Comparative Diagnostic Performance of Wide QRS Tachycardia Algorithms in Dogs:
Bridging Human and Veterinary Cardiology
DOI:
https://doi.org/10.22456/1679-9216.150958Keywords:
arrhythmia, electrocardiography, dog, ventricular tachycardia, supraventricular tachycardiaAbstract
Background: Wide complex tachycardias (WCTs) in dogs pose a significant diagnostic and clinical challenge in veterinary cardiology, often carrying a high risk of syncope, heart failure, or sudden cardiac death. Accurate differentiation between ventricular tachycardias (VTs) and supraventricular tachycardias with aberrancy (SVT-A) is crucial for guiding therapeutic decisions. However, this distinction is frequently obscured by similar electrocardiographic morphologies and inherent electrophysiological complexity. While specific diagnostic algorithms have been developed for both human (e.g., D12V16) and veterinary medicine to improve diagnostic accuracy, their comparative performance and potential for hybrid optimization in canine patients remain largely unexplored. This study aimed to evaluate and compare the diagnostic performance of 2 established electrocardiographic algorithms - D12V16 (human-derived) and Santilli (veterinary-derived) - and a novel hybrid model, for differentiating WCTs in dogs.
Materials, Methods & Results: This retrospective observational study analyzed 25 twelve-lead electrocardiograms (ECGs) from client-owned dogs with sustained WCTs, collected between 2023 and 2024 at NUDIVE / UFPI. A gold standard diagnosis of VT (n = 14) or SVT-A (n = 11) was established by consensus of 3 experienced veterinary cardiologists, who were blinded to the algorithm results. Each algorithm (D12V16, adapted Santilli, and the hybrid model) was applied in a strictly binary format. Diagnostic performance was assessed using accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1-Score, F1-Macro, and Cohen’s Kappa coefficient, with calculations performed using Python. The D12V16 algorithm demonstrated superior overall performance, achieving 92% accuracy, 100% sensitivity for VT (0 false negatives), 81.82% specificity for SVT-A, and a Kappa coefficient of 0.8261, indicating "Almost Perfect" agreement. The adapted Santilli algorithm showed 72% accuracy, 71.43% sensitivity for VT (4 false negatives), 72.73% specificity for SVT-A, and a Kappa of 0.4400 ("Moderate" agreement). The hybrid model exhibited intermediate performance with 84% accuracy and 100% sensitivity for VT (0 false negatives), but a reduced specificity of 63.64% for SVT-A (4 false positives), and a Kappa of 0.6409 ("Substantial" agreement).
Discussion: The D12V16 algorithm proved highly effective for WCT differentiation in dogs, with its 100% VT sensitivity being a critical advantage in acute clinical settings. While the hybrid model showed promise, its lower specificity (higher false positive rate for SVT-A) limits its immediate clinical utility compared to D12V16. These findings advocate for the translational application and validation of human-based algorithms in veterinary cardiology. Future studies with larger canine cohorts and advanced computational approaches, including artificial intelligence, are warranted to further refine WCT diagnostic accuracy in dogs.
Keywords: arrhythmia, electrocardiography, dog, ventricular tachycardia, supraventricular tachycardia.
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