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Background <p>Recurrence of atrial fibrillation (AF) following catheter ablation is a major clinical problem, and there is considerable variability in patient outcomes. The identification of reliable predictors of AF recurrence is critical in developing patient management strategies.</p> Objective <p>To systematically identify and synthesize evidence on independent predictors of atrial fibrillation recurrence following catheter ablation derived from multivariable prognostic models.</p> Methods <p>A systematic review was carried out following the PRISMA 2020 guidelines. A comprehensive search was done using PubMed, PubMed Central, Google Scholar, CORE, and DOAJ. Studies were filtered based on the keywords entered with a publication date ranging from January 2016 to January 2026. The tool used for bias assessment was ROBINS-I. Due to the heterogeneity of the data, the results were synthesized narratively, and descriptive statistics were used to measure study characteristics. Conversely, the certainty of the evidence was assessed using the GRADE approach.</p> Results <p>Out of the identified 24,083 records, after removing the duplicates, 6817 studies were screened, followed by the collection of 148 full-text articles, and finally, 21 studies were selected for the synthesis after meeting all the inclusion criteria. Advanced age, persistent AF phenotype, increased left atrial size, elevated left atrial pressure, renal dysfunction, frailty, and metabolic abnormalities emerged as the most consistent predictors of AF recurrence. Structural and clinical predictors demonstrated high-certainty evidence. Several biomarkers, including soluble ST2, trimethylamine N-oxide, B-type natriuretic peptide, and insulin resistance indices, were independently associated with AF recurrence, although the certainty of evidence was moderate. Machine learning and radiomics-based models demonstrated promising predictive performance but were limited by insufficient external validation and reduced interpretability.</p> Conclusion <p>Atrial fibrillation recurrence after catheter ablation seems to result from the interaction between atrial remodeling, metabolic and inflammation dysfunction, and patients’ clinical features. Conventional predictors based on clinical and structural information seem to be the most robust indicators in terms of prognosis, while novel predictors such as biomarkers and machine-learning approaches require further external validation to be utilized in practice.</p>

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Predictors of atrial fibrillation recurrence after catheter ablation: a systematic review

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月30日

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  • Muhanned Faisal Towfig,
  • Mohamed Salah Khalil Salih,
  • Omer Yahia Elhadi Suliman,
  • Mugtaba Ahmed,
  • Selma Ali,
  • Moiad Faisal Suliman Tawfig,
  • Fatima Salah Khalil Salih

摘要

Background

Recurrence of atrial fibrillation (AF) following catheter ablation is a major clinical problem, and there is considerable variability in patient outcomes. The identification of reliable predictors of AF recurrence is critical in developing patient management strategies.

Objective

To systematically identify and synthesize evidence on independent predictors of atrial fibrillation recurrence following catheter ablation derived from multivariable prognostic models.

Methods

A systematic review was carried out following the PRISMA 2020 guidelines. A comprehensive search was done using PubMed, PubMed Central, Google Scholar, CORE, and DOAJ. Studies were filtered based on the keywords entered with a publication date ranging from January 2016 to January 2026. The tool used for bias assessment was ROBINS-I. Due to the heterogeneity of the data, the results were synthesized narratively, and descriptive statistics were used to measure study characteristics. Conversely, the certainty of the evidence was assessed using the GRADE approach.

Results

Out of the identified 24,083 records, after removing the duplicates, 6817 studies were screened, followed by the collection of 148 full-text articles, and finally, 21 studies were selected for the synthesis after meeting all the inclusion criteria. Advanced age, persistent AF phenotype, increased left atrial size, elevated left atrial pressure, renal dysfunction, frailty, and metabolic abnormalities emerged as the most consistent predictors of AF recurrence. Structural and clinical predictors demonstrated high-certainty evidence. Several biomarkers, including soluble ST2, trimethylamine N-oxide, B-type natriuretic peptide, and insulin resistance indices, were independently associated with AF recurrence, although the certainty of evidence was moderate. Machine learning and radiomics-based models demonstrated promising predictive performance but were limited by insufficient external validation and reduced interpretability.

Conclusion

Atrial fibrillation recurrence after catheter ablation seems to result from the interaction between atrial remodeling, metabolic and inflammation dysfunction, and patients’ clinical features. Conventional predictors based on clinical and structural information seem to be the most robust indicators in terms of prognosis, while novel predictors such as biomarkers and machine-learning approaches require further external validation to be utilized in practice.