Clinical events classification (CEC) in clinical trials: Report on the current landscape and future directions - proceedings from the CEC Summit 2018.
Am Heart J
; 246: 93-104, 2022 04.
Article
em En
| MEDLINE
| ID: mdl-34973948
IMPORTANCE: Clinical events adjudication is pivotal for generating consistent and comparable evidence in clinical trials. The methodology of event adjudication is evolving, but research is needed to develop best practices and spur innovation. OBSERVATIONS: A meeting of stakeholders from regulatory agencies, academic and contract research organizations, pharmaceutical and device companies, and clinical trialists convened in Chicago, IL, for Clinical Events Classification (CEC) Summit 2018 to discuss key topics and future directions. Formal studies are lacking on strategies to optimize CEC conduct, improve efficiency, minimize cost, and generally increase the speed and accuracy of the event adjudication process. Major challenges to CEC discussed included ensuring rigorous quality of the process, identifying safety events, standardizing event definitions, using uniform strategies for missing information, facilitating interactions between CEC members and other trial leadership, and determining the CEC's role in pragmatic trials or trials using real-world data. Consensus recommendations from the meeting include the following: (1) ensure an adequate adjudication infrastructure; (2) use negatively adjudicated events to identify important safety events reported only outside the scope of the primary endpoint; (3) conduct further research in the use of artificial intelligence and digital/mobile technologies to streamline adjudication processes; and (4) emphasize the importance of standardizing event definitions and quality metrics of CEC programs. CONCLUSIONS AND RELEVANCE: As novel strategies for clinical trials emerge to generate evidence for regulatory approval and to guide clinical practice, a greater understanding of the role of the CEC process will be critical to optimize trial conduct and increase confidence in the data generated.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Inteligência Artificial
Tipo de estudo:
Guideline
Limite:
Humans
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article