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Motor Dual-Tasks for Gait Analysis and Evaluation in Post-Stroke Patients
Published on: March 11, 2021
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Automatic Matching Algorithms to Identify Eligible Participants for Stroke Trials: A Proof-of-Concept Study.
Pattarawut Charatpangoon1, Nishita Singh2, Brian H Buck3
1Departments of Biomedical Engineering, the Hotchkiss Brain Institute, University of Calgary, Calgary, Canada.
Summary
Automated clinical trial screening algorithms significantly improve patient identification and reduce screening time. This technology enhances trial efficiency and inclusivity for conditions like stroke.
Area of Science:
- Clinical informatics
- Medical imaging analysis
- Health services research
Background:
- Clinical trial recruitment faces significant challenges, with only 10% of eligible patients enrolling.
- Manual patient screening by frontline clinicians is time-consuming and prone to overlooking eligible individuals, particularly in time-sensitive conditions like stroke.
- Inefficient screening processes hinder trial progress and limit the generalizability of research findings.
Purpose of the Study:
- To develop and evaluate an automated matching algorithm for efficient and inclusive clinical trial participant screening.
- To assess the algorithm's performance in identifying eligible patients across multiple trials.
- To quantify the impact of the algorithm on screening time and resource utilization.
Main Methods:
- A matching algorithm was developed using imaging and clinical data from the AcT trial (NCT03889249).
- The algorithm employed rule-based logic to match patient variables with trial inclusion/exclusion criteria.
- The algorithm was applied to identify potential candidates for six specific trials (EASI-TOC, CATIS-ICAD, CONVINCE, TEMPO-2, ESCAPE-MEVO, ENDOLOW), with performance validated against manual reviews and enrollment data.
Main Results:
- The algorithm identified a greater number of potentially eligible candidates compared to actual enrollment numbers across the evaluated trials.
- The automated screening demonstrated over 90% sensitivity and specificity.
- Screening time was reduced by over 100-fold compared to traditional manual methods.
Conclusions:
- Automated matching algorithms offer a powerful solution for rapidly identifying eligible patients for clinical trials.
- This technology can significantly reduce the resources required for patient enrollment, improving trial efficiency.
- The developed algorithm is adaptable for application in diverse trials and medical conditions.
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