ArticleTranslational pediatrics2023
Adverse events after repair of tetralogy of Fallot: prediction models by machine learning of a retrospective cohort study in western China.
Article in Translational pediatrics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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.
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Who cites it
6 citing papers in PubMed, 5 citations in OpenAlex.
- Echocardiography-based intelligent diagnosis and risk stratification management for tetralogy of Fallot.EBioMedicine · 2026Article
- Real-time deep learning interpretation of echocardiographic video for automated detection of anatomical features associated with tetralogy of fallot in pediatric patients : a feasibility study.Scientific reports · 2026Article
- Artificial Intelligence in Tetralogy of Fallot: From Prenatal Diagnosis to Lifelong Management: A Narrative Review.Bioengineering (Basel, Switzerland) · 2025Review
- Radiomics with structural magnetic resonance imaging, surface morphometry features, neurology scales, and clinical metrics to evaluate the neurodevelopment of preschool children with corrected tetralogy of Fallot.Translational pediatrics · 2024Article
- Applying a Combined Model to Evaluate the Risk of Poor Treatment Outcomes in Rifampicin Resistant Tuberculosis Patients: A Multicenter Retrospective Study.Infection and drug resistance · 2024Article
- The Lifelong Impact of Artificial Intelligence and Clinical Prediction Models on Patients With Tetralogy of Fallot.CJC pediatric and congenital heart disease · 2023Review
Corrections and comments
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Authors and funding
13 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The incidence of clinical adverse events after tetralogy of Fallot (TOF) repair remains high. This study was performed to explore risk factors for adverse events and develop a prediction model through machine learning (ML) to forecast the incidence of clinical adverse events after TOF repair. Methods: A total of 281 participants who were treated with cardiopulmonary bypass (CPB) at our hospital from January 2002 to January 2022 were included in the study. Risk factors for adverse events were explored by composite and comprehensive analyses. Five artificial intelligence (AI) models were used for ML to build prediction models and screen out the model with the best performance in predicting adverse events. Results: CPB time, differential pressure of the right ventricular outflow tract (RVOTDP or DP), and transannular patch repair were identified as the main risk factors for adverse events. The reference point for CPB time was 116.5 minutes and that for right ventricular (RV) outflow tract differential pressure was 70 mmHg. SPO Conclusions: Differential pressure of the RV outflow tract, CPB time, and transannular patch repair are risk factors, and SPO
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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.