Observational studyPloS one2020
Development and validation of a prognostic model predicting symptomatic hemorrhagic transformation in acute ischemic stroke at scale in the OHDSI network.
Observational study in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
23 citing papers in PubMed, 2 syntheses or guidelines pooled it, 35 citations in OpenAlex.
- Secondary use of health records for prediction, detection, and treatment planning in the clinical decision support system: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Traditional and machine learning models for predicting haemorrhagic transformation in ischaemic stroke: a systematic review and meta-analysis.Systematic reviews · 2025Pooled it
- DrugUtilisation: An R Package to Support Drug Utilisation Research Using the OMOP Common Data Model.Pharmacoepidemiology and drug safety · 2026Article
- Evaluating predictive models for hemorrhagic transformation post-mechanical thrombectomy in acute ischemic stroke.Frontiers in neurology · 2025Review
- Towards global model generalizability: independent cross-site feature evaluation for patient-level risk prediction models using the OHDSI network.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- OHDSI Standardized Vocabularies-a large-scale centralized reference ontology for international data harmonization.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Cross Biobank Comparison of Phenomic Profiles.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- A weakly supervised deep learning model integrating noncontrasted computed tomography images and clinical factors facilitates haemorrhagic transformation prediction after intravenous thrombolysis in acute ischaemic stroke patients.Biomedical engineering online · 2023Article
- Development and validation of common data model-based fracture prediction model using machine learning algorithm.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2023Article
- Modern Learning from Big Data in Critical Care: Primum Non Nocere.Neurocritical care · 2022Article
- MRI whole-lesion texture analysis on ADC maps for the prognostic assessment of ischemic stroke.BMC medical imaging · 2022Article
- Applying Machine Learning in Distributed Data Networks for Pharmacoepidemiologic and Pharmacovigilance Studies: Opportunities, Challenges, and Considerations.Drug safety · 2022Review
- Patient-Level Fall Risk Prediction Using the Observational Medical Outcomes Partnership's Common Data Model: Pilot Feasibility Study.JMIR medical informatics · 2022Article
- Article
- Data Consult Service: Can we use observational data to address immediate clinical needs?Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Prediction of Hemorrhagic Transformation after Ischemic Stroke Using Machine Learning.Journal of personalized medicine · 2021Article
- Drawing Reproducible Conclusions from Observational Clinical Data with OHDSI.Yearbook of medical informatics · 2021Article
- Observational
- Real-World Use of Colonoscopy in an Older Population: A Nationwide Standard Cohort Study Using a Common Data Model.Digestive diseases and sciences · 2021Observational
- Prediction of 90-day mortality after surgery for colorectal cancer using standardized nationwide quality-assurance data.BJS open · 2021Article
Corrections and comments
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Authors and funding
23 authors at 11 institutions in 4 countries.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.