Do you know your PDA type?

Morphologic study of patent ductus arteriosus based on computed tomography data in 25 dogs

Heesung Umh, Kyoung-a Youpi, Jeongmin Lee, et al.

Background
The study examines the morphology and dimensions of patent ductus arteriosus (PDA) in dogs using computed tomography (CT). PDA is a common congenital cardiac defect with significant mortality if untreated. Precise morphological characterization is crucial to improve treatment, particularly in selecting appropriately sized occlusion devices for minimally invasive interventions.

Methods
The study retrospectively analyzed CT angiography data from 25 client-owned dogs diagnosed with PDA. Morphological parameters, including minimum duct diameter, ampulla diameter, length, angle, and cross-sectional characteristics, were measured using multiplanar reconstruction tools. Morphology classifications and dimensions were compared to previous methods, and statistical analyses evaluated correlations among variables.

Results
Three PDA morphology types were identified: Type IIA (48%), Type IIB (20%), and Type III (32%). Morphological parameters showed significant correlations between ampulla diameter, body weight, and angle. Cross-sectional analyses revealed that PDA structures were predominantly oval rather than circular. Ampulla diameter measurements using CT were more consistent with actual cross-sectional areas than traditional methods. Device selection criteria based on CT measurements were recommended for accuracy.

Limitations
The study's small sample size and retrospective design limit generalizability. Variations in CT imaging protocols and the lack of direct comparison with other diagnostic modalities within the same patients were additional constraints.

Conclusions
CT provides detailed morphological insights into PDA, offering improved accuracy for pre-procedural planning and device selection. This approach could reduce sizing complications and enhance outcomes in PDA occlusion procedures. The findings support further research using larger datasets to refine CT-based diagnostic and interventional methodologies.

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