American Cancer Society (ACS) clinical trial matching service (CTMS)

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 18501-18501
Author(s):  
T. Gansler ◽  
K. Sharpe ◽  
C. Demler ◽  
H. Eyre
Keyword(s):  
Lung Cancer ◽  
Clinical Trial ◽  
Call Center ◽  
Trial Participation ◽  
Cancer Type ◽  
Eligibility Criteria ◽  
Cancer Society ◽  
Lung Cancer Patients ◽  
Enrollment Rates ◽  
Low Prevalence

18501 Background: The low prevalence of clinical trial participation limits progress in clinical research and practice. ACS assists individuals in finding trials appropriate to their medical and personal situation. Methods: The ACS call center and website provide access to a comprehensive cancer clinical trial database and matching software platform licensed from EmergingMed. This report outlines characteristics and outcomes of constituents who met initial eligibility criteria and requested further information for at least one trial after entering data on their diagnosis and prior treatment. Results: Among 4525 CTMS constituents during 2004–5, 58% were patients, 40% were their relatives and 2% were friends. 62% entered data via the call center, the rest used www.cancer.org (although for quality assurance reasons, all must speak with a call center specialist before receiving trial site contact information). They requested protocols for a median of 6 trials (range 1 to 75); most often requesting information on trials for lung cancer (16.4%), breast cancer (11.4%), colorectal cancer (7.3%), prostate cancer (7.0%), and melanoma (5.2%). Likelihood of trial enrollment varied by cancer type (χ2, p < 0.0001) and was highest with kidney cancer (10.7%), melanoma (7.8%), multiple myeloma (4.4%), head & neck cancer (4.4%), and leukemia (3.8%). We studied several common cancer types in greater detail. For example, in analysis of data from 2004, non-small cell lung cancer patients requesting trial information had more advanced disease than typical patients in the National Cancer Database (χ2, p < 0.001). Enrollment rates for patients with stage I-IIIA and IIIB-IV were 0% and 3.7% (NS), respectively; rates for those with ECOG performance 0–1 and >1 were 4.8% and 0.0% (χ2, p < 0.05). Conclusions: The ACS CTMS can substantially augment participation in clinical trials among patients not recruited into trials by their oncologists. No significant financial relationships to disclose.

Improving hematology/oncology clinical research recruitment via universal prescreening: The VA Connecticut (VACT) Cancer Center experience.

2021 ◽  
Vol 39 (28_suppl) ◽  
pp. 79-79
Author(s):  
Jenny Jing Xiang ◽  
Alicia Roy ◽  
Christine Summers ◽  
Monica Delvy ◽  
Jessica Lee O'Donovan ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Clinical Trials ◽  
Clinical Research ◽  
Cancer Patients ◽  
Cancer Center ◽  
Cancer Type ◽  
Eligibility Criteria ◽  
Research Recruitment ◽  
Lung Cancer Patients ◽  
Study Enrollment

79 Background: Patient-trial matching is a critical step in clinical research recruitment that requires extensive review of clinical data and trial requirements. Prescreening, defined as identifying potentially eligible patients using select eligibility criteria, may streamline the process and increase study enrollment. We describe the real-world experience of implementing a standardized, universal clinical research prescreening protocol within a VA cancer center and its impact on research enrollment. Methods: An IRB approved prescreening protocol was implemented at the VACT Cancer Center in March 2017. All patients with a suspected or confirmed diagnosis of cancer are identified through tumor boards, oncology consults, and clinic lists. Research coordinators perform chart review and manually enter patient demographics, cancer type and stage, and treatment history into a REDCap (Research Electronic Data Capture) database. All clinical trials and their eligibility criteria are also entered into REDCap and updated regularly. REDCap generates real time lists of potential research studies for each patient based on his/her recorded data. The primary oncologist is alerted to a patient’s potential eligibility prior to upcoming clinic visits and thus can plan to discuss clinical research enrollment as appropriate. Results: From March 2017 to December 2020, a total of 2548 unique patients were prescreened into REDCAP. The mean age was 71.5 years, 97.5% were male, and 15.5% were African American. 32.57 % patients had genitourinary cancer, 17.15% had lung cancer, and 46.15% were undergoing malignancy workup. 1412 patients were potentially eligible after prescreening and 556 patients were ultimately enrolled in studies. The number of patients enrolled on therapeutic clinical trials increased after the implementation of the prescreening protocol (35 in 2017, 64 in 2018, 78 in 2019, and 55 in 2020 despite the COVID19 pandemic). Biorepository study enrollment increased from 8 in 2019 to 15 in 2020. The prescreening protocol also enabled 200 patients to be enrolled onto a lung nodule liquid biopsy study from 2017 to 2019. Our prescreening process captured 98.57% of lung cancer patients entered into the cancer registry during the same time period. Conclusions: Universal prescreening streamlined research recruitment operations and was associated with yearly increases in clinical research enrollment at a VA cancer center. Our protocol identified most new lung cancer patients, suggesting that, at least for this malignancy, potential study patients were not missed. The protocol was integral in our program becoming the top accruing VA site for NCI’s National Clinical Trial Network (NCTN) studies since 2019.

Algorithmic matching of genomic profiles to precision cancer medicine clinical trials at DFCI.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 6620-6620
Author(s):  
James Lindsay ◽  
Catherine Del Vecchio Fitz ◽  
Zachary Zwiesler ◽  
Priti Kumari ◽  
Khanh Tu Do ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Precision Medicine ◽  
Open Source ◽  
Patient Specific ◽  
Cancer Type ◽  
Eligibility Criteria ◽  
Enrollment Rates ◽  
And Gender ◽  
Computational Platform

6620 Background: Genomic profiling and access to precision medicine clinical trials are now standard at leading cancer institutes and many community practices. Interpreting patient-specific genomic information and tracking the complex criteria for precision medicine trials requires specialized computational tools, especially for multi-institutional basket studies such as NCI-MATCH and TAPUR. Methods: To address this challenge we have developed an open source computational platform for patient-specific clinical trial matching at Dana-Farber Cancer Institute (DFCI) called MatchMiner, which aides in both patient recruitment to precision medicine trials, as well as decision support for oncologists. Trial matches are computed based on genomic criteria, including mutations, CNAs, and SVs, as well as clinical and demographic information, including cancer type, age, and gender. A formal standard called clinical trial markup language (CTML) to encode complex clinical trial eligibility criteria has also been created. Results: MatchMiner is now available at DFCI. Currently 123 precision medicine clinical trials have been transformed into CTML and 13,000 patient records are available, with over 88% of current patients having at least 1 match (average 2.6). A total of 103 genes are specified as criteria for at least 1 trial. KRAS, TP53, PTEN, PIK3CA and BRAF are the genes driving the most number of matches. General usage statistics and trial enrollment rates are currently being monitored to determine the system effectiveness. As this is an open source initiative, the software is also now publically available at https://github.com/dfci/matchminer. Conclusions: We have developed an open source computational platform that enables patient-specific matching and recruitment to precision medicine clinical trials at DFCI. We are actively seeking collaborators and plan to make CTML a multi-institution standard for encoding complex clinical trial eligibility in a computable form.

scholarly journals Adolescent angst: enrollment on clinical trials

Hematology ◽  
2018 ◽  
Vol 2018 (1) ◽  
pp. 154-160 ◽  
Author(s):  
Theresa H. M. Keegan ◽  
Helen M. Parsons
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Community Setting ◽  
Treatment Setting ◽  
System Level ◽  
Trial Participation ◽  
Cancer Type ◽  
Enrollment Rates ◽  
Clinical Trial Enrollment ◽  
Barriers To Enrollment

Abstract Survival among adolescents and young adults (AYAs) ages 15 to 39 with cancer has not improved to the same extent as that of pediatric and older adult cancer patients, which is thought to relate, in part, to the lower participation of AYAs in clinical trials. Because significant efforts have been made to improve clinical trial enrollment for AYAs, we (1) present contemporary clinical trial enrollment rates by cancer type, sociodemographic characteristics, and treatment setting and (2) discuss provider-, patient-, and system-level barriers to clinical trial participation. Contemporary studies examining clinical trial enrollment among AYAs have continued to find low overall participation relative to pediatric populations, with most studies observing no significant improvements in enrollment over time. In addition to age and cancer type, enrollment varies by treatment setting, health insurance, and race/ethnicity. Access to available clinical trials may be increased by appropriate referral of AYAs to pediatric and adult specialty cancer centers with studies relevant to the AYA population because most AYAs are treated in the community setting. Even with similar access to trials, however, AYAs may be less likely to participate, and therefore, future efforts should focus on better understanding and addressing barriers to enrollment as well as improving education and outreach regarding clinical trials.

Analysis of potential disparities in lung cancer clinical trial participation among Medicare beneficiaries.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e18548-e18548
Author(s):  
Grace E. Mishkin ◽  
Luisa Franzini
Keyword(s):  
Lung Cancer ◽  
Clinical Trial ◽  
Active Treatment ◽  
Cancer Clinical Trial ◽  
Trial Participation ◽  
Medicare Beneficiaries ◽  
Clinical Trial Participation ◽  
Lung Cancer Patients ◽  
Race Ethnicity ◽  
Trial Participants

e18548 Background: Disparities in clinical trial participation can mean disparities in medical advances, and disproportionate participation is an ethical issue even if differential results are not expected. The disparate impact of Covid-19 highlighted the importance of representativeness in trials aiming to prevent or treat disease. Previous analyses suggest there are likely significant differences in cancer clinical trial participants by race, ethnicity, and age. However, these analyses generally compare trial demographics to broad population demographics. Methods: This analysis used the SEER-Medicare linked database with claims data from 2014-2016 to compare lung cancer clinical trial participants to similar lung cancer patients not participating in a treatment trial. We compared the race, ethnicity, sex, age, and number of comorbidities for Medicare beneficiaries with at least one claim for an active treatment for lung cancer to the demographics of Medicare beneficiaries with at least one claim coded with the National Clinical Trials (NCT) number for an active treatment trial in lung cancer. The relationship between clinical trial participation and demographic variables was assessed using chi-square tests for binary variables and t-tests for continuous variables and corrected for multiplicity. A logistic regression model was used to assess robustness of these findings. Clinical trial participants were hypothesized to be more likely to be White, non-Hispanic, and male, and have a lower mean age and fewer comorbidities than the comparable non-trial active treatment population. Results: We compared 1,624 lung cancer clinical trial patients to 34,077 active treatment lung cancer patients. Clinical trial participants were more likely than non-trial active treatment patients to be female (53.6% vs. 50.4%, p = 0.015) or Asian/Pacific Islander (13.0% vs. 5.2%, p < 0.001) and less likely to be Black (5.2% vs. 9.0%, p < 0.001) or White (76.5% vs. 81.4%, p < 0.001). Trial participants had a lower mean age (70.7 vs. 73.7, p < 0.001) and fewer comorbidities (3.0 vs. 4.6, p < 0.001). There was not a significant difference by Hispanic ethnicity (5.2% vs. 4.4%, p = 0.106). The regression analyses supported these findings. Conclusions: Most analyses of clinical trials enrollment do not have a direct comparison group. Because this study is directly comparing lung cancer trial participants and non-participants from the same Medicare beneficiary population, the results fill a gap in our understanding of disparities in cancer clinical trial participation. Trial participants in this analysis were more representative than hypothesized, although the results supported previous findings that Black patients and older patients are underrepresented in cancer trials. There was also lower participation by White patients. Underrepresentation of patients with comorbidities may be due to trial eligibility criteria.

Initial results of a new clinical trial matching service to increase patient participation

2009 ◽  
Vol 27 (15_suppl) ◽  
pp. e17504-e17504
Author(s):  
T. Gansler ◽  
R. Comis ◽  
K. Sharpe ◽  
L. Tis ◽  
M. Jin ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Call Center ◽  
Activity Level ◽  
Disease Stage ◽  
Trial Participation ◽  
Stage Disease ◽  
Enrollment Status ◽  
Enrollment Rates ◽  
Disease Site

e17504 Methods: Data from CTMS constituents and follow-up information describing enrollment status and barriers to trial participation are reviewed. Results: During 15 months of operation the CTMS provided information to 10,997 individuals; 7,521 (68.39%) used the website only, and 3,476 (31.61%) also contacted the ACS call center. Among 981 of the 3,476 (28.22% the basis of analyses below) who consented to and could be reached for follow-up and who answered the question on enrollment status, 119 (12.13%) enrolled in a CT. Trial phase was known for 74 enrollees (phase I: 17 [22.97%]; II: 36 [48.65%]; III: 21 [28.38%]; IV: 0 [0%]). Enrollment was negatively (p < 0.05) associated with poor ECOG functional status and black race, and was positively related to disease stage. Among the 757 individuals with available disease site and enrollment information, those with stomach cancer accounted for the most enrollments (25, 24.75% of all enrollments); followed by melanoma (12, 11.88%) and kidney, renal pelvis, bladder, ureter and urethra (also 12, 11.88%), and breast cancer (11, 10.89%). The highest enrollment rates (% enrollees among individuals with available follow-up) were for multiple myeloma/plasma cell disorders (4/14, 28.57%), melanoma (12/49, 24.49%), primary CNS malignancy (5/31, 16.13%), and soft tissue sarcoma (6/45, 13.33%). The following barriers were significantly associated with non-enrollment: ‘I cannot travel to clinical trial site,‘ ‘I cannot find a clinical trial using the modality or treatment I want,‘ ‘My physical activity level is too low,‘ and ‘I do not have measurable disease or am cancer-free.‘ Conclusions: 12% of CTMS participants with available follow-up data for enrollment status participated in a CT. Several determinants of CT participation were identified. Strategies for eliminating racial disparities, facilitating transportation, and increasing participation among patients with earlier stage disease and more common tumor types must be developed and implemented. No significant financial relationships to disclose.

Sign in / Sign up

Export Citation Format

Share Document