AI-based individual risk prediction of oral anticoagulation in patients with cirrhosis and atrial fibrillation

Abstract 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 p15% 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.