Objective To demonstrate how the Observational Healthcare Data Science and Informatics (OHDSI) collaborative network and standardization can be utilized to externally validate patient-level prediction models at scale. Materials & 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 and a network study was run that enabled the five

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... models to be externally validated across nine datasets spanning three countries and five independent sites. Results The five existing models were able to be integrated into the OHDSI framework for patient-level prediction and their performances in predicting stroke within 1 year of initial atrial fibrillation diagnosis for females were comparable with existing studies. The validation network study took 60 days once the models were replicated and an R package for the study was published to collaborators at https://github.com/OHDSI/StudyProtocolSandbox/tree/master/ExistingStrokeRiskExternalVa lidation. Discussion This study demonstrates the ability to scale up external validation of patientlevel prediction models using a collaboration of researchers and data standardization that enable models to be readily shared across data sites. External validation is necessary to understand the transportability and reproducibility of prediction models, but without collaborative approaches it can take three or more years to be validated by one independent researcher. Conclusion In this paper we show it is possible to both scale-up and speed-up external validation by showing how validation can be done across multiple databases in less than 2 months.