Do tight nosebands have an effect on the upper airways of horses?

BACKGROUND/OBJECTIVES: The public perception relating to the welfare of horses involved with equestrian sports is associated with training methods used and the presentation of horses at events. In this context, very tight nosebands, which are intended to prevent the horse from opening its mouth, also attract a lot of attention. Various studies have evaluated the impact of tight nosebands on stress parameters, whereas the effect of tight nosebands on upper airway function is unknown. Therefore, the aim of the study was to use overground endoscopy to evaluate changes in pharyngeal and laryngeal function when a tight noseband is fitted. Moreover, the ridden horse pain ethogram (RHpE) was applied to investigate signs of discomfort (Dyson et al., 2018). STUDY DESIGN: A randomized, blinded, and prospective study was performed. METHODS: Sixteen warmblood horses consisting of twelve mares and four geldings with a mean age of 11.63 ± 3.53 years were ridden on 2 consecutive days with either loose or tight nosebands (two fingers or no space between bridge of the nose and noseband, respectively) and inserted endoscope in a random order. Videos were taken in a riding arena during a standardized exercise protocol involving beginner level tasks for 30 min in all gaits. For video analysis, freeze frames were prepared and analyzed at the beginning of the expiration phase. Pharyngeal diameter was measured using the pharynx-epiglottis ratio. Other findings (swallowing, pharyngeal collapse, soft palate movements, and secretion) were also evaluated. Moreover, the RHpE was applied. Descriptive statistics and generalized linear mixed effects models were used. Results with a p-value < 0.05 were considered statistically significant. RESULTS: While the pharynx-epiglottis ratio did not change significantly in horses ridden with loose versus tight nosebands, there was an increase in mean grade and total counts of parameters assessed in the pharyngeal region, for example, grade of secretion (1.5 [±SD 0.89] vs. 3.13 [±SD 0.96]; p = 0.0001), axial deviation of the aryepiglottic folds (0.29 [±SD 0.73] vs. 1.33 [±SD 1.44]; p = 0.01), and pharyngeal collapse (0.69 [±SD 0.87] vs. 1.88 [±SD 1.54]; p = 0.005) in horses ridden with tight nosebands. There was no RHpE score above 8 indicating musculoskeletal pain, but the RHpE scores were significantly higher in horses ridden with tight nosebands (p < 0.001). MAIN LIMITATIONS: Video quality was limited when horses showed large amounts of secretion. Another limitation was the small number of horses. CONCLUSIONS: Results add to the evidence obtained in other studies that tight nosebands do not only cause adverse reactions based on the RHpE score such as head behind the vertical or intense staring but also contribute to changes in the pharyngeal region, such as increased secretion and collapse of pharyngeal structures. This may provide further support for future decisions regarding regulations on nosebands.

Comparison of veterinarians and a deep learning tool in the diagnosis of equine ophthalmic diseases.

BACKGROUND/OBJECTIVES: The aim was to compare ophthalmic diagnoses made by veterinarians to a deep learning (artificial intelligence) software tool which was developed to aid in the diagnosis of equine ophthalmic diseases. As equine ophthalmology is a very specialised field in equine medicine, the tool may be able to help in diagnosing equine ophthalmic emergencies such as uveitis. STUDY DESIGN: In silico tool development and assessment of diagnostic performance. METHODS: A deep learning tool which was developed and trained for classification of equine ophthalmic diseases was tested with 40 photographs displaying various equine ophthalmic diseases. The same data set was shown to different groups of veterinarians (equine, small animal, mixed practice, other) using an opinion poll to compare the results and evaluate the performance of the programme. Convolutional Neural Networks (CNN) were trained on 2346 photographs of equine eyes, which were augmented to 9384 images. Two hundred and sixty-one separate unmodified images were used to evaluate the trained network. The trained deep learning tool was used on 40 photographs of equine eyes (10 healthy, 12 uveitis, 18 other diseases). An opinion poll was used to evaluate the diagnostic performance of 148 veterinarians in comparison to the software tool. RESULTS: The probability for the correct answer was 93% for the AI programme. Equine veterinarians answered correctly in 76%, whereas other veterinarians reached 67% probability for the correct diagnosis. MAIN LIMITATIONS: Diagnosis was solely based on images of equine eyes without the possibility to evaluate the inner eye. CONCLUSIONS: The deep learning tool proved to be at least equivalent to veterinarians in assessing ophthalmic diseases in photographs. We therefore conclude that the software tool may be useful in detecting potential emergency cases. In this context, blindness in horses may be prevented as the horse can receive accurate treatment or can be sent to an equine hospital. Furthermore, the tool gives less experienced veterinarians the opportunity to differentiate between uveitis and other ocular anterior segment disease and to support them in their decision-making regarding treatment.

Evaluation of substance P as a biomarker for pain in equine colic.

Equine colic is an important condition associated with acute abdominal pain and one of the leading causes of death in horses. As such, objectively evaluating pain is of interest for attending veterinarians. Pain scales for assessment are present, but no single pain-specific biomarker has been reported. The aim of this study was to determine if substance P (SP) could be a reliable biomarker to reflect pain and serve as a parameter to predict outcome in equine colic. The hypothesis was that horses displaying severe colic signs present with higher values of SP in contrast to those with mild colic signs. Thirty warmblood horses, aged between 3 and 20 years were recruited; evenly distributed (10 horses each) in three colic groups (mild, moderate, severe). To classify the colic signs, the horses were graded by the Equine Acute Abdominal Pain Scale (EAAPS). Clinical examination and EAAPS were performed at arrival in the hospital. Blood samples were collected four times in hourly intervals commencing from arrival. For comparison, already established parameters for prognosticating equine colic (heart rate, serum cortisol, and blood lactate concentration) were also measured. The assumption of increasing SP concentrations along with pain could not be confirmed. SP did not show any association with heart rate, cortisol, lactate, or EAAPS. Whereas the established parameters increased according to the EAAPS, SP remained stable in individual horses regardless of clinical signs, treatment, and disease progression. Consequently, SP was not a reliable parameter to reflect painful conditions or to predict outcome in equine colic.

Evaluation of Substance P as a New Stress Parameter in Horses in a Stress Model Involving Four Different Stress Levels.

Stress has a significant impact on equine welfare. There are some studies on the stress response in horses ridden with tight nosebands, but little is known about other stress parameters than cortisol, which potentially could address an emotional component. In this study, blood samples of a total of 74 warmblood horses were used to establish reference values for plasma substance P (SP) concentrations. Moreover, 16 of these warmblood horses were included in a stress model. Four different stress levels (level 1: horses ridden with loose noseband, level 2: tight noseband, level 3: loose noseband and overground endoscope, level 4: tight noseband and overground endoscope) were applied to evaluate SP as a potential stress parameter in horses. Blood samples were taken at rest (t0) and directly after inducing stress (noseband tightening, insertion of endoscope; t1), as well as after 20 min of riding at all gaits (t2). A ridden horse ethogram was applied and showed that horses in the tight noseband group resorted to other stress-related behavioral issues than horses with loose nosebands. Serum cortisol showed a linear increase concurrent with the increase in stress levels with a significant difference between level 1 and level 4 (p = 0.043), proving that stress factors were adequate to evaluate the stress response, whereas SP did not show a correlation with the stress levels. Furthermore, concentrations of SP differed widely between horses but stayed within more narrow limits in the individual horse. As a conclusion, SP might not be a reliable stress parameter in horses in the applied minor stress model.