Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management. © 2021, The Author(s)

Conceptualising the Integration of Strategies by Clinical Commissioning Groups in England towards the Antibiotic Prescribing Targets for the Quality Premium Financial Incentive Scheme: A Short Report

Background: In order to tackle the public health threat of antimicrobial resistance, improvement in antibiotic prescribing in primary care was included as one of the priorities of the Quality Premium (QP) financial incentive scheme for Clinical Commissioning Groups (CCGs) in England. This paper briefly reports the outcome of a workshop exploring the experiences of antimicrobial stewardship (AMS) leads within CCGs in selecting and adopting strategies to help achieve the QP antibiotic targets. Methods: We conducted a thematic analysis of the notes on discussions and observations from the workshop to identify key themes. Results: Practice visits, needs assessment, peer feedback and audits were identified as strategies integrated in increasing engagement with practices towards the QP antibiotic targets. The conceptual model developed by AMS leads demonstrated possible pathways for the impact of the QP on antibiotic prescribing. Participants raised a concern that the constant targeting of high prescribing practices for AMS interventions might lead to disengagement by these practices. Most of the participants suggested that the effect of the QP might be less about the financial incentive and more about having national targets and guidelines that promote antibiotic prudency. Conclusions: Our results suggest that national targets, rather than financial incentives are key for engaging stakeholders in quality improvement in antibiotic prescribing.</jats:p