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

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...

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Published in:	PloS one Vol. 15; no. 1; p. e0226718
Main Authors:	Wang, Qiong, Reps, Jenna M, Kostka, Kristin Feeney, Ryan, Patrick B, Zou, Yuhui, Voss, Erica A, Rijnbeek, Peter R, Chen, RuiJun, Rao, Gowtham A, Morgan Stewart, Henry, Williams, Andrew E, Williams, Ross D, Van Zandt, Mui, Falconer, Thomas, Fernandez-Chas, Margarita, Vashisht, Rohit, Pfohl, Stephen R, Shah, Nigam H, Kasthurirathne, Suranga N, You, Seng Chan, Jiang, Qing, Reich, Christian, Zhou, Yi
Format:	Journal Article
Language:	English
Published:	United States Public Library of Science 07-01-2020 Public Library of Science (PLoS)
Subjects:	Analysis Biomedical engineering Brain Ischemia - complications Cerebral Hemorrhage - diagnosis Cerebral Hemorrhage - etiology Cerebral infarction Clinical decision making Datasets Decision making Electronic health records Electronic medical records Electronic records Engineering schools Female Follow-Up Studies Health aspects Health informatics Health risks Hemorrhage Humans Infarction Information systems Ischemia Male Medical practice software Medical prognosis Medical records Medical research Medicine Medicine and Health Sciences Middle Aged Models, Statistical Observational studies Patients Physical Sciences Prediction models Prognosis R&D Regression models Research & development Research and Analysis Methods Research funding Retrospective Studies Risk analysis Risk Assessment - methods Risk Factors ROC Curve Stroke Stroke - complications Transformation Transformations North Carolina New York Netherlands United States > US China Indianapolis Indiana California New Jersey
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Summary:	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.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 ObjectType-Undefined-3 Competing Interests: 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.
ISSN:	1932-6203 1932-6203
DOI:	10.1371/journal.pone.0226718