Thromboelastography-Based Risk-Stratified Transfusion Strategy in Acute Stanford Type A Aortic Dissection: A Predictive Model and Prospective Validation
Year: 2026
Type: article
Abstract: Objectives: Perioperative blood transfusion for acute type A aortic dissection (ATAAD) lacks clinical guidelines. This study aims to investigate the application of a thromboelastography (TEG)-based risk-stratified transfusion protocol in these patients. Methods: We conducted a two-stage study. Firstly, a retrospective analysis of ATAAD patients undergoing surgery in 2023 was performed to identify predictors of postoperative/perioperative excessive bleeding and develop a predictive model. Subsequently, a single-center prospective validation study was conducted in 2024, comparing a TEG-based risk-stratified transfusion protocol against conventional empirical transfusion. Results: In the retrospective phase (n = 57), 18 patients (31.6%) developed perioperative excessive bleeding. Preoperative activated clotting time (ACT) and TEG parameters (K-time) were independent predictors. A predictive model incorporating these variables achieved an AUC of 0.788. In the prospective phase (n = 47), 21 patients received the TEG-based risk-stratified transfusion protocol. Compared to the conventional group, the TEG risk-stratified group exhibited significantly lower postoperative drainage volume (p = 0.046), a reduced incidence of perioperative excessive bleeding (4.8% vs. 34.6%, p = 0.033), and lower transfusion costs (p = 0.029), without an increase in total transfusion volume. Conclusions: Preoperative ACT and TEG parameters effectively predict perioperative excessive bleeding in ATAAD patients. Implementing a TEG-based risk-stratified transfusion protocol optimizes blood product utilization, improves clinical outcomes, and reduces costs, offering a promising evidence-based approach for perioperative management.
Source: Journal of Clinical Medicine
DOI: 10.3390/jcm15093446
Authors: Jiawei Zhu, Qiuyong Guo, Yi Jiang, Xinlong Tang, Xiyu Zhu, Hoshun Chong, Yunxing Xue, Jun Pan, Jinfeng Yu, Qing Chen, Fudong Fan, Dongjin Wang
Language: English
Subfield: Biochemistry
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W7160040256
