients with COVID-19, which significantly increases mortality risk, the severity of the disease, and the probability of ICU admission.

SYSTEMATIC REVIEW REGISTRATION

This study has been registered in PROSPERO (CRD42021231844).

Clinical and immune response characteristics among vaccinated persons infected with SARS-CoV-2 delta variant: a retrospective study

OBJECTIVES

This study aimed to observe the clinical and immune response characteristics of vaccinated persons infected with the delta variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Yangzhou, China.

METHODS

We extracted the medical data of 129 patients with delta-variant infection who were admitted to Northern Jiangsu People's Hospital (Yangzhou, China) between August and September, 2021. The patients were grouped according to the number of vaccine doses received into an unvaccinated group: a one-dose group and a two-dose group. The vaccine used was SARS-CoV-2-inactivated vaccine developed by Sinovac. We retrospectively analyzed the patients' epidemiological, clinical, laboratory, and imaging data.

RESULTS

Almost all patients with delta-variant infection in Yangzhou were elderly, and patients with severe/critical illness were over 70 years of age. The rates of severe/critical illness (P=0.006), fever (P=0.025), and dyspnea (P=0.045) were lower in the two-dose group than in the unvaccinated group. Compared to the unvaccinated group, the two-dose group showed significantly higher lymphocyte counts and significantly lower levels of C-reactive protein (CRP), interleukin-6 (IL-6), and D-dimer during hospitalization and a significantly higher positive rate of immunoglobulin G (IgG) antibodies at admission (all P<0.05). The cumulative probabilities of hospital discharge and negative virus conversion were also higher in the two-dose group than in the unvaccinated group (P<0.05).

CONCLUSIONS

Two doses of the SARS-CoV-2-inactivated vaccine were highly effective at limiting symptomatic disease and reducing immune response, while a single dose did not seem to be effective.

Rapid systematic review of respiratory rate as a vital sign of clinical deterioration in COVID-19

OBJECTIVES

This meta-analysis aimed to establish a clinical evidence base for respiratory rate (RR) as a single predictor of early-onset COVID-19. The review also looked to determine the practical implementation of mobile respiratory rate measuring devices where information was available.

METHODS

We focused on domestic settings with older adults. Relevant studies were identified through MEDLINE, Embase, and CENTRAL databases. A snowballing method was also used. Articles published from the beginning of the COVID-19 pandemic (2019) until Feb 2022 were selected. Databases were searched for terms related to COVID-19 and respiratory rate measurements in domestic patients.

RESULTS

A total of 2,889 articles were screened for relevant content, of which 60 full-text publications were included. We compared the Odds Ratios and statistically significant results of both vital signs.

CONCLUSION

Multinational studies across dozens of countries have shown respiratory rate to have predictive accuracy in detecting COVID-19 deterioration. However, considerable variability is present in the data, making it harder to be sure about the effects. There is no meaningful difference in data quality in terms of variability (95% CI intervals) between vital signs as predictors of decline in COVID-19 patients. Contextual and economic factors will likely determine the choice of measurement used.

Admission Predictors of Mortality in Hospitalized COVID-19 Patients-A Serbian Cohort Study

Background: Early prediction of COVID-19 patients’ mortality risk may be beneficial in adequate triage and risk assessment. Therefore, we aimed to single out the independent morality predictors of hospitalized COVID-19 patients among parameters available on hospital admission. Methods: An observational, retrospective−prospective cohort study was conducted on 703 consecutive COVID-19 patients hospitalized in the University Clinical Center Kragujevac between September and December 2021. Patients were followed during the hospitalization, and in-hospital mortality was observed as a primary end-point. Within 24 h of admission, patients were sampled for blood gas and laboratory analysis, including complete blood cell count, inflammation biomarkers and other biochemistry, coagulation parameters, and cardiac biomarkers. Socio-demographic and medical history data were obtained using patients’ medical records. Results: The overall prevalence of mortality was 28.4% (n = 199). After performing multiple regression analysis on 20 parameters, according to the initial univariate analysis, only four independent variables gave statistically significant contributions to the model: SaO2 < 88.5 % (aOR 3.075), IL-6 > 74.6 pg/mL (aOR 2.389), LDH > 804.5 U/L (aOR 2.069) and age > 69.5 years (aOR 1.786). The C-index of the predicted probability calculated using this multivariate logistic model was 0.740 (p < 0.001). Conclusions: Parameters available on hospital admission can be beneficial in predicting COVID-19 mortality.

A Review of Routine Laboratory Biomarkers for the Detection of Severe COVID-19 Disease

As the COVID-19 pandemic continues, there is an urgent need to identify clinical and laboratory predictors of disease severity and prognosis. Once the coronavirus enters the cell, it triggers additional events via different signaling pathways. Cellular and molecular deregulation evoked by coronavirus infection can manifest as changes in laboratory findings. Understanding the relationship between laboratory biomarkers and COVID-19 outcomes would help in developing a risk-stratified approach to the treatment of patients with this disease. The purpose of this review is to investigate the role of hematological (white blood cell (WBC), lymphocyte, and neutrophil count, neutrophil-to-lymphocyte ratio (NLR), platelet, and red blood cell (RBC) count), inflammatory (C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), and lactate dehydrogenase (LDH)), and biochemical (Albumin, aspartate aminotransferase (AST) and alanine aminotransferase (ALT), blood urea nitrogen (BUN), creatinine, D-dimer, total Cholesterol, low-density lipoprotein (LDL), and high-density lipoprotein (HDL)) biomarkers in the pathogenesis of COVID-19 disease and how their levels vary according to disease severity.

Ivermectin role in COVID-19 treatment (IRICT): single-center, adaptive, randomized, double-blind, placebo-controlled, clinical trial

BACKGROUND

To investigate the efficacy and safety of ivermectin compared to hydroxychloroquine and placebo in hospitalized moderate to severe COVID-19 patients.

RESEARCH DESIGN AND METHODS

The study was an adaptive, randomized, double-blinded, controlled, single-center trial. The study was a series of 3-arm comparisons between two different investigational therapeutic agents (ivermectin and hydroxychloroquine) and a placebo. There was interim monitoring to allow early stopping for futility, efficacy, or safety.

RESULTS

Ivermectin decreased survival time from 29 to 18.3 days (HR, 9.8, 95%CI, 3.7-26.2), while it did not shorten the recovery time (HR, 1.02, 95%CI, 0.69-1.5). Subgroup analysis showed an association between ivermectin-related mortality and baseline oxygen saturation level. Moreover, stratified groups showed higher risk among patients on high flow O2. Hydroxychloroquine delayed recovery from 10.1 to 12.5 days (HR, 0.62, 95%CI, 0.4-0.95) and non-significantly decreased survival time from 29 to 26.8 days (HR, 1.47, 95%CI, 0.73-2.9). However, 3 months mortality rates were increased with hydroxychloroquine (RR, 2.05, 95%CI, 1.33-3.16). Neither ivermectin nor hydroxychloroquine increased adverse events and demonstrated safety profile compared to placebo.

CONCLUSIONS

The study recommends against using either ivermectin or hydroxychloroquine for treatment of COVID-19 in hospitalized patients with any degree of severity. Clinical trial registration: www.clinicaltrials.gov identifier is: NCT04746365.

Gut as an Alternative Entry Route for SARS-CoV-2: Current Evidence and Uncertainties of Productive Enteric Infection in COVID-19

The gut has been proposed as a potential alternative entry route for SARS-CoV-2. This was mainly based on the high levels of SARS-CoV-2 receptor expressed in the gastrointestinal (GI) tract, the observations of GI disorders (such as diarrhea) in some COVID-19 patients and the detection of SARS-CoV-2 RNA in feces. However, the underlying mechanisms remain poorly understood. It has been proposed that SARS-CoV-2 can productively infect enterocytes, damaging the intestinal barrier and contributing to inflammatory response, which might lead to GI manifestations, including diarrhea. Here, we report a methodological approach to assess the evidence supporting the sequence of events driving SARS-CoV-2 enteric infection up to gut adverse outcomes. Exploring evidence permits to highlight knowledge gaps and current inconsistencies in the literature and to guide further research. Based on the current insights on SARS-CoV-2 intestinal infection and transmission, we then discuss the potential implication on clinical practice, including on long COVID. A better understanding of the GI implication in COVID-19 is still needed to improve disease management and could help identify innovative therapies or preventive actions targeting the GI tract.

Mechanisms Leading to Gut Dysbiosis in COVID-19: Current Evidence and Uncertainties Based on Adverse Outcome Pathways

Alteration in gut microbiota has been associated with COVID-19. However, the underlying mechanisms remain poorly understood. Here, we outlined three potential interconnected mechanistic pathways leading to gut dysbiosis as an adverse outcome following SARS-CoV-2 presence in the gastrointestinal tract. Evidence from the literature and current uncertainties are reported for each step of the different pathways. One pathway investigates evidence that intestinal infection by SARS-CoV-2 inducing intestinal inflammation alters the gut microbiota. Another pathway links the binding of viral S protein to angiotensin-converting enzyme 2 (ACE2) to the dysregulation of this receptor, essential in intestinal homeostasis-notably for amino acid metabolism-leading to gut dysbiosis. Additionally, SARS-CoV-2 could induce gut dysbiosis by infecting intestinal bacteria. Assessing current evidence within the Adverse Outcome Pathway framework justifies confidence in the proposed mechanisms to support disease management and permits the identification of inconsistencies and knowledge gaps to orient further research.

Body mass index and comorbidities are associated with the duration of COVID-19 symptoms in non-hospitalized patients

Objective

Evidence indicates that people with a high body mass index (BMI) tend to develop more severe forms of coronavirus disease 2019 (COVID-19). In this study, we aimed to determine the association between the duration of COVID-19 symptoms and variables such as BMI, age, presence of comorbidities, and smoking in non-hospitalized patients.

Methods

In this observational cross-sectional analytical study, we analyzed the data of patients with COVID-19 but without severe manifestations. We conducted descriptive statistics, non-parametric tests, and multivariate quasi-Poisson regression in the analysis. The quasi-Poisson regression model was configured with the duration of COVID-19 symptoms as the response variable, and BMI and the presence of comorbidities as the explanatory variables.

Results

Among 302 non-hospitalized patients, we found a significant difference in COVID-19 symptom duration between the overweight group and the group with normal weight. Multivariate quasi-Poisson regression analysis showed that BMI and the presence of comorbidities were associated with the duration of COVID-19 symptoms. On the contrary, sex, age, and smoking status were not related to COVID-19 symptom duration.

Conclusions

BMI and comorbidities were associated with the duration of COVID-19 symptoms in non-hospitalized patients.

Global Prevalence and Potential Influencing Factors of COVID-19 Vaccination Hesitancy: A Meta-Analysis

Countries worldwide have deployed mass COVID-19 vaccination drives, but there are people who are hesitant to receive the vaccine. Studies assessing the factors associated with COVID-19 vaccination hesitancy are inconclusive. This study aimed to assess the global prevalence of COVID-19 vaccination hesitancy and determine the potential factors associated with such hesitancy. We performed an organized search for relevant articles in PubMed, Scopus, and Web of Science. Extraction of the required information was performed for each study. A single-arm meta-analysis was performed to determine the global prevalence of COVID-19 vaccination hesitancy; the potential factors related to vaccine hesitancy were analyzed using a Z-test. A total of 56 articles were included in our analysis. We found that the global prevalence of COVID-19 vaccination hesitancy was 25%. Being a woman, being a 50-year-old or younger, being single, being unemployed, living in a household with five or more individuals, having an educational attainment lower than an undergraduate degree, having a non-healthcare-related job and considering COVID-19 vaccines to be unsafe were associated with a higher risk of vaccination hesitancy. In contrast, living with children at home, maintaining physical distancing norms, having ever tested for COVID-19, and having a history of influenza vaccination in the past few years were associated with a lower risk of hesitancy to COVID-19 vaccination. Our study provides valuable information on COVID-19 vaccination hesitancy, and we recommend special interventions in the sub-populations with increased risk to reduce COVID-19 vaccine hesitancy.

Analysis of Chitinase-3-Like Protein 1, IL-1-Alpha, and IL-6 as Novel Inflammatory Biomarkers for COVID-19

The aim of our study was to investigate the potential role of IL-1-alpha, IL-6, and chitinase 3-like protein-1 (CHI3L1) as potential biomarkers for COVID-19. Sixty adult SARS Cov-2 PCR-positive patients (22 mild, 25 moderate, and 13 severe) and 50 healthy controls were included in this study. The serum levels of CHI3L1, IL-1-alpha, and IL-6 for all study participants were measured by protein-specific ELISAs. Mean serum CHI3L1 levels in patients with severe disease (7,185.5 ± 1,109.4) were significantly higher than in the moderate (3,977.4 ± 1,260.3), mild (1,379.5 ± 598.8), and control (329.5 ± 128.4) groups (P = 0.001). There was no difference in IL-1-alpha levels between the patient and control groups (P = 0.083). IL-6 levels differed significantly, being lowest in the control group (35.9 ± 13.7), 89.1 ± 23.4 in the mild group, 156.2 ± 29.6 in the moderate group, and the highest in the severe group (214.9 ± 28.1) (P = 0.001). A strong significant correlation was found between disease severity and serum IL-6 and CHI3L1 values (r = 0.894 and r = 0.905, respectively, and P < 0.001 for both). Serum CHI3L1 and IL-6 levels exhibited a linear correlation with the clinical course of COVID-19 infection. These results indicate that inhibitors of IL-6 and/or CHI3L1 may provide useful treatments for COVID-19.

Vegan nutrition: a preliminary guide for health professionals

Since the beginning of the 21st century, interest in vegan diets has been rapidly increasing in most countries. Misconceptions about vegan diets are widespread among the general population and health professionals. Vegan diets can be health-promoting and may offer certain important advantages compared to typical Western (and other mainstream) eating patterns. However, adequate dietary sources/supplements of nutrients of focus specific to vegan diets should be identified and communicated. Without supplements/fortified foods, severe vitamin B12 deficiency may occur. Other potential nutrients of focus are calcium, vitamin D, iodine, omega-3 fatty acids, iron, zinc, selenium, vitamin A, and protein. Ensuring adequate nutrient status is particularly important during pregnancy, lactation, infancy, and childhood. Health professionals are often expected to be able to provide advice on the topic of vegan nutrition, but a precise and practical vegan nutrition guide for health professionals is lacking. Consequently, it is important and urgent to provide such a set of dietary recommendations. It is the aim of this article to provide vegan nutrition guidelines, based on current evidence, which can easily be communicated to vegan patients/clients, with the goal of ensuring adequate nutrient status in vegans.

Factors Modulating COVID-19: A Mechanistic Understanding Based on the Adverse Outcome Pathway Framework

Addressing factors modulating COVID-19 is crucial since abundant clinical evidence shows that outcomes are markedly heterogeneous between patients. This requires identifying the factors and understanding how they mechanistically influence COVID-19. Here, we describe how eleven selected factors (age, sex, genetic factors, lipid disorders, heart failure, gut dysbiosis, diet, vitamin D deficiency, air pollution and exposure to chemicals) influence COVID-19 by applying the Adverse Outcome Pathway (AOP), which is well-established in regulatory toxicology. This framework aims to model the sequence of events leading to an adverse health outcome. Several linear AOPs depicting pathways from the binding of the virus to ACE2 up to clinical outcomes observed in COVID-19 have been developed and integrated into a network offering a unique overview of the mechanisms underlying the disease. As SARS-CoV-2 infectibility and ACE2 activity are the major starting points and inflammatory response is central in the development of COVID-19, we evaluated how those eleven intrinsic and extrinsic factors modulate those processes impacting clinical outcomes. Applying this AOP-aligned approach enables the identification of current knowledge gaps orientating for further research and allows to propose biomarkers to identify of high-risk patients. This approach also facilitates expertise synergy from different disciplines to address public health issues.

When Outcomes Diverge: Age and Cardiovascular Risk as Determinants of Mortality and ICU Admission in COVID-19

Background: Hospital mortality and admission to the Intensive Care Unit (ICU) are markers of disease severity in COVID-19 patients. Cardiovascular co-morbidities are one of the main determinants of negative outcomes. In this study we investigated the impact of cardiovascular co-morbidities on mortality and admission to the ICU in first-wave COVID-19 patients. Methods: A multicenter, retrospective, cohort study. A total of 1077 patients were analyzed for mortality and ICU admission. Cardiovascular risk factors were explored as determinants of the outcomes after correction for other confounders. Results: In the multivariable model, after correction for age, only a history of heart failure remained independently associated (p = 0.0013) with mortality (hazard ratio 2.22, 95% confidence interval 1.37 to 3.62). Age showed a mortality risk increase of 8% per year (hazard ratio 1.08, 95% confidence interval 1.05 to 1.10, p = 0.001). The transition from ward to the ICU had, as a single determinant, the age, but in a reversed fashion (hazard ratio 0.96, 95% confidence interval 0.94 to 0.98, p = 0.0002). Conclusions: Once adjusted for the main determinant of mortality (age) heart failure only remained independently associated with mortality. Admission to the ICU was less likely for elderly patients. This may reflect the catastrophic impact of the first wave of COVID-19 pandemic in terms of ICU bed availability in Lombardy, leading to a selection process for ICU admission.

Evaluation of the Charlson Comorbidity Index and Laboratory Parameters as Independent Early Mortality Predictors in Covid 19 Patients

Purpose

Patients and methods

Results

Conclusion

A systematic review and meta-analysis of abnormalities in hematological and biochemical markers among Bangladeshi COVID-19 cases

Background and Aims

Abnormalities in hematological and biochemical markers are assumed to be associated with the progression of COVID-19 disease. This meta-analysis was performed to assess the consequences of abnormalities of biomarkers (D-dimers, C-reactive protein [CRP], serum ferritin, lactate dehydrogenase [LDH], random blood sugar [RBS], absolute neutrophil count [ANC], neutrophil to lymphocyte ratio (NLR), serum creatinine, and hemoglobin) in the Bangladeshi COVID-19 patients.

Methods

The data of biomarker levels in Bangladeshi COVID-19 patients were gathered from five databases: PubMed, ScienceDirect, Web of Science, Google Scholar and Bangladesh Journals Online between January 2020 to March 2022. Review Manager 5.4 was used for the meta-analysis, and Egger's test and Begg-Mazumdar's rank correlation were used to investigate publication bias.

Results

This study included 1542 patients with 567 severe and 975 nonsevere statuses. Based on the accumulated data synthesis, there is a strong correlation between disease severity and different biomarkers, including D-dimer, CRP, ferritin, LDH, RBS, NLR, and serum creatinine (MD = 1.16, p = 0.0004; MD = 22.97, p = 0.003; MD = 419.26, p < 0.00001; MD = 118.37, p = 0.004; MD = 1.96, p = 0.02; MD = 1.26, p = 0.02; and MD = 0.31, p = 0.008, respectively). A significantly decreased correlation was observed for hemoglobin levels in severe COVID-19 patients (MD = -0.73, p = 0.10).

Conclusion

The elevated biomarkers level was noticed in severe cases compared to nonsevere patients, revealing that D-dimer, CRP, ferritin, LDH, RBS, NLR, and serum creatinine are significantly correlated to COVID-19 severity. Only lower hemoglobin level was found to be associated with COVID-19 severity.

Empirical Antibiotic Prescribing in Adult COVID-19 Inpatients over Two Years in Mexico

Background and Objectives: Empirical antibiotic prescribing in patients with coronavirus disease 2019 (COVID-19) has been common even though bacterial coinfections are infrequent. The overuse of antibacterial agents may accelerate the antibiotic resistance crisis. We aimed to evaluate factors predicting empirical antibiotic prescribing to adult COVID-19 inpatients over 2 years (March 2020–February 2021) in Mexico. Materials and Methods: A cross-sectional analysis of a nationwide cohort study was conducted. Hospitalized adults due to laboratory-confirmed COVID-19 were included (n = 214,171). Odds ratios (OR) and 95% confidence intervals (CI), computed by using logistic regression models, were used to evaluate factors predicting empirical antibiotic prescribing. Results: The overall frequency of antibiotic usage was 25.3%. In multiple analysis, the highest risk of antibiotic prescription was documented among patients with pneumonia at hospital admission (OR = 2.20, 95% CI 2.16–2.25). Male patients, those with chronic comorbidities (namely obesity and chronic kidney disease) and longer interval days from symptoms onset to healthcare seeking, were also more likely to receive these drugs. We also documented that, per each elapsed week during the study period, the odds of receiving antibiotic therapy decreased by about 2% (OR = 0.98, 95% CI 0.97–0.99). Conclusion: Our study identified COVID-19 populations at increased risk of receiving empirical antibiotic therapy during the first two years of the pandemic.

Risk of Death in Comorbidity Subgroups of Hospitalized COVID-19 Patients Inferred by Routine Laboratory Markers of Systemic Inflammation on Admission: A Retrospective Study

Our study objective was to construct models using 20 routine laboratory parameters on admission to predict disease severity and mortality risk in a group of 254 hospitalized COVID-19 patients. Considering the influence of confounding factors in this single-center study, we also retrospectively assessed the correlations between the risk of death and the routine laboratory parameters within individual comorbidity subgroups. In multivariate regression models and by ROC curve analysis, a model of three routine laboratory parameters (AUC 0.85; 95% CI: 0.79-0.91) and a model of six laboratory factors (AUC 0.86; 95% CI: 0.81-0.91) were able to predict severity and mortality of COVID-19, respectively, compared with any other individual parameter. Hierarchical cluster analysis showed that inflammatory laboratory markers grouped together in three distinct clusters including positive correlations: WBC with NEU, NEU with neutrophil-to-lymphocyte ratio (NLR), NEU with systemic immune-inflammation index (SII), NLR with SII and platelet-to-lymphocyte ratio (PLR) with SII. When analyzing the routine laboratory parameters in the subgroups of comorbidities, the risk of death was associated with a common set of laboratory markers of systemic inflammation. Our results have shown that a panel of several routine laboratory parameters recorded on admission could be helpful for early evaluation of the risk of disease severity and mortality in COVID-19 patients. Inflammatory markers for mortality risk were similar in the subgroups of comorbidities, suggesting the limited effect of confounding factors in predicting COVID-19 mortality at admission.

Anti-SARS-CoV-2 Titers Predict the Severity of COVID-19

Coronavirus disease 2019 (COVID-19) due to SARS-CoV-2 is associated with a wide spectrum of disease, ranging from asymptomatic infection to acute respiratory distress syndrome. Some biomarkers may predict disease severity. Among them, the anti-SARS-CoV-2 antibody response has been related to severe disease. The aim of this study was to assess the correlation between the anti-SARS-CoV-2 serological response and COVID-19 outcome. Demographic, clinical, and biological data from nasopharyngeal-PCR confirmed COVID-19 hospitalized patients were prospectively collected between April and August 2020 at our institution. All patients had serial weekly serology testing for a maximum of three blood samples or until discharge. Two different serological assays were used: a chemiluminescent assay and an in-house developed Luminex immunoassay. Kinetics of the serological response and correlation between the antibody titers and outcome were assessed. Among the 70 patients enrolled in the study, 22 required invasive ventilation, 29 required non-invasive ventilation or oxygen supplementation, and 19 did not require any oxygen supplementation. Median duration of symptoms upon admission for the three groups were 13, 8, and 9 days, respectively. Antibody titers gradually increased for up to 3 weeks since the onset of symptoms for patients requiring oxygen supplementation with significantly higher antibody titers for patients requiring invasive ventilation. Antibody titers on admission were also significantly higher in severely ill patients and serology performed well in predicting the necessity of invasive ventilation (AUC: 0.79, 95% CI: 0.67–0.9). Serology testing at admission may be a good indicator to identify severe COVID-19 patients who will require invasive mechanical ventilation.

Expanding the spectrum of the hyperferritinemic syndrome, from pathogenic mechanisms to clinical observations, and therapeutic implications

From the introduction of hyperferritinemic syndrome concept, a growing body of evidence has suggested the role of ferritin as a pathogenic mediator and a relevant clinical feature in the management of patients with inflammatory diseases. From a pathogenic point of view, ferritin may directly stimulate the aberrant immune response by triggering the production of pro-inflammatory mediators in inducing a vicious pathogenic loop and contributing to the occurrence of cytokine storm syndrome. The latter has been recently defined as a clinical picture characterised by elevated circulating cytokine levels, acute systemic inflammatory symptoms, and secondary organ dysfunction beyond that which could be attributed to a normal response to a pathogen It is noteworthy that the occurrence of hyperferritinemia may be correlated with the development of the cytokine storm syndrome in the context of an inflammatory disease. In addition to adult onset Still's disease, macrophage activation syndrome, catastrophic anti-phospholipids syndrome, and septic shock, recent evidence has suggested this association between ferritin and life-threatening evolution in patients with systemic lupus erythematosus, with anti-MDA5 antibodies in the context of poly-dermatomyositis, with severe COVID-19, and with multisystem inflammatory syndrome. The possible underlying common inflammatory mechanisms, associated with hyperferritinemia, may led to the similar clinical picture observed in these patients. Furthermore, similar therapeutic strategies could be suggested inhibiting pro-inflammatory cytokines and improving long-term outcomes in these disorders. Thus, it could be possible to expand the spectrum of the hyperferritinemic syndrome to those diseases burdened by a dreadful clinical picture correlated with hyperferritinemia and the occurrence of the cytokine storm syndrome. In addition, the assessment of ferritin may provide useful information to the physicians in clinical practice to manage these patients. Therefore, ferritin may be considered a relevant clinical feature to be used as biomarker in dissecting the unmet needs in the management of these disorders. Novel evidence may thus support an expansion of the spectrum of the hyperferritinemic syndrome to these diseases burdened by a life-threatening clinical picture correlated with hyperferritinemia and the occurrence of the cytokine storm syndrome.

HbA1c level may be a risk factor for oxygen therapy requirement in patients with coronavirus disease 2019

INTRODUCTION

Many clinical studies have identified significant predictors or risk factors for the severity or mortality of coronavirus disease 2019 (COVID-19) cases. However, there are very limited reports on the risk factors for requiring oxygen therapy during hospitalization. In particular, we sought to investigate whether plasma glucose and HbA1c levels could be risk factors for oxygen therapy requirement.

MATERIALS AND METHODS

A single-center, retrospective study was conducted of 131 COVID-19 patients hospitalized at Saitama Medical University Hospital between March 2020 and November 2020. To identify the risk factors for oxygen therapy requirement during hospitalization, a stepwise multivariate binary logistic regression analysis was performed using several clinical parameters commonly obtained on admission, including plasma glucose and HbA1c levels.

RESULTS

Of the 131 patients with COVID-19, 33.6% (44/131) received oxygen therapy during hospitalization. According to the logistic regression analysis, male sex (odds ratio [OR]: 8.76, 95% confidence interval [CI]: 1.65-46.5, P < 0.05), age (OR: 1.07, 95% CI: 1.02-1.12, P < 0.01), HbA1c levels (OR: 1.94, 95% CI: 1.09-3.44, P < 0.05), and serum C-reactive protein (CRP) levels (OR: 2.22, 95% CI: 1.54-3.20, P < 0.01) emerged as independent variables associated with oxygen therapy requirement during hospitalization.

CONCLUSIONS

In addition to male sex, age, and serum CRP levels, HbA1c levels on admission may serve as a risk factor for oxygen therapy requirement during the clinical course of COVID-19, irrespective of diabetes history and status. This may contribute to the efficient delegation of limited numbers of hospital beds to patients at risk for oxygen therapy requirement.

Analysis of Risk Factors for In-Hospital Death Due to COVID-19 in Patients Hospitalised at the Temporary Hospital Located at the National Stadium in Warsaw: A Retrospective Analysis

The outbreak of the SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) pandemic has affected all aspects of social life and brought massive changes to the healthcare sector. The aim of this study was to identify the factors affecting the mortality of COVID-19 patients at a temporary hospital in Warsaw (Poland). The present study was conducted based on a retrospective analysis of the medical records of patients hospitalised at the temporary hospital located at the National Stadium in Warsaw between 1 March 2020 and 30 April 2021. The study included all cases of patients who were brought directly or transferred to the National Hospital from other hospitals for further treatment. With regard to comorbidities, the analysis found that five comorbidities—namely, diabetes (OR = 1.750, 95% CI: 1.009−2.444, p < 0.05), stroke history (OR = 2.408, 95% CI: 1.208−4.801, p < 0.05), renal failure (OR = 2.141, 95% CI: 1.052−4.356, p < 0.05), chronic obstructive pulmonary disease (OR = 2.044, 95% CI: 1.133−3.690, p < 0.05) and heart failure (OR = 1.930, 95% CI: 1.154−3.227, p < 0.05)—had a significant impact on the survival of COVID-19 patients. The analysis identified 14 factors that had a significant impact on the prognosis and mortality of the COVID-19 patients studied.

Clinical Characteristics and Risk Factors Associated with Severe Disease Progression among COVID-19 Patients In Wad Medani Isolation Centers: A Multicenter Retrospective Cross-Sectional Study

Background

Since December 2019, (COVID-19) has had a significant impact on global health systems. Because little is known about the clinical characteristics and risk factors connected with COVID-19 severity in Sudanese patients, it is vital to summarize the clinical characteristics of COVID-19 patients and to investigate the risk factors linked to COVID-19 severity.

Objectives

We aimed to assess the clinical characteristics of COVID-19 patients and look into risk factors associated with COVID-19 severity.

Methods

This is a retrospective cross-sectional study that took place in two Isolation Centers in Wad Medani, Gezira State, Sudan. Four hundred and eighteen patients were included between May 2020 and May 2021. All COVID-19 patients over the age of 18 who were proven COVID-19 positive by nucleic acid testing or had characteristics suggestive of COVID-19 on a chest CT scan and had a complete medical record in the study period were included.

Results

The participants in this study were 418 confirmed COVID-19 cases with a median age of 66.313 years. There were 279 men (66.7%) among the patients. The most prevalent comorbidities were hypertension (n = 195; 46.7%) and diabetes (n = 187; 44.7%). Fever (n = 303; 72.5%), cough (n = 278; 66.5%), and dyspnea (n = 256; 61.2%) were the most prevalent symptoms at the onset of COVID-19. The overall mortality rate (n = 148) was 35.4%. Patients with severe illness had a mortality rate of 42.3% (n = 118). Older age, anemia, neutrophilia, and lymphocytopenia, as well as higher glucose, HbA1c, and creatinine levels, were all linked to severe COVID-19, according to the chi-square test and analysis of variance analysis.

Conclusion

Sixteen variables were found to be associated with COVID-19 severity. These patients are more prone to go through a serious infection and as a result have a greater death rate than those who do not have these characteristics.

Ivermectin and outcomes from Covid‐19 pneumonia: A systematic review and meta‐analysis of randomized clinical trial studies

Ivermectin is an FDA‐approved drug for a parasitic disease that has broad antiviral activity. This study aims to analyse the efficacy of ivermectin in improving the Covid‐19 outcomes. We systematically searched the PubMed, Europe PMC and ClinicalTrials.gov database using specific keywords related to our aims until 10th May 2021. All published randomized clinical trial studies on Covid‐19 and ivermectin were retrieved. The quality of the study was assessed using Jadad scale assessment tool for clinical trial studies. Statistical analysis was done using Review Manager 5.4 software. A total of 19 studies with 2768 Covid‐19 patients were included in this meta‐analysis. This meta‐analysis showed that ivermectin was associated with reduction in severity of Covid‐19 (RR 0.43 [95% CI 0.23–0.81], p  = 0.008), reduction of mortality (RR 0.31 [95% CI 0.15–0.62], p  = 0.001), higher negative RT‐PCR test results rate (RR 1.23 [95% CI 1.01–1.51], p  = 0.04), shorter time to negative RT‐PCR test results (mean difference [MD] −3.29 [95% CI −5.69, −0.89], p  = 0.007), higher symptoms alleviations rate (RR 1.23 [95% CI 1.03−1.46], p  = 0.02), shorter time to symptoms alleviations (MD −0.68 [95% CI −1.07, −0.29], p  = 0.0007) and shorter time to hospital discharge (MD −2.66 [95% CI −4.49, −0.82], p  = 0.004). Our study suggests that ivermectin may offer beneficial effects towards Covid‐19 outcomes. More randomized clinical trial studies are still needed to confirm the results of our study.

Predictors of mortality among hospitalized COVID-19 patients and risk score formulation for prioritizing tertiary care-An experience from South India

Background

We retrospectively data-mined the case records of Reverse Transcription Polymerase Chain Reaction (RT-PCR) confirmed COVID-19 patients hospitalized to a tertiary care centre to derive mortality predictors and formulate a risk score, for prioritizing admission.

Methods and findings

Data on clinical manifestations, comorbidities, vital signs, and basic lab investigations collected as part of routine medical management at admission to a COVID-19 tertiary care centre in Chengalpattu, South India between May and November 2020 were retrospectively analysed to ascertain predictors of mortality in the univariate analysis using their relative difference in distribution among ‘survivors’ and ‘non-survivors’. The regression coefficients of those factors remaining significant in the multivariable logistic regression were utilised for risk score formulation and validated in 1000 bootstrap datasets.

Among 746 COVID-19 patients hospitalised [487 “survivors” and 259 “non-survivors” (deaths)], there was a slight male predilection [62.5%, (466/746)], with a higher mortality rate observed among 40–70 years age group [59.1%, (441/746)] and highest among diabetic patients with elevated urea levels [65.4% (68/104)]. The adjusted odds ratios of factors [OR (95% CI)] significant in the multivariable logistic regression were SaO₂<95%; 2.96 (1.71–5.18), Urea ≥50 mg/dl: 4.51 (2.59–7.97), Neutrophil-lymphocytic ratio (NLR) >3; 3.01 (1.61–5.83), Age ≥50 years;2.52 (1.45–4.43), Pulse Rate ≥100/min: 2.02 (1.19–3.47) and coexisting Diabetes Mellitus; 1.73 (1.02–2.95) with hypertension and gender not retaining their significance. The individual risk scores for SaO₂<95–11, Urea ≥50 mg/dl-15, NLR >3–11, Age ≥50 years-9, Pulse Rate ≥100/min-7 and coexisting diabetes mellitus-6, acronymed collectively as ‘OUR-ARDs score’ showed that the sum of scores ≥ 25 predicted mortality with a sensitivity-90%, specificity-64% and AUC of 0.85.

Conclusions

The ‘OUR ARDs’ risk score, derived from easily assessable factors predicting mortality, offered a tangible solution for prioritizing admission to COVID-19 tertiary care centre, that enhanced patient care but without unduly straining the health system.

Fraction of COVID-19 hospitalizations and deaths attributable to chronic diseases

Evidence shows that chronic diseases are associated with COVID-19 severity and death. This study aims to estimate the fraction of hospitalizations and deaths from COVID-19 attributable to chronic diseases associated to poor nutrition and smoking among adults who tested positive to COVID-19 in Mexico. We analyzed 1,006,541 adults aged ≥20 who tested positive for COVID-19 from March 23 to December 5, 2020. Six chronic diseases were considered: obesity, chronic obstructive pulmonary disease (COPD), hypertension, diabetes, cardiovascular disease, and chronic kidney disease (CKD). We calibrated the database using a bias quantification method to consider undiagnosed disease cases. To estimate the total impact of multiple diseases, we defined a multimorbidity variable according to the number of diseases. Risks of hospitalization and death were estimated with Poisson regression models and used to calculate population attributable fractions (PAFs). Chronic diseases accounted for to 25.4% [95% CI: 24.8%-26.1%], 28.3% (95% CI: 27.8%-28.7%) and 15.3% (95% CI: 14.9%-15.7%) of the hospitalizations among adults below 40, 40-59, and 60 years and older, respectively. For COVID-19-related deaths, 50.1% (95% CI: 48.6%-51.5%), 40.5% (95% CI: 39.7%-41.3%), and 18.7% (95% CI, 18.0%-19.5%) were attributable to chronic diseases in adults under 40, 40-59, and 60 years and older, respectively. Chronic diseases linked to poor nutrition and smoking could have contributed to a large burden of hospitalization and deaths from COVID-19 in Mexico, particularly among younger adults. Medical and structural interventions to curb chronic disease incidence and facilitate disease control are urgently needed.