Warfarin sensitivity is associated with increased hospital mortality in critically Ill patients

Using machine learning to predict the bleeding risk for patients with cardiac valve replacement treated with warfarin in hospitalized

BACKGROUND

Distinguishing warfarin-related bleeding risk at the bedside remains challenging. Studies indicate that warfarin therapy should be suspended when international normalized ratio (INR) ≥ 4.5, or it may sharply increase the risk of bleeding. We aim to develop and validate a model to predict the high bleeding risk in valve replacement patients during hospitalization.

METHOD

Cardiac valve replacement patients from January 2016 to December 2021 across Nanjing First Hospital were collected. Five different machine-learning (ML) models were used to establish the prediction model. High bleeding risk was an INR ≥4.5. The area under the receiver operating characteristic curve (AUC) was used for evaluating the prediction performance of different models. The SHapley Additive exPlanations (SHAP) was used for interpreting the model. We also compared ML with ATRIA score and ORBIT score.

RESULTS

A total of 2376 patients were finally enrolled in this model, 131 (5.5%) of whom experienced the high bleeding risk after anticoagulation therapy of warfarin during hospitalization. The extreme gradient boosting (XGBoost) exhibited the best overall prediction performance (AUC: 0.882, confidence interval [CI] 0.817-0.946, Brier score, 0.158) compared to other prediction models. It also shows superior performance compared with ATRIA score and ORBIT score. The top 5 most influential features in XGBoost model were platelet, thyroid stimulation hormone, body surface area, serum creatinine and white blood cell.

CONCLUSION

A model for predicting high bleeding risk in valve replacement patients who treated with warfarin during hospitalization was successfully developed by using machine learning, which may well assist clinicians to identify patients at high risk of bleeding and allow timely adjust therapeutic strategies in evaluating individual patient.

Pre-hospital use of direct oral anticoagulants agents is associated with a lower risk of major bleeding events in critically ill patients: A single academic center experience

BACKGROUND

The last decade has witnessed significant advancements in direct oral anticoagulants (DOACs), transforming the landscape of anticoagulation therapy. With the uptrend in DOACs use, critical care physicians are encountering more patients with pre-hospital DOACs prescription. Safety and real world outcomes-related data on DOACs use in critically ill patients are scarce.

OBJECTIVE

We assess the risk of major bleeding (MB) events and patient-centered outcomes with pre-hospital use of direct oral anticoagulant agents (DOACs) compared to warfarin therapy.

METHODS

Observational study in a single large academic center from January 1st, 2012, through May 4th, 2018. We included adult critically ill patients with warfarin or one of the DOACs, as active medications at the time of hospital admission. The primary outcome was major bleeding (MB), based on the ISTH criteria RESULTS: 99,481 patients were screened; 558 and 3037 patients were included in the final analysis for the DOAC and warfarin groups, respectively. Multivariable analysis showed that the pre-hospital use of DOACs was associated with lower odds for major bleeding events, GI bleeding, need for endoscopic intervention, hemorrhagic shock, any blood transfusion; but higher odds of intracranial bleeding, as compared to warfarin use. There was no difference in hospital length of stay or ICU-free days.

CONCLUSIONS

Pre-hospital use of DOACs among critically ill patients is associated with lower major bleeding events, GI bleeding, need for endoscopic intervention, and blood transfusion but a higher risk for intracranial bleeding.