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


Autoři: Qiong Wang aff001;  Jenna M. Reps aff003;  Kristin Feeney Kostka aff003;  Patrick B. Ryan aff003;  Yuhui Zou aff007;  Erica A. Voss aff003;  Peter R. Rijnbeek aff003;  RuiJun Chen aff003;  Gowtham A. Rao aff003;  Henry Morgan Stewart aff003;  Andrew E. Williams aff003;  Ross D. Williams aff003;  Mui Van Zandt aff003;  Thomas Falconer aff003;  Margarita Fernandez-Chas aff003;  Rohit Vashisht aff003;  Stephen R. Pfohl aff003;  Nigam H. Shah aff003;  Suranga N. Kasthurirathne aff003;  Seng Chan You aff003;  Qing Jiang aff001;  Christian Reich aff003;  Yi Zhou aff015
Působiště autorů: Biomedical Engineering School, Sun Yat-Sen University, Guangzhou, China aff001;  The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China aff002;  Observational Health Data Sciences and Informatics, New York, New York, United States of America aff003;  Janssen Research and Development, Raritan, New Jersey, United States of America aff004;  IQVIA, Durham, North Carolina, United States of America aff005;  Department of Biomedical Informatics, Columbia University, New York, New York, United States of America aff006;  Department of Neurosurgery, General Hospital of Southern Theatre Command, Guangzhou, China aff007;  Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands aff008;  Department of Medicine, Weill Cornell Medical College, New York, New York, United States of America aff009;  Tufts Medical Center, Institute for Clinical Research and Health Policy Studies, Boston, Massachusetts, United States of America aff010;  Stanford Center for Biomedical Informatics Research, Stanford, California, United States of America aff011;  Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, Indiana, United States of America aff012;  Department of Epidemiology, Indiana University Richard M. Fairbanks School of Public Health, Indianapolis, Indiana, United States of America aff013;  Department of Biomedical informatics, Ajou University School of Medicine, Suwon, Korea aff014;  Department of Biomedical Engineering, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, China aff015
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
prolekare.web.journal.doi_sk: 10.1371/journal.pone.0226718

Souhrn

Background and purpose

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.

Methods

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.

Results

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.

Conclusions

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.

Klíčová slova:

Electronic medical records – Forecasting – Health informatics – Hemorrhage – Hemorrhagic stroke – Infarction – Ischemic stroke – Medical risk factors


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