Cross-sectional shape analysis for risk assessment and prognosis of patients with true lumen narrowing after type-A aortic dissection surgery

TL;DR

This study develops a mathematical and machine learning framework using cross-sectional shape analysis of the true lumen in CT scans to predict risk levels of aortic dissection patients after surgery, aiding early prognosis and treatment planning.

Contribution

The paper introduces a novel shape-based risk assessment framework utilizing form factor and linear discriminant analysis for post-ATAAD patient prognosis.

Findings

The model accurately identified low-risk patients, potentially reducing hospital visits.

It effectively predicted high-risk cases, supporting early intervention.

The framework demonstrated reliable risk classification over a follow-up period of 2.8 years.

Abstract

Background: For acute type-A aortic dissection (ATAAD) surgery, early post-surgery assessment is crucially important for effective treatment plans, underscoring the need for a framework to identify the risk level of aortic dissection cases. We examined true-lumen narrowing during follow-up examinations, collected morphological data 14 days (early stages) after surgery, and assessed patient risk levels over 2.8 years. Purpose: To establish an implementable framework supported by mathematical techniques to predict the risk of aortic dissection patients experiencing true-lumen narrowing after ATAAD surgery. Materials and Methods: This retrospective study analyzed CT data from 21 ATAAD patients. Forty uniformly distributed cross-sectional shapes (CSSs) are derived from each lumen to account for gradual changes in shape. We introduced the form factor (FF) to assess CSS morphology. Linear discriminant analysis (LDA) is used for the risk classification of aortic dissection patients. Leave-one-patient-out cross-validation (LOPO-CV) is used for risk prediction. Results: For this investigation, we examined data of 21 ATAAD patients categorized into high-risk, medium-risk, and low-risk cases based on clinical observations of the range of true-lumen narrowing. Our risk classification machine-learning (ML) model preserving the model's generalizability. The model's predictions reliably identified low-risk patients, thereby potentially reducing hospital visits. It also demonstrated proficiency in accurately predicting the risk for all high-risk patients. Conclusion: The suggested method anticipates the risk linked to aortic enlargement in patients with a narrowing true lumen in the early stage following ATAAD surgery, thereby aiding follow-up doctors in enhancing patient care.