Bickell Nina A, May Benjamin, Havrylchuk Ihor, John Jimmy, Lin Sylvia, Tao Ariana, Yagnik Radhi, Tatonetti Nicholas P
Institute for Health Equity Research, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York City, NY 10029, United States.
Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY 10032, United States.
JAMIA Open. 2024 Nov 18;7(4):ooae131. doi: 10.1093/jamiaopen/ooae131. eCollection 2024 Dec.
To explore implementing regular expressions (RegEx) to streamline patient identification and classification for matching to clinical trials.
To prepare approaches needed to match patients to relevant cancer clinical trials, we combined NCI's Clinical Trials Search API to extract high-level eligibility criteria, including cancer type, stage, receptor/biomarker status, with similar data of patients with appointments in the upcoming week. Using RegEx, we prospectively identified all patients with breast, liver, or lung cancers at treatment decision points at 2 Cancer Centers' and 2 community hospitals', classified their cancer type, stage, and receptor/biomarker status. We evaluated accuracy using RegEx against manual reviews.
Algorithm accuracy to identify patients at treatment decision points revealed 92% True Negative and 53% True Positive rate. Staging accuracy varied from 67% to 95%, and receptor/biomarker status accuracy from 76% to 86%.
Using RegEx significantly reduced the number of patients requiring manual review, demonstrating a reduction in manual labor and potential biases, which can improve efficiency and inclusivity of clinical trial enrollment processes, especially in resource limited or data sensitive environments.
NCT05146297.
探讨实施正则表达式（RegEx）以简化患者识别和分类，以便与临床试验进行匹配。
为准备将患者与相关癌症临床试验进行匹配所需的方法，我们结合了美国国立癌症研究所（NCI）的临床试验搜索应用程序编程接口（API），以提取高级资格标准，包括癌症类型、分期、受体/生物标志物状态，并与未来一周预约就诊患者的类似数据进行对比。我们使用正则表达式，前瞻性地识别了2家癌症中心和2家社区医院在治疗决策点时患有乳腺癌、肝癌或肺癌的所有患者，对他们的癌症类型、分期和受体/生物标志物状态进行了分类。我们使用正则表达式与人工审核评估准确性。
在治疗决策点识别患者的算法准确性显示真阴性率为92%，真阳性率为53%。分期准确性从67%到95%不等，受体/生物标志物状态准确性从76%到86%不等。
使用正则表达式显著减少了需要人工审核的患者数量，表明减少了人工劳动和潜在偏差，这可以提高临床试验入组流程的效率和包容性，尤其是在资源有限或数据敏感的环境中。
NCT05146297。
