Novel Morphological Features for Prediction of Distal Thoracic Aortic Enlargement after Thoracic Endovascular Aortic Repair of DeBakey IIIb Aortic Dissection

Preoperative systemic inflammatory response index predicts long-term outcomes in type B aortic dissection after endovascular repair

Objectives

Inflammation is a hallmark of the initial development and progression of aortic dissection. This study aimed to investigate the value of preoperative inflammatory biomarkers in predicting aorta-related adverse events (AAEs) after thoracic endovascular aortic repair (TEVAR) for type B aortic dissection.

Methods

We included all patients who underwent TEVAR for type B aortic dissection between November 2016 and November 2020 in this single-center, retrospective cohort study. Patients were divided into two groups: the AAEs group (n = 75) and the non-AAEs group (n = 126). Preoperative inflammatory biomarkers were recorded, including neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), systemic immune inflammation index (SII), and systemic inflammatory response index (SIRI). Patients were followed-up for the development of AAEs. Prediction accuracy of inflammatory biomarkers for AAEs were evaluated using the area under the receiver operating characteristic curves.

Results

This study included 201 patients, of whom 80.0% were men, with a mean age of 59.1 ± 12.5 years. A total of 75 patients developed AAEs after TEVAR. The AUCs of NLR, MLR, PLR, SII, and SIRI for AAEs were.746,.782,.534,.625 and.807, respectively. Age and SIRI were independent risk factors for the AAEs after TEVAR (HR 3.264, p <.001; HR 4.281, p <.001, respectively). Survival analysis revealed significantly lower AAE-free status in patients with preoperative SIRI > = 4 (p <.001).

Conclusion

Increased preoperative SIRI and age are independent risk factors for AAEs after TEVAR in type B aortic dissection.

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.

The 301 Classification: A Proposed Modification to the Stanford Type B Aortic Dissection Classification for Thoracic Endovascular Aortic Repair Prognostication

OBJECTIVE

To assess the usefulness of a modified Stanford classification for risk stratification of complications after thoracic endovascular aortic repair (TEVAR) for type B aortic dissection (TBAD).

PATIENTS AND METHODS

This retrospective analysis included 201 patients from an observational multicenter cohort study who underwent TEVAR for TBAD from January 1, 2011, to December 31, 2016. The patients were divided by using a modified Stanford classification, termed 301, into 3 groups: types B1 (n=62) and B3 (n=24), with a true and false lumen, respectively, descending closely along the thoracic vertebral bodies, and type B2 (n=115), a semi-spiral or spiral configuration. The value of the 301 classification in assessing the risk for post-TEVAR thoracic aortic expansion, as main outcome, and other complications was assessed by using the Kaplan-Meier method and multivariable Cox proportional hazards models.

RESULTS

Median follow-up duration was 26.37 months, and the 24-month cumulative rate of freedom from thoracic aortic enlargement was 0.58 (95% CI, 0.25 to 0.81) for type B3, 0.75 (95% CI, 0.64 to 0.83) for type B2, and 0.97 (95% CI, 0.88 to 0.99) for type B1. In the multivariable Cox regression models, types B2 and B3 with type B1 as reference were independently associated with the risk for thoracic aortic expansion (type B2: hazard ratio, 7.81; 95% CI, 1.84 to 33.13; type B3: hazard ratio, 13.91; 95% CI, 2.86 to 67.69).

CONCLUSION

The 301 classification, a modified Stanford classification system in the era of endovascular repair, appears to improve the risk stratification of patients with TBAD undergoing TEVAR.

TRIAL REGISTRATION

Chinese Clinical Trial Registry number: ChiCTR-POC-17011726.