New Cancer Diagnosis After Bleeding in Anticoagulated Patients With Atrial Fibrillation.

Background Bleeding is frequent in patients with atrial fibrillation (AF) treated with oral anticoagulant therapy, and may be the first manifestation of underlying cancer. We sought to investigate to what extent bleeding represents the unmasking of an occult cancer in patients with AF treated with oral anticoagulants. Methods and Results Using data from CardioCHUVI-AF (Retrospective Observational Registry of Patients With Atrial Fibrillation From Vigo's Health Area), 8753 patients with AF aged ≥75 years with a diagnosis of AF between 2014 and 2017 were analyzed. Of them, 2171 (24.8%) experienced any clinically relevant bleeding, and 479 (5.5%) were diagnosed with cancer during a follow-up of 3 years. Among 2171 patients who experienced bleeding, 198 (9.1%) were subsequently diagnosed with cancer. Patients with bleeding have a 3-fold higher hazard of being subsequently diagnosed with new cancer compared with those without bleeding (4.7 versus 1.4 per 100 patient-years; adjusted hazard ratio [HR], 3.2 [95% CI, 2.6-3.9]). Gastrointestinal bleeding was associated with a 13-fold higher hazard of new gastrointestinal cancer diagnosis (HR, 13.4; 95% CI, 9.1-19.8); genitourinary bleeding was associated with an 18-fold higher hazard of new genitourinary cancer diagnosis (HR, 18.1; 95% CI, 12.5-26.2); and bronchopulmonary bleeding was associated with a 15-fold higher hazard of new bronchopulmonary cancer diagnosis (HR, 15.8; 95% CI, 6.0-41.3). For other bleeding (nongastrointestinal, nongenitourinary, nonbronchopulmonary), the HR for cancer was 2.3 (95% CI, 1.5-3.6). Conclusions In patients with AF treated with oral anticoagulant therapy, any gastrointestinal, genitourinary, or bronchopulmonary bleeding was associated with higher rates of new cancer diagnosis. These bleeding events should prompt investigation for cancers at those sites.S

Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets

Background The accuracy of current prediction tools for ischaemic and bleeding events after an acute coronary syndrome (ACS) remains insufficient for individualised patient management strategies. We developed a machine learning-based risk stratification model to predict all-cause death, recurrent acute myocardial infarction, and major bleeding after ACS.Methods Different machine learning models for the prediction of 1-year post-discharge all-cause death, myocardial infarction, and major bleeding (defined as Bleeding Academic Research Consortium type 3 or 5) were trained on a cohort of 19 826 adult patients with ACS (split into a training cohort [80%] and internal validation cohort [20%]) from the BleeMACS and RENAMI registries, which included patients across several continents. 25 clinical features routinely assessed at discharge were used to inform the models. The best-performing model for each study outcome (the PRAISE score) was tested in an external validation cohort of 3444 patients with ACS pooled from a randomised controlled trial and three prospective registries. Model performance was assessed according to a range of learning metrics including area under the receiver operating characteristic curve (AUC).Findings The PRAISE score showed an AUC of 0.82 (95% CI 0.78-0.85) in the internal validation cohort and 0.92 (0.90-0.93) in the external validation cohort for 1-year all-cause death; an AUC of 0.74 (0.70-0.78) in the internal validation cohort and 0.81 (0.76-0.85) in the external validation cohort for 1-year myocardial infarction; and an AUC of 0.70 (0.66-0.75) in the internal validation cohort and 0.86 (0.82-0.89) in the external validation cohort for 1-year major bleeding.Interpretation A machine learning-based approach for the identification of predictors of events after an ACS is feasible and effective. The PRAISE score showed accurate discriminative capabilities for the prediction of all-cause death, myocardial infarction, and major bleeding, and might be useful to guide clinical decision making. Copyright (C) 2021 Elsevier Ltd. All rights reserved