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Aims: Andexanet alfa, a specific antidote to factor Xa (FXa) inhibitors, has been approved for clinical use in several countries, including Japan, based on the results from the phase 3 trial ANNEXA-4. We aimed to assess the efficacy and safety of andexanet alfa treatment in FXa inhibitor-related acute major bleeding in patients enrolled for ANNEXA-4 in Japan., Methods: This prespecified analysis included patients enrolled at Japanese sites in the prospective, open-label, single-arm ANNEXA-4 trial. Eligible patients had major bleeding within 18 hours of oral FXa inhibitor administration. The coprimary efficacy endpoints were percent change in anti-FXa activity and proportion of patients achieving excellent or good hemostatic efficacy 12 hours post-treatment., Results: A total of 19 patients were enrolled, all of whom had intracranial hemorrhage; 16 patients were evaluable for efficacy. Median percent reduction in anti-FXa activity from baseline to nadir was 95.4% in patients taking apixaban, 96.1% in patients taking rivaroxaban, and 82.2% in patients taking edoxaban. Overall, 14/16 patients (88%) achieved excellent or good hemostasis (apixaban, 5/5; rivaroxaban, 6/7; edoxaban, 3/4). Within 30 days, treatment-related adverse events (AEs) and serious AEs occurred in 2 and 5 patients, respectively. One patient died during follow-up, and 2 patients experienced thrombotic events., Conclusion: Treatment with andexanet alfa rapidly reduced anti-FXa activity with favorable hemostatic efficacy in Japanese patients with acute major bleeding. Serious AEs of thrombotic events during rapid reversal of anti-FXa activity arose as particular safety concerns in this population as with previous studies.

Patients with acute myocardial infarction (AMI) triaged as life-threatening are transferred to our emergency medical care center (EMCC). However, data on these patients remain limited. We aimed to compare the characteristics and AMI prognosis of patients transferred to our EMCC with those transferred to our cardiovascular intensive care unit (CICU) using whole and propensity-matched cohorts.We analyzed the data of 256 consecutive AMI patients transferred from the scene to our hospital by ambulance between 2014 and 2017. The EMCC and CICU groups comprised 77 and 179 patients, respectively. There were no significant between-group age or sex differences. Patients in the EMCC group had more disease severity score and had the left main trunk identified as the culprit more frequently (12% versus 0.6%, P < 0.001) than those in the CICU group; however, the number of patients with multiple culprit vessels did not differ. The EMCC group had a longer door-to-reperfusion time (75 [60, 109] minutes versus 60 [40, 86] minutes, P< 0.001) and a higher in-hospital mortality (19% versus 4.5%, P < 0.001), especially from non-cardiac causes (10% versus 0.6%, P < 0.001), than the CICU group. However, peak myocardial creatine phosphokinase did not significantly differ between the groups. The EMCC group had a significantly higher 1-year post-discharge mortality than the CICU group (log-rank, P = 0.032); this trend was maintained after propensity score matching, although the difference was not statistically significant (log-rank, P = 0.094).AMI patients transferred to the EMCC exhibited more severe disease and worse overall in-hospital and non-cardiac mortality than those transferred to the CICU.

Background: Ventilator weaning protocols are commonly implemented for patients receiving mechanical ventilation. However, despite such protocols, the rate of extubation failure remains high. This study analyzed the usefulness and accuracy of machine learning in predicting extubation success., Methods: We retrospectively evaluated data from patients who underwent intubation for respiratory failure and received mechanical ventilation in an intensive care unit (ICU). Information on 57 features, including patient demographics, vital signs, laboratory data, and ventilator data, were extracted. Extubation failure was defined as re-intubation within 72 hours of extubation. For supervised learning, data were labeled as intubation-required or not. We used three learning algorithms (Random Forest, XGBoost, and LightGBM) to predict successful extubation. We also analyzed important features and evaluated the area under curve (AUC) and prediction metrics., Results: Overall, 13 of the 117 included patients required re-intubation. LightGBM had the highest AUC (0.950), followed by XGBoost (0.946) and Random Forest (0.930). The accuracy, precision, and recall performance were 0.897, 0.910, and 0.909 for Random Forest; 0.910, 0.912, and 0.931 for XGBoost; and 0.927, 0.915, and 0.960 for LightGBM, respectively. The most important feature was duration of mechanical ventilation, followed by fraction of inspired oxygen, positive end-expiratory pressure, maximum and mean airway pressures, and Glasgow Coma Scale., Conclusions: Machine learning predicted successful extubation of ICU patients on mechanical ventilation. LightGBM had the best overall performance. Duration of mechanical ventilation was the most important feature in all models.