A Framework for Systematic Assessment of Clinical Trial Population Representativeness Using Electronic Health Records Data.
Appl Clin Inform
; 12(4): 816-825, 2021 08.
Article
em En
| MEDLINE
| ID: mdl-34496418
ABSTRACT
BACKGROUND:
Clinical trials are the gold standard for generating robust medical evidence, but clinical trial results often raise generalizability concerns, which can be attributed to the lack of population representativeness. The electronic health records (EHRs) data are useful for estimating the population representativeness of clinical trial study population.OBJECTIVES:
This research aims to estimate the population representativeness of clinical trials systematically using EHR data during the early design stage.METHODS:
We present an end-to-end analytical framework for transforming free-text clinical trial eligibility criteria into executable database queries conformant with the Observational Medical Outcomes Partnership Common Data Model and for systematically quantifying the population representativeness for each clinical trial.RESULTS:
We calculated the population representativeness of 782 novel coronavirus disease 2019 (COVID-19) trials and 3,827 type 2 diabetes mellitus (T2DM) trials in the United States respectively using this framework. With the use of overly restrictive eligibility criteria, 85.7% of the COVID-19 trials and 30.1% of T2DM trials had poor population representativeness.CONCLUSION:
This research demonstrates the potential of using the EHR data to assess the clinical trials population representativeness, providing data-driven metrics to inform the selection and optimization of eligibility criteria.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Diabetes Mellitus Tipo 2
/
COVID-19
Tipo de estudo:
Prognostic_studies
Limite:
Humans
País/Região como assunto:
America do norte
Idioma:
En
Revista:
Appl Clin Inform
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
Estados Unidos