Use of a clinical trial screening tool to enhance patient accrual

Cancer ◽  
2021 ◽  
Vol 127 (10) ◽  
pp. 1630-1637
Author(s):  
Diane C. St. Germain ◽  
Worta McCaskill‐Stevens
Keyword(s):  
Clinical Trial ◽  
Screening Tool ◽  
Patient Accrual

Implementation of a prospective screening tool for breast cancer clinical trial eligibility at an NCI designated cancer center.

2011 ◽  
Vol 29 (15_suppl) ◽  
pp. 6052-6052 ◽  
Author(s):  
K. H. Lethert ◽  
S. K. Cheng ◽  
D. J. Nauman ◽  
D. M. Dilts ◽  
A. Sandler ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Clinical Trial ◽  
Screening Tool ◽  
Cancer Clinical Trial ◽  
Cancer Center

scholarly journals Prevalence of potentially inappropriate prescribing in a subpopulation of older European clinical trial participants: a cross-sectional study

BMJ Open ◽  
2018 ◽  
Vol 8 (3) ◽  
pp. e019003 ◽  
Author(s):  
David O Riordan ◽  
Carole Elodie Aubert ◽  
Kieran A Walsh ◽  
Anette Van Dorland ◽  
Nicolas Rodondi ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Screening Tool ◽  
Inappropriate Prescribing ◽  
Community Dwelling ◽  
System Level ◽  
Potentially Inappropriate Prescribing ◽  
Duration Of Action ◽  
Significant Difference ◽  
The Impact ◽  
Trial Participants

ObjectivesTo estimate and compare the prevalence and type of potentially inappropriate prescribing (PIP) and potential prescribing omissions (PPOs) among community-dwelling older adults (≥65 years) enrolled to a clinical trial in three European countries.DesignA secondary analysis of the Thyroid Hormone Replacement for Subclinical Hypothyroidism Trial dataset.ParticipantsA subset of 48/80 PIP and 22/34 PPOs indicators from the Screening Tool of Older Persons Prescriptions/Screening Tool to Alert doctors to Right Treatment (STOPP/START) V2 criteria were applied to prescribed medication data for 532/737 trial participants in Ireland, Switzerland and the Netherlands.ResultsThe overall prevalence of PIP was lower in the Irish participants (8.7%) compared with the Swiss (16.7%) and Dutch (12.5%) participants (P=0.15) and was not statistically significant. The overall prevalence of PPOs was approximately one-quarter in the Swiss (25.3%) and Dutch (24%) participants and lower in the Irish (14%) participants (P=0.04) and the difference was statistically significant. The hypnotic Z-drugs were the most frequent PIP in Irish participants, (3.5%, n=4), while it was non-steroidal anti-inflammatory drug and oral anticoagulant combination, sulfonylureas with a long duration of action, and benzodiazepines (all 4.3%, n=7) in Swiss, and benzodiazepines (7.1%, n=18) in Dutch participants. The most frequent PPOs in Irish participants were vitamin D and calcium in osteoporosis (3.5%, n=4). In the Swiss and Dutch participants, they were bone antiresorptive/anabolic therapy in osteoporosis (9.9%, n=16, 8.6%, n=22) respectively. The odds of any PIP after adjusting for age, sex, multimorbidity and polypharmacy were (adjusted OR (aOR)) 3.04 (95% CI 1.33 to 6.95, P<0.01) for Swiss participants and aOR 1.74 (95% CI 0.79 to 3.85, P=0.17) for Dutch participants compared with Irish participants. The odds of any PPOs were aOR 2.48 (95% CI 1.27 to 4.85, P<0.01) for Swiss participants and aOR 2.10 (95% CI 1.11 to 3.96, P=0.02) for Dutch participants compared with Irish participants.ConclusionsThis study has estimated and compared the prevalence and type of PIP and PPOs among this cohort of community-dwelling older people. It demonstrated a significant difference in the prevalence of PPOs between the three populations. Further research is urgently needed into the impact of system level factors as this has important implications for patient safety, healthcare provision and economic costs.

scholarly journals Targeted screening for Alzheimer’s disease clinical trials using data-driven disease progression models

2021 ◽  
Author(s):  
Neil P. Oxtoby ◽  
Cameron Shand ◽  
David M. Cash ◽  
Daniel C. Alexander ◽  
Frederik Barkhof ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Trial ◽  
Cognitive Impairment ◽  
Disease Progression ◽  
Treatment Effect ◽  
Screening Tool ◽  
Data Driven ◽  
Full Cohort ◽  
Computational Screening

ABSTRACTHeterogeneity in Alzheimer’s disease progression contributes to the ongoing failure to demonstrate efficacy of putative disease-modifying therapeutics that have been trialled over the past two decades. Any treatment effect present in a subgroup of trial participants (responders) can be diluted by non-responders who ideally should have been screened out of the trial. How to identify (screen-in) the most likely potential responders is an important question that is still without an answer. Here we pilot a computational screening tool that leverages recent advances in data-driven disease progression modelling to improve stratification. This aims to increase the sensitivity to treatment effect by screening out non-responders, which will ultimately reduce the size, duration, and cost of a clinical trial. We demonstrate the concept of such a computational screening tool by retrospectively analysing a completed double-blind clinical trial of donepezil in people with amnestic mild cognitive impairment (clinicaltrials.gov: NCT00000173), identifying a data-driven subgroup having more severe cognitive impairment who showed clearer treatment response than observed for the full cohort.

Use of a clinical trials screening tool in the NCI Community Oncology Research Program (NCORP) to enhance accrual and promote disparities research.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e14587-e14587
Author(s):  
Diane C. St. Germain ◽  
Troy Budd ◽  
Worta J. McCaskill-Stevens
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Screening Tool ◽  
Social Issues ◽  
Demographic Data ◽  
Specific Information ◽  
Eligibility Criteria ◽  
Oncology Patient ◽  
Oncology Research ◽  
Clinical Trial Accrual

e14587 Background: Despite the identification of multiple patient, clinician, and health systems barriers, accrual to a new generation of cancer clinical trials continues to present challenges. It may be that barrier data is not sufficiently granular. A clinical trial screening tool was developed for use in NCORP to collect trial- and site-specific information as well as broadened demographic data to determine factors that may impact accrual. Methods: The tool was developed with stakeholder input and the NCI Oncology Patient Enrollment Network was used for data input and analyses. Results: From February 2016 to December 2019, 14,340 entries were made in the screening tool. Eighty-two percent of participants consented to participate. Participants screened were female (77%), married (64%) and Caucasian (85%). Fourteen percent of the participants were racial minorities (1% not reported) and 5% were Hispanic or Latino. The mean age was 60 (range 1-95). Thirty-six percent were employed, ≥ 32 hours per week followed by 35% retirees. Income did not vary significantly ( < $25,000(16%), $25,000 - $50,00 (20%), $51,000 - $10,000 (26%), and > $100,000 (19%), and 19% of participants refused to provide income data. Four percent of the participants were uninsured at diagnosis. Seventy-two percent (8,501) of participants screened enrolled in a clinical trial. Of those not enrolled, 49% were ineligible and 48% were eligible but declined to participate. The most common reasons for ineligibility included concurrent disease, abnormal lab or other tests, and patient could not comply with eligibility criteria. The most common reasons eligible participants declined to participate were perception that toxicities were too great and social issues (child care, transportation). Further analysis of the data will include correlation of race/ethnicity, age, income and co-morbidities with enrollment status. Conclusions: The majority of participants approached agreed to participate in the screening tool protocol. Approximately half of the participants were eligible for a trial but declined to participate. These issues will be addressed within the network to enhance accrual. The data collected will also provide opportunities for investigators within the network to develop research questions focused on disparities and clinical trial accrual.

Prospective Evaluation of Cancer Clinical Trial Accrual Patterns: Identifying Potential Barriers to Enrollment

2001 ◽  
Vol 19 (6) ◽  
pp. 1728-1733 ◽  
Author(s):  
Primo N. Lara ◽  
Roger Higdon ◽  
Nelson Lim ◽  
Karen Kwan ◽  
Michael Tanaka ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Private Insurance ◽  
Cancer Clinical Trial ◽  
Cancer Center ◽  
Trial Participation ◽  
Cancer Clinical Trials ◽  
Eligibility Criteria ◽  
Clinical Trial Accrual ◽  
Patient Accrual

PURPOSE: Well-conducted cancer clinical trials are essential for improving patient outcomes. Unfortunately, only 3% of new cancer patients participate in clinical trials. Barriers to patient accrual in cancer clinical trials must be identified and overcome to increase patient participation. MATERIALS AND METHODS: We prospectively tracked factors that potentially affected patient accrual into cancer clinical trials at the University of California Davis Cancer Center. Oncologists seeing new outpatients were asked to complete questionnaires regarding patient characteristics and the physician’s decision-making on patient eligibility, protocol availability, and patient opinions on participation. Statistical analysis was performed to correlate these parameters with subsequent protocol accrual. RESULTS: There were 276 assessable patients. At the initial visits, physicians did not consider clinical trials in 38% (105/276) of patients principally because of a perception of protocol unavailability and poor performance status. Physicians considered 62% (171/276) of patients for participation in clinical trials. Of these, only 53% (91/171) had an appropriate protocol available for site and stage of disease. Seventy-six of 90 patients (84%) with available protocols met eligibility criteria for a particular study. Only 39 of 76 patients (51%) agreed to participate in cancer clinical trials, for an overall accrual rate of 14% (39/276). The remainder (37/76, 49%) declined trial participation despite meeting eligibility criteria. The most common reasons were a desire for other treatment (34%), distance from the cancer center (13%), patient refusal to disclose reason (11%), and insurance denial (8%). Patients with private insurance were less likely to enroll in clinical trials compared to those with government-funded insurance (OR, 0.34; P = .03; 95% CI, 0.13 to 0.9). CONCLUSION: Barriers to cancer clinical trial accrual can be prospectively identified and addressed in the development and conduct of future studies, which may potentially lead to more robust clinical trials enrollment. Investigation of patient perceptions regarding the clinical trials process and the role of third party–payers is warranted.

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