Leveraging electronic health record data for clinical trial planning by assessing eligibility criteria’s impact on patient count and safety

Rogers, James R.; Pavisic, Jovana; Ta, Casey; Liu, Cong; Soroush, Ali; Cheung, Ying Kuen; Hripcsak, George; Weng, Chunhua

doi:10.1016/j.jbi.2022.104032

Cited by 3 publications

(1 citation statement)

References 58 publications

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“…Further research should enhance the models to handle more complex eligibility criteria such as comorbidities or patient safety [43] using semantic AI to improve model performance in real-world clinical settings, automate the generation of NIH-required Inclusion Enrollment Report for investigators to design more achievable plans which embeds NIH principles of DEI, and evaluate the model in future trial recruitment for further improvement.…”

Section: Discussionmentioning

confidence: 99%

Planning and Monitoring Equitable Clinical Trial Enrollment Using Goal Programming

Miao

Das

Bennett

2023

IEEE J. Biomed. Health Inform.

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Randomized clinical trial (RCT) studies are the gold standard for scientific evidence on treatment benefits to patients. RCT outcomes may not be generalizable to clinical practice if the trial population is not representative of the patients for which the treatment is intended. Specifically, enrollment plans may not adequately include groups of patients with protected attributes, such as gender, race, or ethnicity. Inequities in RCTs are a major concern for funding agencies such as the National Institutes of Health (NIH) and for policy makers. We address this challenge by proposing a goal-programming approach, explicitly integrating measurable enrollment goals, to design equitable enrollment plans for RCTs. We evaluate our model in both single and multisite settings using the enrollment criteria and study population from the Systolic Blood Pressure Intervention Trial (SPRINT) study. Our model can successfully generate equitable enrollment plans that satisfy multiple goals such as sample representativeness and minimum total financial cost. Our model can detect deviations from a target plan during the enrollment process and update the plan to reduce deviations in the remaining process. Finally, through appropriate site selection in the planning stage, the model can demonstrate the possibility of enrolling a nationally representative study population if geographic constraints exist in multisite recruitment (e.g., clinical centers in a particular region). Our model can be used to prospectively produce and retrospectively evaluate how equitable enrollment plans are based on subjects' protected attributes, and it allows researchers to provide justifications on validity of scientific analysis and evaluation of subgroup disparities.

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Section: Discussionmentioning

confidence: 99%

Planning and Monitoring Equitable Clinical Trial Enrollment Using Goal Programming

Miao

Das

Bennett

2023

IEEE J. Biomed. Health Inform.

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Broadening Eligibility Criteria and Diversity among Patients for Cancer Clinical Trials

Kaur,

Frahm,

et al. 2024

NEJM Evidence

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Evaluating the robustness of an AI pathfinder application on eligibility criteria in multiple myeloma trials using real-world data and historical trials

Jreich,

Zhang,

Meng

et al. 2024

J. Comp. Eff. Res.

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Background: Eligibility criteria are pivotal in achieving clinical trial success, enabling targeted patient enrollment while ensuring the trial safety. However, overly restrictive criteria hinder enrollment and study result generalizability. Broadening eligibility criteria enhances the trial inclusivity, diversity and enrollment pace. Liu et al. proposed an AI pathfinder method leveraging real-world data to broaden criteria without compromising efficacy and safety outcomes, demonstrating promise in non-small cell lung cancer trials. Aim: To assess the robustness of the methodology, considering diverse qualities of real-world data and to promote its application. Materials/Methods: We revised the AI pathfinder method, applied it to relapsed and refractory multiple myeloma trials and compared it using two real-world data sources. We modified the assessment and considered a bootstrap confidence interval of the AI pathfinder to enhance the decision robustness. Results & conclusion: Our findings confirmed the AI pathfinder's potential in identifying certain eligibility criteria, in other words, prior complications and laboratory tests for relaxation or removal. However, a robust quantitative assessment, accounting for trial variability and real-world data quality, is crucial for confident decision-making and prioritizing safety alongside efficacy.

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Leveraging electronic health record data for clinical trial planning by assessing eligibility criteria’s impact on patient count and safety

Cited by 3 publications

References 58 publications

Planning and Monitoring Equitable Clinical Trial Enrollment Using Goal Programming

Planning and Monitoring Equitable Clinical Trial Enrollment Using Goal Programming

Broadening Eligibility Criteria and Diversity among Patients for Cancer Clinical Trials

Evaluating the robustness of an AI pathfinder application on eligibility criteria in multiple myeloma trials using real-world data and historical trials

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