Automated inter-rater reliability assessment and electronic data collection in a multi-center breast cancer study

<p>Abstract</p> <p>Background</p> <p>The choice between paper data collection methods and electronic data collection (EDC) methods has become a key question for clinical researchers. There remains a need to examine potential benefits, efficiencies, and innovations associated with an EDC system in a multi-center medical record review study.</p> <p>Methods</p> <p>A computer-based automated menu-driven system with 658 data fields was developed for a cohort study of women aged 65 years or older, diagnosed with invasive histologically confirmed primary breast cancer (N = 1859), at 6 Cancer Research Network sites. Medical record review with direct data entry into the EDC system was implemented. An inter-rater and intra-rater reliability (IRR) system was developed using a modified version of the EDC.</p> <p>Results</p> <p>Automation of EDC accelerated the flow of study information and resulted in an efficient data collection process. Data collection time was reduced by approximately four months compared to the project schedule and funded time available for manuscript preparation increased by 12 months. In addition, an innovative modified version of the EDC permitted an automated evaluation of inter-rater and intra-rater reliability across six data collection sites.</p> <p>Conclusion</p> <p>Automated EDC is a powerful tool for research efficiency and innovation, especially when multiple data collection sites are involved.</p

Identification of novel risk loci, causal insights, and heritable risk for Parkinson's disease: a meta-analysis of genome-wide association studies

Background Genome-wide association studies (GWAS) in Parkinson's disease have increased the scope of biological knowledge about the disease over the past decade. We aimed to use the largest aggregate of GWAS data to identify novel risk loci and gain further insight into the causes of Parkinson's disease. Methods We did a meta-analysis of 17 datasets from Parkinson's disease GWAS available from European ancestry samples to nominate novel loci for disease risk. These datasets incorporated all available data. We then used these data to estimate heritable risk and develop predictive models of this heritability. We also used large gene expression and methylation resources to examine possible functional consequences as well as tissue, cell type, and biological pathway enrichments for the identified risk factors. Additionally, we examined shared genetic risk between Parkinson's disease and other phenotypes of interest via genetic correlations followed by Mendelian randomisation. Findings Between Oct 1, 2017, and Aug 9, 2018, we analysed 7·8 million single nucleotide polymorphisms in 37 688 cases, 18 618 UK Biobank proxy-cases (ie, individuals who do not have Parkinson's disease but have a first degree relative that does), and 1·4 million controls. We identified 90 independent genome-wide significant risk signals across 78 genomic regions, including 38 novel independent risk signals in 37 loci. These 90 variants explained 16–36% of the heritable risk of Parkinson's disease depending on prevalence. Integrating methylation and expression data within a Mendelian randomisation framework identified putatively associated genes at 70 risk signals underlying GWAS loci for follow-up functional studies. Tissue-specific expression enrichment analyses suggested Parkinson's disease loci were heavily brain-enriched, with specific neuronal cell types being implicated from single cell data. We found significant genetic correlations with brain volumes (false discovery rate-adjusted p=0·0035 for intracranial volume, p=0·024 for putamen volume), smoking status (p=0·024), and educational attainment (p=0·038). Mendelian randomisation between cognitive performance and Parkinson's disease risk showed a robust association (p=8·00 × 10−7). Interpretation These data provide the most comprehensive survey of genetic risk within Parkinson's disease to date, to the best of our knowledge, by revealing many additional Parkinson's disease risk loci, providing a biological context for these risk factors, and showing that a considerable genetic component of this disease remains unidentified. These associations derived from European ancestry datasets will need to be followed-up with more diverse data. Funding The National Institute on Aging at the National Institutes of Health (USA), The Michael J Fox Foundation, and The Parkinson's Foundation (see appendix for full list of funding sources)

Convalescent plasma in patients admitted to hospital with COVID-19 (RECOVERY): a randomised controlled, open-label, platform trial

SummaryBackground Azithromycin has been proposed as a treatment for COVID-19 on the basis of its immunomodulatoryactions. We aimed to evaluate the safety and efficacy of azithromycin in patients admitted to hospital with COVID-19.Methods In this randomised, controlled, open-label, adaptive platform trial (Randomised Evaluation of COVID-19Therapy [RECOVERY]), several possible treatments were compared with usual care in patients admitted to hospitalwith COVID-19 in the UK. The trial is underway at 176 hospitals in the UK. Eligible and consenting patients wererandomly allocated to either usual standard of care alone or usual standard of care plus azithromycin 500 mg once perday by mouth or intravenously for 10 days or until discharge (or allocation to one of the other RECOVERY treatmentgroups). Patients were assigned via web-based simple (unstratified) randomisation with allocation concealment andwere twice as likely to be randomly assigned to usual care than to any of the active treatment groups. Participants andlocal study staff were not masked to the allocated treatment, but all others involved in the trial were masked to theoutcome data during the trial. The primary outcome was 28-day all-cause mortality, assessed in the intention-to-treatpopulation. The trial is registered with ISRCTN, 50189673, and ClinicalTrials.gov, NCT04381936.Findings Between April 7 and Nov 27, 2020, of 16 442 patients enrolled in the RECOVERY trial, 9433 (57%) wereeligible and 7763 were included in the assessment of azithromycin. The mean age of these study participants was65·3 years (SD 15·7) and approximately a third were women (2944 [38%] of 7763). 2582 patients were randomlyallocated to receive azithromycin and 5181 patients were randomly allocated to usual care alone. Overall,561 (22%) patients allocated to azithromycin and 1162 (22%) patients allocated to usual care died within 28 days(rate ratio 0·97, 95% CI 0·87–1·07; p=0·50). No significant difference was seen in duration of hospital stay (median10 days [IQR 5 to >28] vs 11 days [5 to >28]) or the proportion of patients discharged from hospital alive within 28 days(rate ratio 1·04, 95% CI 0·98–1·10; p=0·19). Among those not on invasive mechanical ventilation at baseline, nosignificant difference was seen in the proportion meeting the composite endpoint of invasive mechanical ventilationor death (risk ratio 0·95, 95% CI 0·87–1·03; p=0·24).Interpretation In patients admitted to hospital with COVID-19, azithromycin did not improve survival or otherprespecified clinical outcomes. Azithromycin use in patients admitted to hospital with COVID-19 should be restrictedto patients in whom there is a clear antimicrobial indication

Modelling decisions to undergo genetic testing for susceptibility to common health conditions: An ancillary study of the Multiplex Initiative

New genetic tests reveal risks for multiple conditions simultaneously, although little is understood about the psychological factors that affect testing uptake. We assessed a conceptual model called the Multiplex Genetic Testing Model (MGTM) using structural equation modeling (SEM). The MGTM delineates worry, perceived severity, perceived risk, response efficacy and attitudes toward testing as predictors of intentions and behavior. Participants were 270 healthy insured adults age 25–40 from the Multiplex Initiative conducted within a health care system in Detroit MI, USA. Participants were offered a genetic test that assessed risk for eight common health conditions. Confirmatory factor analysis revealed that worry, perceived risk and severity clustered into two disease domains: cancer or metabolic conditions. Only perceived severity of metabolic conditions was correlated with general response efficacy (β=0.13, p<0.05), which predicted general attitudes toward testing (β=0.24, p<0.01). Consistent with our hypothesized model, attitudes towards testing were the strongest predictors of intentions to undergo testing (β=0.49, p<0.01), which in turn predicted testing uptake (OR 17.7, β=0.97, p<0.01). The MGTM explained a striking 48% of the variance in intentions and 94% of the variation in uptake. These findings support use of the MGTM to explain psychological predictors of testing for multiple health conditions