Characterizing the diagnostic sensitivity and specificity of pain biomarkers in cattle using receiver operating characteristic curves

Abstract

Biomarkers are used to assess pain and analgesic drug efficacy in livestock. However, often the diagnostic sensitivity and specificity of these biomarkers for different painful conditions over time have not been described. Receiver operating characteristic (ROC) curves are graphical plots that illustrate the diagnostic ability of a test as its discrimination threshold is varied. The objective of this analysis was to use area under the curve (AUC) values derived from ROC analysis to characterize the predictive value of potential pain biomarkers at specific time points following a painful stimulus. The biomarkers included in the analysis were plasma cortisol, salivary cortisol, hair cortisol, infrared thermography (IRT), mechanical nociceptive threshold (MNT), substance P, kinematic gait analysis, and a visual analog scale for pain. A total of 7,992 biomarker outcomes collected from 7 pain studies involving pain associated with castration, dehorning, lameness, and abdominal surgery were included in the analysis. Each study consisted of 3 treatments: uncontrolled pain (tissue damage), no pain (handled controls), and analgesic use (tissue damage, administered a nonsteroidal anti-inflammatory drug). Results comparing analgesic effects to uncontrolled pain consistently yielded AUC values >0.7 (95% confidence interval: 0.40 to 0.99) for plasma cortisol (time points: 1.5, 2, 3, 4, 6, and 8 h), hair cortisol (time point: 62 d), and IRT (time point: 72 h). Results comparing analgesic effects to uncontrolled pain consistently yielded AUC values <0.7 (95% confidence interval: 0.28 to 0.90) for salivary cortisol (6, 13, 20, 34, 48, and 62 d); MNT (6, 25, and 49 h); substance P (1, 2, 3, 4, 6, 8, 12, 18, 24, 48, 72, 96, 120, 144, 312, 480, 816, 1,152, and 1,488 h); kinematic gait analysis including area (8, 16, 48, 72, 96, and 120 h), force (8, 16, 24, 48, 72, 96, and 120 h), and pressure (8, 16, 24, 48, 72, 96, and 120 h); and a visual analog scale for pain (1, 2, 3, 4, 5, and 6 d). These results indicate that ROC analysis can be used to characterize the predictive value of pain biomarkers and provide new knowledge on the diagnostic accuracy of pain biomarkers within this data set. This analysis, using data from 7 studies, was a preliminary approach to identify biomarkers and collection time points that could inform additional analytical approaches or meta-analyses with larger sample sizes, which are needed to further validate these hypotheses and conclusions.