Male-Female Differences in Acute Type B Aortic Dissection

BACKGROUND

Acute type B aortic dissection is a cardiovascular emergency with considerable mortality and morbidity risk. Male-female differences have been observed in cardiovascular disease; however, literature on type B aortic dissection is scarce.

METHODS AND RESULTS

A retrospective cohort study was conducted including all consecutive patients with acute type B aortic dissection between 2007 and 2017 in 4 tertiary hospitals using patient files and questionnaires for late morbidity. In total, 384 patients were included with a follow-up of 6.1 (range, 0.02-14.8) years, of which 41% (n=156) were female. Women presented at an older age than men (67 [interquartile range (IQR), 57-73] versus 62 [IQR, 52-71]; P=0.015). Prior abdominal aortic aneurysm (6% versus 15%; P=0.009), distally extending dissections (71 versus 85%; P=0.001), and clinical malperfusion (18% versus 32%; P=0.002) were less frequently observed in women. Absolute maximal descending aortic diameters were smaller in women (36 [IQR: 33-40] mm versus 39 [IQR, 36-43] mm; P<0.001), while indexed for body surface area diameters were larger in women (20 [IQR, 18-23] mm/m2 versus 19 [IQR, 17-21] mm/m2). No male-female differences were found in treatment choice; however, indications for invasive treatment were different (P<0.001). Early mortality rate was 9.6% in women and 11.8% in men (P=0.60). The 5-year survival was 83% (95% CI, 77-89) for women and 84% (95% CI, 79-89) for men (P=0.90). No male-female differences were observed in late (re)interventions.

CONCLUSIONS

No male-female differences were found in management, early or late death, and morbidity in patients presenting with acute type B aortic dissection, despite distinct clinical profiles at presentation. More details on the impact of age and type of intervention are warranted in future studies.

Factors Affecting False Lumen Thrombosis In Type B Aortic Dissection

BACKGROUND

Complete thrombosis of the false lumen facilitates remodeling of type B aortic dissection (TBAD). Morphological characteristics affect thrombosis in the false lumen.

OBJECTIVES

Discuss the factors present before admission that influence false lumen thrombosis in patients with TBAD.

METHODS

We studied 282 patients diagnosed with TBAD in our hospital between January 2008 and December 2017. We divided the subjects into a thrombotic group and a non-thrombotic group based on whether any thrombus was detectable in the false lumen. We analyzed the differences between the two groups with respect to clinical data, the vertical length of the dissection, and the diameter of the aorta. P values < 0.05 were considered statistically significantly different.

RESULTS

Significant differences between the thrombotic group and non-thrombotic group were found with respect to age (53.92 ± 11.40 vs. 50.36 ± 10.71, p = 0.009) and proportion of patients with renal insufficiency (7.83% vs. 16.38%, p = 0.026). In zones 3-9, the true lumen diameter of the thrombotic group was significantly larger than in the non-thrombotic group (p < 0.05). Binary logistic regression analysis showed that true lumen diameter in zone 5 and renal insufficiency were independent predictors of false lumen thrombosis.

CONCLUSIONS

Age and renal function were associated with thrombosis in the false lumen. Potentially, the difference between the diameter of the true lumen diameter and that of the false lumen may influence the thrombosis of the false lumen.

Emergency Department Visits After Acute Aortic Syndromes

OBJECTIVES

Recent reports document a high rate of readmission after hospitalization for acute aortic syndromes (AAS) that include acute aortic dissections (AD), intramural hematomas (IMH), or penetrating aortic ulcers (PAU). We examined the rate of return to the emergency department (ED) to better understand the utilization of emergent health care services after AAS.

METHODS

Consecutive AAS patients admitted to the vascular surgery service from 2004 to 2020 were included. Patients with type A dissections, arch involvement, or chronic aortic pathology were excluded. The primary outcome was ED visits within 90 days of the original hospitalization.

RESULTS

The study included 79 subjects (62% men, 38% women; mean age, 64+14 years) with AAS (82% AD, 11% IMH, 6% PAU). A total of 54 ED visits related to the AAS occurred within 90 days of the original discharge, each of which incurred a CT angiogram. Twenty-eight (35%) subjects had a mean of 2+2 ED visits, while 51 (65%) subjects had no ED visits. Ninety percent (25/28) of the first ED visits occurred within 1 month of discharge and 53% (15/28) within one week. 17 (61%) subjects were readmitted to the hospital from the ED. Four subjects were found to have progression of AAS on imaging studies and underwent TEVAR during readmission. Comparing subjects who returned to the ED to those who did not, there were no significant differences in demographics, atherosclerotic risk factors except coronary artery disease, type of AAS, number of antihypertensive medications at admission or discharge, operative intervention, length of initial hospital stay, or discharge status. The chief complaint at the first ED visit was pain (n=17), uncontrolled hypertension (n=5), syncope (n=3), and other (n=3).

CONCLUSIONS

These data show that one in three patients with AAS returned to the ED within 90 days of initial discharge. Although returning subjects had a higher number of readmissions, few had progression of AAS that required intervention. Because the vast majority were readmitted for medical therapy, early and frequent clinic follow up may help decrease ED visits and readmissions after AAS.

Cross-Entropy Learning for Aortic Pathology Classification of Artificial Multi-Sensor Impedance Cardiography Signals

An aortic dissection, a particular aortic pathology, occurs when blood pushes through a tear between the layers of the aorta and forms a so-called false lumen. Aortic dissection has a low incidence compared to other diseases, but a relatively high mortality that increases with disease progression. An early identification and treatment increases patients’ chances of survival. State-of-the-art medical imaging techniques have several disadvantages; therefore, we propose the detection of aortic dissections through their signatures in impedance cardiography signals. These signatures arise due to pathological blood flow characteristics and a blood conductivity that strongly depends on the flow field, i.e., the proposed method is, in principle, applicable to any aortic pathology that changes the blood flow characteristics. For the signal classification, we trained a convolutional neural network (CNN) with artificial impedance cardiography data based on a simulation model for a healthy virtual patient and a virtual patient with an aortic dissection. The network architecture was tailored to a multi-sensor, multi-channel time-series classification with a categorical cross-entropy loss function as the training objective. The trained network typically yielded a specificity of (93.9±0.1)% and a sensitivity of (97.5±0.1)%. A study of the accuracy as a function of the size of an aortic dissection yielded better results for a small false lumen with larger noise, which emphasizes the question of the feasibility of detecting aortic dissections in an early state.

Using Machine Learning to Predict Five-Year Reintervention Risk in Type B Aortic Dissection Patients After Thoracic Endovascular Aortic Repair

Purpose: Type B aortic dissection (TBAD) is a high-risk disease, commonly treated with thoracic endovascular aortic repair (TEVAR). However, for the long-term follow-up, it is associated with a high 5-year reintervention rate for patients after TEVAR. There is no accurate definition of prognostic risk factors for TBAD in medical guidelines, and there is no scientific judgment standard for patients’ quality of life or survival outcome in the next five years in clinical practice. A large amount of medical data features makes prognostic analysis difficult. However, machine learning (ML) permits lots of objective data features to be considered for clinical risk stratification and patient management. We aimed to predict the 5-year prognosis in TBAD after TEVAR by Ml, based on baseline, stent characteristics and computed tomography angiography (CTA) imaging data, and provided a certain degree of scientific basis for prognostic risk score and stratification in medical guidelines. Materials and Methods: Dataset we recorded was obtained from 172 TBAD patients undergoing TEVAR. Totally 40 features were recorded, including 14 baseline, 5 stent characteristics and 21 CTA imaging data. Information gain (IG) was used to select features highly associated with adverse outcome. Then, the Gradient Boost classifier was trained using grid search and stratified 5-fold cross-validation, and Its predictive performance was evaluated by the area under the curve (AUC) in the receiver operating characteristic (ROC). Results: Totally 60 patients underwent reintervention during follow-up. Combing 24 features selected by IG, ML model predicted prognosis well in TBAD after TEVAR, with an AUC of 0.816 and a 95% confidence interval of 0.797 to 0.837. Reintervention rate of prediction was slightly higher than the actual (48.2% vs. 34.8%). Conclusion: Machine learning, which combined with baseline, stent characteristics and CTA imaging data for personalized risk computations, effectively predicted reintervention risk in TBAD patients after TEVAR in 5-year follow-up. The model could be used to efficiently assist the clinical management of TBAD patients and prompt high-risk factors.