BACKGROUND
Whether direct oral anticoagulants (DOACs) are a safe and effective alternative to warfarin in patients with atrial fibrillation and mitral stenosis (AF-MS) remains controversial.
OBJECTIVE
To evaluate the effectiveness and safety of DOACs versus warfarin in patients with AF-MS.
DESIGN
Observational cohort study using target trial emulation.
SETTING
Population-wide insurance claims data in Taiwan.
PARTICIPANTS
Patients diagnosed with AF-MS and prescribed either DOACs or warfarin between 1 January 2011 and 31 December 2021 were included in the study.
INTERVENTION
DOACs or warfarin.
MEASUREMENTS
Absolute risk differences (RDs) and risk ratios (RRs) at 1 year and 5 years of follow-up for ischemic stroke, systemic embolism, composite stroke, myocardial infarction (MI), intracranial hemorrhage, gastrointestinal bleeding, bleeding at other critical sites, and all-cause death.
RESULTS
Compared with warfarin, DOACs were associated with an increased risk for ischemic stroke (RD, 4.97 percentage points [95% CI, 1.27 to 8.57 percentage points]; RR, 1.22 [CI, 1.05 to 1.41]) and composite stroke (RD, 5.56 percentage points [CI, 1.77 to 8.97 percentage points]; RR, 1.23 [CI, 1.07 to 1.42]) and a decreased risk for MI (RD, -1.61 percentage points [CI, -3.17 to -0.03 percentage points]; RR, 0.61 [CI, 0.37 to 0.99]) at the 1-year follow-up. Rivaroxaban increased the risk for ischemic stroke during both short- and long-term follow-up periods. The 2 groups did not differ in risks for bleeding or all-cause death.
LIMITATION
Limited sample size, lack of detailed information on MS severity, and lack of international normalized ratio measurements.
CONCLUSION
In Asian patients with AF-MS, DOACs were associated with an increased 1-year risk for stroke but a decreased risk for MI compared with warfarin.
PRIMARY FUNDING SOURCE
Research Grants Council of Hong Kong.
Yu Yang, Chin-Yao Shen, M. Cheng et al.· Annals of Internal Medicine· 0 citations
With the increasing use of multiple medications in clinical practice, accurate and interpretable prediction of organ-level adverse drug reactions (ADRs) induced by drug combinations is essential for drug safety assessment and precision medicine. Existing knowledge graph (KG)-based methods primarily model biomedical associations but leave direct structure-level interactions within drug pairs undercharacterized, while molecular representation methods often rely on whole-molecule or latent substructure encodings, offering limited chemically meaningful evidence for ADR risks. This study proposes MolADR, a multiscale complementary learning framework that integrates GNN-based KG learning with dual-granularity molecular cross-attention modeling to combine macro-level biomedical associations with microlevel molecular interaction cues. Under an emerging-drug setting, MolADR achieves PR-AUC scores of 81.17 ± 3.97, 84.04 ± 4.67, and 74.37 ± 9.90 on three data sets, consistently outperforming state-of-the-art baselines, with further analyses supporting its robustness and suggesting its ability to highlight chemically plausible atoms and functional groups for organ-level ADR prediction.
Yifan Qi, Q. Ren, Chen-Xu Wang et al.· Journal of Chemical Informat...· 0 citations
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