{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Reps JM"],"funding":["Health Promotion Administration, Ministry of Health and Welfare","NLM NIH HHS","Health Promotion Administration, Ministry of Health and Welfare (TW)","Innovative Medicines Initiative Joint Undertaking"],"pagination":["102"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC7201646"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["20(1)"],"pubmed_abstract":["<h4>Background</h4>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.<h4>Methods</h4>Five previously published prognostic models (ATRIA, CHADS<sub>2</sub>, CHADS<sub>2</sub>VASC, 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.<h4>Results</h4>The five existing models were "],"journal":["BMC medical research methodology"],"pubmed_title":["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."],"pmcid":["PMC7201646"],"funding_grant_id":["806968","R01 LM011369","HI16C0992"],"pubmed_authors":["You SC","Falconer T","Callahan A","Lim HS","Reps JM","Park RW","Ryan PB","Williams RD","Minty E","Rijnbeek P"],"additional_accession":[]},"is_claimable":false,"name":"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.","description":"<h4>Background</h4>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.<h4>Methods</h4>Five previously published prognostic models (ATRIA, CHADS<sub>2</sub>, CHADS<sub>2</sub>VASC, 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.<h4>Results</h4>The five existing models were ","dates":{"release":"2020-01-01T00:00:00Z","publication":"2020 May","modification":"2026-05-06T21:42:16.037Z","creation":"2020-05-22T19:39:54Z"},"accession":"S-EPMC7201646","cross_references":{"pubmed":["32375693"],"doi":["10.1186/s12874-020-00991-3"]}}