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Kapadi A, Turner-Uaandja H, Holley R, Wicks K, Hamrang L, Turner B, van Staa T, Bowden C, Keane A, Price G, Faivre-Finn C, French D, Sanders C, Holm S, Devaney S. Exploring Consent to Use Real-World Data in Lung Cancer Radiotherapy: Decision of a Citizens' Jury for an 'Informed Opt-Out' Approach. HEALTH CARE ANALYSIS 2025; 33:192-213. [PMID: 39924606 PMCID: PMC12053208 DOI: 10.1007/s10728-025-00510-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/30/2024] [Indexed: 02/11/2025]
Abstract
An emerging approach to complement randomised controlled trial (RCT) data in the development of radiotherapy treatments is to use routinely collected 'real-world' data (RWD). RWD is the data collected as standard-of-care about all patients during their usual cancer care pathway. Given the nature of this data, important questions remain about the permissibility and acceptability of using RWD in routine practice. We involved and engaged with patients, carers and the public in a two-day citizens' jury to understand their views and obtain decisions regarding two key issues: (1) preferred approaches to consent for the use of RWD within the context of patients receiving radiotherapy for lung cancer in RAPID-RT and (2) how RWD use should be best communicated to patients. Individual views were polled using questionnaires at various stages of the jury, whilst group discussion activities prompted further dialogue about the rationale behind choices of consent. Key decisions obtained from the jury include: (1) an opt-out approach to consent for the use of RWD; (2) the opt-out approach to consent should be informed. Furthermore, it was advised that information and communication regarding the consent process and use of RWD should be accessible, clear and available in a variety of formats. It is important that the consent process for patient data use is underpinned by principles of autonomy and transparency with clear channels of communication between those asking for and giving consent. Moreover, the process of seeking consent from patients should be proportionate to the risks presented from their participation.
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Affiliation(s)
- Arbaz Kapadi
- Manchester Centre for Health Psychology, Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
| | - Hannah Turner-Uaandja
- Vocal, Research and Innovation Division, Manchester University NHS Foundation Trust, Manchester, UK
| | - Rebecca Holley
- Department of Radiotherapy Related Research, Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
| | - Kate Wicks
- Department of Radiotherapy Related Research, Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
| | | | | | - Tjeerd van Staa
- Centre for Health Informatics and Health Data Research UK North, Division of Informatics, Imaging and Data Science, School of Health Sciences, The University of Manchester, Manchester, UK
| | - Catherine Bowden
- Centre for Social Ethics and Policy, Division of Law, School of Social Sciences, The Faculty of Humanities, The University of Manchester, Manchester, UK
| | - Annie Keane
- Vocal, Research and Innovation Division, Manchester University NHS Foundation Trust, Manchester, UK
| | - Gareth Price
- Department of Radiotherapy Related Research, Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
| | - Corinne Faivre-Finn
- Department of Radiotherapy Related Research, Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
- Department of Clinical Oncology, The Christie NHS Foundation Trust, Manchester, UK
| | - David French
- Manchester Centre for Health Psychology, Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
| | - Caroline Sanders
- Centre for Primary Care and Health Services Research, NIHR Greater Manchester Patient Safety Research Collaboration, Division of Population Health, Health Services Research and Primary Care, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
| | - Søren Holm
- Centre for Social Ethics and Policy, Division of Law, School of Social Sciences, The Faculty of Humanities, The University of Manchester, Manchester, UK
| | - Sarah Devaney
- Centre for Social Ethics and Policy, Division of Law, School of Social Sciences, The Faculty of Humanities, The University of Manchester, Manchester, UK.
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Li R, Assadi HS, Zhao X, Matthews G, Mehmood Z, Grafton-Clarke C, Limbachia V, Hall R, Kasmai B, Hughes M, Thampi K, Hewson D, Stamatelatou M, Swoboda PP, Swift AJ, Alabed S, Nair S, Spohr H, Curtin J, Gurung-Koney Y, van der Geest RJ, Vassiliou VS, Zhong L, Garg P. Automated Quantification of Simple and Complex Aortic Flow Using 2D Phase Contrast MRI. MEDICINA (KAUNAS, LITHUANIA) 2024; 60:1618. [PMID: 39459405 PMCID: PMC11509448 DOI: 10.3390/medicina60101618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2024] [Revised: 08/16/2024] [Accepted: 09/28/2024] [Indexed: 10/28/2024]
Abstract
(1) Background and Objectives: Flow assessment using cardiovascular magnetic resonance (CMR) provides important implications in determining physiologic parameters and clinically important markers. However, post-processing of CMR images remains labor- and time-intensive. This study aims to assess the validity and repeatability of fully automated segmentation of phase contrast velocity-encoded aortic root plane. (2) Materials and Methods: Aortic root images from 125 patients are segmented by artificial intelligence (AI), developed using convolutional neural networks and trained with a multicentre cohort of 160 subjects. Derived simple flow indices (forward and backward flow, systolic flow and velocity) and complex indices (aortic maximum area, systolic flow reversal ratio, flow displacement, and its angle change) were compared with those derived from manual contours. (3) Results: AI-derived simple flow indices yielded excellent repeatability compared to human segmentation (p < 0.001), with an insignificant level of bias. Complex flow indices feature good to excellent repeatability (p < 0.001), with insignificant levels of bias except flow displacement angle change and systolic retrograde flow yielding significant levels of bias (p < 0.001 and p < 0.05, respectively). (4) Conclusions: Automated flow quantification using aortic root images is comparable to human segmentation and has good to excellent repeatability. However, flow helicity and systolic retrograde flow are associated with a significant level of bias. Overall, all parameters show clinical repeatability.
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Affiliation(s)
- Rui Li
- Norwich Medical School, University of East Anglia, Norfolk NR4 7TJ, UK; (R.L.); (H.S.A.); (G.M.); (B.K.); (M.H.); (V.S.V.)
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norfolk NR4 7UY, UK; (Z.M.); (C.G.-C.); (V.L.); (R.H.); (K.T.); (D.H.); (M.S.); (S.N.); (H.S.); (J.C.); (Y.G.-K.)
| | - Hosamadin S. Assadi
- Norwich Medical School, University of East Anglia, Norfolk NR4 7TJ, UK; (R.L.); (H.S.A.); (G.M.); (B.K.); (M.H.); (V.S.V.)
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norfolk NR4 7UY, UK; (Z.M.); (C.G.-C.); (V.L.); (R.H.); (K.T.); (D.H.); (M.S.); (S.N.); (H.S.); (J.C.); (Y.G.-K.)
| | - Xiaodan Zhao
- National Heart Research Institute Singapore, National Heart Centre Singapore, 5 Hospital Drive, Singapore 169609, Singapore; (X.Z.); (L.Z.)
| | - Gareth Matthews
- Norwich Medical School, University of East Anglia, Norfolk NR4 7TJ, UK; (R.L.); (H.S.A.); (G.M.); (B.K.); (M.H.); (V.S.V.)
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norfolk NR4 7UY, UK; (Z.M.); (C.G.-C.); (V.L.); (R.H.); (K.T.); (D.H.); (M.S.); (S.N.); (H.S.); (J.C.); (Y.G.-K.)
| | - Zia Mehmood
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norfolk NR4 7UY, UK; (Z.M.); (C.G.-C.); (V.L.); (R.H.); (K.T.); (D.H.); (M.S.); (S.N.); (H.S.); (J.C.); (Y.G.-K.)
| | - Ciaran Grafton-Clarke
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norfolk NR4 7UY, UK; (Z.M.); (C.G.-C.); (V.L.); (R.H.); (K.T.); (D.H.); (M.S.); (S.N.); (H.S.); (J.C.); (Y.G.-K.)
| | - Vaishali Limbachia
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norfolk NR4 7UY, UK; (Z.M.); (C.G.-C.); (V.L.); (R.H.); (K.T.); (D.H.); (M.S.); (S.N.); (H.S.); (J.C.); (Y.G.-K.)
| | - Rimma Hall
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norfolk NR4 7UY, UK; (Z.M.); (C.G.-C.); (V.L.); (R.H.); (K.T.); (D.H.); (M.S.); (S.N.); (H.S.); (J.C.); (Y.G.-K.)
| | - Bahman Kasmai
- Norwich Medical School, University of East Anglia, Norfolk NR4 7TJ, UK; (R.L.); (H.S.A.); (G.M.); (B.K.); (M.H.); (V.S.V.)
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norfolk NR4 7UY, UK; (Z.M.); (C.G.-C.); (V.L.); (R.H.); (K.T.); (D.H.); (M.S.); (S.N.); (H.S.); (J.C.); (Y.G.-K.)
| | - Marina Hughes
- Norwich Medical School, University of East Anglia, Norfolk NR4 7TJ, UK; (R.L.); (H.S.A.); (G.M.); (B.K.); (M.H.); (V.S.V.)
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norfolk NR4 7UY, UK; (Z.M.); (C.G.-C.); (V.L.); (R.H.); (K.T.); (D.H.); (M.S.); (S.N.); (H.S.); (J.C.); (Y.G.-K.)
| | - Kurian Thampi
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norfolk NR4 7UY, UK; (Z.M.); (C.G.-C.); (V.L.); (R.H.); (K.T.); (D.H.); (M.S.); (S.N.); (H.S.); (J.C.); (Y.G.-K.)
| | - David Hewson
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norfolk NR4 7UY, UK; (Z.M.); (C.G.-C.); (V.L.); (R.H.); (K.T.); (D.H.); (M.S.); (S.N.); (H.S.); (J.C.); (Y.G.-K.)
| | - Marianna Stamatelatou
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norfolk NR4 7UY, UK; (Z.M.); (C.G.-C.); (V.L.); (R.H.); (K.T.); (D.H.); (M.S.); (S.N.); (H.S.); (J.C.); (Y.G.-K.)
| | - Peter P. Swoboda
- Division of Biomedical Imaging, Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds LS2 9JT, UK;
| | - Andrew J. Swift
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield S10 2TN, UK; (A.J.S.); (S.A.)
- Department of Clinical Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield S10 2JF, UK
| | - Samer Alabed
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield S10 2TN, UK; (A.J.S.); (S.A.)
- Department of Clinical Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield S10 2JF, UK
| | - Sunil Nair
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norfolk NR4 7UY, UK; (Z.M.); (C.G.-C.); (V.L.); (R.H.); (K.T.); (D.H.); (M.S.); (S.N.); (H.S.); (J.C.); (Y.G.-K.)
| | - Hilmar Spohr
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norfolk NR4 7UY, UK; (Z.M.); (C.G.-C.); (V.L.); (R.H.); (K.T.); (D.H.); (M.S.); (S.N.); (H.S.); (J.C.); (Y.G.-K.)
| | - John Curtin
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norfolk NR4 7UY, UK; (Z.M.); (C.G.-C.); (V.L.); (R.H.); (K.T.); (D.H.); (M.S.); (S.N.); (H.S.); (J.C.); (Y.G.-K.)
| | - Yashoda Gurung-Koney
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norfolk NR4 7UY, UK; (Z.M.); (C.G.-C.); (V.L.); (R.H.); (K.T.); (D.H.); (M.S.); (S.N.); (H.S.); (J.C.); (Y.G.-K.)
| | - Rob J. van der Geest
- Department of Radiology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands;
| | - Vassilios S. Vassiliou
- Norwich Medical School, University of East Anglia, Norfolk NR4 7TJ, UK; (R.L.); (H.S.A.); (G.M.); (B.K.); (M.H.); (V.S.V.)
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norfolk NR4 7UY, UK; (Z.M.); (C.G.-C.); (V.L.); (R.H.); (K.T.); (D.H.); (M.S.); (S.N.); (H.S.); (J.C.); (Y.G.-K.)
| | - Liang Zhong
- National Heart Research Institute Singapore, National Heart Centre Singapore, 5 Hospital Drive, Singapore 169609, Singapore; (X.Z.); (L.Z.)
- Duke-NUS Medical School, National University of Singapore, 8 College Road, Singapore 169857, Singapore
| | - Pankaj Garg
- Norwich Medical School, University of East Anglia, Norfolk NR4 7TJ, UK; (R.L.); (H.S.A.); (G.M.); (B.K.); (M.H.); (V.S.V.)
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norfolk NR4 7UY, UK; (Z.M.); (C.G.-C.); (V.L.); (R.H.); (K.T.); (D.H.); (M.S.); (S.N.); (H.S.); (J.C.); (Y.G.-K.)
- Division of Biomedical Imaging, Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds LS2 9JT, UK;
- Department of Clinical Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield S10 2JF, UK
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3
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Assadi H, Alabed S, Li R, Matthews G, Karunasaagarar K, Kasmai B, Nair S, Mehmood Z, Grafton-Clarke C, Swoboda PP, Swift AJ, Greenwood JP, Vassiliou VS, Plein S, van der Geest RJ, Garg P. Development and validation of AI-derived segmentation of four-chamber cine cardiac magnetic resonance. Eur Radiol Exp 2024; 8:77. [PMID: 38992116 PMCID: PMC11239622 DOI: 10.1186/s41747-024-00477-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 04/30/2024] [Indexed: 07/13/2024] Open
Abstract
BACKGROUND Cardiac magnetic resonance (CMR) in the four-chamber plane offers comprehensive insight into the volumetrics of the heart. We aimed to develop an artificial intelligence (AI) model of time-resolved segmentation using the four-chamber cine. METHODS A fully automated deep learning algorithm was trained using retrospective multicentre and multivendor data of 814 subjects. Validation, reproducibility, and mortality prediction were evaluated on an independent cohort of 101 subjects. RESULTS The mean age of the validation cohort was 54 years, and 66 (65%) were males. Left and right heart parameters demonstrated strong correlations between automated and manual analysis, with a ρ of 0.91-0.98 and 0.89-0.98, respectively, with minimal bias. All AI four-chamber volumetrics in repeatability analysis demonstrated high correlation (ρ = 0.99-1.00) and no bias. Automated four-chamber analysis underestimated both left ventricular (LV) and right ventricular (RV) volumes compared to ground-truth short-axis cine analysis. Two correction factors for LV and RV four-chamber analysis were proposed based on systematic bias. After applying the correction factors, a strong correlation and minimal bias for LV volumetrics were observed. During a mean follow-up period of 6.75 years, 16 patients died. On stepwise multivariable analysis, left atrial ejection fraction demonstrated an independent association with death in both manual (hazard ratio (HR) = 0.96, p = 0.003) and AI analyses (HR = 0.96, p < 0.001). CONCLUSION Fully automated four-chamber CMR is feasible, reproducible, and has the same real-world prognostic value as manual analysis. LV volumes by four-chamber segmentation were comparable to short-axis volumetric assessment. TRIALS REGISTRATION ClinicalTrials.gov: NCT05114785. RELEVANCE STATEMENT Integrating fully automated AI in CMR promises to revolutionise clinical cardiac assessment, offering efficient, accurate, and prognostically valuable insights for improved patient care and outcomes. KEY POINTS • Four-chamber cine sequences remain one of the most informative acquisitions in CMR examination. • This deep learning-based, time-resolved, fully automated four-chamber volumetric, functional, and deformation analysis solution. • LV and RV were underestimated by four-chamber analysis compared to ground truth short-axis segmentation. • Correction bias for both LV and RV volumes by four-chamber segmentation, minimises the systematic bias.
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Affiliation(s)
- Hosamadin Assadi
- Department of Cardiovascular and Metabolic Health, Norwich Medical School, University of East Anglia, Norwich, Norfolk, UK
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, Norfolk, UK
| | - Samer Alabed
- Department of Infection, Immunity & Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Rui Li
- Department of Cardiovascular and Metabolic Health, Norwich Medical School, University of East Anglia, Norwich, Norfolk, UK
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, Norfolk, UK
| | - Gareth Matthews
- Department of Cardiovascular and Metabolic Health, Norwich Medical School, University of East Anglia, Norwich, Norfolk, UK
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, Norfolk, UK
| | - Kavita Karunasaagarar
- Department of Infection, Immunity & Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Bahman Kasmai
- Department of Cardiovascular and Metabolic Health, Norwich Medical School, University of East Anglia, Norwich, Norfolk, UK
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, Norfolk, UK
| | - Sunil Nair
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, Norfolk, UK
| | - Zia Mehmood
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, Norfolk, UK
| | - Ciaran Grafton-Clarke
- Department of Cardiovascular and Metabolic Health, Norwich Medical School, University of East Anglia, Norwich, Norfolk, UK
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, Norfolk, UK
| | - Peter P Swoboda
- Division of Biomedical Imaging, Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
| | - Andrew J Swift
- Department of Infection, Immunity & Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - John P Greenwood
- Division of Biomedical Imaging, Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
| | - Vassilios S Vassiliou
- Department of Cardiovascular and Metabolic Health, Norwich Medical School, University of East Anglia, Norwich, Norfolk, UK
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, Norfolk, UK
| | - Sven Plein
- Division of Biomedical Imaging, Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
| | - Rob J van der Geest
- Department of Radiology, Division of Image Processing, Leiden University Medical Center, Leiden, The Netherlands
| | - Pankaj Garg
- Department of Cardiovascular and Metabolic Health, Norwich Medical School, University of East Anglia, Norwich, Norfolk, UK.
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, Norfolk, UK.
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Dong Y, Mun SK, Wang Y. A blockchain-enabled sharing platform for personal health records. Heliyon 2023; 9:e18061. [PMID: 37496910 PMCID: PMC10366433 DOI: 10.1016/j.heliyon.2023.e18061] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 06/30/2023] [Accepted: 07/05/2023] [Indexed: 07/28/2023] Open
Abstract
Background Longitudinal personal health record (PHR) provides a foundation for managing patients' health care, but we do not have such a system in the U.S. except for the patients in the Department of Veterans Affairs. Such a gap exists mainly in the rest of the U.S. by the fact that patients' electronic health records are scattered across multiple health care facilities and often not shared due to privacy, security, and business interests concerns from both patients and health care organizations. In addition, patients have ethical concerns related to consent. To patients, data security, privacy, and consent are based on trustfulness, rather than patients' engagement in ensuring only authorized people can view their PHRs with patient-managed granularity. Resolving these challenges is an important step in making longitudinal PHR useful for patient care. Objective This research aims to design and implement a blockchain-enabled sharing platform prototype for PHR with desired patient-controlled data security, privacy, and consent granularity. Methods Built upon our prior work of a blockchain-enabled access control (BAC) model, we design a blockchain-enabled sharing platform for PHR with patient-controlled security, privacy, and consent granularity. We further implement the construct by building a prototypical platform among a patient and two typical health care organizations. Health organizations that hold the patient's electronic health records can join the platform with trust based on the validation from the patient. The mutual trust can be established through a rigorous validation process by both the patient and the built-in Hyperledger Fabric blockchain consensus mechanism. Results We proposed a system trusted by patients and health care providers and constructed a Web-based PHR sharing platform with patient-controlled security, privacy, and consent granularity. We analyzed the system scalability in three aspects and showed millisecond range of performance when simultaneously changing access permissions on hundreds of PHRs. Consent, security and privacy of the model are ensured by the merits of the BAC model. We discovered the current blockchain model limits the system scalability due to using a non-graphical database. A new graphical database is suggested for future improvements. Conclusions In this research, we report a solution to electronically sharing and managing patients' electronic health records originating from multiple organizations, focusing on privacy, security, and granularity control of consent in the U.S. Specifically, the system protects data security and privacy, and provides auditability, scalability, distributedness, patient consent autonomy, and zero-trust capabilities. The prototypical instantiation of the designed model suggested the feasibility of combining emerging blockchain technology with next generation access control model to tackle a longstanding longitudinal PHR problem.
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Patient responses to passive enrollment into a large, pragmatic clinical trial: A qualitative content analysis. Contemp Clin Trials 2022; 121:106925. [PMID: 36108887 DOI: 10.1016/j.cct.2022.106925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 09/01/2022] [Accepted: 09/08/2022] [Indexed: 01/27/2023]
Abstract
BACKGROUND While passive enrollment or "opt-out" recruitment methods facilitate pragmatic clinical trials, they pose unique challenges, and it is unclear how participants feel about them. Here, we describe patient responses to passive enrollment into the Watch the Spot Trial, a pragmatic trial comparing two sets of guidelines for small lung nodule follow-up. METHODS For this nested qualitative study, we analyzed participant-initiated calls and emails. We performed a qualitative content analysis, using a team-coding approach to identify reasons that eligible participants contacted the study team. We calculated the proportion of contacts containing each code, and how often each code coincided with study opt-outs and other codes. RESULTS Of 23,412 eligible participants across seven sites, 1494 (6.4%) contacted the study team, with 1560 total contacts. Among the total contacts, the most common codes (i.e., reasons for contacting the team) were study opt-outs (n = 614, 39.0%), clarification of study procedures (n = 328, 21.0%), and unawareness of the nodule prior to research notification (n = 244, 15.6%). The least common codes were concerns about sharing of protected health information with the study team (n = 22, 1.4%) or outside of the healthcare system (n = 26, 1.7%), and disapproval of the opt-out approach (n = 10, 0.6%); most patients with these concerns opted-out. Nodule unawareness sometimes coincided with anger (n = 24) or distress (n = 15), and questions about nodule care sometimes coincided with distress (n = 20) and questions about follow-up surveys (n = 26). CONCLUSION Most participants did not report concerns about passive enrollment. Patient perspectives are an invaluable resource for minimizing risks and inconveniences of future pragmatic trials using this recruitment method.
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Kennedy N, Nelson S, Jerome RN, Edwards TL, Stroud M, Wilkins CH, Harris PA. Recruitment and retention for chronic pain clinical trials: a narrative review. Pain Rep 2022; 7:e1007. [PMID: 38304397 PMCID: PMC10833632 DOI: 10.1097/pr9.0000000000001007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 03/22/2022] [Accepted: 04/02/2022] [Indexed: 11/25/2022] Open
Abstract
Opioid misuse is at a crisis level. In response to this epidemic, the National Institutes of Health has funded $945 million in research through the Helping to End Addiction Long-term (HEAL) Pain Management Initiative, including funding to the Vanderbilt Recruitment Innovation Center (RIC) to strategize methods to catalyze participant recruitment. The RIC, recognizing the challenges presented to clinical researchers in recruiting individuals experiencing pain, conducted a review of evidence in the literature on successful participant recruitment methods for chronic pain trials, in preparation for supporting the HEAL Pain trials. Study design as it affects recruitment was reviewed, with issues such as sufficient sample size, impact of placebo, pain symptom instability, and cohort characterization being identified as problems. Potential solutions found in the literature include targeted electronic health record phenotyping, use of alternative study designs, and greater clinician education and involvement. For retention, the literature reports successful strategies that include maintaining a supportive staff, allowing virtual study visits, and providing treatment flexibility within the trial. Community input on study design to identify potential obstacles to recruitment and retention was found to help investigators avoid pitfalls and enhance trust, especially when recruiting underrepresented minority populations. Our report concludes with a description of generalizable resources the RIC has developed or adapted to enhance recruitment and retention in the HEAL Pain studies. These resources include, among others, a Recruitment and Retention Plan Template, a Competing Trials Tool, and MyCap, a mobile research application that interfaces with Research Electronic Data Capture (REDCap).
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Affiliation(s)
- Nan Kennedy
- Vanderbilt Institute for Clinical and Translational Research, Nashville, TN, USA
| | - Sarah Nelson
- Vanderbilt Institute for Clinical and Translational Research, Nashville, TN, USA
| | - Rebecca N. Jerome
- Vanderbilt Institute for Clinical and Translational Research, Nashville, TN, USA
| | - Terri L. Edwards
- Vanderbilt Institute for Clinical and Translational Research, Nashville, TN, USA
| | - Mary Stroud
- Vanderbilt Institute for Clinical and Translational Research, Nashville, TN, USA
| | - Consuelo H. Wilkins
- Vanderbilt Institute for Clinical and Translational Research, Nashville, TN, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Internal Medicine, Meharry Medical College, Nashville, TN, USA
- Office of Health Equity, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Paul A. Harris
- Vanderbilt Institute for Clinical and Translational Research, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
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7
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Price G, Devaney S, French DP, Holley R, Holm S, Kontopantelis E, McWilliam A, Payne K, Proudlove N, Sanders C, Willans R, van Staa T, Hamrang L, Turner B, Parsons S, Faivre-Finn C. Can Real-world Data and Rapid Learning Drive Improvements in Lung Cancer Survival? The RAPID-RT Study. Clin Oncol (R Coll Radiol) 2022; 34:407-410. [PMID: 35000827 DOI: 10.1016/j.clon.2021.12.017] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 11/29/2021] [Accepted: 12/21/2021] [Indexed: 11/25/2022]
Affiliation(s)
- G Price
- The Christie NHS Foundation Trust, Manchester, UK; Division of Cancer Sciences, The University of Manchester, The Christie NHS Foundation Trust, Manchester, UK.
| | - S Devaney
- Centre for Social Ethics and Policy, The University of Manchester, Manchester, UK
| | - D P French
- Manchester Centre of Health Psychology, The University of Manchester, Manchester, UK
| | - R Holley
- Division of Cancer Sciences, The University of Manchester, The Christie NHS Foundation Trust, Manchester, UK
| | - S Holm
- Centre for Social Ethics and Policy, The University of Manchester, Manchester, UK
| | - E Kontopantelis
- Centre for Health Services Research, Division of Informatics, Imaging and Data Science, The University of Manchester, Manchester, UK
| | - A McWilliam
- The Christie NHS Foundation Trust, Manchester, UK; Division of Cancer Sciences, The University of Manchester, The Christie NHS Foundation Trust, Manchester, UK
| | - K Payne
- Manchester Centre for Health Economics, Health Sciences Research Group, The University of Manchester, Manchester, UK
| | - N Proudlove
- Alliance Manchester Business School, The University of Manchester, Manchester, UK
| | - C Sanders
- NIHR Patient Safety Translational Research Centre, The University of Manchester, Manchester, UK
| | - R Willans
- Data Analytics Unit, National Institute for Health and Care Excellence, Manchester, UK
| | - T van Staa
- Centre for Health Informatics & Health Data Research UK North, Division of Informatics, Imaging and Data Science, School of Health Sciences, The University of Manchester, Manchester, UK
| | - L Hamrang
- RAPID-RT PPI Advisory Group, Manchester, UK
| | - B Turner
- RAPID-RT PPI Advisory Group, Manchester, UK
| | | | - C Faivre-Finn
- The Christie NHS Foundation Trust, Manchester, UK; Division of Cancer Sciences, The University of Manchester, The Christie NHS Foundation Trust, Manchester, UK
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8
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Price G, Mackay R, Aznar M, McWilliam A, Johnson-Hart C, van Herk M, Faivre-Finn C. Learning healthcare systems and rapid learning in radiation oncology: Where are we and where are we going? Radiother Oncol 2021; 164:183-195. [PMID: 34619237 DOI: 10.1016/j.radonc.2021.09.030] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 09/02/2021] [Accepted: 09/26/2021] [Indexed: 01/31/2023]
Abstract
Learning health systems and rapid-learning are well developed at the conceptual level. The promise of rapidly generating and applying evidence where conventional clinical trials would not usually be practical is attractive in principle. The connectivity of modern digital healthcare information systems and the increasing volumes of data accrued through patients' care pathways offer an ideal platform for the concepts. This is particularly true in radiotherapy where modern treatment planning and image guidance offers a precise digital record of the treatment planned and delivered. The vision is of real-world data, accrued by patients during their routine care, being used to drive programmes of continuous clinical improvement as part of standard practice. This vision, however, is not yet a reality in radiotherapy departments. In this article we review the literature to explore why this is not the case, identify barriers to its implementation, and suggest how wider clinical application might be achieved.
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Affiliation(s)
- Gareth Price
- The University of Manchester, Manchester Academic Health Science Centre, The Christie NHS Foundation Trust, United Kingdom.
| | - Ranald Mackay
- The University of Manchester, Manchester Academic Health Science Centre, The Christie NHS Foundation Trust, United Kingdom
| | - Marianne Aznar
- The University of Manchester, Manchester Academic Health Science Centre, The Christie NHS Foundation Trust, United Kingdom
| | - Alan McWilliam
- The University of Manchester, Manchester Academic Health Science Centre, The Christie NHS Foundation Trust, United Kingdom
| | - Corinne Johnson-Hart
- The University of Manchester, Manchester Academic Health Science Centre, The Christie NHS Foundation Trust, United Kingdom
| | - Marcel van Herk
- The University of Manchester, Manchester Academic Health Science Centre, The Christie NHS Foundation Trust, United Kingdom
| | - Corinne Faivre-Finn
- The University of Manchester, Manchester Academic Health Science Centre, The Christie NHS Foundation Trust, United Kingdom
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9
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Thakur N, Lovinsky-Desir S, Appell D, Bime C, Castro L, Celedón JC, Ferreira J, George M, Mageto Y, Mainous III AG, Pakhale S, Riekert KA, Roman J, Ruvalcaba E, Sharma S, Shete P, Wisnivesky JP, Holguin F. Enhancing Recruitment and Retention of Minority Populations for Clinical Research in Pulmonary, Critical Care, and Sleep Medicine: An Official American Thoracic Society Research Statement. Am J Respir Crit Care Med 2021; 204:e26-e50. [PMID: 34347574 PMCID: PMC8513588 DOI: 10.1164/rccm.202105-1210st] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Background: Well-designed clinical research needs to obtain information that is applicable to the general population. However, most current studies fail to include substantial cohorts of racial/ethnic minority populations. Such underrepresentation may lead to delayed diagnosis or misdiagnosis of disease, wide application of approved interventions without appropriate knowledge of their usefulness in certain populations, and development of recommendations that are not broadly applicable.Goals: To develop best practices for recruitment and retention of racial/ethnic minorities for clinical research in pulmonary, critical care, and sleep medicine.Methods: The American Thoracic Society convened a workshop in May of 2019. This included an international interprofessional group from academia, industry, the NIH, and the U.S. Food and Drug Administration, with expertise ranging from clinical and biomedical research to community-based participatory research methods and patient advocacy. Workshop participants addressed historical and current mistrust of scientific research, systemic bias, and social and structural barriers to minority participation in clinical research. A literature search of PubMed and Google Scholar was performed to support conclusions. The search was not a systematic review of the literature.Results: Barriers at the individual, interpersonal, institutional, and federal/policy levels were identified as limiting to minority participation in clinical research. Through the use of a multilevel framework, workshop participants proposed evidence-based solutions to the identified barriers.Conclusions: To date, minority participation in clinical research is not representative of the U.S. and global populations. This American Thoracic Society research statement identifies potential evidence-based solutions by applying a multilevel framework that is anchored in community engagement methods and patient advocacy.
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10
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Challenges to and facilitators of occupational epidemiology research in the UK. Health Policy 2020; 124:772-780. [PMID: 32482438 DOI: 10.1016/j.healthpol.2020.05.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Revised: 04/29/2020] [Accepted: 05/04/2020] [Indexed: 11/23/2022]
Abstract
This study investigated the challenges and facilitators of occupational epidemiology (OE) research in the UK, and evaluated the impact of these challenges. Semi-structured in-depth interviews with leading UK-based OE researchers, and a survey of UK-based OE researchers were conducted. Seven leading researchers were interviewed, and there were 54 survey respondents. Key reported challenges for OE were diminishing resources during recent decades, influenced by social, economic and political drivers, and changing fashions in research policy. Consequently, the community is getting smaller and less influential. These challenges may have negatively affected OE research, causing it to fail to keep pace with recent methodological development and impacting its output of high-quality research. Better communication with, and support from other researchers and relevant policy and funding stakeholders was identified as the main facilitators to OE research. Many diseases were initially discovered in workplaces, as these make exceptionally good study populations to accurately assess exposures. Due to the decline of manufacturing industry, there is a perception that occupational diseases are now a thing of the past. Nevertheless, new occupational exposures remain under-evaluated and the UK has become reliant on overseas epidemiology. This has been exacerbated by the decline in the academic occupational medicine base. Maintaining UK-based OE research is hence necessary for the future development of occupational health services and policies for the UK workforce.
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11
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Moss C, Haire A, Cahill F, Enting D, Hughes S, Smith D, Sawyer E, Davies A, Zylstra J, Haire K, Rigg A, Van Hemelrijck M. Guy's cancer cohort - real world evidence for cancer pathways. BMC Cancer 2020; 20:187. [PMID: 32178645 PMCID: PMC7077127 DOI: 10.1186/s12885-020-6667-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Accepted: 02/21/2020] [Indexed: 12/15/2022] Open
Abstract
Background The burden of disease due to cancer remains substantial. Since the value of real-world evidence has also been recognised by regulatory agencies, we established a Research Ethics Committee (REC) approved research database for cancer patients (Reference: 18/NW/0297). Construction and content Guy’s Cancer Cohort introduces the concept of opt-out consent processes for research in a subset of oncology patients diagnosed and treated at a large NHS Trust in the UK. From April 2016 until March 2017, 1388 eligible patients visited Guy’s and St Thomas’ NHS Foundation Trust (GSTT) for breast cancer management. For urological cancers this number was 1757 and for lung cancer 677. The Cohort consists of a large repository of routinely collected clinical data recorded both retrospectively and prospectively. The database contains detailed clinical information collected at various timepoints across the treatment pathway inclusive of diagnostic data, and data on disease progression, recurrence and survival. Conclusions Guy’s Cancer Cohort provides a valuable infrastructure to answer a wide variety of research questions of a clinical, mechanistic, and supportive care nature. Clinical research using this database will result in improved patient safety and experience. Guy’s Cancer Cohort promotes collaborative research and will accept applications for the release of anonymised datasets for research purposes.
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Affiliation(s)
- C Moss
- King's College London, School of Cancer and Pharmaceutical Sciences, Translational Oncology and Urology Research (TOUR), Guy's Hospital, 3rd Floor Bermondsey Wing, London, SE1 9RT, UK.
| | - A Haire
- King's College London, School of Cancer and Pharmaceutical Sciences, Translational Oncology and Urology Research (TOUR), Guy's Hospital, 3rd Floor Bermondsey Wing, London, SE1 9RT, UK
| | - F Cahill
- King's College London, School of Cancer and Pharmaceutical Sciences, Translational Oncology and Urology Research (TOUR), Guy's Hospital, 3rd Floor Bermondsey Wing, London, SE1 9RT, UK
| | - D Enting
- King's College London, School of Cancer and Pharmaceutical Sciences, Translational Oncology and Urology Research (TOUR), Guy's Hospital, 3rd Floor Bermondsey Wing, London, SE1 9RT, UK.,Comprehensive Cancer Centre, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - S Hughes
- King's College London, School of Cancer and Pharmaceutical Sciences, Translational Oncology and Urology Research (TOUR), Guy's Hospital, 3rd Floor Bermondsey Wing, London, SE1 9RT, UK.,Comprehensive Cancer Centre, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - D Smith
- Comprehensive Cancer Centre, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - E Sawyer
- Comprehensive Cancer Centre, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - A Davies
- Department of Upper Gastrointestinal Surgery, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - J Zylstra
- Department of Upper Gastrointestinal Surgery, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - K Haire
- South East London (SEL) Accountable Cancer Network, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - A Rigg
- Comprehensive Cancer Centre, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - M Van Hemelrijck
- King's College London, School of Cancer and Pharmaceutical Sciences, Translational Oncology and Urology Research (TOUR), Guy's Hospital, 3rd Floor Bermondsey Wing, London, SE1 9RT, UK
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