Search

Background: There is little evidence to guide the perioperative management of patients on a direct oral anticoagulant (DOAC) in the absence of a last known dose. Quantitative serum titers may be ordered, but there is little evidence supporting this., Aims: This multi-center retrospective cohort study of consecutive surgical in-patients with a DOAC assay, performed over a five-year period, aimed to characterize preoperative DOAC assay orders and their impact on perioperative outcomes., Materials and Methods: Patients prescribed regular DOAC (both prophylactic and therapeutic dosing) with a preoperative DOAC assay were included. The DOAC assay titer was evaluated against endpoints. Further, patients with an assay were compared against anticoagulated patients who did not receive a preoperative DOAC assay. The primary endpoint was major bleeding. Secondary endpoints included perioperative hemoglobin change, blood transfusions, idarucizumab or prothrombin complex concentrate administration, postoperative thrombosis, in-hospital mortality and reoperation. Adjusted and unadjusted linear regression models were used for continuous data. Binary logistic models were performed for dichotomous outcomes., Results: 1065 patients were included, 232 had preoperative assays. Assays were ordered most commonly by Spinal (11.9%), Orthopedics (15.4%), and Neurosurgery (19.4%). For every 10 ng/ml increase in titer, the hemoglobin decreases by 0.5066 g/L and the odds of a preoperative reversal increases by 13%. Compared to those without an assay, patients with preoperative DOAC assays had odds 1.44× higher for major bleeding, 2.98× higher for in-hospital mortality and 16.3× higher for receiving anticoagulant reversal., Conclusion: A preoperative DOAC assay order was associated with worse outcomes despite increased reversal administration. However, the DOAC assay titer can reflect the patient's likelihood of bleeding.

Automated information extraction might be able to assist with the collection of stroke key performance indicators (KPI). The feasibility of using natural language processing for classification‐based KPI and datetime field extraction was assessed. Using free‐text discharge summaries, random forest models achieved high levels of performance in classification tasks (area under the receiver operator curve 0.95–1.00). The datetime field extraction method was successful in 29 of 43 (67.4%) cases. Further studies are indicated. [ABSTRACT FROM AUTHOR]