Competing for patients: an ethical framework for recruiting patients with brain tumors into clinical trials

Recruitment across two decades of NIH-funded Alzheimer's disease clinical trials

BACKGROUND

Timely accrual of a representative sample is a key factor in whether Alzheimer's disease (AD) clinical trials successfully answer the scientific questions under study. Studies in other fields have observed that, over time, recruitment to trials has become increasingly reliant on larger numbers of sites, with declines in the average per-site recruitment rate. Here, we examined the trends in recruitment over a 20-year period of NIH-funded AD clinical trials conducted by the Alzheimer's Disease Cooperative Study (ADCS), a temporally consistent network of sites devoted to interventional research.

METHODS

We performed retrospective analyses of eleven ADCS randomized clinical trials. To examine the recruitment planning, we calculated the expected number of participants to be enrolled per site for each trial. To examine the actual trial recruitment rates, we quantified the number of participants enrolled per site per month.

RESULTS

No effects of time were observed on recruitment planning or overall recruitment rates across trials. No trial achieved an overall recruitment rate greater than one subject per site per month. We observed the fastest recruitment rates in trials with no competition and the slowest in trials that overlapped in time. The highest recruitment rates were consistently seen early within trials and declined over the course of studies.

CONCLUSIONS

Trial recruitment projections should plan for fewer than one participant randomized per site per month and consider the number of other AD trials being conducted concurrently.

Neuro-Oncology Patients as Human Research Subjects: Ethical Considerations for Cognitive and Behavioral Testing for Research Purposes

Simple Summary

Previous publications have elaborated on the exposure of ethical issues surrounding the enrollment and neurological testing of brain cancer patients into clinical studies. Existing literature has been tailored to provide insight on how to overcome ethical challenges for clinical team members but not for the research component that runs in parallel. The aim of this paper is to highlight the obstacles that researchers encounter when obtaining informed consent and administering language, cognitive or behavioral tasks for the sole purpose of research. Researchers should be encouraged to practice their best judgment and effectively communicate the purpose of the study while emphasizing the voluntary participation of neurologically impaired cancer patients. The solutions proposed in this paper can serve as future reference and a guide on maintaining a transparent balance between research and clinical testing for both researchers and clinical team members in the neuro-oncology field.

Abstract

Language, cognition, and behavioral testing have become a fundamental component of standard clinical care for brain cancer patients. Many existing publications have identified and addressed potential ethical issues that are present in the biomedical setting mostly centering around the enrollment of vulnerable populations for therapeutic clinical trials. Well-established guides and publications have served as useful tools for clinicians; however, little has been published for researchers who share the same stage but administer tests and collect valuable data solely for non-therapeutic investigational purposes derived from voluntary patient participation. Obtaining informed consent and administering language, cognition, and behavioral tasks for the sole purpose of research involving cancer patients that exhibit motor speech difficulties and cognitive impairments has its own hardships. Researchers may encounter patients who experience emotional responses during tasks that challenge their existing impairments. Patients may have difficulty differentiating between clinical testing and research testing due to similarity of task design and their physician’s dual role as a principal investigator in the study. It is important for researchers to practice the proposed methods emphasized in this article to maintain the overall well-being of patients while simultaneously fulfilling the purpose of the study in a research setting.

Allocation of Opportunities to Participate in Clinical Trials during the Covid-19 Pandemic and Other Public Health Emergencies

Covid-19 raised many novel ethical issues including regarding the allocation of opportunities to participate in clinical trials during a public health emergency. In this article, we explore how hospitals that have a scarcity of trial opportunities, either overall or in a specific trial, can equitably allocate those opportunities in the context of an urgent medical need with limited therapeutic interventions. We assess the three main approaches to allocating trial opportunities discussed in the literature: patient choice, physician referral, and randomization/lottery. As, we argue, none of the three typical approaches are ethically ideal for allocating trial opportunities in the pandemic context, many hospitals have instead implemented hybrid solutions. We offer practical guidance to support those continuing to face these challenges, and we analyze options for the future.

Improving the Efficiency of Clinical Trial Recruitment Using an Ensemble Machine Learning to Assist With Eligibility Screening

Objective

Efficiently identifying eligible patients is a crucial first step for a successful clinical trial. The objective of this study was to test whether an approach using electronic health record (EHR) data and an ensemble machine learning algorithm incorporating billing codes and data from clinical notes processed by natural language processing (NLP) can improve the efficiency of eligibility screening.

Methods

We studied patients screened for a clinical trial of rheumatoid arthritis (RA) with one or more International Classification of Diseases (ICD) code for RA and age greater than 35 years, from a tertiary care center and a community hospital. The following three groups of EHR features were considered for the algorithm: 1) structured features, 2) the counts of NLP concepts from notes, 3) health care utilization. All features were linked to dates. We applied random forest and logistic regression with least absolute shrinkage and selection operator penalty against the following two standard approaches: 1) one or more RA ICD code and no ICD codes related to exclusion criteria (Screen_RAICD1+EX) and 2) two or more RA ICD codes (Screen_RAICD2). To test the portability, we trained the algorithm at one institution and tested it at the other.

Results

In total, 3359 patients at Brigham and Women’s Hospital (BWH) and 642 patients at Faulkner Hospital (FH) were studied, with 461 (13.7%) eligible patients at BWH and 84 (13.4%) at FH. The application of the algorithm reduced ineligible patients from chart review by 40.5% at the tertiary care center and by 57.0% at the community hospital. In contrast, Screen_RAICD2 reduced patients for chart review by 2.7% to 11.3%; Screen_RAICD1+EX reduced patients for chart review by 63% to 65% but excluded 22% to 27% of eligible patients.

Conclusion

The ensemble machine learning algorithm incorporating billing codes and NLP data increased the efficiency of eligibility screening by reducing the number of patients requiring chart review while not excluding eligible patients. Moreover, this approach can be trained at one institution and applied at another for multicenter clinical trials.

A Time-and-Motion Study of Clinical Trial Eligibility Screening in a Pediatric Emergency Department

OBJECTIVE

Challenges with efficient patient recruitment including sociotechnical barriers for clinical trials are major barriers to the timely and efficacious conduct of translational studies. We conducted a time-and-motion study to investigate the workflow of clinical trial enrollment in a pediatric emergency department.

METHODS

We observed clinical research coordinators during 3 clinically staffed shifts. One clinical research coordinator was shadowed at a time. Tasks were marked in 30-second intervals and annotated to include patient screening, patient contact, performing procedures, and physician contact. Statistical analysis was conducted on the patient enrollment activities.

RESULTS

We conducted fifteen 120-minute observations from December 12, 2013, to January 3, 2014 and shadowed 8 clinical research coordinators. Patient screening took 31.62% of their time, patient contact took 18.67%, performing procedures took 17.6%, physician contact was 1%, and other activities took 31.0%.

CONCLUSIONS

Screening patients for eligibility constituted the most time. Automated screening methods could help reduce this time. The findings suggest improvement areas in recruitment planning to increase the efficiency of clinical trial enrollment.

The Effects of Brain Tumours upon Medical Decision-Making Capacity

Purpose of Review

Informed consent is the integral part of good medical practice in patients with brain tumours. Capacity to consent may be affected by the brain disorder or its treatment. We intend to draw upon the current neuro-oncology literature to discuss the influence intracranial tumours have upon patients’ capacity to consent to treatment and research.

Recent Findings

We performed a systematic review of studies of capacity to consent for treatment or research in patients with intracranial tumours. The search retrieved 1597 papers of which 8 were considered eligible for review.

Summary

Although there are obvious inherent limitations to solely assessing cognition, most research consistently demonstrated increased risk of incapacity in brain tumour patients with cognitive impairment. Specific items in cognitive screening batteries, for example Semantic Verbal Fluency Test (SVFT), Hopkins Verbal Learning Test (HVLT-Recall), and Trail Making Test A/B (TMT), are simple, easily applied tests that may act as significant red flags to identify patients at increased risk of incapacity and who subsequently will require additional cognitive/psychiatric evaluation or more formal tests for capacity to consent for treatment or research.

Ethical issues in competing clinical trials

The proliferation of clinical trials in the last decade and the relatively limited number of experienced clinical trial sites in comparison has created in some sites an environment of clinical trial abundance. As clinical trial protocols typically restrict patients from concurrent clinical trial participation, and patients may be eligible for more than one trial at any given time, selecting the best trial for an individual patient requires evaluation of not only the merits of the individual trials but also patient preferences. This article highlights some potential ethical issues which should be considered when clinical trials are raised as a treatment option and when patients are eligible for more than one trial at the time of evaluation.

A tripartite model for recruiting African-Americans into fatherhood intervention research

Many studies have examined factors influencing African-American (AA) participation in research studies. But none inform the recruitment of AA men into fatherhood intervention research. Our purpose is to describe the recruitment and enrollment framework of the Dedicated African American Dad (DAAD) Study, a randomized controlled trial (RCT) designed to test a fatherhood intervention against a financial literacy comparison condition. AA nonresident (AANR) fathers are fathers who do not reside with their child on a full-time basis. Fathers attended 10 group-based sessions; and father and mother informants completed research interviews at baseline, postintervention, and 12 weeks postintervention. The DAAD Study tripartite model is a system of strategies that address three factors that individually and cooperatively affect recruitment of AANR fathers into research: community partnerships; study infrastructure; and recruitment personnel. The intersection of these three components forms a recruitment nexus that can be used to guide community-based research. The DAAD study serves as an exemplar of recruitment challenges, strategies, and lessons learned.

Failed Randomized Clinical Trials in Radiation Oncology: What Can We Learn?

PURPOSE

Randomized clinical trials (RCTs) are essential to evidence-based medicine, yet a significant proportion fail to be completed. In radiation oncology, factors contributing to trial failure are not well understood. We sought to compare completed and incomplete clinical trials involving radiation therapy (RT) to identify predictors of trial failure.

METHODS AND MATERIALS

We undertook a review of ClinicalTrials.gov to identify RCTs involving RT. Eligible trials mandated external beam RT in ≥1 arm of the study and were registered between September 27, 2007, and December 31, 2010. Univariate and multivariate logistic regression analyses were performed to determine factors predictive of trial failure.

RESULTS

We included 134 eligible studies, of which 94 (70.1%) were successful and 40 (29.9%) failed. The reasons for trial failure were categorized as follows: lack of accrual (57.5%), inadequate funding (15.0%), drug unavailability (7.5%), interim data-monitoring report recommendations (7.5%), and other (12.5%). Over time, significantly more trials were failing to be completed (P = .010), with rates increasing from 11.8% (before 2007) to 34.0% (2007-2008) to 39.5% (2009-2012). On univariate analysis, predictors of failure were trials with a surgical comparator (odds ratio [OR], 8.12; P = .013), government sponsorship (vs non-government; OR, 3.67; P = .025), inclusion of a safety endpoint (OR, 2.85; P = .022), and studies starting after 2006 (P = .033). On multivariate analysis, surgical trials were strongly predictive of failure (OR, 12.30; P = .025) while behavioral trials were associated with success (OR, 0.11; P = .045).

CONCLUSIONS

RT RCTs involving ≥1 surgical arms are at a very high risk of failure, with 75% failing to be completed. In contrast, behavioral studies are associated with study completion, with 94% of studies being successful. Future RT trials involving surgical interventions should consider novel methods to reduce the risk of trial failure.

When clinical trials compete: prioritising study recruitment

It is not uncommon for multiple clinical trials at the same institution to recruit concurrently from the same patient population. When the relevant pool of patients is limited, as it often is, trials essentially compete for participants. There is evidence that such a competition is a predictor of low study accrual, with increased competition tied to increased recruitment shortfalls. But there is no consensus on what steps, if any, institutions should take to approach this issue. In this article, we argue that an institutional policy that prioritises some trials for recruitment ahead of others is ethically permissible and indeed prima facie preferable to alternative means of addressing recruitment competition. We motivate this view by appeal to the ethical importance of minimising the number of studies that begin but do not complete, thereby exposing their participants to unnecessary risks and burdens in the process. We then argue that a policy of prioritisation can be fair to relevant stakeholders, including participants, investigators and funders. Finally, by way of encouraging and helping to frame future debate, we propose some questions that would need to be addressed when identifying substantive ethical criteria for prioritising between studies.

Ethical and scientific considerations for patient enrollment into concurrent clinical trials

Researchers and institutional review boards often consider it inappropriate for patients to be asked to consent to more than one study despite there being no regulatory prohibition on co-enrollment in most countries. There are however ethical, safety, statistical, and practical considerations relevant to co-enrollment, particularly in surgery and perioperative medicine, but co-enrollment can be done if such concerns can be resolved. Preventing eligible patients from co-enrolling in studies which they would authentically value participating in, and whose material risks and benefits they understand, violates their autonomy--and thus contravenes a fundamental principle of research ethics. Statistical issues must be considered but can be addressed. In most cases each trial can be analyzed separately and validly using standard intention to treat principles; selection and other biases can be avoided if enrollment into the second trial is not dependent upon randomized treatment in the first trial; and valid interaction analyses can be performed for each trial by considering the patient's status in the other trial at the time of randomization in the index trial. Clinical research with a potential to inform and improve clinical practice is valuable and should be supported. The ethical, safety, statistical, and practical aspects of co-enrollment can be managed, providing greater opportunity for research-led improvements in clinical practice.

Automated clinical trial eligibility prescreening: increasing the efficiency of patient identification for clinical trials in the emergency department

Objectives (1) To develop an automated eligibility screening (ES) approach for clinical trials in an urban tertiary care pediatric emergency department (ED); (2) to assess the effectiveness of natural language processing (NLP), information extraction (IE), and machine learning (ML) techniques on real-world clinical data and trials.

Data and methods We collected eligibility criteria for 13 randomly selected, disease-specific clinical trials actively enrolling patients between January 1, 2010 and August 31, 2012. In parallel, we retrospectively selected data fields including demographics, laboratory data, and clinical notes from the electronic health record (EHR) to represent profiles of all 202795 patients visiting the ED during the same period. Leveraging NLP, IE, and ML technologies, the automated ES algorithms identified patients whose profiles matched the trial criteria to reduce the pool of candidates for staff screening. The performance was validated on both a physician-generated gold standard of trial–patient matches and a reference standard of historical trial–patient enrollment decisions, where workload, mean average precision (MAP), and recall were assessed.

Results Compared with the case without automation, the workload with automated ES was reduced by 92% on the gold standard set, with a MAP of 62.9%. The automated ES achieved a 450% increase in trial screening efficiency. The findings on the gold standard set were confirmed by large-scale evaluation on the reference set of trial–patient matches.

Discussion and conclusion By exploiting the text of trial criteria and the content of EHRs, we demonstrated that NLP-, IE-, and ML-based automated ES could successfully identify patients for clinical trials.