Retrospective Evaluation of Acid-Base Disorders in Dogs With Hyperlactatemia Using Traditional and Semiquantitative Analysis: 3866 Cases.
Saint-Pierre LM, Epstein SE, Hopper K · Journal of Veterinary Emergency and Critical Care · 14 August 2026
Metabolic acidosis and type A hyperlactatemia are strongly associated with increased mortality in hyperlactatemic dogs.
This large retrospective study evaluated acid-base disorders in 3,866 dogs with hyperlactatemia (plasma lactate >2 mmol/L) using both traditional and semiquantitative (Stewart) approaches at a university teaching hospital. Gastrointestinal disorders were the most common underlying disease category (29.3%), while circulatory shock was the most frequent mechanism of hyperlactatemia (19.1%). Traditional acid-base analysis identified abnormalities in 66.9% of dogs, with primary respiratory acidosis being most common (15.7%), though metabolic acidosis—primary or mixed—was present in 37.5% of patients. Semiquantitative analysis revealed that 70.7% of dogs had simultaneous acidifying and alkalinizing processes, with unmeasured anions being the predominant acidifying effect (44.1%). This highlights the complexity of acid-base disturbances that traditional analysis alone may undercharacterize. Critically, metabolic acidosis was associated with significantly higher mortality (46.9%) compared to dogs without metabolic acidosis (19.8%, p<0.001). Type A hyperlactatemia (tissue hypoxia-driven) carried substantially higher mortality (52.5%) than Type B (30.4%, p<0.001). The study underscores the prognostic importance of identifying metabolic acidosis in hyperlactatemic dogs and suggests the semiquantitative approach offers additional mechanistic insight beyond traditional methods. Clinicians managing hyperlactatemic dogs should consider both analytical frameworks to better understand underlying pathophysiology and guide treatment, particularly given the strong association between metabolic acidosis, type A hyperlactatemia, and adverse outcomes.
This summary was distilled by AI and may occasionally misinterpret data. Confirm critical details with the primary literature before clinical application.