Dynamics of Anti-S IgG Antibodies Titers after the Second Dose of COVID-19 Vaccines in the Manual and Craft Worker Population of Qatar.

There is limited seroepidemiological evidence on the magnitude and long-term durability of antibody titers of mRNA and non-mRNA vaccines in the Qatari population. This study was conducted to generate evidence on long-term anti-S IgG antibody titers and their dynamics in individuals who have completed a primary COVID-19 vaccination schedule. A total of 300 male participants who received any of the following vaccines BNT162b2/Comirnaty, mRNA-1273, ChAdOx1-S/Covishield, COVID-19 Vaccine Janssen/Johnson, or BBIBP-CorV or Covaxin were enrolled in our study. All sera samples were tested by chemiluminescent microparticle immunoassay (CMIA) for the quantitative determination of IgG antibodies to SARS-CoV-2, receptor-binding domain (RBD) of the S1 subunit of the spike protein of SARS-CoV-2. Antibodies against SARS-CoV-2 nucleocapsid (SARS-CoV-2 N-protein IgG) were also determined. Kaplan-Meier survival curves were used to compare the time from the last dose of the primary vaccination schedule to the time by which anti-S IgG antibody titers fell into the lowest quartile (range of values collected) for the mRNA and non-mRNA vaccines. Participants vaccinated with mRNA vaccines had higher median anti-S IgG antibody titers. Participants vaccinated with the mRNA-1273 vaccine had the highest median anti-S-antibody level of 13,720.9 AU/mL (IQR 6426.5 to 30,185.6 AU/mL) followed by BNT162b2 (median, 7570.9 AU/mL; IQR, 3757.9 to 16,577.4 AU/mL); while the median anti-S antibody titer for non-mRNA vaccinated participants was 3759.7 AU/mL (IQR, 2059.7-5693.5 AU/mL). The median time to reach the lowest quartile was 3.53 months (IQR, 2.2-4.5 months) and 7.63 months (IQR, 6.3-8.4 months) for the non-mRNA vaccine recipients and Pfizer vaccine recipients, respectively. However, more than 50% of the Moderna vaccine recipients did not reach the lowest quartile by the end of the follow-up period. This evidence on anti-S IgG antibody titers should be considered for informing decisions on the durability of the neutralizing activity and thus protection against infection after the full course of primary vaccination in individuals receiving different type (mRNA verus non-mRNA) vaccines and those with natural infection.

Duration of COVID-19 mRNA Vaccine Effectiveness against Severe Disease.

Waning immunity following administration of mRNA-based COVID-19 vaccines remains a concern for many health systems. We undertook a study to determine if recent reports of waning for severe disease could have been attributed to design-related bias by conducting a study only among those detected with a first SARS-CoV-2 infection. We used a matched case-control study design with the study base being all individuals with first infection with SARS-CoV-2 reported in the State of Qatar between 1 January 2021 and 20 February 2022. Cases were those detected with first SARS-CoV-2 infection requiring intensive care (hard outcome), while controls were those detected with first SARS-CoV-2 infection who recovered without the need for intensive care. Cases and controls were matched in a 1:30 ratio for the calendar month of infection and the comorbidity category. Duration and magnitude of conditional vaccine effectiveness against requiring intensive care and the number needed to vaccinate (NNV) to prevent one more case of COVID-19 requiring intensive care was estimated for the mRNA (BNT162b2/mRNA-1273) vaccines. Conditional vaccine effectiveness against requiring intensive care was 59% (95% confidence interval (CI), 50 to 76) between the first and second dose, and strengthened to 89% (95% CI, 85 to 92) between the second dose and 4 months post the second dose in persons who received a primary course of the vaccine. There was no waning of vaccine effectiveness in the period from 4 to 6, 6 to 9, and 9 to 12 months after the second dose. This study demonstrates that, contrary to mainstream reports using hierarchical measures of effectiveness, conditional vaccine effectiveness against requiring intensive care remains robust till at least 12 months after the second dose of mRNA-based vaccines.

The Economic Impact of Optimizing a COVID-19 Management Protocol in Pre-Existing Cardiovascular Disease Patients.

This study answers the question of whether the health care costs of managing COVID-19 in preexisting cardiovascular diseases (CVD) patients increased or decreased as a consequence of evidence-based efforts to optimize the initial COVID-19 management protocol in a CVD group of patients. A retrospective cohort study was conducted in preexisting CVD patients with COVID-19 in Hamad Medical Corporation, Qatar. From the health care perspective, only direct medical costs were considered, adjusted to their 2021 values. The impact of revising the protocol was a reduction in the overall costs in non-critically ill patients from QAR15,447 (USD 4243) to QAR4337 (USD 1191) per patient, with an economic benefit of QAR11,110 (USD 3051). In the critically ill patients, however, the cost increased from QAR202,094 (USD 55,505) to QAR292,856 (USD 80,433) per patient, with added cost of QAR90,762 (USD 24,928). Overall, regardless of critical care status, the optimization of the initial COVID-19 protocols in patients with preexisting CVD did not reduce overall health care costs, but increased it by QAR80,529 (USD 22,117) per patient.

The prevalence of adaptive immunity to COVID-19 and reinfection after recovery - a comprehensive systematic review and meta-analysis.

This study aims to estimate the prevalence and longevity of detectable SARS-CoV-2 antibodies and T and B memory cells after recovery. In addition, the prevalence of COVID-19 reinfection and the preventive efficacy of previous infection with SARS-CoV-2 were investigated. A synthesis of existing research was conducted. The Cochrane Library, the China Academic Journals Full Text Database, PubMed, and Scopus, and preprint servers were searched for studies conducted between 1 January 2020 to 1 April 2021. Included studies were assessed for methodological quality and pooled estimates of relevant outcomes were obtained in a meta-analysis using a bias adjusted synthesis method. Proportions were synthesized with the Freeman-Tukey double arcsine transformation and binary outcomes using the odds ratio (OR). Heterogeneity was assessed using the I2 and Cochran's Q statistics and publication bias was assessed using Doi plots. Fifty-four studies from 18 countries, with around 12,000,000 individuals, followed up to 8 months after recovery, were included. At 6-8 months after recovery, the prevalence of SARS-CoV-2 specific immunological memory remained high; IgG - 90.4% (95%CI 72.2-99.9, I2 = 89.0%), CD4+ - 91.7% (95%CI 78.2-97.1y), and memory B cells 80.6% (95%CI 65.0-90.2) and the pooled prevalence of reinfection was 0.2% (95%CI 0.0-0.7, I2 = 98.8). Individuals previously infected with SARS-CoV-2 had an 81% reduction in odds of a reinfection (OR 0.19, 95% CI 0.1-0.3, I2 = 90.5%). Around 90% of recovered individuals had evidence of immunological memory to SARS-CoV-2, at 6-8 months after recovery and had a low risk of reinfection.RegistrationPROSPERO: CRD42020201234.

The epidemic volatility index, a novel early warning tool for identifying new waves in an epidemic.

Early warning tools are crucial for the timely application of intervention strategies and the mitigation of the adverse health, social and economic effects associated with outbreaks of epidemic potential such as COVID-19. This paper introduces, the Epidemic Volatility Index (EVI), a new, conceptually simple, early warning tool for oncoming epidemic waves. EVI is based on the volatility of newly reported cases per unit of time, ideally per day, and issues an early warning when the volatility change rate exceeds a threshold. Data on the daily confirmed cases of COVID-19 are used to demonstrate the use of EVI. Results from the COVID-19 epidemic in Italy and New York State are presented here, based on the number of confirmed cases of COVID-19, from January 22, 2020, until April 13, 2021. Live daily updated predictions for all world countries and each of the United States of America are publicly available online. For Italy, the overall sensitivity for EVI was 0.82 (95% Confidence Intervals: 0.75; 0.89) and the specificity was 0.91 (0.88; 0.94). For New York, the corresponding values were 0.55 (0.47; 0.64) and 0.88 (0.84; 0.91). Consecutive issuance of early warnings is a strong indicator of main epidemic waves in any country or state. EVI's application to data from the current COVID-19 pandemic revealed a consistent and stable performance in terms of detecting new waves. The application of EVI to other epidemics and syndromic surveillance tasks in combination with existing early warning systems will enhance our ability to act swiftly and thereby enhance containment of outbreaks.

Development and Validation of an Early Scoring System for Prediction of Disease Severity in COVID-19 Using Complete Blood Count Parameters.

The coronavirus disease 2019 (COVID-19) after outbreaking in Wuhan increasingly spread throughout the world. Fast, reliable, and easily accessible clinical assessment of the severity of the disease can help in allocating and prioritizing resources to reduce mortality. The objective of the study was to develop and validate an early scoring tool to stratify the risk of death using readily available complete blood count (CBC) biomarkers. A retrospective study was conducted on twenty-three CBC blood biomarkers for predicting disease mortality for 375 COVID-19 patients admitted to Tongji Hospital, China from January 10 to February 18, 2020. Machine learning based key biomarkers among the CBC parameters as the mortality predictors were identified. A multivariate logistic regression-based nomogram and a scoring system was developed to categorize the patients in three risk groups (low, moderate, and high) for predicting the mortality risk among COVID-19 patients. Lymphocyte count, neutrophils count, age, white blood cell count, monocytes (%), platelet count, red blood cell distribution width parameters collected at hospital admission were selected as important biomarkers for death prediction using random forest feature selection technique. A CBC score was devised for calculating the death probability of the patients and was used to categorize the patients into three sub-risk groups: low (<=5%), moderate (>5% and <=50%), and high (>50%), respectively. The area under the curve (AUC) of the model for the development and internal validation cohort were 0.961 and 0.88, respectively. The proposed model was further validated with an external cohort of 103 patients of Dhaka Medical College, Bangladesh, which exhibits in an AUC of 0.963. The proposed CBC parameter-based prognostic model and the associated web-application, can help the medical doctors to improve the management by early prediction of mortality risk of the COVID-19 patients in the low-resource countries.

Prevalence and determinants of symptomatic COVID-19 infection among children and adolescents in Qatar: a cross-sectional analysis of 11 445 individuals.

There is a paucity of evidence about the prevalence and risk factors for symptomatic infection among children. This study aimed to describe the prevalence of symptomatic coronavirus disease 2019 (COVID-19) and its risk factors in children and adolescents aged 0-18 years in Qatar. We conducted a cross-sectional study of all children aged 0-18 years diagnosed with COVID-19 using polymerase chain reaction in Qatar during the period 1st March to 31st July 2020. A generalised linear model with a binomial family and identity link was used to assess the association between selected factors and the prevalence of symptomatic infection. A total of 11 445 children with a median age of 8 years (interquartile range (IQR) 3-13 years) were included in this study. The prevalence of symptomatic COVID-19 was 36.6% (95% confidence interval (CI) 35.7-37.5), and it was similar between children aged <5 years (37.8%), 5-9 years (34.3%) and 10 + years (37.3%). The most frequently reported symptoms among the symptomatic group were fever (73.5%), cough (34.8%), headache (23.2%) and sore throat (23.2%). Fever (82.8%) was more common in symptomatic children aged <5 years, while cough (38.7%) was more prevalent in those aged 10 years or older, compared to other age groups. Variables associated with an increased risk of symptomatic infection were; contact with confirmed cases (RD 0.21; 95% CI 0.20-0.23; P = 0.001), having visited a health care facility (RD 0.54; 95% CI 0.45-0.62; P = 0.001), and children aged under 5 years (RD 0.05; 95% CI 0.02-0.07; P = 0.001) or aged 10 years or older (RD 0.04; 95% CI 0.02-0.06; P = 0.001). A third of the children with COVID-19 were symptomatic with a higher proportion of fever in very young children and a higher proportion of cough in those between 10 and 18 years of age.

Efficacy of chloroquine and hydroxychloroquine in treating COVID-19 infection: A meta-review of systematic reviews and an updated meta-analysis.

OBJECTIVE: To synthesize findings from systematic reviews and meta-analyses on the efficacy and safety of chloroquine (CQ) and hydroxychloroquine (HCQ) with or without Azithromycin for treating COVID-19, and to update the evidence using a meta-analysis. METHODS: A comprehensive search was carried out in electronic databases for systematic reviews, meta-analyses and experimental studies which investigated the efficacy and safety of CQ, HCQ with or without Azithromycin to treat COVID-19. Findings from the reviews were synthesised using tables and forest plots and the quality effect model was used for the updated meta-analysis. The main outcomes were mortality, the need for intensive care services, disease exacerbation, viral clearance and occurrence of adverse events. RESULTS: Thirteen reviews with 40 primary studies were included. Two meta-analyses reported a high risk of mortality, with ORs of 2.2 and 3.0, and the two others found no association between HCQ and mortality. Findings from two meta-analyses showed that HCQ with Azithromycin increased the risk of mortality, with similar ORs of 2.5. The updated meta-analysis of experimental studies showed that the drugs were not effective in reducing mortality (RR 1.1, 95%CI 1.0-1.3, I2 = 0.0%), need for intensive care services (OR 1.1, 95%CI 0.9-1.4, I2 = 0.0%), virological cure (OR 1.5, 95%CI 0.5-4.4, I2 = 39.6%) or disease exacerbation (OR 1.2, 95%CI 0.3-5.9, I2 = 31.9%) but increased the odds of adverse events (OR 12,3, 95%CI 2.5-59.9, I2 = 76.6%). CONCLUSION: There is conclusive evidence that CQ and HCQ, with or without Azithromycin are not effective in treating COVID-19 or its exacerbation. REGISTRATION: PROSPERO: CRD42020191353.

An Early Warning Tool for Predicting Mortality Risk of COVID-19 Patients Using Machine Learning.

COVID-19 pandemic has created an extreme pressure on the global healthcare services. Fast, reliable, and early clinical assessment of the severity of the disease can help in allocating and prioritizing resources to reduce mortality. In order to study the important blood biomarkers for predicting disease mortality, a retrospective study was conducted on a dataset made public by Yan et al. in [1] of 375 COVID-19 positive patients admitted to Tongji Hospital (China) from January 10 to February 18, 2020. Demographic and clinical characteristics and patient outcomes were investigated using machine learning tools to identify key biomarkers to predict the mortality of individual patient. A nomogram was developed for predicting the mortality risk among COVID-19 patients. Lactate dehydrogenase, neutrophils (%), lymphocyte (%), high-sensitivity C-reactive protein, and age (LNLCA)-acquired at hospital admission-were identified as key predictors of death by multi-tree XGBoost model. The area under curve (AUC) of the nomogram for the derivation and validation cohort were 0.961 and 0.991, respectively. An integrated score (LNLCA) was calculated with the corresponding death probability. COVID-19 patients were divided into three subgroups: low-, moderate-, and high-risk groups using LNLCA cutoff values of 10.4 and 12.65 with the death probability less than 5%, 5-50%, and above 50%, respectively. The prognostic model, nomogram, and LNLCA score can help in early detection of high mortality risk of COVID-19 patients, which will help doctors to improve the management of patient stratification.

Pharmacotherapy in COVID-19 patients: a review of ACE2-raising drugs and their clinical safety.

The COVID-19 pandemic is caused by the severe acute-respiratory-syndrome-coronavirus-2 that uses ACE2 as its receptor. Drugs that raise serum/tissue ACE2 levels include ACE inhibitors (ACEIs) and angiotensin-II receptor blockers (ARBs) that are commonly used in patients with hypertension, cardiovascular disease and/or diabetes. These comorbidities have adverse outcomes in COVID-19 patients that might result from pharmacotherapy. Increasing ACE2 could potentially increase the risk of infection, severity or mortality in COVID-19 or it might be protective as it forms angiotensin-(1-7) which exhibits anti-inflammatory/anti-oxidative effects and prevents diabetes- and/or hypertension-induced end-organ damage. Thus, there existed clinical uncertainty. Here, we review studies implicating 15 classes of drugs in increasing ACE2 levels in vivo and the available literature on the clinical safety of these drugs in COVID-19 patients. Further, in a re-analysis of clinical data from a meta-analysis of 9 studies, we show that ACEIs/ARBs usage was not associated with an increased risk of all-cause mortality. Literature suggests that ACEIs/ARBs usage generally appears to be clinically safe though their use in severe COVID-19 patients might increase the risk of acute renal injury. For definitive clarity, further clinical and mechanistic studies are needed in assessing the safety of all classes of ACE2 raising medications.