Key result
Integrated XGBoost machine learning model predicts persistent atrial fibrillation with an AUC of ~0.99.
Why the study?
Patients with paroxysmal AF may progress to persistent AF, motivating the identification of associated metabolites using machine learning to predict this progression and guide clinical treatment.
Observational (n=100)
Yes
A machine learning model incorporating targeted metabolomics and clinical features accurately differentiates persistent from paroxysmal atrial fibrillation.
Background Atrial fibrillation (AF), one of the most common cardiac arrhythmias worldwide, carries a high risk of severe complications. Patients diagnosed with paroxysmal atrial fibrillation (PAF) may progress to persistent atrial fibrillation (PerAF) following a period of time. This study aimed to identify metabolites associated with PerAF using machine learning (ML) and predict the probability of PAF progressing to PerAF, enabling the adjustment of subsequent treatments in clinical practice. Methods AF patients without any anticoagulant therapy within the last 7 days were enrolled between July 2020 and July 2022 at two hospitals in China. Targeted metabolomic profiling was performed on the participating patients’ plasma. Differential metabolites and clinical features were identified through univariate and multivariate analyses. Patients were divided into discovery and validation cohorts (70%:30%). Four ML models (logistic regression, random forest, XGBoost, and LightGBM) were developed for PerAF prediction. Model predictive performance was measured mainly using the area under the curve (AUC). Results One hundred patients (65 PerAF, 35 PAF) were enrolled. Eight metabolites (kynurenine, N-A cetylaspartic acid, glyceric acid, adipic acid, citramalic acid, malic acid, isocitric acid, and oxoglutaric acid) and two clinical features (NT-proBNP and uric acid) were significantly associated with PerAF. Pathway enrichment analysis highlighted alterations in the citrate cycle and glyoxylate/dicarboxylate metabolism. XGBoost was chosen for establishing the final model since its predictive performance outperformed that of the other algorithms. The model based on clinical parameters, metabolites, and demographics achieved the highest AUC in both the discovery cohort (0.751 (95% CI [0.631~0.867])) and validation cohort (0.985 (95% CI [0.940~1.000])). A simplified model with three features (NT-proBNP, citramalic acid, and uric acid) retained robust performance. Conclusions This study identified eight PerAF-related metabolites via targeted metabolomics and ML, and developed accurate predictive models (including a simplified, clinically feasible model) with favorable predictive performance for PerAF risk stratification. Future directions should include large-scale multi-center external validation, comprehensive adjustment for potential confounding factors, and the application of multiple metabolic platforms to deeply explore AF-related metabolic alterations.
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Gao et al. (2026) conducted an observational in Atrial fibrillation (n=100). Metabolomic and clinical features was evaluated on Prediction of persistent atrial fibrillation in the validation cohort (AUC 0.985, 95% CI 0.940-1.000). An integrated XGBoost machine learning model incorporating clinical parameters, metabolites, and demographics accurately predicted persistent atrial fibrillation with an AUC of 0.985.
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