Elective surgery system strengthening: development, measurement, and validation of the surgical preparedness index across 1632 hospitals in 119 countries.

BACKGROUND The 2015 Lancet Commission on global surgery identified surgery and anaesthesia as indispensable parts of holistic health-care systems. However, COVID-19 exposed the fragility of planned surgical services around the world, which have also been neglected in pandemic recovery planning. This study aimed to develop and validate a novel index to support local elective surgical system strengthening and address growing backlogs. METHODS First, we performed an international consultation through a four-stage consensus process to develop a multidomain index for hospital-level assessment (surgical preparedness index; SPI). Second, we measured surgical preparedness across a global network of hospitals in high-income countries (HICs), middle-income countries (MICs), and low-income countries (LICs) to explore the distribution of the SPI at national, subnational, and hospital levels. Finally, using COVID-19 as an example of an external system shock, we compared hospitals' SPI to their planned surgical volume ratio (SVR; ie, operations for which the decision for surgery was made before hospital admission), calculated as the ratio of the observed surgical volume over a 1-month assessment period between June 6 and Aug 5, 2021, against the expected surgical volume based on hospital administrative data from the same period in 2019 (ie, a pre-pandemic baseline). A linear mixed-effects regression model was used to determine the effect of increasing SPI score. FINDINGS In the first phase, from a longlist of 103 candidate indicators, 23 were prioritised as core indicators of elective surgical system preparedness by 69 clinicians (23 [33%] women; 46 [67%] men; 41 from HICs, 22 from MICs, and six from LICs) from 32 countries. The multidomain SPI included 11 indicators on facilities and consumables, two on staffing, two on prioritisation, and eight on systems. Hospitals were scored from 23 (least prepared) to 115 points (most prepared). In the second phase, surgical preparedness was measured in 1632 hospitals by 4714 clinicians from 119 countries. 745 (45·6%) of 1632 hospitals were in MICs or LICs. The mean SPI score was 84·5 (95% CI 84·1-84·9), which varied between HIC (88·5 [89·0-88·0]), MIC (81·8 [82·5-81·1]), and LIC (66·8 [64·9-68·7]) settings. In the third phase, 1217 (74·6%) hospitals did not maintain their expected SVR during the COVID-19 pandemic, of which 625 (51·4%) were from HIC, 538 (44·2%) from MIC, and 54 (4·4%) from LIC settings. In the mixed-effects model, a 10-point increase in SPI corresponded to a 3·6% (95% CI 3·0-4·1; p<0·0001) increase in SVR. This was consistent in HIC (4·8% [4·1-5·5]; p<0·0001), MIC (2·8 [2·0-3·7]; p<0·0001), and LIC (3·8 [1·3-6·7%]; p<0·0001) settings. INTERPRETATION The SPI contains 23 indicators that are globally applicable, relevant across different system stressors, vary at a subnational level, and are collectable by front-line teams. In the case study of COVID-19, a higher SPI was associated with an increased planned surgical volume ratio independent of country income status, COVID-19 burden, and hospital type. Hospitals should perform annual self-assessment of their surgical preparedness to identify areas that can be improved, create resilience in local surgical systems, and upscale capacity to address elective surgery backlogs. FUNDING National Institute for Health Research (NIHR) Global Health Research Unit on Global Surgery, NIHR Academy, Association of Coloproctology of Great Britain and Ireland, Bowel Research UK, British Association of Surgical Oncology, British Gynaecological Cancer Society, and Medtronic

Phenotype Similarity Regression for Identifying the Genetic Determinants of Rare Diseases.

Rare genetic disorders, which can now be studied systematically with affordable genome sequencing, are often caused by high-penetrance rare variants. Such disorders are often heterogeneous and characterized by abnormalities spanning multiple organ systems ascertained with variable clinical precision. Existing methods for identifying genes with variants responsible for rare diseases summarize phenotypes with unstructured binary or quantitative variables. The Human Phenotype Ontology (HPO) allows composite phenotypes to be represented systematically but association methods accounting for the ontological relationship between HPO terms do not exist. We present a Bayesian method to model the association between an HPO-coded patient phenotype and genotype. Our method estimates the probability of an association together with an HPO-coded phenotype characteristic of the disease. We thus formalize a clinical approach to phenotyping that is lacking in standard regression techniques for rare disease research. We demonstrate the power of our method by uncovering a number of true associations in a large collection of genome-sequenced and HPO-coded cases with rare diseases.This work was supported by NIHR award RG65966 (D.G. and E.T.) and the Medical Research Council programme grant MC UP 0801/1 (D.G. and S.R.). The NIHR BioResource – Rare Diseases projects were approved by Research Ethics Committees in the UK and appropriate national ethics authorities in non-UK enrolment centres (see Supplemental Note). We are grateful to Dr William J Astle for advice on the statistical model and for providing comments on the manuscript. We are particularly thankful to the BPD project members for granting access to detailed HPO terms of patientsThis is the final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.ajhg.2016.01.00

‘All hands-on deck’, working together to develop UK standards for public involvement in research

Background: Public involvement in research is an established part of the research process in the UK, however there remain questions about what good public involvement in research looks and feels like. Until now public involvement practitioners, researchers and members of the public have looked for answers in examples shared across networks, published case studies, guidance and research articles. Pulling these strands together, the UK Standards for Public Involvement provides six statements (standards) about public involvement in research. They were produced by a partnership of organisations from Scotland, Northern Ireland, Wales and England with contributions from involvement practitioners, public partners, researchers and research funders. Main body: Each standard has reflective questions, which are designed to encourage standard users to use approaches and behaviours that improve involvement, over time. The standards are designed to be used as a practical tool, and reflect the agreed hallmarks of good public involvement in research for example, flexibility in approaches used, shared learning, and mutual respect. The standards development process is described from the initial idea and scoping, via the appraisal of existing standard sets and integration of values and principles in public involvement in research. The collaborative writing process of and consultation on the draft standard set is described, together with what changed as a result of feedback. The initiation of a year-long testing programme with forty participating research organisations, the experiential feedback and the resulting changes to the standards is summarised. Conclusion: This commentary paper describes, in some detail, a process to develop a set of six standards for public involvement in research in the UK. Producing a complex, national public involvement initiative is not without its challenges, and in supplementary material partnership members reflect on and share their experiences of standards development. The next phase of integration and implementation is explored with concluding comments from those that tested and helped improve the standards

Coagulation factor V is a T-cell inhibitor expressed by leukocytes in COVID-19

This work was supported by the NIHR BioResource, the NIHR Cambridge Biomedical Research Centre and the NIHR Cambridge Clinical Research Facility. The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. We thank NIHR BioResource volunteers for their participation, and gratefully acknowledge NIHR BioResource centres, NHS Trusts and staff for their contribution. FV plasma assays were performed by the NIHR Cambridge Biochemical Assay Laboratory. The Cambridge University Hospitals Research Tissue Bank is supported by the NIHR Cambridge Biomedical Research Centre. FV constructs were prepared and expressed by Peak Proteins. Neutrophil proteins were characterized at the Mass Spectrometry Facility at the University of Dundee and the QMRI flow cytometry and cell sorting facility. Sequencing was supported by Paul Coupland from the CRUK Cambridge Institute Genomics Core. The graphical abstract was produced using Biorender (https://biorender.com/). The work was funded by awards from NIHR to the NIHR BioResource and the NIHR Cambridge Biomedical Research Centre, Evelyn Trust, Addenbrooke's Charitable Trust, UKRI/NIHR funding through the UK Coronavirus Immunology Consortium (UK-CIC) and a CSO award (COV/DUN/20/01). KGCS holds a Wellcome Trust Investigator award. BG holds an award from the Aging Biology Foundation Europe to BG. RKG holds a Wellcome Senior Fellowship (WT108082AIA). PK is supported by the Australian and New Zealand Society of Nephrology and the Royal Australasian College of Physicians. SRW holds a Wellcome Trust Senior Clinical Fellowship (209220). ERW holds a Wellcome Clinical Training Fellowship award (108717/Z/15/Z). NM was supported by a DFG Research Fellowship. PFC is a Wellcome Trust Principal Research Fellow (212219/Z/18/Z), and a NIHR Senior Investigator, and receives support from the Medical Research Council (MRC) Mitochondrial Biology Unit, the MRC International Centre for Genomic Medicine in Neuromuscular Disease, the Leverhulme Trust, an MRC research grant, and an Alzheimer's Society Project Grant. JAN holds a Wellcome Trust Senior Research Fellowship (215477/Z/19/Z).Peer reviewedPublisher PD

A Fast Association Test for Identifying Pathogenic Variants Involved in Rare Diseases

We present a rapid and powerful inference procedure for identifying loci associated with rare hereditary disorders using Bayesian model comparison. Under a baseline model, disease risk is fixed across all individuals in a study. Under an association model, disease risk depends on a latent bipartition of rare variants into pathogenic and non-pathogenic variants, the number of pathogenic alleles that each individual carries, and the mode of inheritance. A parameter indicating presence of an association and the parameters representing the pathogenicity of each variant and the mode of inheritance can be inferred in a Bayesian framework. Variant-specific prior information derived from allele frequency databases, consequence prediction algorithms, or genomic datasets can be integrated into the inference. Association models can be fitted to different subsets of variants in a locus and compared using a model selection procedure. This procedure can improve inference if only a particular class of variants confers disease risk and can suggest particular disease etiologies related to that class. We show that our method, called BeviMed, is more powerful and informative than existing rare variant association methods in the context of dominant and recessive disorders. The high computational efficiency of our algorithm makes it feasible to test for associations in the large non-coding fraction of the genome. We have applied BeviMed to whole-genome sequencing data from 6,586 individuals with diverse rare diseases. We show that it can identify multiple loci involved in rare diseases, while correctly inferring the modes of inheritance, the likely pathogenic variants, and the variant classes responsible.This work was supported by NIHR award RG65966 (D.G. and E.T.) and the Medical Research Council program grant MC_UP_0801/1 (D.G. and S.R.)