Outcomes of thoracic endovascular repair for type B aortic dissection with multichanneled morphology

Deep learning-based radiomics of computed tomography angiography to predict adverse events after initial endovascular repair for acute uncomplicated Stanford type B aortic dissection

PURPOSE

This study aimed to construct a predictive model integrating deep learning-derived radiomic features from computed tomography angiography (CTA) and clinical biomarkers to forecast postoperative adverse events (AEs) in patients with acute uncomplicated Stanford type B aortic dissection (uTBAD) undergoing initial thoracic endovascular aortic repair (TEVAR).

METHODS

We retrospectively evaluated 369 patients treated with TEVAR for acute uTBAD from January 2015 to December 2022. A three-dimensional (3D) deep convolutional neural network (CNN) automated radiomic feature extraction from CTA images. Feature selection, using Analysis of Variance (ANOVA) and the Least Absolute Shrinkage and Selection Operator (LASSO) algorithms, refined a radiomic score (Rad-Score). This score, alongside clinical parameters, was modelled via Extreme Gradient Boosting (XGBoost) analysis. Model calibration was assessed by calibration curves.

RESULTS

The integration of the Rad-Score with clinical factors including albumin and C-reactive protein levels moderately enhanced predictive efficiency, exhibiting an area under the curve (AUC) of 1.000 (95%CI, 1.000-1.000) in the training cohort and 0.990 (95%CI, 0.966-1.000) in the internal validation cohort. In an independent validation cohort from another hospital, the combined model yielded an AUC of 0.985 (95%CI, 0.965-1.000), with an accuracy, precision, sensitivity, and specificity of 0.92, 0.92, 0.94, and 0.91, respectively.

CONCLUSIONS

The synergistic application of deep learning-based radiomics from CTA and clinical indicators holds promise for anticipating AEs post-initial thoracic endovascular aortic repair in patients with acute uTBAD. The clinical utility of the constructed combined model, offering prognostic foresight during follow-up, has been substantiated.

Morphological features and endovascular repair for type B multichanneled aortic dissection: A case report

BACKGROUND

Among the various types of aortic dissection, multichanneled aortic dissection (MCAD) differs from classic double-channeled aortic dissection and involves the formation of an additional false lumen in the aortic wall or the flaps. It is considered a relatively rare condition with high perioperative mortality and morbidity. However, the morphological characteristic and the optimal therapeutic strategy for MCAD has not been fully determined.

CASE SUMMARY

A 64-year-old man presented to our hospital with severe epigastric abdominal pain radiating to the back that was associated with nausea without emesis. A computed tomography angiogram was performed that revealed a type B aortic dissection with multiple channels extending from the level of the left subclavian artery to the bilateral femoral arteries. We used a medical three-dimensional modeling (3D) modeling system to identify the location and extension of multiple lumens from different angles. It also precisely located the two primary entries leading to the false lumens, which helped us to exclude the two false lumens with one stent-graft.

CONCLUSION

By applying medical 3D modeling system, we discover the fragility of aortic wall and the collapse of true lumen caused by the multiple false lumens are the two morphological features of MCAD.

TEVAR for complicated and uncomplicated type B aortic dissection-Systematic review and meta-analysis

BACKGROUND

Type B aortic dissection (TBAD), is defined as a dissection involving the aorta distal to left subclavian artery with the ascending aorta and the aortic arch not affected. TBAD is classified due to the time frame and presence of complications. Complicated TBAD (co-TBAD) patients have a greater mortality rate than uncomplicated TBAD (un-TBAD) and thoracic endovascular aortic repair (TEVAR) is considered the gold-standard intervention for these clinical challenges.

METHODS

We undertook a systematic review of the literature regarding TEVAR intervention in co-TBAD and un-TBAD. A comprehensive search was undertaken across four major databases and was evaluated and assessed until June 2020.

RESULTS

A total of 16,104 patients were included in the study (7772 patients co-TBAD and 8352 un-TBAD). A significantly higher proportion of comorbidities were seen in co-TBAD patients compared with un-TBAD. Acute dissection was more frequent in the co-TBAD group (73.55% vs. 66.91%), while chronic dissection was more common in un-TBAD patients (33.8% vs. 70.73%). Postprocedure stroke was higher in co-TBAD (5.85% vs. 3.92%; p < .01), while postprocedural renal failure was higher in un-TBAD patients (7.23 vs. 11.38%; p < .01). No difference was observed in in-hospital mortality however the 30 days mortality was higher in the co-TBAD group. One-year survival was higher in the uncomplicated group but this difference was not observed in the 5-year survival.

CONCLUSION

In our analysis we can appreciate that despite significantly higher comorbidities in the co-TBAD cohort, there was no difference in in-hospital mortality between the two groups and the 5-year survival did not have any difference.

Prediction of Distal Aortic Enlargement after Proximal Repair of Aortic Dissection Using Machine Learning

OBJECTIVES

This study aimed to construct a risk prediction model for distal aortic enlargement in patients with type B aortic dissection (TBAD) treated with proximal thoracic endovascular aortic repair (TEVAR).

METHODS

From June 2010 to June 2016, patients with TBAD who underwent proximal TEVAR were retrospectively analyzed. A total of 38 clinical and imaging variables were collected. Univariable logistic regression was conducted to explore potential risk factors associated with distal aortic enlargement. Elastic net regression was employed to select significantly influential variables. Then, machine learning algorithms (logistic regression (LR), artificial neutral network (ANN), random forest and support vector machine) were applied to build risk prediction models. The area under the receiver operating characteristic curve (AUC), sensitivity and specificity were used to evaluate the performance of these models.

RESULTS

A total of 503 patients were enrolled in this study. During the follow-up, 105 (20.9%) patients were identified as having distal aortic enlargement, and 69 (13.7%) patients were found to have distal aortic aneurysm formation. Five patients were identified with aortic rupture. True lumen collapse and multi-false lumens were two potential risk factors for distal aortic enlargement after proximal repair of TBAD. The LR model performed the best in predicting distal aortic enlargement, with the highest sensitivity (96.7%) and an AUC of 0.773. The best model for predicting distal aneurysm formation was the ANN model, which yielded the highest AUC (0.876) and a specificity of 79.1%.

CONCLUSIONS

Machine learning approaches can produce accurate predictions of distal aortic enlargement after proximal repair of TBAD, which potentially benefits subsequent management.

Intravascular Ultrasound-Assisted Endovascular Treatment of Mesenteric Malperfusion in a Multichannel Aortic Dissection With Full True Lumen Collapse

PURPOSE

To describe endovascular treatment of mesenteric malperfusion in a multichannel aortic dissection (MCAD) with full true lumen (TL) collapse following thoracic endovascular aortic repair (TEVAR).

CASE REPORT

A 54-year-old man presented with chronic mesenteric ischemia and a previous TEVAR for MCAD complicated by superior mesenteric artery (SMA) malperfusion. Computed tomography angiography (CTA) demonstrated a 3-channel aortic dissection with a "false-true-false" configuration. The SMA was malperfused through the collapsed TL. CTA also showed a secondary entry tear, measuring 18 mm in diameter, at the end of the previous endograft. Direct open surgery or endovascular revascularization of the SMA was not feasible. A plan was devised to improve SMA perfusion by increasing the TL inflow. With the assistance of intravascular ultrasound (IVUS), an endograft was placed through one false lumen in the abdominal aorta and through the TL in the descending thoracic aorta to seal the secondary entry tear. Symptoms of mesenteric ischemia resolved 2 days after the procedure. At 1 year, he is asymptomatic, has gained weight, and has improved SMA perfusion and remodeling of the 3-channel dissection on CTA.

CONCLUSION

IVUS imaging can help evaluate the complex hemodynamics of MCAD. Patient-specific endovascular treatment of MCAD with mesenteric malperfusion seems to be a feasible bailout alternative treatment for urgent, complex cases without reconstruction options.

Hemodynamic evaluation using four-dimensional flow magnetic resonance imaging for a patient with multichanneled aortic dissection

The hemodynamic function of multichanneled aortic dissection (MCAD) requires close monitoring and effective management to avoid potentially catastrophic sequelae. This report describes a 47-year-old man who underwent endovascular repair based on findings from four-dimensional (4D) flow magnetic resonance imaging of an MCAD. The acquired 4D flow data revealed complex, bidirectional flow patterns in the false lumens and accelerated blood flow in the compressed true lumen. The collapsed abdominal true lumen expanded unsatisfactorily after primary tear repair, which required further remodeling with bare stents. This case study demonstrates that hemodynamic analysis using 4D flow magnetic resonance imaging can help understand the complex pathologic changes of MCAD.