Biomechanical Study On Whether To Intervene Again After Stanford Type A Aortic Dissection Surgery And Prediction Model Of The Effect Of Thoracoabdominal Aortic Replacemen

Objective:A residual false lumen progress risk prediction model was constructed based on computational fluid dynamics and biomechanical parameters were calculated to assess the factors affecting the long-term reintervention after the first operation.Methods:Patients with Stanford type A aortic dissection admitted to Fuwai hospital in were retrospectively collected and divided into control group and thoracoabdominal aortic replacement group according to long-term prognosis or history of reintervention.The biomechanical and fluid parameters of the descending aorta were calculated based on the early CTA imaging data after first operation.The differences of parameters between the two groups were compared to explore the risk factors.Results:A total of 24 patients were included from January 2015 to May 2021.The average age was 47.88±9.84 years old,and 87.5%were male.The wall shear stress of intimal flap in true lumen was higher(33.42±13.28Pa vs 21.67±6.28Pa,p=0.031)and the proportion of high relative residence time area in false lumen was lower(0.14±0.06s/Pa vs 0.36±0.18s/Pa,p=0.018)in the thoracoabdominal aortic replacement group than in the control group.The balance position of luminal pressure difference in the descending aorta was closer to the opening of the left subclavicular artery(22.00±3.91cm vs 36.00±1.77cm,p<0.001),and the false lumen pressure was greater than the true lumen pressure in the thoracoabdominal aortic replacement group.Conclusions:The computational fluid dynamics method can simplify and visualize the complex human blood flow and postoperative structure based on the mathematical model.The higher wall shear stress of the vascular wall or intimal flap,the lower relative residence time in the false lumen and the lumen pressure balance point moving to the proximal are the risk factors for poor remodeling of the descending aorta and reintervention.Objective:To summarize the operative characteristics,short-term outcomes and long-term prognosis of thoracoabdominal aortic replacement(TAAR)in our center in the last decade.Methods:270 consecutive patients who received TAAR in our hospital from 2012 to 2021 were retrospectively analyzed.Univariate and multivariate logistic regression were performed for the analysis of predictors of early death.Cox proportional hazard regression was performed for the analysis of late mortality.Survival over time and freedom from vascular events were estimated by the Kaplan-Meier curve.Results:Among all patients(mean age 41.44±11.57 years,males accounted for 73.1%)enrolled in this study.The early death rate was 7.8%,and cumulative survival rate was 93%at lyear,91%at 3 years,89%at 5 years and 87%at 10 years.The false lumen complete thrombosis(OR 1.27,95%CI 1.16-1.40;p=0.001)and lactic acid level(OR 1.41,95%CI 1.13-1.75;p=0.002)was independent risk factors for early death.More blood drainage volume(OR 1.11,95%CI 1.01-1.21;p=0.034)and paraplegia(OR 6.95,95%CI 1.5131.95;p=0.013)were independent risk factors for late mortality.Conclusions:The outcomes of TAAR were satisfactory and reliable.TAAR should be performed as early as possible if the patient is judged to be indicated for surgery,while a delay may increase thrombosis and is not conducive to perioperative recovery.Objective:Spinal cord injury(SCI)is a common and serious complication in the early stage of thoracoabdominal aortic repair,which significantly increases the mortality and disability rate.Most of the existing studies were based on traditional modeling to predict the risk factors of SCI with surgical data.This study is based on an improved machine learning method,which uses comprehensive perioperative data-driven learning to predict the occurrence of SCI.Methods:270 patients were included in the study,and 66 potentially meaningful variables were selected from 84 original variables as input data to drive machine learning.The improved machine learning algorithm(PSO-FLXGBoost)was used to stratify and rank the risk factors,and finally SHAP was used to assign attributional values for interpretation.Results:This PSO-FLXGBoost model has a high AUC(0.895)and a good prediction effect.The SHAP analysis of the four most important variables showed that lower intraoperative hemoglobin(<70g/L),higher preoperative D-Dimer(>6μg/ml)and platelet(>250*10^9/L),and prolonged operation time(>500min)can significantly increase the probability of SCI.Conclusions:An improved machine learning method was developed to predict SCI after thoracoabdominal aortic repair.The research demonstrated that intraoperative hemoglobin,preoperative coagulation status and operation time are crucial for SCI prediction.