Jack W. O’Sullivan

Bio: Jack W. O’Sullivan is an academic researcher from Stanford University. The author has contributed to research in topics: Medicine & Population. The author has an hindex of 15, co-authored 54 publications receiving 897 citations. Previous affiliations of Jack W. O’Sullivan include Health Science University & Royal Brisbane and Women's Hospital.

Improving reporting standards for polygenic scores in risk prediction studies

Abstract: Polygenic risk scores (PRSs), which often aggregate results from genome-wide association studies, can bridge the gap between initial discovery efforts and clinical applications for the estimation of disease risk using genetics. However, there is notable heterogeneity in the application and reporting of these risk scores, which hinders the translation of PRSs into clinical care. Here, in a collaboration between the Clinical Genome Resource (ClinGen) Complex Disease Working Group and the Polygenic Score (PGS) Catalog, we present the Polygenic Risk Score Reporting Standards (PRS-RS), in which we update the Genetic Risk Prediction Studies (GRIPS) Statement to reflect the present state of the field. Drawing on the input of experts in epidemiology, statistics, disease-specific applications, implementation and policy, this comprehensive reporting framework defines the minimal information that is needed to interpret and evaluate PRSs, especially with respect to downstream clinical applications. Items span detailed descriptions of study populations, statistical methods for the development and validation of PRSs and considerations for the potential limitations of these scores. In addition, we emphasize the need for data availability and transparency, and we encourage researchers to deposit and share PRSs through the PGS Catalog to facilitate reproducibility and comparative benchmarking. By providing these criteria in a structured format that builds on existing standards and ontologies, the use of this framework in publishing PRSs will facilitate translation into clinical care and progress towards defining best practice.

Prevalence and outcomes of incidental imaging findings: umbrella review

Abstract: Objective To provide an overview of the evidence on prevalence and outcomes of incidental imaging findings. Design Umbrella review of systematic reviews. Data sources Searches of MEDLINE, EMBASE up to August 2017; screening of references in included papers. Eligibility criteria Criteria included systematic reviews and meta-analyses of observational studies that gave a prevalence of incidental abnormalities (“incidentalomas”). An incidental imaging finding was defined as an imaging abnormality in a healthy, asymptomatic patient or an imaging abnormality in a symptomatic patient, where the abnormality was not apparently related to the patient’s symptoms. Primary studies that measured the prevalence of incidentalomas in patients with a history of malignancy were also considered in sensitivity analyses. Results 20 systematic reviews (240 primary studies) were identified from 7098 references from the database search. Fifteen systematic reviews provided data to quantify the prevalence of incidentalomas, whereas 18 provided data to quantify the outcomes of incidentalomas (13 provided both). The prevalence of incidentalomas varied substantially between imaging tests; it was less than 5% for chest computed tomography for incidental pulmonary embolism in patients with and without cancer and whole body positron emission tomography (PET) or PET/computed tomography (for patients with and without cancer). Conversely, incidentalomas occurred in more than a third of images in cardiac magnetic resonance imaging (MRI), chest computed tomography (for incidentalomas of thorax, abdomen, spine, or heart), and computed tomography colonoscopy (for extra-colonic incidentalomas). Intermediate rates occurred with MRI of the spine (22%) and brain (22%). The rate of malignancy in incidentalomas varied substantially between organs; the prevalence of malignancy was less than 5% in incidentalomas of the brain, parotid, and adrenal gland. Extra-colonic, prostatic, and colonic incidentalomas were malignant between 10% and 20% of the time, whereas renal, thyroid, and ovarian incidentalomas were malignant around a quarter of the time. Breast incidentalomas had the highest percentage of malignancy (42%, 95% confidence interval 31% to 54%). Many assessments had high between-study heterogeneity (15 of 20 meta-analyses with I2 >50%). Conclusions There is large variability across different imaging techniques both in the prevalence of incidentalomas and in the prevalence of malignancy for specific organs. This umbrella review will aid clinicians and patients weigh up the pros and cons of requesting imaging scans and will help with management decisions after an incidentaloma diagnosis. Our results can underpin the creation of guidelines to assist these decisions. Systematic review registration PROSPERO: CRD42017075679.

Overdiagnosis: what it is and what it isn’t

Abstract: > Why then, can one desire too much of a good thing? > > William Shakespeare, As You Like It (1600) Rosalind’s question, as she is about to marry Orlando, is purely rhetorical—she thinks that one cannot desire too much of a good thing. Nevertheless, trite though it may be, it is true that one can sometimes have it. It is certainly true of healthcare and has been referred to as ‘too much medicine’,1 although because of potential confusion with ‘too much medication’ a better term might be ‘too much healthcare’. This includes too much screening of asymptomatic individuals, too much investigation of those with symptoms, too much reliance on biomarkers, too many quasi-diseases, too much diagnosis, often leading to too much treatment, sometimes cost-ineffective, medicines that are too costly and too rapidly approved for marketing, too many adverse reactions, and too much inappropriate monitoring. And too much healthcare implies too little effective healthcare. An older term, ‘overdiagnosis’ has been used to refer to a more restricted set of items. And although the term can be traced back as far as 1955,2 it is still difficult to define satisfactorily. Broadly, overdiagnosis means making people patients unnecessarily, by identifying problems that were never going to cause harm or by medicalising ordinary life experiences through expanded definitions of diseases. Overdiagnosis has two major causes: overdetection and overdefinition of disease. While the forms of overdiagnosis differ, the consequences are the same: diagnoses that ultimately cause more harm than benefit. Confusion about what constitutes overdiagnosis undermines progress to a solution. Here we aim to draw boundaries around what overdiagnosis is and to exclude what it is not. Overdetection refers to the identification of abnormalities that were never going to cause harm, abnormalities that do not progress, that progress too slowly to cause symptoms or harm during a person’s remaining lifetime, or that …

Improving reporting standards for polygenic scores in risk prediction studies

Abstract: Polygenic risk scores (PRS), often aggregating the results from genome-wide association studies, can bridge the gap between the initial discovery efforts and clinical applications for disease risk estimation. However, there is remarkable heterogeneity in the reporting of these risk scores. This lack of adherence to reporting standards hinders the translation of PRS into clinical care. The ClinGen Complex Disease Working Group, in a collaboration with the Polygenic Score (PGS) Catalog, have updated the Genetic Risk Prediction (GRIPS) Reporting Statement to the current state of the field and to enable downstream utility. Drawing upon experts in epidemiology, statistics, disease-specific applications, implementation, and policy, this 22-item reporting framework defines the minimal information needed to interpret and evaluate a PRS, especially with respect to any downstream clinical applications. Items span detailed descriptions of the study population (recruitment method, key demographic and clinical characteristics, inclusion/exclusion criteria, and outcome definition), statistical methods for both PRS development and validation, and considerations for potential limitations of the published risk score and downstream clinical utility. Additionally, emphasis has been placed on data availability and transparency to facilitate reproducibility and benchmarking against other PRS, such as deposition in the publicly available PGS Catalog. By providing these criteria in a structured format that builds upon existing standards and ontologies, the use of this framework in publishing PRS will facilitate translation of PRS into clinical care and progress towards defining best practices. Summary In recent years, polygenic risk scores (PRS) have increasingly been used to capture the genome-wide liability underlying many human traits and diseases, hoping to better inform an individual’s genetic risk. However, a lack of adherence to existing reporting standards has hindered the translation of this important tool into clinical and public health practice; in particular, details necessary for benchmarking and reproducibility are underreported. To address this gap, the ClinGen Complex Disease Working Group and Polygenic Score (PGS) Catalog have updated the Genetic Risk Prediction (GRIPS) Reporting Statement into the 22-item Polygenic Risk Score Reporting Statement (PRS-RS). This framework provides the minimal information expected of authors to promote the validity, transparency, and reproducibility of PRS by encouraging authors to detail the study population, statistical methods, and potential clinical utility of a published score. The widespread adoption of this framework will encourage rigorous methodological consideration and facilitate benchmarking to ensure high quality scores are translated into the clinic.

The Answer Is 17 Years, What Is the Question

Abstract: This study aimed to review the literature describing and quantifying time lags in the health research translation process. Papers were included in the review if they quantified time lags in the development of health interventions. The study identified 23 papers. Few were comparable as different studies use different measures, of different things, at different time points. We concluded that the current state of knowledge of time lags is of limited use to those responsible for R&D and knowledge transfer who face difficulties in knowing what they should or can do to reduce time lags. This effectively ‘blindfolds’ investment decisions and risks wasting effort. The study concludes that understanding lags first requires agreeing models, definitions and measures, which can be applied in practice. A second task would be to develop a process by which to gather these data.

Meta-analysis of prevalence

Abstract: Meta-analysis is a method to obtain a weighted average of results from various studies. In addition to pooling effect sizes, meta-analysis can also be used to estimate disease frequencies, such as incidence and prevalence. In this article we present methods for the meta-analysis of prevalence. We discuss the logit and double arcsine transformations to stabilise the variance. We note the special situation of multiple category prevalence, and propose solutions to the problems that arise. We describe the implementation of these methods in the MetaXL software, and present a simulation study and the example of multiple sclerosis from the Global Burden of Disease 2010 project. We conclude that the double arcsine transformation is preferred over the logit, and that the MetaXL implementation of multiple category prevalence is an improvement in the methodology of the meta-analysis of prevalence.

Measurement of Blood Pressure in Humans: A Scientific Statement From the American Heart Association.

Abstract: The accurate measurement of blood pressure (BP) is essential for the diagnosis and management of hypertension. This article provides an updated American Heart Association scientific statement on BP measurement in humans. In the office setting, many oscillometric devices have been validated that allow accurate BP measurement while reducing human errors associated with the auscultatory approach. Fully automated oscillometric devices capable of taking multiple readings even without an observer being present may provide a more accurate measurement of BP than auscultation. Studies have shown substantial differences in BP when measured outside versus in the office setting. Ambulatory BP monitoring is considered the reference standard for out-of-office BP assessment, with home BP monitoring being an alternative when ambulatory BP monitoring is not available or tolerated. Compared with their counterparts with sustained normotension (ie, nonhypertensive BP levels in and outside the office setting), it is unclear whether adults with white-coat hypertension (ie, hypertensive BP levels in the office but not outside the office) have increased cardiovascular disease risk, whereas those with masked hypertension (ie, hypertensive BP levels outside the office but not in the office) are at substantially increased risk. In addition, high nighttime BP on ambulatory BP monitoring is associated with increased cardiovascular disease risk. Both oscillometric and auscultatory methods are considered acceptable for measuring BP in children and adolescents. Regardless of the method used to measure BP, initial and ongoing training of technicians and healthcare providers and the use of validated and calibrated devices are critical for obtaining accurate BP measurements.