ReviewInternational journal of cardiology. Cardiovascular risk and prevention2026
Efficacy and comparative performance of machine learning models for stroke risk prediction in hypertensive patients: A systematic review and meta-analysis.
Review in International journal of cardiology. Cardiovascular risk and prevention, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prediction models for mortality in patients with sepsis: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- Machine learning and Regression-Based models for prediction of postoperative atrial fibrillation following coronary artery bypass grafting: A systematic review and meta-analysis.International journal of cardiology. Cardiovascular risk and prevention · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Stroke poses a significant health burden among hypertensive patients, where traditional risk models often lack precision. Machine learning (ML) has shown promise in enhancing prediction accuracy by integrating diverse data sources. Methods: Following PRISMA guidelines, we searched 5 databases from inception to September 2025. Eligible studies reported the performance of ML models in hypertensive cohorts. Data were pooled using random-effects models, with heterogeneity assessed via I Results: Ten studies (n = 13,299 stroke cases) were included. Pooled sensitivity was 0.88 (95 % CI: 0.80-0.93), specificity 0.88 (95 % CI: 0.77-0.94), positive likelihood ratio 7.1 (95 % CI: 3.4-15.1), negative likelihood ratio 0.14 (95 % CI: 0.08-0.26), and AUC-ROC 0.94 (95 % CI: 0.91-0.96), indicating good discriminative ability. Heterogeneity was high for both sensitivity (I Conclusion: ML models demonstrate good performance for stroke prediction in hypertensive patients. However, heterogeneity underscores the need for standardized approaches. This evidence, rated moderate by GRADE, supports ML integration in clinical practice for improved prevention.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.