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

Autor:	Marino ML; Comprehensive Cancer Center (CCC Munich LMU), LMU University Hospital, Munich, Germany., Kazmaier L; Comprehensive Cancer Center (CCC Munich LMU), LMU University Hospital, Munich, Germany., Krendelsberger A; Comprehensive Cancer Center (CCC Munich LMU), LMU University Hospital, Munich, Germany., Müller S; Comprehensive Cancer Center (CCC Munich LMU), LMU University Hospital, Munich, Germany; Comprehensive Cancer Center, Technical University of Munich Hospital Rechts der Isar, Munich, Germany., Kesting S; Preventive Pediatrics, Department of Sport and Health Sciences, Technical University of Munich, Munich, Germany; Department of Pediatrics and Children's Cancer Research Centre, TUM School of Medicine, Kinderklinik München Schwabing, Technical University of Munich, Munich, Germany., Fey T; Comprehensive Cancer Center (CCC Munich LMU), LMU University Hospital, Munich, Germany., Nasseh D; Comprehensive Cancer Center (CCC Munich LMU), LMU University Hospital, Munich, Germany.
Jazyk:	angličtina
Zdroj:	Health informatics journal [Health Informatics J] 2024 Jan-Mar; Vol. 30 (1), pp. 14604582241235632.
DOI:	10.1177/14604582241235632
Abstrakt:	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. Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Databáze:	MEDLINE
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