scholarly journals Erratum to “Use of a clinical trial screening tool to enhance accrual”

Cancer ◽  
2021 ◽  
Keyword(s):  
Clinical Trial ◽  
Screening Tool

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.

Development of a drug-drug interaction screening tool for oncology clinical trial enrollment.

2018 ◽  
Vol 36 (30_suppl) ◽  
pp. 315-315
Author(s):  
Lauren A Marcath ◽  
Taylor D Coe ◽  
Bruce G. Redman ◽  
Daniel Louis Hertz
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Patient Safety ◽  
Drug Interaction ◽  
Screening Tool ◽  
Clinical Trial Data ◽  
Trial Data ◽  
Screening Tools ◽  
Cancer Center ◽  
Clinical Trial Enrollment

315 Background: Screening drug-drug interactions (DDI) for subjects enrolling in oncology clinical trials is critical to ensuring patient safety and the validity of clinical trial data. We previously reported that DDI screening is not uniformly conducted when screening patients for enrollment into SWOG clinical trials and found that at the University of Michigan Rogel Cancer Center up to 24.2% of subjects enrolled in National Clinical Trial Network (NCTN) trials had a DDI. Screening tools aid in DDI reduction in clinical practice, but none have been created for clinical trial enrollment. Our objective was to develop a clinical trial specific DDI screening tool based on features requested by the end-users of the tool at U-M. Methods: Semi-structured and informal interviews were conducted with all data managers who enroll patients into NCTN clinical trials at the U-M cancer center. Data managers were asked about their current workflow and desired features of a DDI screening tool. Responses were combined and reviewed for feasibility. Desired features were conveyed to PEPID, LLC (Phoenix, AZ) for tool development. Results: Four data managers were interviewed. Protocol-guided screening was a key workflow feature, which was completed by gathering DDI information primarily from the exclusion criteria and drug information sections of each respective protocol, Google, CredibleMeds, and the Indiana University P450 Drug Interaction Table. Consequently, a critical feature was the display of drug characteristics with wording that aligned with that in the protocol including transporter and CYP450 substrates, inhibitors, or inducers and QT prolongation potential. Additional desirable features included separate entry of study and concomitant drugs, filtering to display only DDI with study drugs, and PDF export of results. PEPID developed a prototype tool including these desired attributes for a prospective implementation pilot study. Conclusions: A first generation clinical trial specific DDI screening tool was developed based on end-user feedback. We are designing a prospective study to determine whether implementation of this tool can reduce DDI, enhance patient safety, and ensure validity of clinical trial data.

Sign in / Sign up

Export Citation Format

Share Document