Impact of detecting potentially serious incidental findings during multi-modal imaging

Gibson, Lorna M; Littlejohns, Thomas J.; Adamska, Ligia; Garratt, Steve; Doherty, Nicola; Maskell, Giles; Parker, Michael; Brownsword, Roger; Matthews, Paul M.; Collins, Rory; Allen, Naomi E.; Sellors, Jonathan; Sudlow, Cathie

doi:10.12688/wellcomeopenres.13181.3

Cited by 18 publications

(10 citation statements)

References 20 publications

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“…This pattern is biologically plausible and has been seen in data from primary studies. 21 25 26 28 53 The heterogeneity between included studies, relative rarity of potentially serious incidental findings, methods of meta-analyses, and conservative calculation of confidence intervals could have obscured true differences in the prevalence of potentially serious incidental findings between regions. Results on incidental findings from ongoing imaging studies based on large populations (including the UK Biobank imaging substudy, which by late October 2018 had imaged more than 30 000 of an intended 100 000 participants) should be able to confirm or refute this pattern in future.…”

Section: Discussionmentioning

confidence: 99%

Potentially serious incidental findings on brain and body magnetic resonance imaging of apparently asymptomatic adults: systematic review and meta-analysis

Gibson

Paúl

Chappell

et al. 2018

BMJ

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ObjectivesTo determine prevalence and types of potentially serious incidental findings on magnetic resonance imaging (MRI) in apparently asymptomatic adults, describe factors associated with potentially serious incidental findings, and summarise information on follow-up and final diagnoses.DesignSystematic review and meta-analyses.Data sourcesCitation searches of relevant articles and authors’ files in Medline and Embase (from inception to 25 April 2017).Review methodsEligible studies included prevalence and types of incidental findings detected among apparently asymptomatic adults undergoing MRI of the brain, thorax, abdomen, or brain and body. Data on study population and methods, prevalence and types of incidental findings, and final diagnoses were extracted. Pooled prevalence was estimated by random effects meta-analysis, and heterogeneity by τ2 statistics.Main outcome measuresPrevalence of potentially serious incidental findings on MRI of the brain, thorax, abdomen, and brain and body.ResultsOf 5905 retrieved studies, 32 (0.5%) met the inclusion criteria (n=27 643 participants). Pooled prevalence of potentially serious incidental findings was 3.9% (95% confidence interval 0.4% to 27.1%) on brain and body MRI, 1.4% (1.0% to 2.1%) on brain MRI, 1.3% (0.2% to 8.1%) on thoracic MRI, and 1.9% (0.3% to 12.0%) on abdominal MRI. Pooled prevalence rose after including incidental findings of uncertain potential seriousness (12.8% (3.9% to 34.3%), 1.7% (1.1% to 2.6%), 3.0% (0.8% to 11.3%), and 4.5% (1.5% to 12.9%), respectively). There was generally substantial heterogeneity among included studies. About half the potentially serious incidental findings were suspected malignancies (brain, 0.6% (95% confidence interval 0.4% to 0.9%); thorax, 0.6% (0.1% to 3.1%); abdomen, 1.3% (0.2% to 9.3%); brain and body, 2.3% (0.3% to 15.4%)). There were few informative data on potential sources of between-study variation or factors associated with potentially serious incidental findings. Limited data suggested that relatively few potentially serious incidental findings had serious final diagnoses (48/234, 20.5%).ConclusionsA substantial proportion of apparently asymptomatic adults will have potentially serious incidental findings on MRI, but little is known of their health consequences. Systematic, long term follow-up studies are needed to better inform on these consequences and the implications for policies on feedback of potentially serious incidental findings.Systematic review registrationProspero CRD42016029472.

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Section: Discussionmentioning

confidence: 99%

Potentially serious incidental findings on brain and body magnetic resonance imaging of apparently asymptomatic adults: systematic review and meta-analysis

Gibson

Paúl

Chappell

et al. 2018

BMJ

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“…Likewise, some contemporary research studies, such as All of Us (which aims to enroll 1 million or more diverse participants to enable research on a wide range of common and rare diseases) and Project Baseline (which involves deep health data collection in a longitudinal cohort of at least 10 000 participants to map human health and health transitions), are planning to return many individual research results in an effort to create value. 3 While most published experiences about the return of research results to study participants have demonstrated mixed benefits and risks and are limited to studies of returning genetic or imaging results, [4][5][6] little evidence is available to answer key questions: what results to return and when and how to return them (Box).…”

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confidence: 99%

Return of Research Results to Study Participants

Wong

Hernandez

Califf

2018

JAMA

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Research data may be of great utility to study participants and their clinicians in understanding and managing the health of patients who participate in research, depending on the clinical context and nature of results. In addition, increasing amounts of digital data are generated by commercial health products, such as accelerometers and smart watches, and in clinical care. With increasing digitization and data availability, researchers, clinicians, developers, and patients encounter the issue of how to consume, analyze, and make sense of those data. The return of individual results in the context of research studies provides a useful, structured environment to develop an empirical framework, and lessons learned may be applicable to other cases of health information consumerism.Increasing calls for the return of research results to study participants align with a cultural shift in health care toward participant engagement and open science. 1,2 In this construct, returning individual research data is

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“…Access to independently standardized and validated controls in a variety of matrices (plasma, serum, urine, etc) for established and emerging biomarkers would be a valuable resource for investigators and facilitate more consistent biomarker results between laboratories. This in turn supports the goals of improved data rigor and reproducibility, and the more ethical discussion regarding disclosure of validated data to study participants [23–25].…”

Section: Discussionmentioning

confidence: 53%

A computational solution to improve biomarker reproducibility during long-term projects

et al. 2019

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Biomarkers are fundamental to basic and clinical research outcomes by reporting host responses and providing insight into disease pathophysiology. Measuring biomarkers with research-use ELISA kits is universal, yet lack of kit standardization and unexpected lot-to-lot variability presents analytic challenges for long-term projects. During an ongoing two-year project measuring plasma biomarkers in cancer patients, control concentrations for one biomarker (PF) decreased significantly after changes in ELISA kit lots. A comprehensive operations review pointed to standard curve shifts with the new kits, an analytic variable that jeopardized data already collected on hundreds of patient samples. After excluding other reasonable contributors to data variability, a computational solution was developed to provide a uniform platform for data analysis across multiple ELISA kit lots. The solution ( ELISAtools ) was developed within open-access R software in which variability between kits is treated as a batch effect. A defined best-fit Reference standard curve is modelled, a unique Shift factor “S” is calculated for every standard curve and data adjusted accordingly. The averaged S factors for PF ELISA kit lots #1–5 ranged from -0.086 to 0.735, and reduced control inter-assay variability from 62.4% to <9%, within quality control limits. S factors calculated for four other biomarkers provided a quantitative metric to monitor ELISAs over the 10 month study period for quality control purposes. Reproducible biomarker measurements are essential, particularly for long-term projects with valuable patient samples. Use of research-use ELISA kits is ubiquitous and judicious use of this computational solution maximizes biomarker reproducibility.

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Impact of detecting potentially serious incidental findings during multi-modal imaging

Abstract: There are limited data on the impact of feedback of incidental

Cited by 18 publications

References 20 publications

Potentially serious incidental findings on brain and body magnetic resonance imaging of apparently asymptomatic adults: systematic review and meta-analysis

Potentially serious incidental findings on brain and body magnetic resonance imaging of apparently asymptomatic adults: systematic review and meta-analysis

Return of Research Results to Study Participants

A computational solution to improve biomarker reproducibility during long-term projects

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