The effect of acute aortic dissection itself on coagulopathy or surgery-related coagulopathy has never been specifically studied. The aim of the present study was to perioperatively describe consumption coagulopathy in patients with acute aortic dissection. Sixty-six patients with acute type A aortic dissection were enrolled in this study from January 2015 to September 2016. Thirty-six patients with thoracic aortic aneurysms were used as a control group during the same period. Consumption coagulopathy was evaluated using standard laboratory tests, enzyme-linked immunosorbent assay and thromboelastograghy at five perioperative time-points. A significant reduction in clotting factors and fibrinogen was observed at the onset of acute aortic dissection. Enzyme-linked immunosorbent assay and thromboelastograghy also revealed a persistent systemic activation of the coagulation system and the consumption of clotting factors. In contrast, although platelet counts were consistently low, we did not find that platelet function was more impaired in the acute aortic dissection group than the control group. After surgery, clotting factors and fibrinogen were more impaired than platelet function. Thus, we proposed that hemostatic therapy should focus on the rapid and sufficient supplementation of clotting factors and fibrinogen to improve consumption coagulopathy in patients with acute aortic dissection.

There are relatively few articles on the relationship between serum albumin and acute kidney injury (AKI). Therefore, the objective of this research was to study the relationship between serum albumin and AKI in patients who were undergoing surgery for acute type A aortic dissection. Methods: We retrospectively collected data from 624 patients attending a Chinese hospital between January 2015 and June 2017. The target independent variable was serum albumin measured before surgery after hospital admission, and the dependent variable was AKI, defined in accordance with the Kidney Disease Improving Global Outcomes (KDIGO) criteria. Results: The mean age of these 624 selected patients was 48.5 ± 11.1 years, and almost 73.7% were male. A nonlinear association was detected between serum albumin and AKI; the turning point was 32 g/L. The risk of AKI decreased gradually as the serum albumin level increased up to 32 g/L (adjusted OR = 0.87; 95% CI 0.82–0.92; p < 0.001). When the serum albumin level exceeded 32 g/L, the level of serum albumin was not associated with the risk of AKI (OR = 1.01, 95% CI 0.94–1.08; p = 0.769). Conclusions: The findings suggest that preoperative serum albumin below 32 g/L was an independent risk factor for AKI in patients undergoing surgery for acute type A aortic dissection. Trial registration: A retrospective cohort study.

In Federated Learning (FL), data communication among clients is denied. However, it is difficult to learn from the decentralized client data, which is under-sampled, especially for segmentation tasks that need to extract enough contextual semantic information. Existing FL studies always average client models to one global model in segmentation tasks while neglecting the diverse knowledge extracted by the models. To maintain and utilize the diverse knowledge, we propose a novel training paradigm called Federated Learning with Z-average and Cross-teaching (FedZaCt) to deal with segmentation tasks. From the model parameters’ aspect, the Z-average method constructs individual client models, which maintain diverse knowledge from multiple client data. From the model distillation aspect, the Cross-teaching method transfers the other client models’ knowledge to supervise the local client model. In particular, FedZaCt does not have the global model during the training process. After training, all client models are aggregated into the global model by averaging all client model parameters. The proposed methods are applied to two medical image segmentation datasets including our private aortic dataset and a public HAM10000 dataset. Experimental results demonstrate that our methods can achieve higher Intersection over Union values and Dice scores.

Background: Changes in the intrinsic coagulation pathway during aortic arch surgery in patients with acute aortic dissection (AAD) have not yet been reported. The aim of this study is to describe the changes in intrinsic coagulation factor XII, explore its function and find a new target for the treatment of coagulopathy during surgery. Methods: Eighty-eight patients undergoing emergent surgery for AAD were enrolled. Changes in the intrinsic and extrinsic coagulation pathways were evaluated at 5 different timepoints during the perioperative period by measuring intrinsic coagulation factor XII, extrinsic coagulation factor VII and some intrinsic upstream stimulating factors. The 88 patients were also divided into two groups according to whether reoperation for coagulopathy was required after surgery. Results: Both coagulation factors XII and VII demonstrated a significant and similar change during the perioperative period. These factors decreased significantly during hypothermia circulation arrest (P<0.001) and recovered to normal levels by 24 hours after surgery. Among the intrinsic upstream stimulating factors, bradykinin (BK) demonstrated a similar changing trend with coagulation factors XII and VII, while other stimulating factors did not. However, compared with factor VII, factor XII demonstrated a greater decline during surgery. The proportion of decline of factor XII from anesthesia induction to hypothermia circulation arrest was 42%, whereas the proportion of decline of factor VII during the same period was 20% (P<0.001). Moreover, factor VII recovered to preoperative levels 4 hours after surgery with a relatively faster speed (P<0.001) while factor XII had not recovered (P=0.010). The independent t-test and Wilcoxon test showed that coagulation factor XII levels during hypothermia circulation arrest (P=0.002), total dosage of fibrinogen (P=0.027), total dosage of packed red blood cells (PRBCs) (P=0.006) and total dosage of fresh frozen plasma (FFP) (P=0.022) during the perioperative period were significantly different between the patients who did or did not require reoperation for coagulopathy. Multivariable logistic regression analysis suggested that the factor XII level during hypothermia circulation arrest was an independent risk factor for reoperation for coagulopathy [odds ratio (OR): 1.342, 95% confidence interval (CI): 1.058–1.570; P=0.012]. Conclusions: Factor XII levels are more influenced by surgery and require a longer period of time to recover to preoperative levels compared with factor VII, and the level of factor XII during hypothermia circulation arrest might be an independent risk factor for reoperation for coagulopathy. Therefore, supplementation of coagulation factor XII and its upstream stimulating factors might be a promising therapeutic modality in the future.

Objective:To determine the effect of renal artery stenosis (RAS) resulting from acute type B aortic dissection (ATBAD) with thoracic endovascular aortic repair (TEVAR) on early prognosis in patients with ATBAD.Methods:A total of 129 ATBAD patients in the National Acute Aortic Syndrome Database (AASCN) who underwent TEVAR between 2019 and 2020 were enrolled in our study. Patients were divided into two groups: the RAS group and the non-RAS group.Results:There were 21 RAS patients (16.3%) and 108 non-RAS patients (83.7%) in our cohort. No patient in our cohort died during the 1-month follow-up. There was no significant difference in preoperative creatinine clearance rate (CCr) between the two groups (90.6 ± 46.1 μmol/L in the RAS group vs. 78.7 ± 39.2 μmol/L in the non-RAS group,P= 0.303) but the RAS group had a significantly lower estimated glomerular filtration rate (eGFR) than the non-RAS group (83.3 ± 25.0 vs. 101.9 ± 26.9 ml/min, respectively;P= 0.028).One month after TEVAR, CCr was significantly higher (99.0 ± 68.1 vs. 78.5 ± 25.8 ml/min,P= 0.043) and eGFR (81.7 ± 23.8 vs. 96.0 ± 20.0 ml/min,P= 0.017) was significantly lower in the RAS group than in the non-RAS group.Conclusions:In ATBAD, RAS could result in acute kidney injury (AKI) in the early stage after TEVAR. The RAS group had a high incidence of hypertension. These results suggest that patients with RAS may need further treatment.

BACKGROUD : Ischemic stroke remains a devastating postoperative complication in Type A aortic dissection (TAAD) patients, contributing significantly to elevated mortality rates. Identifying reliable predictors for ischemic stroke risk is crucial for implementing timely clinical interventions. This study endeavors to develop and validate a machine learning-based predictive model for ischemic stroke risk stratification in TAAD patients undergoing surgical treatment. Methods : This retrospective cohort study analyzed 430 TAAD patients who underwent total aortic arch replacement with frozen elephant trunk implantation at Beijing Anzhen Hospital (2015-2021). The cohort was randomly partitioned into training (70%, n=301) and validation (30%, n=129) sets. Feature selection was performed using Boruta algorithm, with variables demonstrating P<0.1 in univariate analysis subsequently incorporated into multivariate logistic regression. Ten machine learning models were evaluated through receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and calibration plots. Model interpretability was enhanced via Shapley Additive Explanations (SHAP), while restricted cubic splines (RCS) elucidated potential non-linear/liner relationships between predictors and result. Results: The GBM model demonstrated superior predictive performance compared to all other models, achieving an area under the curve (AUC) of 0.804 in the validation cohort. SHAP analysis identified the following key predictors of postoperative ischemic stroke: age, history of cerebrovascular disease, cardiopulmonary bypass time(CPBT), intraoperative blood loss volume(IBLV), and preoperative systolic blood pressure(SBP).Furthermore,RCS were independently constructed for each continuous variable to explore variable-outcome relationships. Conclusion: The Gradient Boosting Machine (GBM) model demonstrates the best predictive capacity for postoperative ischemic stroke in TAAD patients, offering clinicians a clinically actionable tool for early postoperative risk stratification and personalized therapeutic optimization.