Computed tomography angiography assessment of the degree of simple coarctation of the aorta and its relationship with surgical outcome: A retrospective analysis

Regional Arch Measurements Differ Between Imaging Modalities in Infants With Aortic Coarctation

BACKGROUND

Aortic arch measurements provide a framework for surgical decision-making in neonatal aortic coarctation, specifically in the determination of approach for arch repair by lateral thoracotomy vs median sternotomy. The purpose of this study was to evaluate our experience with transthoracic echocardiography (TTE) and computed tomography angiography (CTA) in the preoperative evaluation of infants with aortic coarctation, specifically comparing arch dimensions as a function of imaging modality.

METHODS

Imaging data were reviewed for all infants undergoing surgical repair of aortic coarctation at our institution from 2012 to 2022. Infants with both TTE and CTA evaluations were included. Aortic measurements were compared at predefined anatomic regions including ascending aorta, proximal arch, distal arch, and isthmus.

RESULTS

During the study period, 372 infants underwent surgical coarctation repair; 72 (19.4%) infants had TTE and CTA arch evaluations preoperatively. Significant discrepancies between imaging modalities were defined by poor correlation coefficients and absolute measurement differences and were most prominent in the proximal aortic arch (R2 = 0.23 [-4.4 to 3.2 mm]) and isthmus regions (R2 = 0.11 [-4.2 to 1.7 mm]). Improved correlation was demonstrated in the ascending aorta (R2 = 0.63) and distal aortic arch (R2 = 0.54).

CONCLUSIONS

Significant variability exists between TTE- and CTA-derived aortic measurements in infants with coarctation, with proximal arch measurements demonstrating the poorest correlation. This anatomic location represents a commonly used arch region for the determination of approach for repair of neonatal aortic coarctation. Thus, these findings have important implications for current preoperative surgical decision-making paradigms and future prospective study to minimize the risk of residual or recurrent arch obstruction.

Research and application of intelligent image processing technology in the auxiliary diagnosis of aortic coarctation

Objective

To explore the application of the proposed intelligent image processing method in the diagnosis of aortic coarctation computed tomography angiography (CTA) and to clarify its value in the diagnosis of aortic coarctation based on the diagnosis results.

Methods

Fifty-three children with coarctation of the aorta (CoA) and forty children without CoA were selected to constitute the study population. CTA was performed on all subjects. The minimum diameters of the ascending aorta, proximal arch, distal arch, isthmus, and descending aorta were measured using manual and intelligent methods, respectively. The Wilcoxon signed-rank test was used to analyze the differences between the two measurements. The surgical diagnosis results were used as the gold standard, and the diagnostic results obtained by the two measurement methods were compared with the gold standard to quantitatively evaluate the diagnostic results of CoA by the two measurement methods. The Kappa test was used to analyze the consistency of intelligence diagnosis results with the gold standard.

Results

Whether people have CoA or not, there was a significant difference (p < 0.05) in the measurements of the minimum diameter at most sites using the two methods. However, close final diagnoses were made using the intelligent method and the manual. Meanwhile, the intelligent measurement method obtained higher accuracy, specificity, and AUC (area under the curve) compared to manual measurement in diagnosing CoA based on Karl's classification (accuracy = 0.95, specificity = 0.9, and AUC = 0.94). Furthermore, the diagnostic results of the intelligence method applied to the three criteria agreed well with the gold standard (all kappa ≥ 0.8). The results of the comparative analysis showed that Karl's classification had the best diagnostic effect on CoA.

Conclusion

The proposed intelligent method based on image processing can be successfully applied to assist in the diagnosis of CoA.