ArticleEuropean heart journal. Quality of care & clinical outcomes2022
Improving dynamic stroke risk prediction in non-anticoagulated patients with and without atrial fibrillation: comparing common clinical risk scores and machine learning algorithms.
Article in European heart journal. Quality of care & clinical outcomes, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it, 58 citations in OpenAlex.
- Evaluation of machine learning models for stroke risk prediction in atrial fibrillation: a systematic review and meta-analysis.BMC cardiovascular disorders · 2026Pooled it
- Comparative predictive performance of three machine learning algorithms for acute radiation enteritis risk among patients with cervical cancer undergoing radiotherapy: A prospective cohort study.Asia-Pacific journal of oncology nursing · 2026Article
- Machine-learning-based cardiovascular mortality prediction using a cumulative PMScientific reports · 2026Observational
- Secondary Prevention of AFAIS: Deploying Traditional Regression, Machine Learning, and Deep Learning Models to Validate and Update CHA2DS2-VASc for 90-Day Recurrence.Journal of clinical medicine · 2025Article
- Prediction of major outcomes in patients with malignant hypertension using machine learning: A report from the West Birmingham malignant hypertension registry.European journal of clinical investigation · 2025Article
- Cost-effectiveness of a clinical decision support system for atrial fibrillation: an RCT-based modelling study.European heart journal. Digital health · 2025Article
- Interpretable Independent Recurrent Networks for Forecasting Stroke in Atrial Fibrillation.JACC. Asia · 2025Article
- Machine-learning model for predicting left atrial thrombus in patients with paroxysmal atrial fibrillation.BMC cardiovascular disorders · 2025Article
- Stroke prediction in elderly patients with atrial fibrillation using machine learning combined clinical and left atrial appendage imaging phenotypic features.BMC cardiovascular disorders · 2025Article
- Explainable artificial intelligence for stroke risk stratification in atrial fibrillation.European heart journal. Digital health · 2025Review
- Machine learning and deep learning predictive models for prognosis in patients with atrial fibrillation: a systematic review and meta-analysis.BMJ digital health & AI · 2025Article
- Fair prediction of 2-year stroke risk in patients with atrial fibrillation.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Computational Medicine: What Electrophysiologists Should Know to Stay Ahead of the Curve.Current cardiology reports · 2024Review
- Evaluating Machine Learning Models for Stroke Prognosis and Prediction in Atrial Fibrillation Patients: A Comprehensive Meta-Analysis.Diagnostics (Basel, Switzerland) · 2024Article
- Great debate: device-detected subclinical atrial fibrillation should be treated like clinical atrial fibrillation.European heart journal · 2024Article
- The Application of Artificial Intelligence in Atrial Fibrillation Patients: From Detection to Treatment.Reviews in cardiovascular medicine · 2024Review
- Prediction of atrial fibrillation and stroke using machine learning models in UK Biobank.Heliyon · 2024Article
- Atrial fibrillation: stroke prevention.The Lancet regional health. Europe · 2024Review
- Current and Future Use of Artificial Intelligence in Electrocardiography.Journal of cardiovascular development and disease · 2023Review
- Transient ischemic attack events and incident cardiovascular and non-cardiovascular complications: Observations from a large diversified multimorbid cohort.European stroke journal · 2023Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimsDiversified cardiovascular/non-cardiovascular multi-morbid risk and efficient machine learning algorithms may facilitate improvements in stroke risk prediction, especially in newly diagnosed non-anticoagulated atrial fibrillation (AF) patients where initial decision-making on stroke prevention is needed. Therefore the aims of this article are to study common clinical risk assessment for stroke risk prediction in AF/non-AF cohorts together with cardiovascular/ non-cardiovascular multi-morbid conditions; to improve stroke risk prediction using machine learning approaches; and to compare the improved clinical prediction rules for multi-morbid conditions using machine learning algorithms. METHODS AND
resultsWe used cohort data from two health plans with 6 457 412 males/females contributing 14,188,679 person-years of data. The model inputs consisted of a diversified list of comorbidities/demographic/ temporal exposure variables, with the outcome capturing stroke event incidences. Machine learning algorithms used two parametric and two nonparametric techniques. The best prediction model was derived on the basis of non-linear formulations using machine learning criteria, with the highest c-index was obtained for logistic regression [0.892; 95% confidence interval (CI) 0.886-0.898] with consistency on external validation (0.891; 95% CI 0.882-0.9). These were significantly higher than those based on the conventional stroke risk scores (CHADS2: 0.7488, 95% CI 0.746-0.7516; CHA2DS2-VASc: 0.7801, 95% CI 0.7772-0.7831) and multi-morbid index (0.8508, 95% CI 0.8483-0.8532). The machine learning algorithm had good internal and external calibration and net benefit values.
conclusionIn this large cohort of newly diagnosed non-anticoagulated AF/non-AF patients, large improvements in stroke risk prediction can be shown with cardiovascular/non-cardiovascular multi-morbid index and a machine learning approach accounting for dynamic changes in risk factors.
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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.