SynthesisBMC cardiovascular disorders2024
Using artificial intelligence to predict post-operative outcomes in congenital heart surgeries: a systematic review.
Synthesis in BMC cardiovascular disorders, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 12 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
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Role of artificial intelligence in predicting the renal function after nephrectomy in renal cell carcinoma: a systematic review and meta-analysis.International urology and nephrology · 2025Pooled it
- Development and validation of a machine learning-based prediction model for prolonged length of stay after laparoscopic gastrointestinal surgery: a secondary analysis of the FDP-PONV trial.BMC gastroenterology · 2025Trial
- From Automated ECG Interpretation to Multimodal Cardiovascular Intelligence: The Evolution of Artificial Intelligence in Cardiovascular Medicine.Medical sciences (Basel, Switzerland) · 2026Review
- Artificial intelligence, extended reality and computational modelling in cross-sectional cardiovascular imaging in congenital heart disease: a narrative review.Cardiovascular diagnosis and therapy · 2026Review
- Exposure-based stratification of postoperative complications in major cardiac surgical procedures: a retrospective cohort study using the MOVER database.Frontiers in cardiovascular medicine · 2026Article
- Application of artificial intelligence in predicting the results of open-heart surgery: a scoping review.BMC medical informatics and decision making · 2025Article
- Review
- Artificial Intelligence in Adult Congenital Heart Disease: Diagnostic and Therapeutic Applications and Future Directions.Reviews in cardiovascular medicine · 2025Review
- Advancements in Machine Learning for Precision Diagnostics and Surgical Interventions in Interconnected Musculoskeletal and Visual Systems.Journal of clinical medicine · 2025Review
- Review
- Machine learning-based prediction of hemodynamically significant patent ductus arteriosus in preterm neonates: a pioneering insight.Frontiers in pediatrics · 2025Article
- Development and prospective evaluation of a machine learning model to predict serious cardiac outcomes among paediatric cardiac inpatients.BMJ digital health & AI · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionCongenital heart disease (CHD) represents the most common group of congenital anomalies, constitutes a significant contributor to the burden of non-communicable diseases, highlighting the critical need for improved risk assessment tools. Artificial intelligence (AI) holds promise in enhancing outcome predictions for congenital cardiac surgery. This study aims to systematically review the utilization of AI in predicting post-operative outcomes in this population.
methodsFollowing PRISMA guidelines, a comprehensive search of Pubmed, Scopus, and Web of Science databases was conducted. Two independent reviewers screened articles based on predefined criteria. Included studies focused on AI models predicting various post-operative outcomes in congenital heart surgery.
resultsThe review included 35 articles, primarily published within the last four years, indicating growing interest in AI applications. Models predominantly targeted mortality and survival (n = 16), prolonged length of hospital or ICU stay (n = 7), postoperative complications (n = 6), prolonged mechanical ventilatory support time (n = 4), with additional focus on specific outcomes such as peri-ventricular leucomalacia (n = 2) and malnutrition (n = 1). Performance metrics, such as area under the curve (AUC), ranged from 0.52 to 0.997. Notably, these AI models consistently outperformed traditional risk stratification categories. For instance, in assessing the risk of morbidity and mortality, the AI models demonstrated superior performance compared to conventional methods.
conclusionAI-driven prediction models show significant promise in improving outcome predictions for congenital heart surgery. They surpass traditional risk prediction tools not only in immediate postoperative risks but also in long-term outcomes such as 1-year survival and malnutrition. Further studies with robust external validation are necessary to assess the practical applicability of these models in clinical settings. The protocol of this review was prospectively registered on PROSPERO (CRD42024550942).
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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.