How can current oncological datasets be adjusted to support the automated patient recruitment in clinical trials?

Marino, Maria-Luisa ; Kazmaier, Lara ; Krendelsberger, Antonia ; Müller, Silvia; Kesting, Sabine ; Fey, Theres und Nasseh, Daniel (2024): How can current oncological datasets be adjusted to support the automated patient recruitment in clinical trials? In: Health Informatics Journal, Bd. 30 [Forthcoming]

Abstract

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.

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Medizin > Klinikum der LMU München
• Themengebiete: 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit
• ISSN: 1460-4582
• Bemerkung: First published online March 16, 2024
• Sprache: Englisch