Feasibility and evaluation of a large-scale external validation approach for patient-level prediction in an international data network: validation of models predicting stroke in female patients newly diagnosed with atrial fibrillation.

ABSTRACT:

Background

To demonstrate how the Observational Healthcare Data Science and Informatics (OHDSI) collaborative network and standardization can be utilized to scale-up external validation of patient-level prediction models by enabling validation across a large number of heterogeneous observational healthcare datasets.

Methods

Five previously published prognostic models (ATRIA, CHADS₂, CHADS₂VASC, Q-Stroke and Framingham) that predict future risk of stroke in patients with atrial fibrillation were replicated using the OHDSI frameworks. A network study was run that enabled the five models to be externally validated across nine observational healthcare datasets spanning three countries and five independent sites.

Results

The five existing models were

SUBMITTER: Reps JM 

PROVIDER: S-EPMC7201646 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature