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CardiologyStudy2 min read · distilled by Vetree AI

Pre-anaesthetic risk assessment and management of dogs with myxomatous mitral valve disease: a spectrum of care narrative review.

Levinzon I, Köster L, Vettorato E, Estrada AH · Journal of Small Animal Practice · 21 March 2026

Clinical bottom line

Integrate accessible diagnostics and risk stratification tools to safely manage anesthesia in dogs with myxomatous mitral valve disease.

Summary

Myxomatous mitral valve disease (MMVD) is the most prevalent cardiac disorder in canine primary care, affecting approximately 10% of dogs and frequently necessitating anesthesia for routine procedures. This narrative review addresses limitations of the American Society of Anesthesiologists (ASA) physical status classification system for risk stratification in MMVD patients, particularly Stage B1 and B2 dogs that demonstrate significant disease variability within identical ASA categories. While comprehensive echocardiography remains the gold standard for staging, financial constraints and extended waiting periods often preclude specialist referral. The authors present a practical, accessible framework for pre-anesthetic risk assessment integrating readily available diagnostic modalities: physical examination, radiographic measurements (vertebral heart size and vertebral left atrial size), cardiac biomarkers (N-terminal pro-B-type natriuretic peptide), and point-of-care ultrasound. The review provides tailored anaesthetic protocols for diverse clinical scenarios, from cases with minimal diagnostics through ambiguous presentations to confirmed cardiomegaly, incorporating structured management strategies for intraoperative complications. This spectrum-of-care approach acknowledges that while specialist cardiology evaluation remains optimal, practical alternatives can effectively guide safe anesthetic management in this commonly encountered condition, improving accessibility and outcomes for companion dogs with MMVD.

CardiologyAnesthesiaSmall AnimalInternal Medicine

This summary was distilled by AI and may occasionally misinterpret data. Confirm critical details with the primary literature before clinical application.