CC BY-NC-ND 4.0 · Methods Inf Med 2021; 60(01/02): 009-020
DOI: 10.1055/s-0041-1724107
Original Article

Analysis of Not Structurable Oncological Study Eligibility Criteria for Improved Patient-Trial Matching

Julia Dieter*
1  Deparment of Medical Informatics for Translational Oncology, German Cancer Research Center, Heidelberg, Germany
,
Friederike Dominick*
1  Deparment of Medical Informatics for Translational Oncology, German Cancer Research Center, Heidelberg, Germany
,
Alexander Knurr
1  Deparment of Medical Informatics for Translational Oncology, German Cancer Research Center, Heidelberg, Germany
,
Janko Ahlbrandt
1  Deparment of Medical Informatics for Translational Oncology, German Cancer Research Center, Heidelberg, Germany
,
Frank Ückert
1  Deparment of Medical Informatics for Translational Oncology, German Cancer Research Center, Heidelberg, Germany
› Author Affiliations

Abstract

Background Higher enrolment rates of cancer patients into clinical trials are necessary to increase cancer survival. As a prerequisite, an improved semiautomated matching of patient characteristics with clinical trial eligibility criteria is needed. This is based on the computer interpretability, i.e., structurability of eligibility criteria texts. To increase structurability, the common content, phrasing, and structuring problems of oncological eligibility criteria need to be better understood.

Objectives We aimed to identify oncological eligibility criteria that were not possible to be structured by our manual approach and categorize them by the underlying structuring problem. Our results shall contribute to improved criteria phrasing in the future as a prerequisite for increased structurability.

Methods The inclusion and exclusion criteria of 159 oncological studies from the Clinical Trial Information System of the National Center for Tumor Diseases Heidelberg were manually structured and grouped into content-related subcategories. Criteria identified as not structurable were analyzed further and manually categorized by the underlying structuring problem.

Results The structuring of criteria resulted in 4,742 smallest meaningful components (SMCs) distributed across seven main categories (Diagnosis, Therapy, Laboratory, Study, Findings, Demographics, and Lifestyle, Others). A proportion of 645 SMCs (13.60%) was not possible to be structured due to content- and structure-related issues. Of these, a subset of 415 SMCs (64.34%) was considered not remediable, as supplementary medical knowledge would have been needed or the linkage among the sentence components was too complex. The main category “Diagnosis and Study” contained these two subcategories to the largest parts and thus were the least structurable. In the inclusion criteria, reasons for lacking structurability varied, while missing supplementary medical knowledge was the largest factor within the exclusion criteria.

Conclusion Our results suggest that further improvement of eligibility criterion phrasing only marginally contributes to increased structurability. Instead, physician-based confirmation of the matching results and the exclusion of factors harming the patient or biasing the study is needed.

* Equally contributing authors.




Publication History

Received: 19 August 2020

Accepted: 08 December 2020

Publication Date:
22 April 2021 (online)

© 2021. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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