Development and validation of a prognostic model predicting symptomatic hemorrhagic transformation in acute ischemic stroke at scale in the OHDSI network
| Autor: | Ruijun Chen, Erica A. Voss, Thomas Falconer, Gowtham A. Rao, Kristin Kostka, Yi Zhou, Ross D. Williams, Suranga N. Kasthurirathne, Rohit Vashisht, Seng Chan You, Qing Jiang, Margarita Fernandez-Chas, Andrew E. Williams, Peter R. Rijnbeek, Patrick B. Ryan, Jenna Reps, Stephen R. Pfohl, Mui Van Zandt, Nigam H. Shah, Henry Morgan Stewart, Qiong Wang, Yuhui Zou, Christian G. Reich |
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| Přispěvatelé: | Medical Informatics |
| Rok vydání: | 2020 |
| Předmět: |
Male
Epidemiology Infarction Electronic Medical Records Logistic regression Pathology and Laboratory Medicine Vascular Medicine Brain Ischemia Database and Informatics Methods 0302 clinical medicine Mathematical and Statistical Techniques Risk Factors Medicine and Health Sciences 030212 general & internal medicine education.field_of_study Multidisciplinary Cerebral infarction Statistics Middle Aged Prognosis Stroke Hemorrhagic Stroke Neurology Cohort Physical Sciences Medicine Female Cohort study Research Article medicine.medical_specialty Science Cerebrovascular Diseases Population Health Informatics Hemorrhage Research and Analysis Methods Risk Assessment 03 medical and health sciences Signs and Symptoms Diagnostic Medicine medicine Humans Statistical Methods education Cerebral Hemorrhage Retrospective Studies Ischemic Stroke Models Statistical business.industry Retrospective cohort study medicine.disease ROC Curve Medical Risk Factors Emergency medicine Observational study business 030217 neurology & neurosurgery Mathematics Follow-Up Studies Forecasting |
| Zdroj: | PLoS ONE PLoS One (print), 15. Public Library of Science PLoS ONE, Vol 15, Iss 1, p e0226718 (2020) |
| ISSN: | 1932-6203 |
| Popis: | Background and purposeHemorrhagic 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.MethodsWe 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.ResultsIn 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.ConclusionsA 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. |
| Databáze: | OpenAIRE |
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