Perioperative changes in coagulation and platelet contribution to clot strength after transcatheter aortic valve implantation: A study using thromboelastography and conventional markers.

INTRODUCTION: Thrombotic and haemorrhagic complications have been reported following transcatheter aortic valve implantation (TAVI). However, few reports have studied perioperative changes in coagulation and platelet function after TAVI. Furthermore, there are no clear guidelines for antithrombotic therapy. This study aimed to examine the perioperative changes in coagulation and platelet contribution to clot strength after TAVI using thromboelastography (TEG 6s; Hemonetics). METHODS: This prospective observational study included 15 patients scheduled to undergo TAVI for severe aortic stenosis. TEG 6s global haemostasis was used to record three measurements: on the day of surgery (immediately before the operation) and postoperative days 1 and 3. The reaction time R to thrombosis; K and α, which represent the rate of thrombosis; and the maximum amplitude (MA) were measured from the kaolin thromboelastography (TEG) curves. The coagulation index (CI) was calculated from the measurement results to assess thrombotic tendency. In addition, MA was converted to elastic clot strength, and platelet function was assessed by the difference, Gp, in elastic strength depending on platelet activation. RESULTS: R and K decreased, and α tended to increase in kaolin TEG on days 1 and 3 after TAVI, indicating elevated coagulation function compared with the preoperative period, but MA and CI did not show significant changes. Gp decreased significantly on days 1 and 3, suggesting a decrease in the platelet contribution to clot strength. CONCLUSIONS: Compared with the preoperative period, coagulation tended to increase, and platelet contribution to clot strength decreased from days 1 to 3 after TAVI.

Machine learning-based causal models for predicting the response of individual patients to dexamethasone treatment as prophylactic antiemetic.

Risk-based strategies are widely used for decision making in the prophylaxis of postoperative nausea and vomiting (PONV), a major complication of general anesthesia. However, whether risk is associated with individual treatment effect remains uncertain. Here, we used machine learning-based algorithms for estimating the conditional average treatment effect (CATE) (double machine learning [DML], doubly robust [DR] learner, forest DML, and generalized random forest) to predict the treatment response heterogeneity of dexamethasone, the first choice for prophylactic antiemetics. Electronic health record data of 2026 adult patients who underwent general anesthesia from January to June 2020 were analyzed. The results indicated that only a small subset of patients respond to dexamethasone treatment, and many patients may be non-responders. Estimated CATE did not correlate with predicted risk, suggesting that risk may not be associated with individual treatment responses. The current study suggests that predicting treatment responders by CATE models may be more appropriate for clinical decision making than conventional risk-based strategy.