Key Barriers to Clinical Trial Enrolment: A Survey of Clinical Trial Staff at an NCI Designated Cancer Center

Background: Clinical trials, key elements of the processes that account for many of the recent advances in cancer care, are becoming more complex and challenging to conduct. The Stephenson Cancer Center (SCC) has been the lead accruer to NCI-LAP trials over the past three years, and in addition, fields investigator initiated and industry sponsored trials. To identify opportunities for continued improvement in clinical trial enrolment, we sought to identify the obstacles encountered by our clinical trial staff in these activities. Method: We conducted a survey of our research staff including all research nurses and disease site coordinators who participate in recruitment, screening, consenting, data collection and compliance. The survey, sent by email to the clinical trial list-serve at SCC (90 staff member), invited respondents to enumerate obstacles to patient participation in clinical trials. We then performed a follow up meeting with our research coordinators to clarify responses. A total of 26 responses from 90 respondents were received and tabulated by disease site. Results: The most commonly reported obstacles to enrolment were, in descending order: communication/language barriers, cultural bias, time/procedure commitment, and complexity of the trial protocol, financial logistics, comorbidities, and stringent trial criteria. Respondents identified 83 obstacles as frequently encountered obstacles to enrolment. The 83 reported obstacles were classified into 9 categories and organized by disease site as presented in tabular format (below). The most commonly identified obstacles to patient enrolment were communication and language barriers. In patients for whom Spanish is the primary language this was a universal obstacle, as there is a lack of consistent Spanish consents across the clinical trial portfolio. Cultural bias, as an obstacle was manifested as a general mistrust by prospective trial participants of experimental therapies and clinical trials. After communication and cultural bias as barriers, travel requirements and the associated expenses playing a role in patients from rural areas were identified as the most commonly encountered barrier. The complexity of trial protocols and the associated large number of clinic visits, frequent laboratory and imaging tests were also identified as common obstacles. Clinical trial complexity with strict inclusion and exclusion criteria and trial-specified biopsies were frequently cited. Implications: In this descriptive study, common barriers to patient enrolment in clinical trials were identified by clinical trial staff. Assessing barriers encountered by clinical trial staff is infrequently used as a metric for improving clinical trial enrolment, but provides important perspective. In our study, some obstacles are inherent in our patient populations, others appear to be actionable. Development of Spanish language consents and specific programs to overcome negative bias regarding clinical trials are potential areas for improvement. The complexity of clinical trial protocols and the increasingly strict inclusion/exclusion criteria, are issues that will require consideration and action at the level of the cooperative groups and industry. Disclosures No relevant conflicts of interest to declare.

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Evaluating the Financial Impact of Clinical Trials in Oncology: Results From a Pilot Study From the Association of American Cancer Institutes/Northwestern University Clinical Trials Costs and Charges Project

Journal of Clinical Oncology ◽

10.1200/jco.2000.18.15.2805 ◽

2000 ◽

Vol 18 (15) ◽

pp. 2805-2810 ◽

Cited By ~ 27

Author(s):

Charles L. Bennett ◽

Tammy J. Stinson ◽

Victor Vogel ◽

Lyn Robertson ◽

Donald Leedy ◽

...

Keyword(s):

Clinical Trial ◽

Clinical Trials ◽

Pilot Study ◽

Medical Care ◽

Phase Ii ◽

Third Party ◽

Disease Stage ◽

Cancer Center ◽

Policy Debate ◽

Economic Analyses

PURPOSE: Medical care for clinical trials is often not reimbursed by insurers, primarily because of concern that medical care as part of clinical trials is expensive and not part of standard medical practice. In June 2000, President Clinton ordered Medicare to reimburse for medical care expenses incurred as part of cancer clinical trials, although many private insurers are concerned about the expense of this effort. To inform this policy debate, the costs and charges of care for patients on clinical trials are being evaluated. In this Association of American Cancer Institutes (AACI) Clinical Trials Costs and Charges pilot study, we describe the results and operational considerations of one of the first completed multisite economic analyses of clinical trials. METHODS: Our pilot effort included assessment of total direct medical charges for 6 months of care for 35 case patients who received care on phase II clinical trials and for 35 matched controls (based on age, sex, disease, stage, and treatment period) at five AACI member cancer centers. Charge data were obtained for hospital and ancillary services from automated claims files at individual study institutions. The analyses were based on the perspective of a third-party payer. RESULTS: The mean age of the phase II clinical trial patients was 58.3 years versus 57.3 years for control patients. The study population included persons with cancer of the breast (n = 24), lung (n = 18), colon (n = 16), prostate (n = 4), and lymphoma (n = 8). The ratio of male-to-female patients was 3:4, with greater than 75% of patients having stage III to IV disease. Total mean charges for treatment from the time of study enrollment through 6 months were similar: $57,542 for clinical trial patients and $63,721 for control patients (1998 US$; P = .4) CONCLUSION: Multisite economic analyses of oncology clinical trials are in progress. Strategies that are not likely to overburden data managers and clinicians are possible to devise. However, these studies require careful planning and coordination among cancer center directors, finance department personnel, economists, and health services researchers.

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Artificial Intelligence Tool for Optimizing Eligibility Screening for Clinical Trials in a Large Community Cancer Center

JCO Clinical Cancer Informatics ◽

10.1200/cci.19.00079 ◽

2020 ◽

pp. 50-59 ◽

Cited By ~ 3

Author(s):

J. Thaddeus Beck ◽

Melissa Rammage ◽

Gretchen P. Jackson ◽

Anita M. Preininger ◽

Irene Dankwa-Mullan ◽

...

Keyword(s):

Breast Cancer ◽

Clinical Trial ◽

Clinical Trials ◽

Language Processing ◽

Medical Records ◽

Cancer Center ◽

Patients With Cancer ◽

Attribute Extraction ◽

Time Required ◽

Manual Review

PURPOSE Less than 5% of patients with cancer enroll in clinical trials, and 1 in 5 trials are stopped for poor accrual. We evaluated an automated clinical trial matching system that uses natural language processing to extract patient and trial characteristics from unstructured sources and machine learning to match patients to clinical trials. PATIENTS AND METHODS Medical records from 997 patients with breast cancer were assessed for trial eligibility at Highlands Oncology Group between May and August 2016. System and manual attribute extraction and eligibility determinations were compared using the percentage of agreement for 239 patients and 4 trials. Sensitivity and specificity of system-generated eligibility determinations were measured, and the time required for manual review and system-assisted eligibility determinations were compared. RESULTS Agreement between system and manual attribute extraction ranged from 64.3% to 94.0%. Agreement between system and manual eligibility determinations was 81%-96%. System eligibility determinations demonstrated specificities between 76% and 99%, with sensitivities between 91% and 95% for 3 trials and 46.7% for the 4th. Manual eligibility screening of 90 patients for 3 trials took 110 minutes; system-assisted eligibility determinations of the same patients for the same trials required 24 minutes. CONCLUSION In this study, the clinical trial matching system displayed a promising performance in screening patients with breast cancer for trial eligibility. System-assisted trial eligibility determinations were substantially faster than manual review, and the system reliably excluded ineligible patients for all trials and identified eligible patients for most trials.

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Abstract 4764: An analysis of factors associated with oncology clinical trial activation at University of Illinois Cancer Center Oncology Clinical Trials Office

10.1158/1538-7445.am2018-4764 ◽

2018 ◽

Author(s):

Mary A. Otoo ◽

Michelle Uriostigue Preza ◽

Margaret Gavor ◽

Darlene Kitterman ◽

Oana Danciu

Keyword(s):

Clinical Trial ◽

Clinical Trials ◽

Cancer Center ◽

University Of Illinois ◽

Factors Associated ◽

Oncology Clinical Trial ◽

Oncology Clinical Trials

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Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI Extension

BMJ ◽

10.1136/bmj.m3164 ◽

2020 ◽

pp. m3164 ◽

Cited By ~ 6

Author(s):

Xiaoxuan Liu ◽

Samantha Cruz Rivera ◽

David Moher ◽

Melanie J Calvert ◽

Alastair K Denniston

Keyword(s):

Artificial Intelligence ◽

Clinical Trial ◽

Clinical Trials ◽

Clinical Trial Design ◽

Trial Protocols ◽

General Readership ◽

Peer Reviewers ◽

Multi Stakeholder ◽

Analysis Of Error

Abstract The CONSORT 2010 (Consolidated Standards of Reporting Trials) statement provides minimum guidelines for reporting randomised trials. Its widespread use has been instrumental in ensuring transparency when evaluating new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate impact on health outcomes. The CONSORT-AI extension is a new reporting guideline for clinical trials evaluating interventions with an AI component. It was developed in parallel with its companion statement for clinical trial protocols: SPIRIT-AI. Both guidelines were developed through a staged consensus process, involving a literature review and expert consultation to generate 29 candidate items, which were assessed by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed on in a two-day consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The CONSORT-AI extension includes 14 new items, which were considered sufficiently important for AI interventions, that they should be routinely reported in addition to the core CONSORT 2010 items. CONSORT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention is integrated, the handling of inputs and outputs of the AI intervention, the human-AI interaction and providing analysis of error cases. CONSORT-AI will help promote transparency and completeness in reporting clinical trials for AI interventions. It will assist editors and peer-reviewers, as well as the general readership, to understand, interpret and critically appraise the quality of clinical trial design and risk of bias in the reported outcomes.

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Identification of Cancer Care and Protocol Characteristics Associated With Recruitment in Breast Cancer Clinical Trials

Journal of Clinical Oncology ◽

10.1200/jco.2007.15.3726 ◽

2008 ◽

Vol 26 (27) ◽

pp. 4458-4465 ◽

Cited By ~ 26

Author(s):

Julie Lemieux ◽

Pamela J. Goodwin ◽

Kathleen I. Pritchard ◽

Karen A. Gelmon ◽

Louise J. Bordeleau ◽

...

Keyword(s):

Breast Cancer ◽

Clinical Trial ◽

Clinical Trials ◽

Cancer Care ◽

Primary Objective ◽

Cancer Clinical Trials ◽

Cooperative Groups ◽

Number Of Patients ◽

Trial Protocols ◽

Few Data

Purpose It is estimated that only 5% of patients with cancer participate in a clinical trial. Barriers to participation may relate to available protocols, physicians, and patients, but few data exist on barriers related to cancer care environments and protocol characteristics. Methods The primary objective was to identify characteristics of cancer care environments and clinical trial protocols associated with a low recruitment into breast cancer clinical trials. Secondary objectives were to determine yearly recruitment fraction onto clinical trials from 1997 to 2002 in Ontario, Canada, and to compare recruitment fraction among years. Questionnaires were sent to hospitals requesting characteristics of cancer care environments and to cooperative groups/pharmaceutical companies for information on protocols and the number of patients recruited per hospital/year. Poisson regression was used to estimate the recruitment fraction. Results Questionnaire completion rate varied between 69% and 100%. Recruitment fraction varied between 5.4% and 8.5% according to year. More than 30% of patients were diagnosed in hospitals with no available trials. In multivariate analysis, the following characteristics were associated with recruitment: use of placebo versus not (relative risk [RR] = 0.80; P = .05), nonmetastatic versus metastatic trial (RR = 2.80; P < .01), and for nonmetastatic trials, protocol allowing an interval of 12 weeks or longer versus less than 12 weeks (from diagnosis, surgery, or end of therapy) before enrollment (RR = 1.36; P < .01). Conclusion Allowable interval of 12 weeks or longer to randomly assign patients in clinical trials could help recruitment. In our study, absence of an available clinical trial represented the largest barrier to recruitment.

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Cancer clinical trials available in the community setting: A 5-year analysis from 1999–2004

Journal of Clinical Oncology ◽

10.1200/jco.2006.24.18_suppl.6056 ◽

2006 ◽

Vol 24 (18_suppl) ◽

pp. 6056-6056

Author(s):

J. K. Keller ◽

J. Bowman ◽

J. A. Lee ◽

M. A. Mathiason ◽

K. A. Frisby ◽

...

Keyword(s):

Clinical Trial ◽

Clinical Trials ◽

Cancer Patients ◽

Community Setting ◽

Disease Stage ◽

Cancer Center ◽

Newly Diagnosed ◽

Community Based ◽

Eligibility Criteria ◽

Major Barrier

6056 Background: Less than 5% of newly diagnosed cancer patients are accrued into clinical trials. In the community setting, the lack of appropriate clinical trials is a major barrier. Our prospective study in 2004 determined that 58% of newly diagnosed adult cancer patients at our community-based cancer center didn’t have a clinical trial available appropriate for their disease stage. Among those with clinical trials, 23% were subsequently found to be ineligible (Go RS, et al. Cancer 2006, in press). However, the availability of clinical trials may vary from year to year. Methods: A retrospective study was conducted to determine what clinical trials were available for newly diagnosed adult cancer patients at our institution from June 1999-July 2004. The study also investigated the proportions of newly diagnosed patients who had a clinical trial available appropriate for type and stage of disease and patients accrued. Results: Over the 5-year period, 207 (82, 87, 99, 102, 117, years 1–5, respectively) trials were available. Most (50.7%) trials were for the following cancers: breast (15.5%), lung (13.5%), head and neck (7.7%), colorectal (7.2%) and lymphoma (6.8%). ECOG (53%), RTOG (26%), and CTSU (9%) provided the majority of the trials. A total of 5,776 new adult cancer patients were seen during this period. Overall, 60% of the patients had a trial available appropriate for type and stage of their cancer, but only 103 (3%) were enrolled. There was a significant upward trend in the proportions of patients with available trials over the years (60.2%, 55.9%, 59.2%, 60.7%, 63.9%, years 1–5, respectively; Mantel-Haenszel P=.008). The proportion of patients with a trial available was highest for prostate (97.3%), lung (90.9%), and breast (73.9%), and lowest for melanoma (17.1%), renal (11.6%), and bladder (7.2%). The majority of patients accrued to trials had the following cancers: breast (32%), lung (17%), lymphoma (9%), colon (7%), and prostate (5%). Conclusions: Nearly half of the newly diagnosed adult patients at our center had no trials available appropriate for type and stage of their cancers. It is likely that if strict clinical trial eligibility criteria were applied, approximately 2/3 of our patients would not be eligible for a clinical trial. No significant financial relationships to disclose.

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Metastatic colorectal cancer (mCRC) patterns of care: Implications for clinical trial design

Journal of Clinical Oncology ◽

10.1200/jco.2007.25.18_suppl.4079 ◽

2007 ◽

Vol 25 (18_suppl) ◽

pp. 4079-4079 ◽

Cited By ~ 1

Author(s):

C. S. Denlinger ◽

M. A. Collins ◽

Y. Wong ◽

S. Litwin ◽

N. J. Meropol

Keyword(s):

Clinical Trial ◽

Decision Making ◽

Clinical Trials ◽

Trial Design ◽

Clinical Trial Design ◽

New Drugs ◽

Comprehensive Cancer Center ◽

Cancer Center ◽

Treatment Change ◽

Therapy Regimen

4079 Background: New approaches have expanded options for patients (pts) with mCRC. To characterize current practice paradigms that might bear on clinical trial design, we analyzed decision-making and treatment patterns in pts treated at a Comprehensive Cancer Center since the introduction of cetuximab (CET), and bevacizumab (BV). Methods: A retrospective review of all pts diagnosed with mCRC between 3/1/04 and 8/28/06 treated at Fox Chase Cancer Center. Results: 160 pts were treated, with 157 pts receiving at least one therapy regimen by 10 attending oncologists. There were 350 changes in therapy with 246 (70%) including continuation of at least one prior drug (92 BV, 111 fluoropyrimidines, 43 other). The most common reasons for treatment change were toxicity (33%), progressive disease (PD) (29%), treatment breaks (15%), and metastasectomy (11%) ( Table ). PD was a more common cause for treatment discontinuation in later phases of treatment (18% initial regimen vs. 36% subsequent regimens, p=0.0002). 24% of pts treated with oxaliplatin (OX) discontinued due to neuropathy. Hypersensitivity caused discontinuation in 5% of pts with OX and 7% of pts with CET. Resection of metastases was undertaken in 38% of pts. 43% of these pts received neoadjuvant therapy, and 56% received adjuvant therapy. 30% of pts have died, 29% remain on active treatment, 28% are on a treatment break, 3% are on hospice, and 11% are lost to follow-up. Conclusions: PD is no longer the primary reason for change of therapy in pts with mCRC. Metastasectomy is common and OX neuropathy is often treatment-limiting. These findings have important implications for endpoint selection and design of clinical trials in mCRC. Future clinical trials in mCRC must recognize treatment complexities and capture key components of decision-making that may result in prolonged survival. Furthermore, treatment breaks represent a potential window for the evaluation of new drugs. [Table: see text] No significant financial relationships to disclose.

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Accelerating the clinical trial start-up process for early-phase cancer studies: A test of process change to support rapid activation in an academic medical center.

Journal of Clinical Oncology ◽

10.1200/jco.2013.31.15_suppl.e17507 ◽

2013 ◽

Vol 31 (15_suppl) ◽

pp. e17507-e17507 ◽

Cited By ~ 1

Author(s):

Sheilah K Hurley ◽

Therica M Miller ◽

Rebecca Flores Stella ◽

Keren Dunn ◽

Ryan Schroeder ◽

...

Keyword(s):

Clinical Trial ◽

Clinical Trials ◽

Medical Center ◽

Academic Medical Center ◽

Cancer Center ◽

Small Series ◽

Academic Medical ◽

One Year ◽

Activation Times ◽

High Level

e17507 Background: Clinical trial sponsors have strong scientific, financial, and regulatory interests in rapidly activating studies at participating sites. Academic medical centers have difficulty activating trials within a few weeks of sponsor agreement because, among other inefficiencies, they engage the necessary committee reviews, regulatory approvals, contracting, and budgeting in serial fashion. Incremental revisions in such workflows do not result in strong improvements. Methods: We redesigned our institutional workflow to complete clinical trial activation tasks within six weeks. Historical procedures were replaced rather than scrutinized. A high level leadership committee was required to change and integrate procedures across the medical center, and engage sponsors to improve their turnaround times. A web-based collaborative workflow tracking tool was created to help coordinate the necessary tasks and measure performance. Six clinical trials from the Cancer Center portfolio were used to test and improve the new workflow. Results: Clinical trial activation redesign took one year. For the six studies used as tests of change, the activation times were 49, 54, 78, 58, 62, and 32 days. Times in excess of 6 weeks were largely due to sponsor delays. Conclusions: Considerable effort is required to significantly alter a complex workflow like clinical trial activation. Appropriate priorities, leadership, staffing, and tools are required. Markedly shortened study activation for a small series of cancer trials taught our academic medical center lessons that will be useful for improving the process for all clinical trials, and will make us a better partner for pharmaceutical and academic sponsors as well as for investigator initiated research. [Table: see text]

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Best supportive care (BSC) in published clinical trials.

Journal of Clinical Oncology ◽

10.1200/jco.2013.31.15_suppl.9560 ◽

2013 ◽

Vol 31 (15_suppl) ◽

pp. 9560-9560 ◽

Cited By ~ 1

Author(s):

Amy Pickar Abernethy ◽

Ryan David Nipp ◽

David Currow ◽

Nathan Cherny ◽

Florian Strasser ◽

...

Keyword(s):

Systematic Review ◽

Clinical Trial ◽

Clinical Trials ◽

Supportive Care ◽

Symptom Management ◽

Symptom Assessment ◽

Best Supportive Care ◽

Delphi Panel ◽

Random Allocation ◽

Exclusion Criteria

9560 Background: BSC as a control arm in clinical trials is poorly defined. A systematic review was conducted to evaluate clinical trial concordance with published, consensus-based framework for BSC delivery in trials. Methods: A consensus-based Delphi panel previously identified 4 key domains of BSC delivery in trials: multidisciplinary care; supportive care documentation; symptom assessment at least as often as the intervention arm; and guideline-based symptom management. A systematic review of trials including BSC control arms assessed BSC concordance to the consensus-based domains. Databases were searched from 2002-2012 using search strings: “cancer”; “best supportive care”; “randomized” or “random allocation”; and “supportive” or “palliative.” Exclusion criteria were: no BSC arm, non-human trial, not randomized, not English, not advanced cancer, or not including anticancer therapy. Data were independently extracted by 2 reviewers and scored by 4 reviewers for conformance with consensus-based BSC framework. Results: 373 articles were retrieved, 17 retained after applying exclusion criteria. Overall, trials conformed to <18% of the consensus-based BSC standards. 35% of articles offered a detailed description of BSC. 65% reported baseline and regular symptom assessment, and 47% reported using validated symptom assessment measures. 35% reported symptom assessment at identical intervals in both experimental and BSC arms. None listed an evidence-based guideline for symptom management. None of the multicenter trials reported standardization of BSC across sites. No studies reported educating patients on symptom management or goals of anti-cancer therapy. No studies reported offering access to palliative care specialists, social workers, financial or spiritual counseling. Conclusions: Reporting of BSC in trials is incomplete, resulting in uncertain internal and external validity. Such poorly defined interventions and variation between sites is unacceptable for other aspects of a clinical trial. Unless it is truly best supportive care, such studies may risk systematically over-estimating the clinical effect of the comparator arms. Standardization of a BSC delivery framework is needed to improve trial design and data generalization.

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