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How can current oncological datasets be adjusted to support the automated patient recruitment in clinical trials?
Maria-Luisa Marino1, Lara Kazmaier1, Antonia Krendelsberger1
1Comprehensive Cancer Center (CCC Munich LMU), LMU University Hospital, Munich, Germany.
Objectives:
This study aims to identify necessary adjustments required in existing oncological datasets to effectively support automated patient recruitment.
Methods:
We extracted and categorized the inclusion and exclusion criteria from 115 oncological trials registered on ClinicalTrials.gov in 2022. These criteria were then compared with the content of the oBDS (Oncological Base Dataset version 3.0), Germany's legally mandated oncological data standard.
Results:
The analysis revealed that 42.9% of generalized inclusion and exclusion criteria are typically present as data fields in the oBDS. On average, 54.6% of all criteria per trial were covered. Notably, certain criteria such as comorbidities, pregnancy status, and laboratory values frequently appeared in trial protocols but were absent in the oBDS.
Conclusion:
The omission of criteria, notably comorbidities, within the oBDS restricts its functionality to support trial recruitment. Addressing this limitation would enhance its overall effectiveness. Furthermore, the implications of these findings extend beyond Germany, suggesting potential relevance and applicability to oncological datasets globally.
Insights
Oncological datasets need adjustments for automated patient recruitment. Key criteria like comorbidities are missing, limiting trial enrollment effectiveness globally.
Area of Science:
- Oncology
- Medical Informatics
- Clinical Trial Management
Background:
- Automated patient recruitment is crucial for efficient clinical trials.
- Existing oncological datasets may not fully support automated recruitment processes.
- The Oncological Base Dataset (oBDS) is Germany's standard for oncological data.
Purpose of the Study:
- To identify necessary adjustments in oncological datasets for effective automated patient recruitment.
- To assess the compatibility of clinical trial inclusion/exclusion criteria with the oBDS.
- To highlight data gaps that hinder trial recruitment.
Main Methods:
- Extracted and categorized inclusion/exclusion criteria from 115 oncological trials (ClinicalTrials.gov, 2022).
- Compared trial criteria against Germany's oBDS version 3.0.
- Analyzed data field presence and coverage of trial criteria within the oBDS.
Main Results:
- Only 42.9% of generalized criteria are typically present in the oBDS.
- An average of 54.6% of all trial-specific criteria were covered by the oBDS.
- Comorbidities, pregnancy status, and lab values were frequently missing from the oBDS despite being common trial criteria.
Conclusions:
- Omission of critical criteria (e.g., comorbidities) in the oBDS limits its utility for patient recruitment.
- Enhancing the oBDS with missing data fields would improve its effectiveness.
- Findings have global implications for oncological dataset development and trial recruitment strategies.
