ArticleJournal of the neurological sciences2021
Predicting short and long-term mortality after acute ischemic stroke using EHR.
Article in Journal of the neurological sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 2 of them syntheses that pooled it.
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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
28 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Predictive Value of Machine Learning for Poststroke Mortality Risk: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Global performance of machine learning models to predict all-cause mortality: systematic review and meta-analysis.Scientific reports · 2025Pooled it
- Optimizing stroke mortality prediction: a comparative study of sampling, cost-sensitive learning, and threshold adjustment.BMC medical informatics and decision making · 2026Article
- Comparative Analysis of Regularised Logistic Regression and Random Forest Models for In-hospital or 30-day Post-Discharge Mortality Prediction within the Hospital Standardised Mortality Ratio Framework.Healthcare informatics research · 2026Article
- Risk prediction of postoperative acute kidney injury in patients undergoing off-pump coronary artery bypass grafting based on machine learning.The Journal of international medical research · 2026Article
- A Heterogeneous Graph Neural Network Framework for Multi-Horizon Stroke Mortality Prediction.medRxiv : the preprint server for health sciences · 2026Article
- Association of Anemia with Mortality and Functional Prognosis in Patients with First-Ever Ischemic Stroke: A Retrospective Cohort Study in Tunisia.Vascular health and risk management · 2026Article
- Treatment-Specific Risk Scales for Identifying High-Risk Patients With Poor Prognosis in Acute Ischemic Stroke: A Cohort Study From the National Neurological Medical Center of China.CNS neuroscience & therapeutics · 2025Article
- The link between red blood cell distribution width and 3-month prognosis in acute ischemic stroke patients: a secondary analysis of a cohort study.BMC neurology · 2025Article
- Machine learning-based prediction of 90-day prognosis and in-hospital mortality in hemorrhagic stroke patients.Scientific reports · 2025Article
- Evaluating Machine Learning Models for Stroke Prognosis and Prediction in Atrial Fibrillation Patients: A Comprehensive Meta-Analysis.Diagnostics (Basel, Switzerland) · 2024Article
- Development and Validation of a Predictive Score for Three-Year Mortality in Acute Ischemic Stroke Patients.Medicina (Kaunas, Lithuania) · 2024Article
- Utilizing machine learning to facilitate the early diagnosis of posterior circulation stroke.BMC neurology · 2024Article
- Article
- Development and internal validation of machine learning-based models and external validation of existing risk scores for outcome prediction in patients with ischaemic stroke.European heart journal. Digital health · 2024Article
- A comprehensive review for artificial intelligence on neuroimaging in rehabilitation of ischemic stroke.Frontiers in neurology · 2024Review
- Machine learning-based prediction of one-year mortality in ischemic stroke patients.Oxford open neuroscience · 2024Article
- Predicting 90-Day Prognosis in Ischemic Stroke Patients Post Thrombolysis Using Machine Learning.Journal of personalized medicine · 2023Article
- An integrated pipeline for prediction of Clostridioides difficile infection.Scientific reports · 2023Article
- Thoughts and Considerations of Studies on Smart Devices for Cerebrovascular Disease Detection and Progression Monitoring.Mayo Clinic proceedings. Digital health · 2023Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
objectiveDespite improvements in treatment, stroke remains a leading cause of mortality and long-term disability. In this study, we leveraged administrative data to build predictive models of short- and long-term post-stroke all-cause-mortality.
methodsThe study was conducted and reported according to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guideline. We used patient-level data from electronic health records, three algorithms, and six prediction windows to develop models for post-stroke mortality.
resultsWe included 7144 patients from which 5347 had survived their ischemic stroke after two years. The proportion of mortality was between 8%(605/7144) within 1-month, to 25%(1797/7144) for the 2-years window. The three most common comorbidities were hypertension, dyslipidemia, and diabetes. The best Area Under the ROC curve(AUROC) was reached with the Random Forest model at 0.82 for the 1-month prediction window. The negative predictive value (NPV) was highest for the shorter prediction windows - 0.91 for the 1-month - and the best positive predictive value (PPV) was reached for the 6-months prediction window at 0.92. Age, hemoglobin levels, and body mass index were the top associated factors. Laboratory variables had higher importance when compared to past medical history and comorbidities. Hypercoagulation state, smoking, and end-stage renal disease were more strongly associated with long-term mortality.
conclusionAll the selected algorithms could be trained to predict the short and long-term mortality after stroke. The factors associated with mortality differed depending on the prediction window. Our classifier highlighted the importance of controlling risk factors, as indicated by laboratory measures.
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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.