Implementation of a rule-based algorithm to find patients eligible for cancer clinical trials
Objective To explore implementing regular expressions (RegEx) to streamline patient identification and classification for matching to clinical trials. Materials and Methods To prepare approaches needed to match patients to relevant cancer clinical trials, we combined NCI’s Clinical Trials Search API...
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| Vydané v: | JAMIA open Ročník 7; číslo 4; s. ooae131 |
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| Médium: | Journal Article |
| Jazyk: | English |
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United States
Oxford University Press
01.12.2024
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| Abstract | Objective
To explore implementing regular expressions (RegEx) to streamline patient identification and classification for matching to clinical trials.
Materials and Methods
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.
Results
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%.
Discussion and Conclusion
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.
Trial Registration
NCT05146297
Lay Summary
Advancing cancer care requires a robust clinical trial infrastructure. Current approaches to identify patients potentially eligible for clinical trials are notoriously inefficient, requiring laborious manual review. We developed a systematic approach using a relatively simple Regular Expressions’ coding algorithm to identify cancer patients at treatment decision points when trials would be most relevant and classify them in preparation for matching clinical trials to patients. We achieved high algorithm accuracy identifying patients who were not at treatment decision points and moderate to high accuracy identifying stage and receptor/biomarker status. Regular Expressions can significantly reduce manual case review and improve the efficiency and inclusivity of clinical trial enrollment processes, especially in resource-limited or data-sensitive environments. |
|---|---|
| AbstractList | Objective To explore implementing regular expressions (RegEx) to streamline patient identification and classification for matching to clinical trials. Materials and Methods 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. Results 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%. Discussion and Conclusion 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. Trial Registration NCT05146297 To explore implementing regular expressions (RegEx) to streamline patient identification and classification for matching to clinical trials.ObjectiveTo 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.Materials and MethodsTo 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%.ResultsAlgorithm 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.Discussion and ConclusionUsing 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.Trial RegistrationNCT05146297. Objective To explore implementing regular expressions (RegEx) to streamline patient identification and classification for matching to clinical trials. Materials and Methods 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. Results 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%. Discussion and Conclusion 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. Trial Registration NCT05146297 Lay Summary Advancing cancer care requires a robust clinical trial infrastructure. Current approaches to identify patients potentially eligible for clinical trials are notoriously inefficient, requiring laborious manual review. We developed a systematic approach using a relatively simple Regular Expressions’ coding algorithm to identify cancer patients at treatment decision points when trials would be most relevant and classify them in preparation for matching clinical trials to patients. We achieved high algorithm accuracy identifying patients who were not at treatment decision points and moderate to high accuracy identifying stage and receptor/biomarker status. Regular Expressions can significantly reduce manual case review and improve the efficiency and inclusivity of clinical trial enrollment processes, especially in resource-limited or data-sensitive environments. Objective: To explore implementing regular expressions (RegEx) to streamline patient identification and classification for matching to clinical trials. Materials and Methods: 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. Results: 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%. Discussion and Conclusion: 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. Trial Registration: NCT05146297 Lay Summary Advancing cancer care requires a robust clinical trial infrastructure. Current approaches to identify patients potentially eligible for clinical trials are notoriously inefficient, requiring laborious manual review. We developed a systematic approach using a relatively simple Regular Expressions' coding algorithm to identify cancer patients at treatment decision points when trials would be most relevant and classify them in preparation for matching clinical trials to patients. We achieved high algorithm accuracy identifying patients who were not at treatment decision points and moderate to high accuracy identifying stage and receptor/biomarker status. Regular Expressions can significantly reduce manual case review and improve the efficiency and inclusivity of clinical trial enrollment processes, especially in resource-limited or data- sensitive environments. Key words: cancer clinical trials; racial disparities; regular expressions; algorithms. Trial Registration: NCT05146297 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. |
| Audience | Academic |
| Author | May, Benjamin Bickell, Nina A John, Jimmy Lin, Sylvia Havrylchuk, Ihor Tao, Ariana Yagnik, Radhi Tatonetti, Nicholas P |
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| References_xml | – volume: 4 start-page: 680 year: 2020 ident: 2024111811184225700_ooae131-B10 article-title: Natural language processing to ascertain cancer outcomes from medical oncologist notes publication-title: JCO Clin Cancer Inform doi: 10.1200/CCI.20.00020 – volume: 177 start-page: 484 year: 2024 ident: 2024111811184225700_ooae131-B15 article-title: The impact of health care algorithms on racial and ethnic disparities: a systematic review publication-title: Ann Intern Med doi: 10.7326/M23-296 – year: 2018 ident: 2024111811184225700_ooae131-B1 – volume: 14 start-page: e1 year: 2018 ident: 2024111811184225700_ooae131-B4 article-title: Representation of minorities and women in oncology clinical trials: review of the past 14 years publication-title: J Oncol Pract doi: 10.1200/JOP.2017.025288 – volume: 40 start-page: 2163 year: 2022 ident: 2024111811184225700_ooae131-B8 article-title: Increasing racial and ethnic diversity in cancer clinical trials: an American Society of Clinical Oncology and Association of Community Cancer Centers Joint Research Statement publication-title: J Clin Oncol doi: 10.1200/JCO.22.00754 – volume: 5 start-page: 622 year: 2021 ident: 2024111811184225700_ooae131-B6 article-title: Clinical inflection point detection on the basis of EHR data to identify clinical trial-ready patients with cancer publication-title: JCO Clin Cancer Inform doi: 10.1200/CCI.20.00184 – volume: 8 start-page: 29270 year: 2020 ident: 2024111811184225700_ooae131-B13 article-title: CREGEX: a biomedical text classifier based on automatically generated regular expressions publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2972205 – volume: 111 start-page: 245 year: 2019 ident: 2024111811184225700_ooae131-B3 article-title: Systematic review and meta-analysis of the magnitude of structural, clinical, and physician and patient barriers to cancer clinical trial participation publication-title: J Natl Cancer Inst doi: 10.1093/jnci/djy221 – volume: 21 start-page: 850 year: 2014 ident: 2024111811184225700_ooae131-B11 article-title: Research and applications: learning regular expressions for clinical text classification publication-title: J Am Med Inform Assoc doi: 10.1136/amiajnl-2013-002411 – volume: 7 start-page: e2300009 year: 2023 ident: 2024111811184225700_ooae131-B14 article-title: Automated matching of patients to clinical trials: a patient-centric natural language processing approach for pediatric leukemia publication-title: JCO Clin Cancer Inform doi: 10.1200/cci.23.00009 – volume: 19 start-page: 1728 year: 2001 ident: 2024111811184225700_ooae131-B2 article-title: Prospective evaluation of cancer clinical trial accrual patterns: identifying potential barriers to enrollment publication-title: J Clin Oncol doi: 10.1200/JCO.2001.19.6.1728 – volume: 6 start-page: e2322515 year: 2023 ident: 2024111811184225700_ooae131-B5 article-title: Racial and ethnic inequities in US oncology clinical trial participation from 2017 to 2022 publication-title: JAMA Netw Open doi: 10.1001/jamanetworkopen.2023.22515 – volume: 8 start-page: e2300507 year: 2024 ident: 2024111811184225700_ooae131-B9 article-title: Identifying oncology clinical trial candidates using artificial intelligence predictions of treatment change: a pilot implementation study publication-title: JCO Precis Oncol doi: 10.1200/PO.23.00507 – volume: 24 start-page: 781 year: 2017 ident: 2024111811184225700_ooae131-B12 article-title: Automated classification of eligibility criteria in clinical trials to facilitate patient-trial matching for specific patient populations publication-title: J Am Med Inform Assoc doi: 10.1093/jamia/ocw176 – volume: 29 start-page: 197 year: 2021 ident: 2024111811184225700_ooae131-B7 article-title: A systematic review on natural language processing systems for eligibility prescreening in clinical research publication-title: J Am Med Inform Assoc doi: 10.1093/jamia/ocab228 |
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To explore implementing regular expressions (RegEx) to streamline patient identification and classification for matching to clinical trials.... To explore implementing regular expressions (RegEx) to streamline patient identification and classification for matching to clinical trials. To prepare... Objective: To explore implementing regular expressions (RegEx) to streamline patient identification and classification for matching to clinical trials.... Trial Registration: NCT05146297 Objective To explore implementing regular expressions (RegEx) to streamline patient identification and classification for matching to clinical trials.... To explore implementing regular expressions (RegEx) to streamline patient identification and classification for matching to clinical trials.ObjectiveTo explore... |
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| SubjectTerms | Accuracy Algorithms Biomarkers Brief Communications Cancer Cancer patients Care and treatment Clinical trials Computational linguistics Hospitals Language processing Liver Liver cancer Lung cancer Medical colleges Natural language interfaces United States |
| Title | Implementation of a rule-based algorithm to find patients eligible for cancer clinical trials |
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