Primary care patient interests in joining a planned multi-cancer early detection clinical trial

INTRODUCTION

Clinical trials are being conducted and are being planned to assess the safety and efficacy of multi-cancer early detection (MCED) tests for use in cancer screening. This study aimed to determine the feasibility of primary care patient outreach in recruiting participants to a planned MCED clinical trial, assess patient interest in trial participation, and measure decisional conflict related to participation.

METHODS

The research team used the electronic medical record of a large, urban health care system to identify primary care patients 50-80 years of age who were potentially eligible for a planned MCED trial. We mailed information about the planned MCED trial to identified patients and then contacted the patients by telephone to obtain consent and administer a baseline survey. Subsequently, we contacted consented patients to complete an interview to review the mailed information and elicit perceptions about trial participation. Finally, a research coordinator administered an endpoint telephone survey to assess patient interest in and decisional conflict related to joining the trial.

RESULTS

We randomly identified 1000 eligible patients and were able to make contact with 690 (69%) by telephone. Of the patients contacted, 217 (31%) completed the decision counseling session and 219 (32%) completed the endpoint survey. Among endpoint survey respondents, 177 (81%) expressed interest in joining the MCED trial and 162 (74%) reported low decisional conflict.

CONCLUSIONS

Most patients were contacted and about a quarter of those contacted expressed interest in and low decisional conflict about joining the planned MCED trial. Research is needed to determine how to optimize patient outreach and engage patients in shared decision-making about MCED trial participation.

Patients' selection and trial matching in early-phase oncology clinical trials

BACKGROUND

Early-phase clinical trials (EPCT) represent an important part of innovations in medical oncology and a valuable therapeutic option for patients with metastatic cancers, particularly in the era of precision medicine. Nevertheless, adult patients' participation in oncology clinical trials is low, ranging from 2% to 8% worldwide, with unequal access, and up to 40% risk of early discontinuation in EPCT, mostly due to cancer-related complications.

DESIGN

We review the tools and initiatives to increase patients' orientation and access to early phase cancer clinical trials, and to limit early discontinuation.

RESULTS

New approaches to optimize the early-phase clinical trial referring process in oncology include automatic trial matching, tools to facilitate the estimation of patients' prognostic and/or to better predict patients' eligibility to clinical trials. Classical and innovative approaches should be associated to double patient recruitment, improve clinical trial enrollment experience and reduce early discontinuation rates.

CONCLUSIONS

Whereas EPCT are essential for patients to access the latest medical innovations in oncology, offering the appropriate trial when it is relevant for patients should increase by organizational and technological innovations. The oncologic community will need to closely monitor their performance, portability and simplicity for implementation in daily clinical practice.

Virtual clinical trials: A tool for predicting patients who may benefit from treatment beyond progression with pembrolizumab in non-small cell lung cancer

Enrolling patients in immunotherapy clinical trials is becoming increasingly competitive. Virtual clinical trials can help investigators answer key questions despite this. For example, pembrolizumab is the recommended first-line treatment for non-small cell lung cancer (NSCLC) with no driver alterations and a programmed death ligand 1 (PD-L1) Tumor Proportion Score ≥50%. Salvage therapies for relapsed/refractory patients are limited. Retrospective studies suggest that a subset of patients may benefit from pembrolizumab beyond progression; these results have not been validated in a prospective study. We constructed digital twins of patients and simulated clinical trials to predict the best salvage therapy after progressive disease (PD) on pembrolizumab. Response dynamics were evaluated at the lesion level to represent patients who experience systemic PD while individual lesions continue shrinking. With >25,000 radiographic lesion measurements from >500 patients, we simulated responses to pembrolizumab, chemotherapy, and PD on pembrolizumab followed by either pembrolizumab beyond progression or salvage chemotherapy. Switching all progressors to salvage chemotherapy was suboptimal. Virtual trials predicted progression-free survival (PFS) from pembrolizumab beyond progression to be comparable with salvage chemotherapy in patients whose PD was due to nontarget progression. A PFS-optimized regimen may improve disease control rates ≥15%. Pembrolizumab beyond progression may benefit a subset of patients with PD-L1-high, driver alteration-free NSCLC, but prospective studies are warranted.

Competition for recruitment in SARS-CoV-2 Trials in the United States: a longitudinal cohort analysis

OBJECTIVE

Competition among trials for patient enrollment can impede recruitment. We hypothesized that this occurred early in the COVID-19 pandemic, when an unprecedented number of clinical trials were launched. We performed a simple and multivariable regression analysis evaluating the relationship between the proportion of SARS-CoV-2 investigational trial sites within each USA state with unsuccessful patient-participant recruitment and: (i) the proportion of cases required to reach state recruitment goals; (ii) state population based on data from the US Census; and, (iii) number of trial sites per state.

RESULTS

Our study included 151 clinical trials. The proportion of trials with successful recruitment was 72.19% (109 of 151 trials). We did not find a significant relationship between unsuccessful patient-participant recruitment, state recruitment goals, state population or the number of trial sites per state in both our simple and multivariable regression analyses. Our results do not suggest that early in the COVID-19 pandemic, competition for patient-participants impeded successful recruitment in SARS-CoV-2 trials. This may reflect the unique circumstances of the first few months of the pandemic in the United States, in which the number and location of SARS-CoV-2 cases was sufficient to meet trial recruitment requirements, despite the large number of trials launched.

Opportunities for Participation in Randomized Controlled Trials for Patients with Multiple Myeloma: Trial Access Depends on Restrictive Eligibility Criteria and Patient Expectations

Simple Summary

Over the past decade, randomized controlled trials as an established instrument of evidence-based medicine have contributed fundamentally to the development and approval of new substances. However, it has been frequently shown that less than 5% of adult cancer patients enroll in clinical trials. Barriers to trial participation have been extensively studied, but the rate of trial participation has not changed substantially. In this retrospective analysis, we found that 53% of newly diagnosed multiple myeloma patients met the eligibility criteria, while only 38% of relapsed refractory patients were eligible. Moreover, our data show for the first time that eligible patients tend to become more reluctant to participate over the course of the disease, with 42% of newly diagnosed patients consenting, and only 7% of relapsed/refractory patients consenting. Thus, our results may assist with trial design improvements and address patient expectations and priorities in order to increase enrollment.

Abstract

Randomized controlled trials (RCT) are the driver of therapeutic innovations. However, it has been frequently shown that less than 5% of adult cancer patients enroll in clinical trials, although 70% of patients are considered as being willing to participate. Barriers to trial participation have been extensively studied. Although there is evidence that trial participation correlates with improved survival and reduced mortality, the rate of participation has not changed substantially. We provide retrospective data from a single-center analysis of 411 patients with multiple myeloma (MM) who were treated at the University Hospital Duesseldorf in Germany between January 2014 and December 2016. Each patient was analyzed for the real-world possibility of participating in a clinical study, based on the inclusion and exclusion (I/E) criteria and the recruiting period of open studies. The overall rate of study participation was 19%. A total of 53% of NDMM patients were eligible for first-line studies (GMMG-HD6, LenaMain). Of these, 80% consented to enrolment (42% of all). In contrast, only 38% of the RRMM population was eligible (GMMG-Relapse, Castor, Tourmaline, Admyre). Of these, only 22% (7% of all) consented. This was confirmed by virtual analysis, showing that only 29% of all RRMM patients would have been eligible for six internationally recruiting trials leading to later drug approval. The majority of cases were rendered ineligible by only one I/E criterion. The most common criteria were study-specific (prior therapies or refractory disease to a specific drug), kidney disease, and previous malignancy, followed by internal, neurologic, and infectious disease. In summary, this single-center analysis showed that I/E criteria permit study participation for most NNDM patients, with a dramatic decrease in the RRMM population. This is aggravated by the fact that the willingness for study participation also significantly declines in RRMM. Thus, addressing patient expectations and priorities seems to be the most promising approach to increasing patient enrollment in clinical trials.

Strategies to Advance Equity in Cancer Clinical Trials

Cancer clinical trials are critical for testing new treatments, yet less than 5% of patients with cancer enroll in these trials. Minority groups, elderly individuals, and rural populations are particularly underrepresented in cancer treatment trials. Strategies for advancing equity in cancer clinical trials for these populations include (1) optimizing clinical trial matching by broadening eligibility criteria, screening all patients for trial eligibility, expanding the number of trials against which patients are screened, and following up on all patient matches with an enrollment invitation; (2) conducting site self-assessments to identify clinical-, patient-, provider-, and system-level barriers that contribute to low rates of clinical trial screening and enrollment; (3) creating a quality improvement plan that addresses the barriers to enrollment and incorporates the use of tools and strategies such as clinical trial checklists; workforce development and trainings to improve cultural competence and reduce unconscious bias; guides to promote community education, outreach and engagement with cancer clinical trials; screening and accrual logs designed to measure participation by demographics; models of informed consent that improve understanding; clinical trial designs that reduce accessibility barriers; use of cancer clinical trial patient navigators; and programs to eliminate barriers to participation and out-of-pocket expenses; and (4) working with stakeholders to develop both protocols that are inclusive of diverse populations' geographic locations, and strategies to access those trials. These actions will support greater access for populations that have remained underrepresented in cancer clinical trials and thereby increase the generalizability and efficiency of cancer research.

Prostate cancer clinical trial completion: The role of geography

BACKGROUND/AIMS

One in five cancer clinical trials fails with another third failing to meet enrollment goals. Prior efforts to improve enrollment focus on patient facing interventions, but geographic factors such as regional cancer incidence may doom trials before they even begin. For these reasons, we examined associations of regional prostate cancer incidence with trial termination, and identified scientifically-underserved areas where future trials might thrive.

METHODS

We merged US phase 2-3 prostate cancer clinical trial data from ClinicalTrials.gov with prostate cancer incidence data from statecancerprofiles.cancer.gov. We matched trial information from 293 closed and 560 active trials with incidence data for 2947 counties. Using multivariable logistic regression, we identified associations with trial termination. We identified 'scientifically-underserved' counties with the highest cancer incidence quintile (>61 annual cases) but lowest active trials quintile (0 or 1 trial).

RESULTS

Of 293 closed trials, one in three was terminated (n = 96, 32.8%). On multivariable analysis, only lower regional prostate cancer incidence was associated with higher likelihood of premature trial termination (OR 0.98, 95% CI [0.96-0.99] for every 100 cases, p = 0.03). We identified 188 counties with >61 annual prostate cancer cases but 0 or 1 active trials, indicating potential scientifically-underserved areas.

CONCLUSIONS

In this novel study, we found prostate cancer trials in areas with low prostate cancer incidence were more likely to fail. We also identified scientifically-underserved areas where trials might thrive. Our findings provide a more nuanced understanding of clinical trial feasibility and upstream opportunities for improvement.

Automated Electronic Health Record-Based Tool for Identification of Patients With Metastatic Disease to Facilitate Clinical Trial Patient Ascertainment

PURPOSE

To facilitate identification of clinical trial participation candidates, we developed a machine learning tool that automates the determination of a patient's metastatic status, on the basis of unstructured electronic health record (EHR) data.

METHODS

This tool scans EHR documents, extracting text snippet features surrounding key words (such as metastatic, progression, and local). A regularized logistic regression model was trained and used to classify patients across five metastatic categories: highly likely and likely positive, highly likely and likely negative, and unknown. Using a real-world oncology database of patients with solid tumors with manually abstracted information as reference, we calculated sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). We validated the performance in a real-world data set, evaluating accuracy gains upon additional user review of tool's outputs after integration into clinic workflows.

RESULTS

In the training data set (N = 66,532), the model sensitivity and specificity (% [95% CI]) were 82.4 [81.9 to 83.0] and 95.5 [95.3 to 96.7], respectively; the PPV was 89.3 [88.8 to 90.0], and the NPV was 94.0 [93.8 to 94.2]. In the validation sample (n = 200 from five distinct care sites), after user review of model outputs, values increased to 97.1 [85.1 to 99.9] for sensitivity, 98.2 [94.8 to 99.6] for specificity, 91.9 [78.1 to 98.3] for PPV, and 99.4 [96.6 to 100.0] for NPV. The model assigned 163 of 200 patients to the highly likely categories. The error prevalence was 4% before and 2% after user review.

CONCLUSION

This tool infers metastatic status from unstructured EHR data with high accuracy and high confidence in more than 75% of cases, without requiring additional manual review. By enabling efficient characterization of metastatic status, this tool could mitigate a key barrier for patient ascertainment and clinical trial participation in community clinics.