@article{RN398,
   author = {Reps, J. M. and Williams, R. D. and You, S. C. and Falconer, T. and Minty, E. and Callahan, A. and Ryan, P. B. and Park, R. W. and Lim, H. S. and Rijnbeek, P.},
   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},
   journal = {BMC Med Res Methodol},
   volume = {20},
   number = {1},
   pages = {102},
   note = {1471-2288 Reps, Jenna M Orcid: 0000-0002-2970-0778 Williams, Ross D You, Seng Chan Falconer, Thomas Minty, Evan Callahan, Alison Ryan, Patrick B Park, Rae Woong Lim, Hong-Seok Rijnbeek, Peter R01 LM011369/LM/NLM NIH HHS/United States HI16C0992/Health Promotion Administration, Ministry of Health and Welfare (TW)/International 806968/Innovative Medicines Initiative Joint Undertaking/International Journal Article Research Support, Non-U.S. Gov't England 2020/05/08 BMC Med Res Methodol. 2020 May 6;20(1):102. doi: 10.1186/s12874-020-00991-3.},
   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, CHADS(2), CHADS(2)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. RESULTS: The five existing models were able to be integrated into the OHDSI framework for patient-level prediction and they obtained mean c-statistics ranging between 0.57-0.63 across the 6 databases with sufficient data to predict stroke within 1 year of initial atrial fibrillation diagnosis for females with atrial fibrillation. This was comparable with existing validation studies. The validation network study was run across nine datasets within 60 days once the models were replicated. An R package for the study was published at https://github.com/OHDSI/StudyProtocolSandbox/tree/master/ExistingStrokeRiskExternalValidation. CONCLUSION: This study demonstrates the ability to scale up external validation of patient-level prediction models using a collaboration of researchers and a data standardization that enable models to be readily shared across data sites. External validation is necessary to understand the transportability or reproducibility of a prediction model, but without collaborative approaches it can take three or more years for a model to be validated by one independent researcher. 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. We recommend that researchers developing new prediction models use the OHDSI network to externally validate their models.},
   keywords = {*Atrial Fibrillation/diagnosis/epidemiology
Feasibility Studies
Female
Humans
Prognosis
Reproducibility of Results
*Stroke/diagnosis/epidemiology
Collaborative network
External validation
Patient-level prediction
Prognostic model
Transportability ,
ISSN = 1471-2288 ,
DOI = 10.1186/s12874-020-00991-3},
   year = {2020},
   type = {Journal Article}
}

{"_id":"QB8w9ibsYXisjRCv3","bibbaseid":"reps-williams-you-falconer-minty-callahan-ryan-park-etal-feasibilityandevaluationofalargescaleexternalvalidationapproachforpatientlevelpredictioninaninternationaldatanetworkvalidationofmodelspredictingstrokeinfemalepatientsnewlydiagnosedwithatrialfibrillation-2020","author_short":["Reps, J. M.","Williams, R. D.","You, S. C.","Falconer, T.","Minty, E.","Callahan, A.","Ryan, P. B.","Park, R. W.","Lim, H. S.","Rijnbeek, P."],"bibdata":{"bibtype":"article","type":"Journal Article","author":[{"propositions":[],"lastnames":["Reps"],"firstnames":["J.","M."],"suffixes":[]},{"propositions":[],"lastnames":["Williams"],"firstnames":["R.","D."],"suffixes":[]},{"propositions":[],"lastnames":["You"],"firstnames":["S.","C."],"suffixes":[]},{"propositions":[],"lastnames":["Falconer"],"firstnames":["T."],"suffixes":[]},{"propositions":[],"lastnames":["Minty"],"firstnames":["E."],"suffixes":[]},{"propositions":[],"lastnames":["Callahan"],"firstnames":["A."],"suffixes":[]},{"propositions":[],"lastnames":["Ryan"],"firstnames":["P.","B."],"suffixes":[]},{"propositions":[],"lastnames":["Park"],"firstnames":["R.","W."],"suffixes":[]},{"propositions":[],"lastnames":["Lim"],"firstnames":["H.","S."],"suffixes":[]},{"propositions":[],"lastnames":["Rijnbeek"],"firstnames":["P."],"suffixes":[]}],"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","journal":"BMC Med Res Methodol","volume":"20","number":"1","pages":"102","note":"1471-2288 Reps, Jenna M Orcid: 0000-0002-2970-0778 Williams, Ross D You, Seng Chan Falconer, Thomas Minty, Evan Callahan, Alison Ryan, Patrick B Park, Rae Woong Lim, Hong-Seok Rijnbeek, Peter R01 LM011369/LM/NLM NIH HHS/United States HI16C0992/Health Promotion Administration, Ministry of Health and Welfare (TW)/International 806968/Innovative Medicines Initiative Joint Undertaking/International Journal Article Research Support, Non-U.S. Gov't England 2020/05/08 BMC Med Res Methodol. 2020 May 6;20(1):102. doi: 10.1186/s12874-020-00991-3.","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, CHADS(2), CHADS(2)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. RESULTS: The five existing models were able to be integrated into the OHDSI framework for patient-level prediction and they obtained mean c-statistics ranging between 0.57-0.63 across the 6 databases with sufficient data to predict stroke within 1 year of initial atrial fibrillation diagnosis for females with atrial fibrillation. This was comparable with existing validation studies. The validation network study was run across nine datasets within 60 days once the models were replicated. An R package for the study was published at https://github.com/OHDSI/StudyProtocolSandbox/tree/master/ExistingStrokeRiskExternalValidation. CONCLUSION: This study demonstrates the ability to scale up external validation of patient-level prediction models using a collaboration of researchers and a data standardization that enable models to be readily shared across data sites. External validation is necessary to understand the transportability or reproducibility of a prediction model, but without collaborative approaches it can take three or more years for a model to be validated by one independent researcher. 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. We recommend that researchers developing new prediction models use the OHDSI network to externally validate their models.","keywords":"*Atrial Fibrillation/diagnosis/epidemiology Feasibility Studies Female Humans Prognosis Reproducibility of Results *Stroke/diagnosis/epidemiology Collaborative network External validation Patient-level prediction Prognostic model Transportability , ISSN = 1471-2288 , DOI = 10.1186/s12874-020-00991-3","year":"2020","bibtex":"@article{RN398,\r\n author = {Reps, J. M. and Williams, R. D. and You, S. C. and Falconer, T. and Minty, E. and Callahan, A. and Ryan, P. B. and Park, R. W. and Lim, H. S. and Rijnbeek, P.},\r\n 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},\r\n journal = {BMC Med Res Methodol},\r\n volume = {20},\r\n number = {1},\r\n pages = {102},\r\n note = {1471-2288 Reps, Jenna M Orcid: 0000-0002-2970-0778 Williams, Ross D You, Seng Chan Falconer, Thomas Minty, Evan Callahan, Alison Ryan, Patrick B Park, Rae Woong Lim, Hong-Seok Rijnbeek, Peter R01 LM011369/LM/NLM NIH HHS/United States HI16C0992/Health Promotion Administration, Ministry of Health and Welfare (TW)/International 806968/Innovative Medicines Initiative Joint Undertaking/International Journal Article Research Support, Non-U.S. Gov't England 2020/05/08 BMC Med Res Methodol. 2020 May 6;20(1):102. doi: 10.1186/s12874-020-00991-3.},\r\n 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, CHADS(2), CHADS(2)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. RESULTS: The five existing models were able to be integrated into the OHDSI framework for patient-level prediction and they obtained mean c-statistics ranging between 0.57-0.63 across the 6 databases with sufficient data to predict stroke within 1 year of initial atrial fibrillation diagnosis for females with atrial fibrillation. This was comparable with existing validation studies. The validation network study was run across nine datasets within 60 days once the models were replicated. An R package for the study was published at https://github.com/OHDSI/StudyProtocolSandbox/tree/master/ExistingStrokeRiskExternalValidation. CONCLUSION: This study demonstrates the ability to scale up external validation of patient-level prediction models using a collaboration of researchers and a data standardization that enable models to be readily shared across data sites. External validation is necessary to understand the transportability or reproducibility of a prediction model, but without collaborative approaches it can take three or more years for a model to be validated by one independent researcher. 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. 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