Anticoagulation Changes Following Major and Clinically Relevant Nonmajor Bleeding Events in Non-valvular Atrial Fibrillation Patients.

Background: Bleeding events are common complications of oral anticoagulant drugs, including both warfarin and the direct oral anticoagulants (DOACs). Some patients have their anticoagulant changed or discontinued after experiencing a bleeding event, while others continue the same treatment. Differences in anticoagulation management between warfarin- and DOAC-treated patients following a bleeding event are unknown. Methods: Patients with non-valvular atrial fibrillation from six anticoagulation clinics taking warfarin or DOAC therapy who experienced an International Society of Thrombosis and Haemostasis (ISTH)-defined major or clinically relevant non-major (CRNM) bleeding event were identified between 2016 and 2020. The primary outcome was management of the anticoagulant following bleeding (discontinuation, change in drug class, and restarting of same drug class). DOAC- and warfarin-treated patients were propensity matched based on the individual elements of the CHA2DS2-VASc and HAS-BLED scores as well as the severity of the bleeding event. Results: Of the 509 patients on warfarin therapy and 246 on DOAC therapy who experienced a major or CRNM bleeding event, the majority of patients continued anticoagulation therapy. The majority of warfarin (231, 62.6%) and DOAC patients (201, 81.7%) restarted their previous anticoagulation. Conclusion: Following a bleeding event, most patients restarted anticoagulation therapy, most often with the same type of anticoagulant that they previously had been taking.

Derivation and external validation of a simple risk score to predict in-hospital mortality in patients hospitalized for COVID-19

BackgroundAs SARS-CoV-2 continues to spread, and hospitals are treating a large number of patients with COVID-19, easy-to-use risk models that predict hospital mortality can assist in clinical decision making and triage. ObjectiveAs SARS-CoV-2 continues to spread, easy-to-use risk models that predict hospital mortality can assist in clinical decision making and triage. We aimed to develop a risk score model for in-hospital mortality in patients hospitalized with COVID-19 that was robust across hospitals and used clinical factors that are readily available and measured standardly across hospitals. MethodsIn this observational study we developed a risk score model using data collected by trained abstractors for patients in 20 diverse hospitals across the state of Michigan (Mi-COVID19) who were discharged between March 5, 2020 and August 14, 2020. Patients who tested positive for SARS-CoV-2 during hospitalization or were discharged with an ICD-10 code for COVID-19 (U07.1) were included. We employed an iterative forward selection approach to consider the inclusion of 145 potential risk factors available at hospital presentation. Model performance was externally validated with patients from 19 hospitals in the Mi-COVID19 registry not used in model development. We shared the model in an easy-to-use online application that allows the user to predict in-hospital mortality risk for a patient if they have any subset of the variables in the final model. ResultsOur final model includes the patients age, first recorded respiratory rate, first recorded pulse oximetry, highest creatinine level on day of presentation, and hospitals COVID-19 mortality rate. No other factors showed sufficient incremental model improvement to warrant inclusion. The AUC for the derivation and validation sets were .796 and .829 respectively. ConclusionsRisk of in-hospital mortality in COVID-19 patients can be reliably estimated using a few factors, which are standardly measured and available to physicians very early in a hospital encounter.