Post-vaccination outcomes in association with four COVID-19 vaccines in the Kingdom of Bahrain

Clinical outcomes and risk factors for SARS-CoV-2 breakthrough cases following vaccination with BNT162b2, CoronaVac, or ChAdOx1-S: A retrospective cohort study in Malaysia

Background

The SARS-CoV-2 pandemic drove global vaccination. However, breakthrough infections raised concerns about vaccine performance, leading the World Health Organization (WHO) to recommend investigations thereof. This study aimed to evaluate the clinical outcomes (time to breakthrough infection, intensive care unit [ICU] admission, and in-hospital mortality) of hospitalised patients with SARS-CoV-2 breakthrough infection. This was the primary outcome and the risk factors associated with its severity were the secondary outcomes.

Methods

This retrospective cohort study at a multispecialty tertiary hospital in Selangor, Malaysia included 200 fully adult vaccinated patients, with confirmed SARS-CoV-2 infection, admitted from September 2021 to February 2022. Participants were selected by simple random sampling. Infection severity was categorised as CAT 2-3 (mild-moderate) and 4-5 (severe-critical).

Results

The time to breakthrough infection was significantly longer for BNT162B2 recipients (128.47 ± 46.21 days) compared to CoronaVac (94.09 ± 48.71 days; P = 0.001) and ChAdOx1-S recipients (90.80 ± 37.59 days; P = 0.019). No significant associations were found between SARS-CoV-2-related ICU admission, mortality, and the vaccines. Multivariable analysis identified vaccine type, variant of concern, ethnicity, and hypertension as significant predictors of severity. BNT162b2 and ChAdOx1-S recipients had significantly (81 % and 74 %, respectively) lower odds of CAT 4-5 infection compared to CoronaVac recipients. Indian patients had a significantly (83 %) lower chance of CAT 4-5 infection compared to Malay patients. Patients with breakthrough infections during the Omicron period had a significantly (58 %) lower risk of CAT 4-5 compared to those in the Delta period. The CAT 4-5 risk was significantly (nearly threefold) higher in hypertensive patients.

Conclusion

The results support the Malaysian Ministry of Health's recommended booster three months after primary vaccination and the WHO's recommended heterologous booster following CoronaVac. Certain ethnic groups, hypertensive patients, and viral variants may require attention in future pandemics.

Assessing the prognostic utility of clinical and radiomic features for COVID-19 patients admitted to ICU: challenges and lessons learned

Severe cases of COVID-19 often necessitate escalation to the Intensive Care Unit (ICU), where patients may face grave outcomes, including mortality. Chest X-rays play a crucial role in the diagnostic process for evaluating COVID-19 patients. Our collaborative efforts with Michigan Medicine in monitoring patient outcomes within the ICU have motivated us to investigate the potential advantages of incorporating clinical information and chest X-ray images for predicting patient outcomes. We propose an analytical workflow to address challenges such as the absence of standardized approaches for image pre-processing and data utilization. We then propose an ensemble learning approach designed to maximize the information derived from multiple prediction algorithms. This entails optimizing the weights within the ensemble and considering the common variability present in individual risk scores. Our simulations demonstrate the superior performance of this weighted ensemble averaging approach across various scenarios. We apply this refined ensemble methodology to analyze post-ICU COVID-19 mortality, an occurrence observed in 21% of COVID-19 patients admitted to the ICU at Michigan Medicine. Our findings reveal substantial performance improvement when incorporating imaging data compared to models trained solely on clinical risk factors. Furthermore, the addition of radiomic features yields even larger enhancements, particularly among older and more medically compromised patients. These results may carry implications for enhancing patient outcomes in similar clinical contexts.

Evaluation of the Diagnostic Performance of Two Automated SARS-CoV-2 Neutralization Immunoassays following Two Doses of mRNA, Adenoviral Vector, and Inactivated Whole-Virus Vaccinations in COVID-19 Naïve Subjects

BACKGROUND

Limited data are available on humoral responses determined by automated neutralization tests following the administration of the three different types of COVID-19 vaccinations. Thus, we here evaluated anti-SARS-CoV-2 neutralizing antibody titers via two different neutralization assays in comparison to total spike antibody levels.

METHODS

Healthy participants (n = 150) were enrolled into three subgroups who were tested 41 (22-65) days after their second dose of mRNA (BNT162b2/mRNA-1273), adenoviral vector (ChAdOx1/Gam-COVID-Vac) and inactivated whole-virus (BBIBP-CorV) vaccines, with no history or serologic evidence of prior SARS-CoV-2 infection. Neutralizing antibody (N-Ab) titers were analyzed on a Snibe Maglumi^® 800 instrument and a Medcaptain Immu F6^® Analyzer in parallel to anti-SARS-CoV-2 S total antibody (S-Ab) levels (Roche Elecsys^® e602).

RESULTS

Subjects who were administered mRNA vaccines demonstrated significantly higher SARS-CoV-2 N-Ab and S-Ab levels compared to those who received adenoviral vector and inactivated whole-virus vaccinations (p < 0.0001). N-Ab titers determined by the two methods correlated with each other (r = 0.9608; p < 0.0001) and S-Ab levels (r = 0.9432 and r = 0.9324; p < 0.0001, respectively). Based on N-Ab values, a new optimal threshold of Roche S-Ab was calculated (166 BAU/mL) for discrimination of seropositivity showing an AUC value of 0.975 (p < 0.0001). Low post-vaccination N-Ab levels (median value of 0.25 μg/mL or 7.28 AU/mL) were measured in those participants (n = 8) who were infected by SARS-CoV-2 within 6 months after immunizations.

CONCLUSION

Both SARS-CoV-2 N-Ab automated assays are effective to evaluate humoral responses after various COVID-19 vaccines.

Covid-19 vaccination reported side effects and hesitancy among the Syrian population: a cross-sectional study

INTRODUCTION

Studying post-vaccination side effects and identifying the reasons behind low vaccine uptake are pivotal for overcoming the pandemic.

METHODS

This cross-sectional study was distributed through social media platforms and face-to-face interviews. Data from vaccinated and unvaccinated participants were collected and analyzed using the chi-square test, multivariable logistic regression to detect factors associated with side effects and severe side effects.

RESULTS

Of the 3509 participants included, 1672(47.6%) were vaccinated. The most common reason for not taking the vaccine was concerns about the vaccine's side effects 815(44.4). The majority of symptoms were mild 788(47.1%), followed by moderate 374(22.3%), and severe 144(8.6%). The most common symptoms were tiredness 1028(61.5%), pain at the injection site 933(55.8%), and low-grade fever 684(40.9%). Multivariable logistic regression analysis revealed that <40 years (vs. ≥40; OR: 2.113, p-value = 0.008), females (vs. males; OR: 2.245, p-value< .001), did not receive influenza shot last year (vs. did receive Influenza shot last year OR: 1.697, p-value = 0.041), AstraZeneca (vs. other vaccine brands; OR: 2.799, p-value< .001), co-morbidities (vs. no co-morbidities; OR: 1.993, p-value = 0.008), and diabetes mellitus (vs. no diabetes mellitus; OR: 2.788, p-value = 0.007) were associated with severe post-vaccine side effects. Serious side effects reported were blood clots 5(0.3%), thrombocytopenia 2(0.1%), anaphylaxis 1(0.1%), seizures 1(0.1%), and cardiac infarction 1(0.1%).

CONCLUSION

Our study revealed that most side effects reported were mild in severity and self-limiting. Increasing the public's awareness of the nature of the vaccine's side effects would reduce the misinformation and improve the public's trust in vaccines. Larger studies to evaluate rare and serious adverse events and long-term side effects are needed, so people can have sufficient information and understanding before making an informed consent which is essential for vaccination.

Efficacy of COVID-19 vaccination in organized group: results of a prospective study

   Objective: to assess the efficacy of COVID-19 vaccination against in organized group.   Materials and methods: A total of 122 adults, employees of a higher education institution participated in the study. Study participants were observed prospectively and filled out a questionnaire where they indicated their age, presence of chronic diseases, history of COVID-19 and vaccination status.   Findings: the study participants were divided into two groups: 59 vaccinated (48.36 %) and 63 unvaccinated (51.64 %) individuals with no differences in age between the groups. There were significantly fewer confirmed cases of COVID-19 in the vaccinated group (р = 0,0008457, df = 1; χ2 = 11,138), significant differences (p = 0.0084; df = 4; χ2 =13.678) were observed in the number of cases among study participants based on their vaccination status.   Conclusion: participants diagnosed with pneumonia were 75 % unvaccinated (p = 0,00729; df = 1; χ2 = 7,2). All hospitalized study participants were unvaccinated (p = 0,004678; χ2 =8,0). None of the vaccinated participants needed respiratory support (p = 0,0455; df = 1; χ2 = 4,0). Chronic disease in vaccinated subjects made a significant (p = 0,04563; df = 2; χ2 = 6,1743) impact on COVID-19 severity.

Impact of the COVID-19 Pandemic on Gyne-Oncological Treatment-A Retrospective Single-Center Analysis of a German University Hospital with 30,525 Patients

The study pursues the objective of drawing a comparison between the data of gyne-oncology, gynecology, and obstetrics patient collectives of a German university hospital regarding the progression of patient number and corresponding treatment data during the five-year period of 2017-2021 to assess the impact of the COVID-19 pandemic on gyne-oncological treatment. Descriptive assessment is based on data extracted from the database of the hospital controlling system QlikView^® for patients hospitalized at the Department of Gynecology and Obstetrics of Marburg University Hospital. Gynecology and gyne-oncology experience a maintained decline in patient number (n_Gynecology: -6% 2019 to 2020, -5% 2019 to 2021; n_{Gyne-Oncology}: -6% 2019 to 2020, -2% 2019 to 2021) with varying effects on the specific gyne-oncological main diagnoses. Treatment parameters remain unchanged in relative assessment, but as gyne-oncology constitutes the dominating revenue contributor in gynecology (35.1% of patients, 52.9% of revenue, 2021), the extent of the decrease in total revenue (-18%, 2019 to 2020, -14%, 2019 to 2021) surpasses the decline in patient number. The study displays a negative impact on the gynecology care situation of a German university hospital for the entire pandemic, with an even greater extent on gyne-oncology. This development not only endangers the quality of medical service provision but collaterally pressurizes gynecology service providers.

Distributed lag inspired machine learning for predicting vaccine-induced changes in COVID-19 hospitalization and intensive care unit admission

Distributed lags play important roles in explaining the short-run dynamic and long-run cumulative effects of features on a response variable. Unlike the usual lag length selection, important lags with significant weights are selected in a distributed lag model (DLM). Inspired by the importance of distributed lags, this research focuses on the construction of distributed lag inspired machine learning (DLIML) for predicting vaccine-induced changes in COVID-19 hospitalization and intensive care unit (ICU) admission rates. Importance of a lagged feature in DLM is examined by hypothesis testing and a subset of important features are selected by evaluating an information criterion. Akin to the DLM, we demonstrate the selection of distributed lags in machine learning by evaluating importance scores and objective functions. Finally, we apply the DLIML with supervised learning for forecasting daily changes in COVID-19 hospitalization and ICU admission rates in United Kingdom (UK) and United States of America (USA). A sharp decline in hospitalization and ICU admission rates are observed when around 40% people are vaccinated. For one percent more vaccination, daily changes in hospitalization and ICU admission rates are expected to reduce by 4.05 and 0.74 per million after 14 days in UK, and 5.98 and 1.04 per million after 20 days in USA, respectively. Long-run cumulative effects in the DLM demonstrate that the daily changes in hospitalization and ICU admission rates are expected to jitter around the zero line in a long-run. Application of the DLIML selects fewer lagged features but provides qualitatively better forecasting outcome for data-driven healthcare service planning.