Key result
Machine learning outperforms ORBIT-AF and CHA2DS2-VASc in predicting major bleeding and ischemic stroke.
Why the study?
Oral anticoagulation requires careful administration in atrial fibrillation patients with liver cirrhosis, warranting a dose-selection calculator that considers the risk-to-benefit ratio.
Does a machine learning-based risk prediction model improve the prediction of major bleeding and ischemic stroke compared to existing models in patients with atrial fibrillation and liver cirrhosis?
Observational (n=600)
Does a machine learning-based risk prediction model improve the prediction of major bleeding and ischemic stroke compared to existing models in patients with atrial fibrillation and liver cirrhosis?
Effect estimate: c-statistic 0.768 for major bleeding and 0.743 for ischemic stroke
p-value: p=<0.05
A machine learning-based model using readily available clinical variables effectively predicts individual risks for major bleeding and ischemic stroke in AF patients with liver cirrhosis, outperforming traditional risk scores.
May aid OAC dosing in AF with cirrhosis; leaves open prospective validation before use.
Background Oral anticoagulation (OAC) should be carefully administered especially among atrial fibrillation (AF) patients with liver cirrhosis (LC). We developed OAC-dose selection calculator considering risk-to-benefit ratio for such population. Methods A total of 600 AF patients with LC (420 derivation/180 validation sets) were recruited. Machine learning (ML)-based risk prediction model for major bleeding (MB) and ischemic stroke (including transient ischemic attack) were constructed, using components of CHA2DS2-VASc and Child-Pugh scores. Results In the derivation set, our ML-based model showed significantly higher c-statistics to predict MB (0.768) and ischemic stroke (0.743), compared to each existing model, i.e. ORBIT-AF for MB (0.642) and CHA2DS2-VASc for ischemic stroke (0.663) (both p<0.05). Both net reclassification improvement and integrated discrimination improvement indices were significantly positive (p<0.05). Proportions with 3-year absolute risk increases (ARI)>30% for MB, compared to non-OAC, increased across low-dose direct oral anticoagulants (DOACs) (12%), standard-dose DOACs (18%), and warfarin (21%). Proportions with 3-year absolute risk reductions (ARR)>15% for ischemic stroke, compared to non-OAC, also increased across low-dose DOACs (17%), standard-dose DOACs (22%), and warfarin (32%). The optimal cutoffs of ARI for MB and ARR for ischemic stroke when using OAC vs. non-OAC were >2.4%/year and >1.2%/year, respectively, with c-statistics (95% confidence intervals) of 0.81 (0.78-0.83) and 0.75 (0.72-0.79). Conclusions By ML-based model, individual risks for MB and ischemic stroke can be effectively predicted through readily available clinical variables among AF patients with LC, also guiding appropriate OAC dosing.
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Kim et al. (2026) conducted an observational in Atrial fibrillation with liver cirrhosis (n=600). Machine learning-based risk prediction model vs. ORBIT-AF and CHA2DS2-VASc models was evaluated on Prediction of major bleeding and ischemic stroke (c-statistic 0.768 for major bleeding and 0.743 for ischemic stroke, p=<0.05). A machine learning-based model predicted major bleeding (c-statistic 0.768 vs 0.642) and ischemic stroke (0.743 vs 0.663) significantly better than ORBIT-AF and CHA2DS2-VASc (P<0.05).
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