TY - JOUR

T1 - Predictive modeling toward refinement of behavior-based pain assessment in horses

AU - Trindade, Pedro Henrique Esteves

AU - Barreto da Rocha, Paula

AU - Driessen, Bernd

AU - McDonnell, Sue M.

AU - Hopster, Klaus

AU - Zarucco, Laura

AU - Gozalo-Marcilla, Miguel

AU - Hopster-Iversen, Charlotte

AU - Rocha, Thamiris Kristine Gonzaga da

AU - Taffarel, Marilda Onghero

AU - Alonso, Bruna Bodini

AU - Schauvliege, Stijn

AU - Mello, João Fernando Serrajordia Rocha de

AU - Luna, Stelio Pacca Loureiro

N1 - Publisher Copyright: © 2023 Elsevier B.V.

PY - 2023

Y1 - 2023

N2 - After 25 years of studies on methodologies for behavioral assessment of equine pain, the Unesp-Botucatu Horse Acute Pain Scale (UHAPS) and the Orthopedic Composite Pain Scale (CPS) were recently considered suboptimal instruments to assess pain in hospitalized horses. However, the combination of the two instruments has never been examined. The objective was to investigate whether the merging, mining, and weighting of UHAPS and CPS behavioral items in a single instrument using a predictive model could improve the capacity to diagnose pain in horses. A previously video-collected behavioral database of 42 horses admitted to three different hospitals for orthopedic or soft tissue surgery was used. Multilevel binomial logistic regression models were used to merge, mine, and weight the behaviors of both instruments. The classification quality between the model and the instruments was compared by the area under the curve (AUC) and its 95% confidence interval. The short model containing 25% of the behaviors of the two instruments showed a higher AUC (98.64 [98.16 – 99.12]; p < 0.0001) than the UHAPS (84.63 [82.08 – 87.18]) and CPS (88.62 [86.56 – 90.66]), independently. We conclude that merging, mining, and weighting the UHAPS and CPS behavior items into a single predictive model appears to be a promising strategy to improve pain diagnostic skill and promote equine welfare.

AB - After 25 years of studies on methodologies for behavioral assessment of equine pain, the Unesp-Botucatu Horse Acute Pain Scale (UHAPS) and the Orthopedic Composite Pain Scale (CPS) were recently considered suboptimal instruments to assess pain in hospitalized horses. However, the combination of the two instruments has never been examined. The objective was to investigate whether the merging, mining, and weighting of UHAPS and CPS behavioral items in a single instrument using a predictive model could improve the capacity to diagnose pain in horses. A previously video-collected behavioral database of 42 horses admitted to three different hospitals for orthopedic or soft tissue surgery was used. Multilevel binomial logistic regression models were used to merge, mine, and weight the behaviors of both instruments. The classification quality between the model and the instruments was compared by the area under the curve (AUC) and its 95% confidence interval. The short model containing 25% of the behaviors of the two instruments showed a higher AUC (98.64 [98.16 – 99.12]; p < 0.0001) than the UHAPS (84.63 [82.08 – 87.18]) and CPS (88.62 [86.56 – 90.66]), independently. We conclude that merging, mining, and weighting the UHAPS and CPS behavior items into a single predictive model appears to be a promising strategy to improve pain diagnostic skill and promote equine welfare.

KW - Algorithm

KW - Body language

KW - Logistic regression

KW - Pain assessment

KW - Welfare

U2 - 10.1016/j.applanim.2023.106059

DO - 10.1016/j.applanim.2023.106059

M3 - Journal article

AN - SCOPUS:85171347716

VL - 267

JO - Applied Animal Behaviour Science

JF - Applied Animal Behaviour Science

SN - 0168-1591

M1 - 106059

ER -