SynthesisFrontiers in cardiovascular medicine2025
Artificial intelligence in electrocardiogram-based prediction of heart failure: a systematic review and meta-analysis.
Synthesis in Frontiers in cardiovascular medicine, 2025. 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
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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.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence technology in aortic valve disease: a decade of scientometric and narrative review.Frontiers in cardiovascular medicine · 2026Pooled it
- Advancements in artificial intelligence for the localization of premature ventricular contraction origins.Frontiers in cardiovascular medicine · 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
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Heart failure (HF) continues to pose a significant global health challenge, characterized by an increasing prevalence. Early identification of individuals at the highest risk of developing HF and implementing interventions can prevent and delay disease progression. The application of artificial intelligence (AI) to electrocardiograms (ECGs) presents a novel strategy for early prediction; however, the effectiveness and generalizability of this approach necessitate systematic evaluation. Objective: To systematically evaluate the performance of AI models based on ECGS in predicting HF. Methods: This study was registered on PROSPERO (CRD420251012231). Following the PRISMA guidelines, we conducted a systematic literature search across multiple databases, including PubMed, IEEE Xplore, Medline, and Embase, for studies published between 2005 and 2025. The inclusion criteria focused on AI models based on ECGs that reported performance metrics such as the AUROC (Area Under the Receiver Operating Characteristic Curve)/C-statistic. Meta-analysis was performed by employing a random-effects model to evaluate the efficacy of AI in predicting HF through the pooled AUROC/ C-statistic. Additionally, we conducted heterogeneity analyses using I Results: A total of five studies involving 11 cohorts and 1,728,134 participants were included in the analysis. The pooled AUROC/C-statistic was found to be 0.76 (95% CI: 0.74-0.78; Conclusions: Theoretically, artificial intelligence-enabled electrocardiogram (AI-ECG) models demonstrate promising applicability for predicting HF; however, their effectiveness remains uncertain due to a high risk of bias and a lack of clinical validity studies. Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD420251012231, PROSPERO CRD420251012231.
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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.