Bayesian evaluation of the accuracy of a thoracic auscultation scoring system in dairy calves with bronchopneumonia using a standard lung sound nomenclature

Abstract Background Although thoracic auscultation (AUSC) in calves is quick and easy to perform, the definition of lung sounds is highly variable and leads to poor to moderate accuracy in diagnosing bronchopneumonia (BP). Hypothesis/Objectives Evaluate the diagnostic accuracy of an AUSC scoring system based on a standard lung sound nomenclature at different cut‐off values, accounting for the absence of a gold standard test for BP diagnosis. Animals Three hundred thirty‐one calves. Methods We considered the following pathological lung sounds: increased breath sounds (score 1), wheezes and crackles (score 2), increased bronchial sounds (score 3), and pleural friction rubs (score 4). Thoracic auscultation was categorized as AUSC1 (positive calves for scores ≥1), AUSC2 (positive calves for scores ≥2), and AUSC3 (positive calves for scores ≥3). The accuracy of AUSC categorizations was determined using 3 imperfect diagnostic tests with a Bayesian latent class model and sensitivity analysis (informative vs weakly informative vs noninformative priors and with vs without covariance between ultrasound and clinical scoring). Results Based on the priors used, the sensitivity (95% Bayesian confidence interval [BCI]) of AUSC1 ranged from 0.89 (0.80‐0.97) to 0.95 (0.86‐0.99), with a specificity (95% BCI) of 0.54 (0.45‐0.71) to 0.60 (0.47‐0.94). Removing increased breath sounds from the categorizations resulted in increased specificity (ranging between 0.97 [0.93‐0.99] and 0.98 [0.94‐0.99] for AUSC3) at the cost of decreased sensitivity (0.66 [0.54‐0.78] to 0.81 [0.65‐0.97]). Conclusions and Clinical Importance A standardized definition of lung sounds improved AUSC accuracy for BP diagnosis in calves.