Beta blockers are associated with lower all-cause mortality among HFpEF patients.

BACKGROUND: The evidence regarding beta blocker (BB) benefit in heart failure with preserved ejection fraction (HFpEF) remains inconclusive, leading to consideration of BB withdrawal in this population. OBJECTIVES: In this study, we retrospectively analyzed the association of BB on all-cause mortality in HFpEF patients. METHODS: This is a single-center retrospective cohort study of 20,206 patients with left ventricular ejection fraction (EF) ≥ 50% who were hospitalized with decompensated HF between January 2011 and March 2020. Survival is reported at 30 days, 1 year, and 3 years. A secondary analysis comparing mortality for patients on BB with additional indications including hypertension (HTN), coronary artery disease (CAD), and atrial fibrillation (AF) was completed. Mortality was compared between patients on BB and additional therapies of spironolactone or angiotensin-converting enzyme inhibitors/angiotensin receptor blockers (ACEi/ARBs). RESULTS: BB showed lower all-cause mortality at 30 days, 1 year, and 3 years (p < 0.0001). This association with lower all-cause mortality was validated by a supplementary propensity score-matched analysis. At 3 years, there was significant mortality reduction with addition of BB to either spironolactone (p = 0.0359) or ACEi/ARBs (p < 0.0001). CONCLUSION: In a large single-center retrospective registry, BB use was associated with lower mortality in HFpEF patients with a recent decompensated HF hospitalization. The mortality benefit persisted in those treated with spironolactone or ACEi/ARBs, and in those with AF. This provocative data further highlights the uncertainty of the benefit of BB use in this cohort and calls for re-consideration of BB withdrawal, especially in those tolerating it well, without conclusive, large, and randomized trials showing lack of benefit or harm.

Machine Learning - Based Bleeding Risk Predictions in Atrial Fibrillation Patients on Direct Oral Anticoagulants.

Importance: Accurately predicting major bleeding events in non-valvular atrial fibrillation (AF) patients on direct oral anticoagulants (DOACs) is crucial for personalized treatment and improving patient outcomes, especially with emerging alternatives like left atrial appendage closure devices. The left atrial appendage closure devices reduce stroke risk comparably but with significantly fewer non-procedural bleeding events. Objective: To evaluate the performance of machine learning (ML) risk models in predicting clinically significant bleeding events requiring hospitalization and hemorrhagic stroke in non-valvular AF patients on DOACs compared to conventional bleeding risk scores (HAS-BLED, ORBIT, and ATRIA) at the index visit to a cardiologist for AF management. Design: Prognostic modeling with retrospective cohort study design using electronic health record (EHR) data, with clinical follow-up at one-, two-, and five-years. Setting: University of Pittsburgh Medical Center (UPMC) system. Participants: 24,468 non-valvular AF patients aged ≥18 years treated with DOACs, excluding those with prior history of significant bleeding, other indications for DOACs, on warfarin or contraindicated to DOACs. Exposures: DOAC therapy for non-valvular AF. Main Outcomes and Measures: The primary endpoint was clinically significant bleeding requiring hospitalization within one year of index visit. The models incorporated demographic, clinical, and laboratory variables available in the EHR at the index visit. Results: Among 24,468 patients, 553 (2.3%) had bleeding events within one year, 829 (3.5%) within two years, and 1,292 (5.8%) within five years of index visit. We evaluated multivariate logistic regression and ML models including random forest, classification trees, k-nearest neighbor, naive Bayes, and extreme gradient boosting (XGBoost) which modestly outperformed HAS-BLED, ATRIA, and ORBIT scores in predicting clinically significant bleeding at 1-year follow-up. The best performing model (random forest) showed area under the curve (AUC-ROC) 0.76 (0.70-0.81), G-Mean score of 0.67, net reclassification index 0.14 compared to 0.57 (0.50-0.63), G-Mean score of 0.57 for HASBLED score, p-value for difference <0.001. The ML models had improved performance compared to conventional risk across time-points of 2-year and 5-years and within the subgroup of hemorrhagic stroke. SHAP analysis identified novel risk factors including measures from body mass index, cholesterol profile, and insurance type beyond those used in conventional risk scores. Conclusions and Relevance: Our findings demonstrate the superior performance of ML models compared to conventional bleeding risk scores and identify novel risk factors highlighting the potential for personalized bleeding risk assessment in AF patients on DOACs.