ArticleEuropean heart journal2022
Critical appraisal of artificial intelligence-based prediction models for cardiovascular disease.
Article in European heart journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06847100 (Development of Artificial Intelligence Models to Predict Intrahospital Atrial Fibrillation and Long-term Coronary Event Recurrence in High-risk Patients), which is not on this map. Cited by 67 papers, 6 of them syntheses 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.
Development of Artificial Intelligence Models to Predict Intrahospital Atrial Fibrillation and Long-term Coronary Event Recurrence in High-risk Patients: PerCard Study
Who cites it
67 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- Future promise, current clinical ambiguity: a systematic review of machine learning algorithm outputs predicting risk of cardiovascular disease.Open heart · 2026Pooled it
- Prediction models for self-harm and suicide: a systematic review and critical appraisal.BMC medicine · 2025Pooled it
- Prognostic models for patients suffering a heart failure with a preserved ejection fraction: a systematic review.ESC heart failure · 2024Pooled it
- Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review.BMC medicine · 2024Pooled it
- Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools.Anesthesiology · 2024Pooled it
- Systematic evaluation of machine learning models for postoperative surgical site infection prediction.PloS one · 2024Pooled it
- Development and validation of a prediction model for moderate-to-severe radiation dermatitis in patients with nasopharyngeal carcinoma: A machine learning study.Asia-Pacific journal of oncology nursing · 2026Article
- Cardiovascular Diseases: Exploring Epidemiology, Pathophysiology, Risk Factors, and Medicinal Plant-Based Therapies: A Narrative Review.Health science reports · 2026Article
- Predicting mitral valve repair durability using artificial intelligence: promises, pitfalls, and the path to clinical practice.Journal of thoracic disease · 2026Article
- Blood-based obesity biomarkers and their relevance for disease risk.Nature reviews. Endocrinology · 2026Review
- Establishing T1 and ECV mapping based nomogram differentiate acute ischemic and nonischemic myocardial injury: a proof-of-concept study.BMC medical imaging · 2026Article
- Comparative study of 11 machine learning algorithms for predicting recurrence risk after atrial fibrillation catheter ablation based on a real-world cohort: a retrospective study.BMC cardiovascular disorders · 2026Article
- A pipeline for developing deep learning prognostic prediction models in cardiac magnetic resonance image analysis.European heart journal. Digital health · 2026Article
- Machine learning and multi-omics technologies for precision cardiovascular medicine: advancing diagnosis, risk prediction, and therapeutic guidance.Frontiers in cardiovascular medicine · 2026Review
- Objective Assessment of Pathological ECG Waveforms Using Gender-specific Normal Waveform Distributions.Arrhythmia & electrophysiology review · 2026Article
- Development and Temporal Validation of a Logistic Regression-Based Risk Score for Identifying Life-Threatening Chest Pain at Emergency Department Triage: A Retrospective Cohort Study.Vascular health and risk management · 2026Observational
- Critical Appraisal Tools for Evaluating Artificial Intelligence in Clinical Studies: Scoping Review.Journal of medical Internet research · 2025Article
- A machine learning model using echocardiographic myocardial strain to detect myocardial ischemia.Internal and emergency medicine · 2025Article
- Medicine, healthcare and the AI act: gaps, challenges and future implications.European heart journal. Digital health · 2025Article
- Combating cardiovascular disease disparities: The potential role of artificial intelligence.American journal of preventive cardiology · 2025Article
7 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
The medical field has seen a rapid increase in the development of artificial intelligence (AI)-based prediction models. With the introduction of such AI-based prediction model tools and software in cardiovascular patient care, the cardiovascular researcher and healthcare professional are challenged to understand the opportunities as well as the limitations of the AI-based predictions. In this article, we present 12 critical questions for cardiovascular health professionals to ask when confronted with an AI-based prediction model. We aim to support medical professionals to distinguish the AI-based prediction models that can add value to patient care from the AI that does not.
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What OpenQuestion 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 OpenQuestion graph.