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PLoS One. 2020 Jan 7;15(1):e0226718. doi: 10.1371/journal.pone.0226718. eCollection 2020.

Development and validation of a prognostic model predicting symptomatic hemorrhagic transformation in acute ischemic stroke at scale in the OHDSI network.

Author information

Biomedical Engineering School, Sun Yat-Sen University, Guangzhou, China.
The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Observational Health Data Sciences and Informatics, New York, New York, United States of America.
Janssen Research and Development, Raritan, New Jersey, United States of America.
IQVIA, Durham, North Carolina, United States of America.
Department of Biomedical Informatics, Columbia University, New York, New York, United States of America.
Department of Neurosurgery, General Hospital of Southern Theatre Command, Guangzhou, China.
Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands.
Department of Medicine, Weill Cornell Medical College, New York, New York, United States of America.
Tufts Medical Center, Institute for Clinical Research and Health Policy Studies, Boston, Massachusetts, United States of America.
Stanford Center for Biomedical Informatics Research, Stanford, California, United States of America.
Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, Indiana, United States of America.
Department of Epidemiology, Indiana University Richard M. Fairbanks School of Public Health, Indianapolis, Indiana, United States of America.
Department of Biomedical informatics, Ajou University School of Medicine, Suwon, Korea.
Department of Biomedical Engineering, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, China.



Hemorrhagic transformation (HT) after cerebral infarction is a complex and multifactorial phenomenon in the acute stage of ischemic stroke, and often results in a poor prognosis. Thus, identifying risk factors and making an early prediction of HT in acute cerebral infarction contributes not only to the selections of therapeutic regimen but also, more importantly, to the improvement of prognosis of acute cerebral infarction. The purpose of this study was to develop and validate a model to predict a patient's risk of HT within 30 days of initial ischemic stroke.


We utilized a retrospective multicenter observational cohort study design to develop a Lasso Logistic Regression prediction model with a large, US Electronic Health Record dataset which structured to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). To examine clinical transportability, the model was externally validated across 10 additional real-world healthcare datasets include EHR records for patients from America, Europe and Asia.


In the database the model was developed, the target population cohort contained 621,178 patients with ischemic stroke, of which 5,624 patients had HT within 30 days following initial ischemic stroke. 612 risk predictors, including the distance a patient travels in an ambulance to get to care for a HT, were identified. An area under the receiver operating characteristic curve (AUC) of 0.75 was achieved in the internal validation of the risk model. External validation was performed across 10 databases totaling 5,515,508 patients with ischemic stroke, of which 86,401 patients had HT within 30 days following initial ischemic stroke. The mean external AUC was 0.71 and ranged between 0.60-0.78.


A HT prognostic predict model was developed with Lasso Logistic Regression based on routinely collected EMR data. This model can identify patients who have a higher risk of HT than the population average with an AUC of 0.78. It shows the OMOP CDM is an appropriate data standard for EMR secondary use in clinical multicenter research for prognostic prediction model development and validation. In the future, combining this model with clinical information systems will assist clinicians to make the right therapy decision for patients with acute ischemic stroke.

Conflict of interest statement

Dr. Reps, Dr. Ryan, Ms. Voss and Dr. Rao are employees of Janssen Research and Development. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

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