Accuracy of history, physical examination, cardiac biomarkers, and biochemical variables in identifying dogs with stage B2 degenerative mitral valve disease

Abstract

Abstract Background Treatment is indicated in dogs with preclinical degenerative mitral valve disease (DMVD) and cardiomegaly (stage B2). This is best diagnosed using echocardiography; however, relying upon this limits access to accurate diagnosis. Objectives To evaluate whether cardiac biomarker concentrations can be used alongside other clinical data to identify stage B2 dogs. Animals Client‐owned dogs (n = 1887) with preclinical DMVD prospectively sampled in Germany, the United Kingdom, and the United States. Methods Dogs that met inclusion criteria and were not receiving pimobendan (n = 1245) were used for model development. Explanatory (multivariable logistic regression) and predictive models were developed using clinical observations, biochemistry, and cardiac biomarker concentrations, with echocardiographically confirmed stage B2 disease as the outcome. Receiver operating characteristic curves assessed the ability to identify stage B2 dogs. Results Age, appetite, serum alanine aminotransferase activity, body condition, serum creatinine concentration, murmur intensity, and plasma N‐terminal propeptide of B‐type natriuretic peptide (NT‐proBNP) concentration were independently associated with the likelihood of being stage B2. The discriminatory ability of this explanatory model (area under curve [AUC], 0.84; 95% confidence interval [CI], 0.82‐0.87) was superior to NT‐proBNP (AUC, 0.77; 95% CI, 0.74‐0.80) or the vertebral heart score alone (AUC, 0.76; 95% CI, 0.69‐0.83). A predictive logistic regression model could identify the probability of being stage B2 (AUC test set, 0.86; 95% CI, 0.81‐0.91). Conclusion and Clinical Importance Our findings indicate accessible measurements could be used to screen dogs with preclinical DMVD. Encouraging at‐risk dogs to seek further evaluation could result in a greater proportion of cases being appropriately managed.

Keywords

• canine
• cardiac biomarker
• machine learning
• NT‐proBNP
• prediction