BACKGROUND: The quantitative effect of strong electrolytes, pCO2 , and plasma protein concentration in determining plasma pH and bicarbonate concentrations can be demonstrated with the physicochemical approach. Plasma anion gap (AG) and strong ion gap (SIG) are used to assess the presence or absence of unmeasured anions. HYPOTHESES: The physicochemical approach is useful for detection and explanation of acid-base disorders in horses with colitis. AG and SIG accurately predict hyperlactatemia in horses with colitis. ANIMALS: Fifty-four horses with acute colitis and diarrhea. METHODS: Retrospective study. Physicochemical variables were calculated for each patient. ROC curves were generated to analyze sensitivity and specificity of AG and SIG for predicting hyperlactatemia. RESULTS: Physicochemical interpretation of acid-base events indicated that strong ion metabolic acidosis was present in 39 (72%) horses. Mixed strong ion acidosis and decreased weak acid (hypoproteinemia) alkalosis was concomitantly present in 17 (30%) patients. The sensitivity and specificity of AG and SIG to predict hyperlactatemia (L-lactate > 5 mEq/L) were 100% (95% CI, 66.4-100; P < .0001) and 84.4% (95% CI, 70.5-93.5 P < .0001). Area under the ROC curve for AG and SIG for predicting hyperlactatemia was 0.95 (95% CI, 0.86-0.99) and 0.93 (95% CI, 0.83-0.99), respectively. CONCLUSION AND CLINICAL RELEVANCE: These results emphasize the importance of strong ions and proteins in the maintenance of the acid-base equilibria. AG and SIG were considered good predictors of clinically relevant hyperlactatemia.
BACKGROUND: The quantitative effect of strong electrolytes, pCO2 , and plasma protein concentration in determining plasma pH and bicarbonate concentrations can be demonstrated with the physicochemical approach. Plasma anion gap (AG) and strong ion gap (SIG) are used to assess the presence or absence of unmeasured anions. HYPOTHESES: The physicochemical approach is useful for detection and explanation of acid-base disorders in horses with colitis. AG and SIG accurately predict hyperlactatemia in horses with colitis. ANIMALS: Fifty-four horses with acute colitis and diarrhea. METHODS: Retrospective study. Physicochemical variables were calculated for each patient. ROC curves were generated to analyze sensitivity and specificity of AG and SIG for predicting hyperlactatemia. RESULTS: Physicochemical interpretation of acid-base events indicated that strong ion metabolic acidosis was present in 39 (72%) horses. Mixed strong ion acidosis and decreased weak acid (hypoproteinemia) alkalosis was concomitantly present in 17 (30%) patients. The sensitivity and specificity of AG and SIG to predict hyperlactatemia (L-lactate > 5 mEq/L) were 100% (95% CI, 66.4-100; P < .0001) and 84.4% (95% CI, 70.5-93.5 P < .0001). Area under the ROC curve for AG and SIG for predicting hyperlactatemia was 0.95 (95% CI, 0.86-0.99) and 0.93 (95% CI, 0.83-0.99), respectively. CONCLUSION AND CLINICAL RELEVANCE: These results emphasize the importance of strong ions and proteins in the maintenance of the acid-base equilibria. AG and SIG were considered good predictors of clinically relevant hyperlactatemia.
Authors: Diego E Gomez; Mathilde Leclere; Luis G Arroyo; Lynna Li; Emily John; Tiago Afonso; Flavie Payette; Shannon Darby Journal: Can Vet J Date: 2022-10 Impact factor: 1.075
Authors: Francisco A Uzal; Luis G Arroyo; Mauricio A Navarro; Diego E Gomez; Javier Asín; Eileen Henderson Journal: J Vet Diagn Invest Date: 2021-11-11 Impact factor: 1.569
Authors: Diego E Gomez; Jeanne Lofstedt; Luis G Arroyo; Maureen Wichtel; Tammy Muirhead; Henri Stämpfli; J Trenton McClure Journal: Can Vet J Date: 2017-10 Impact factor: 1.008
Authors: Luis G Arroyo; Alison Moore; Sofia Bedford; Diego E Gomez; Omid Teymournejad; Qingming Xiong; Khemraj Budachetri; Hannah Bekebrede; Yasuko Rikihisa; John D Baird Journal: Can Vet J Date: 2021-06 Impact factor: 1.008
Authors: Diego E Gomez; Sébastien Buczinski; Shannon Darby; Megan Palmisano; Sarah S K Beatty; Robert J Mackay Journal: J Vet Intern Med Date: 2020-09-23 Impact factor: 3.333
Authors: Diego E Gomez; Sofia Bedford; Shannon Darby; Megan Palmisano; Robert J MacKay; David L Renaud Journal: J Vet Intern Med Date: 2020-11-03 Impact factor: 3.333