AZD1222 effectiveness against severe COVID-19 in individuals with comorbidity or frailty: The RAVEN cohort study

Meeraus, Wilhelmine; Joy, Mark; Ouwens, Mario; Taylor, Kathryn S.; Venkatesan, Sudhir; Dennis, John; Tran, Trung N.; Dashtban, Ashkan; Fan, Xuejuan; Williams, Robert; Morris, Tamsin; Carty, Lucy; Kar, Debasish; Hoang, Uy; Feher, Michael; Forbes, Anna; Jamie, Gavin; Hinton, William; Sanecka, Kornelia; Byford, Rachel; Anand, Sneha N.; Hobbs, F.D. Richard; Clifton, David A.; Pollard, Andrew J.; Taylor, Sylvia; de Lusignan, Simon

doi:10.1016/j.jinf.2024.106129

Cited by 4 publications

(1 citation statement)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…During the COVID-19 pandemic, with linkage to existing NHS England datasets, RSC data were also used to understand its epidemiology and assess vaccine effectiveness and safety [24][25][26][27]. This modified version of the CMMS was used to assess the real-world effectiveness of the Oxford-AstraZeneca Covid-19 Vaccine in England (RAVEN) study, which was run on the RSC using linked data from NHS England [28].…”

Section: Introductionmentioning

confidence: 99%

Creating a Modified Version of the Cambridge Multimorbidity Score (CMMS) to Predict Mortality in People Over 16 Years in the English Nationwide General Practice Extraction Service Data for Pandemic Planning and Research (GDPPR) Dataset: Model Development and Validation (Preprint)

Kar,

Taylor,

Joy

et al. 2024

Journal of Medical Internet Research

View full text Add to dashboard Cite

Background: No single multimorbidity measure is validated for use in NHS England's General Practice Extraction Service Data for Pandemic Planning and Research (GDPPR), the nationwide primary care dataset created for coronavirus disease 19 (COVID-19) pandemic research. A single morbidity measure is advantageous when there is a need to adjust for multimorbidity, such as modelling the effectiveness of vaccinations against COVID-19, as including multiple individual morbidities is challenging. The Cambridge Multimorbidity Score (CMMS) is a validated tool for predicting mortality risk. However, the number of Systematised Nomenclature of Medicine clinical terms (SNOMED CT) for the GDPPR dataset is limited and does not define all the conditions used to calculate the CMMS Objective: To develop and validate a modified version of CMMS using the clinical terms available for the GDPPR. Methods:We used pseudonymised data from the Oxford-Royal College of General Practitioners Research and Surveillance Centre (RCGP RSC), which has a more extensive SNOMED CT list. From the 37 conditions used in the original CMMS model, we selected conditions either with: (a) high prevalence ratio (? 85%), calculated as the prevalence in the RSC data set as defined by the GDPPR set of SNOMED CT codes, divided by the prevalence as defined by the RSC set of SNOMED CT codes, or (b) conditions with lower prevalence ratio but with high predictive value. The resulting set of conditions was included in Cox proportional hazard models to determine the 1-year mortality risk in a development dataset (n=300,000) and construct a new CMMS model, following the original CMMS, with variable reduction and parsimony, achieved by backward elimination and Akaike information stopping criterion. Model validation involved obtaining 1-year mortality estimates for a synchronous dataset (n=150,000) and 1-year and 5-year mortality estimates for an asynchronous dataset (n=150,000).Results: The initial model contained 22 conditions and our final model included 17 conditions. The conditions overlapped with those of a modified CMMS, which we previously developed using RSC data and the more extensive RSC SNOMED CT list. For 1-year mortality, discrimination was high in both the derivation and validation datasets (Harrell's C=0.92), and 5-year mortality was slightly lower (Harrell's C= 0.90), and the calibration was reasonable following an adjustment for over-fitting. The performance was similar to that of both the original and previous modified CMMS models.

show abstract

Section: Introductionmentioning

confidence: 99%

Creating a Modified Version of the Cambridge Multimorbidity Score (CMMS) to Predict Mortality in People Over 16 Years in the English Nationwide General Practice Extraction Service Data for Pandemic Planning and Research (GDPPR) Dataset: Model Development and Validation (Preprint)

Kar,

Taylor,

Joy

et al. 2024

Journal of Medical Internet Research

View full text Add to dashboard Cite

show abstract

Vaccinations, cardiovascular drugs, hospitalization, and mortality in COVID-19 and Long COVID

Dashtban,

Mizani,

Pasea

et al. 2024

International Journal of Infectious Diseases

View full text Add to dashboard Cite

Progress and prospects of mRNA-based drugs in pre-clinical and clinical applications

Shi,

Wang

et al. 2024

Sig Transduct Target Ther

View full text Add to dashboard Cite

In the last decade, messenger ribonucleic acid (mRNA)-based drugs have gained great interest in both immunotherapy and non-immunogenic applications. This surge in interest can be largely attributed to the demonstration of distinct advantages offered by various mRNA molecules, alongside the rapid advancements in nucleic acid delivery systems. It is noteworthy that the immunogenicity of mRNA drugs presents a double-edged sword. In the context of immunotherapy, extra supplementation of adjuvant is generally required for induction of robust immune responses. Conversely, in non-immunotherapeutic scenarios, immune activation is unwanted considering the host tolerability and high expression demand for mRNA-encoded functional proteins. Herein, mainly focused on the linear non-replicating mRNA, we overview the preclinical and clinical progress and prospects of mRNA medicines encompassing vaccines and other therapeutics. We also highlight the importance of focusing on the host-specific variations, including age, gender, pathological condition, and concurrent medication of individual patient, for maximized efficacy and safety upon mRNA administration. Furthermore, we deliberate on the potential challenges that mRNA drugs may encounter in the realm of disease treatment, the current endeavors of improvement, as well as the application prospects for future advancements. Overall, this review aims to present a comprehensive understanding of mRNA-based therapies while illuminating the prospective development and clinical application of mRNA drugs.

show abstract

AZD1222 effectiveness against severe COVID-19 in individuals with comorbidity or frailty: The RAVEN cohort study

Cited by 4 publications

References 67 publications

Creating a Modified Version of the Cambridge Multimorbidity Score (CMMS) to Predict Mortality in People Over 16 Years in the English Nationwide General Practice Extraction Service Data for Pandemic Planning and Research (GDPPR) Dataset: Model Development and Validation (Preprint)

Creating a Modified Version of the Cambridge Multimorbidity Score (CMMS) to Predict Mortality in People Over 16 Years in the English Nationwide General Practice Extraction Service Data for Pandemic Planning and Research (GDPPR) Dataset: Model Development and Validation (Preprint)

Vaccinations, cardiovascular drugs, hospitalization, and mortality in COVID-19 and Long COVID

Progress and prospects of mRNA-based drugs in pre-clinical and clinical applications

Contact Info

Product

Resources

About