ArticleiScience2023
Predicting stroke and myocardial infarction risk in Takayasu arteritis with automated machine learning models.
Article in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 10 citations in OpenAlex.
- Development and validation of an in-hospital cardiogenic shock prediction model for AMI patients based on machine learning.BMC cardiovascular disorders · 2026Article
- Internal carotid artery involvement and stroke risk in Takayasu arteritis: a case-control study.Rheumatology international · 2026Article
- Applications of Artificial Intelligence in Vasculitides: A Systematic Review.ACR open rheumatology · 2025Review
- The mortality and acute complications of large vessel vasculitis patients hospitalized with COVID-19 in the US: a nationwide inpatient sample analysis (2020).Rheumatology international · 2025Article
- Predictive models for secondary epilepsy in patients with acute ischemic stroke within one year.eLife · 2024Article
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Few models exist for predicting severe ischemic complications (SIC) in patients with Takayasu arteritis (TA). We conducted a retrospective analysis of 703 patients with TA from January 2010 to December 2019 to establish an SIC prediction model for TA. SIC was defined as ischemic stroke and myocardial infarction. SIC was present in 97 of 703 (13.8%) patients with TA. Common iliac artery, coronary artery, internal carotid artery, subclavian artery, vertebral artery, renal artery involvement, chest pain, hyperlipidemia, absent pulse, higher BMI, vascular occlusion, asymmetric blood pressure in both upper limbs, visual disturbance, and older age were selected as predictive risk factors. Considering both discrimination and calibration performance, the Weighted Subspace Random Forest model was the most optimal model, boasting an area under the curve of 0.773 (95% confidence interval [0.652, 0.894]) in the validation cohort. Effective models for predicting SIC in TA may help clinicians identify high-risk patients and make targeted interventions.
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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.