Diet Helps Pain Related Behaviors?

Hello, Summarians!

I have to tell the truth. AI and it’s potential applications in the animal world intrigues me. The heart murmur algorithm looks amazing. It does need some more validation but…

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Diet Change Effects on Behavior in Jumpers

This study investigated the effects of dietary changes on gastric health and pain expression in show-jumping horses, focusing on Equine Gastric Ulcer Syndrome (EGUS), which includes Equine Squamous Gastric Disease (ESGD) and Equine Glandular Gastric Disease (EGGD). EGUS is prevalent among sport horses and is linked to high-starch and high-sugar diets commonly fed to support athletic performance. The researchers aimed to determine whether a structured, low-starch, and low-sugar diet could reduce gastric ulcers and alleviate pain-related behaviors during riding without additional medical treatment. 

Using the Ridden Horse Pain Ethogram (RHpE) to assess pain behaviors and the iJUMP saddle equipped with an accelerometer to record locomotor variables during jumping, the study evaluated horses before and after implementing the dietary change. Results showed a significant decrease in both squamous and glandular gastric lesions following the diet alteration. Pain scores during riding also decreased, and there was a positive correlation between gastric ulcer severity and pain expression, indicating that gastric lesions contribute to discomfort during exercise. 

While the iJUMP saddle detected some differences in balance and consistency over jumps, many locomotor variables did not show significant changes post-dietary intervention, possibly due to the small sample size and variability among horses. The RHpE proved to be a reliable tool for identifying discomfort related to gastric disease during ridden exercise, suggesting that gastric ulcers should be considered when horses display altered behavior under saddle. 

The study concluded that dietary modifications alone could promote gastric healing and reduce pain expression in high-performance horses, offering a potential alternative to anti-ulcer medications, which can be costly and have adverse effects. These findings have important implications for the management of gastric ulcers in equine athletes, emphasizing the role of diet in maintaining gastric health and improving welfare. The RHpE could serve as an accessible tool for riders, trainers, and veterinarians to detect gastric pain and prompt further investigation in horses exhibiting behavioral changes during exercise. 

Pineau V, ter Woort F, Julien F, et al. Improvement of gastric disease and ridden horse pain ethogram scores with diet adaptation in sport horses. J Vet Intern Med. 2024; 38(6): 3297-3308. doi:10.1111/jvim.17223

Bottom line — May reduce pain expression in high-performance horses.

Vets -Should I Stay or Should I Go?

The Veterinary Workforce Study highlights critical insights into the current and future needs of the veterinary workforce in the United States. A significant shortage of 14,000 to 24,000 companion animal veterinarians is projected by 2030, prompting expansions in veterinary education and training. While previous research in various countries has examined factors influencing entry and retention in veterinary practice, the U.S. focus has largely been on rural areas, where concerns about salary and work-life balance have been central to retention challenges. 

The study used a cross-sectional survey to identify key factors influencing veterinarians’ decisions to stay in or leave their practices. It found that most respondents perceived a need for more practitioners, with nonrural areas reporting a greater immediate and long-term demand for additional veterinarians compared to rural areas. The demand for small animal care was most pronounced, though respondents also noted needs in food animal, equine, and exotic animal care. Preventative and emergency care were the most needed services. 

The survey revealed that most veterinarians intend to stay in their current practices, with work-life balance, relationships with colleagues and clients, and practice culture emerging as key factors in retention. Conversely, work-life balance, time off, and practice atmosphere were leading reasons for leaving, with retirement being a prominent factor. Respondents working fewer than 40 hours per week and avoiding on-call duty indicated a prioritization of work-life balance, highlighting its importance in recruitment and retention. 

Community characteristics also played a role, with nonrural practitioners reporting greater satisfaction with income opportunities, healthcare, and amenities, while rural veterinarians expressed stronger community attachment and a preference for rural family life. The study emphasized the potential value of integrating rural upbringing and community attachment into veterinary training to encourage rural practice adoption. 

Limitations of the study included selection and recall bias, as well as overrepresentation of certain regions, which may have influenced the findings. Overall, the study underscores the importance of addressing work-life balance, practice culture, and community attachment in strategies to recruit and retain veterinarians, particularly in rural areas. 

Banse, H. E., Hunter, A. S., Althouse, G., Anderson, S., Boudreaux, B., Chaney, K., Garden, O. A., Navarre, C., Roush, J. K., Schleining, J. A., Seneca, E., Shivley, J., Ward, J., & Raczkoski, B. (2024). Cross-sectional survey of rural and nonrural veterinarians indicates similarities in intent to stay or leave practice and differences in community attachment and satisfaction. Journal of the American Veterinary Medical Association https://doi.org/10.2460/javma.24.08.0513 

Bottom line — It depends …

Algorithm to Grade Heart Murmurs

Auscultation, or listening to heart sounds, is a crucial part of a dog's physical examination, particularly for identifying heart murmurs that indicate heart diseases like myxomatous mitral valve disease (MMVD). The intensity of a heart murmur correlates with the severity of certain conditions, aiding in staging the disease and informing treatment decisions such as the administration of pimobendan in stage B2 MMVD to delay heart failure. Traditionally, differentiating disease stages requires echocardiography, a resource-intensive and costly diagnostic tool. 

However, murmur grading through auscultation is subjective and suffers from significant inter-observer variability, making it less reliable for diagnostic criteria. Moreover, proficiency in auscultation has declined, highlighting the need for more objective and accessible diagnostic methods. Electronic stethoscopes offer a solution by recording heart sounds for digital analysis, paving the way for machine-learning algorithms to automatically detect and grade heart murmurs. While substantial progress has been made in human medicine due to large datasets, similar advancements in veterinary medicine have been limited by the lack of extensive heart sound databases in dogs. 

The study aimed to design and evaluate a machine-learning algorithm capable of grading heart murmurs in dogs using electronic stethoscope recordings, thereby assisting in staging preclinical MMVD. The objectives included collecting a large dataset of heart sound recordings with clinical diagnoses from a diverse canine population, training a machine-learning algorithm by adapting models initially developed for humans (a process known as transfer learning), and assessing the algorithm's accuracy compared to expert cardiologists, especially in distinguishing between stage B1 and B2 MMVD. 

By compiling what is believed to be the largest database of annotated canine heart sound recordings to date, the study enabled robust training of the algorithm. The machine-learning model demonstrated high accuracy in detecting and grading heart murmurs across various intensities, showing particularly high sensitivity for moderate to severe cases and conditions like pulmonic stenosis. The transfer learning approach proved effective; the algorithm initially trained on human heart sounds generalized well to canine data, and its performance improved significantly after fine-tuning with the new dog-specific dataset. 

Automating murmur detection offers the advantage of reducing inter and intra-observer variability inherent in manual auscultation. Although the study couldn't fully investigate this potential due to only one cardiologist reviewing each case, it was noted that discrepancies between the algorithm and the expert were often minor, typically differing by just one grade. The algorithm's murmur intensity predictions were also valuable in differentiating between stage B1 and B2 MMVD, achieving an area under the curve (AUC) of 0.861. This performance is comparable to previous studies that utilized additional biomarkers like N-terminal pro-B-type natriuretic peptide (NT-proBNP), suggesting that the algorithm could be a reliable standalone tool for this purpose. 

The study acknowledges limitations, such as the relatively small sample size of 217 B1 and 126 B2 MMVD cases, leading to wide confidence intervals and necessitating further data collection to enhance statistical certainty. Additionally, the data were collected in specialist referral centers rather than primary care settings, which may affect the algorithm's performance in general practice due to differences in recording quality and environmental noise. 

Future work should focus on prospective evaluations in primary care environments to assess the algorithm's practical utility where it could have the most significant impact. This includes determining appropriate probability thresholds for referrals and possibly integrating the algorithm with other diagnostic tools or biomarkers to improve accuracy further. Prospective studies could also compare the algorithm's performance directly with that of general practitioners to evaluate its effectiveness in routine clinical use. 

In conclusion, the study demonstrates that a machine-learning algorithm can accurately detect and grade heart murmurs in dogs using electronic stethoscope recordings, assisting in the staging of preclinical MMVD. This technology holds promise for lowering the skill barrier associated with auscultation, reducing subjectivity and variability in murmur grading, and improving early detection and management of common cardiac diseases in dogs. By facilitating timely referrals for echocardiography and appropriate treatments while minimizing unnecessary procedures, such an algorithm could significantly enhance veterinary cardiac care. 

McDonald A, Novo Matos J, Silva J, et al. A machine-learning algorithm to grade heart murmurs and stage preclinical myxomatous mitral valve disease in dogs. J Vet Intern Med. 2024; 38(6): 2994-3004. doi:10.1111/jvim.17224 

Bottom line — Potentially helpful in certain situations.

Just putting things in perspective …