Peer-reviewed veterinary case report
Predicting dog nasal diseases with simple tests and no anesthesia
By Nakazawa, Yuta et al.·Published in The Journal of veterinary medical science·2023·School of Veterinary Medicine, Japan·View original on PubMed →
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Original publication title: Construction of diagnostic prediction model for canine nasal diseases using less invasive examinations without anesthesia.
- Species:
- dog
Plain-English summary
A group of dogs with nasal problems were studied to find out if less invasive tests could help diagnose their conditions without needing anesthesia. The researchers looked at clinical signs and X-rays to create models that could predict issues like tumors or fungal infections. They found that these models were quite accurate, helping to identify serious conditions effectively. This approach could allow for quicker diagnosis and treatment planning for dogs with nasal diseases, potentially avoiding the need for more invasive procedures.
People also search for: dog nasal disease symptoms · non-invasive tests for dog nasal problems · dog nasal tumor diagnosis · canine sinus infection treatment
Abstract
Advanced imaging techniques under general anesthesia are frequently employed to achieve a definitive diagnosis of canine nasal diseases. However, these examinations may not be performed immediately in all cases. This study aimed to construct prediction models for canine nasal diseases using less-invasive examinations such as clinical signs and radiography. Dogs diagnosed with nasal disease between 2010 and 2020 were retrospectively investigated to construct a prediction model (Group M; GM), and dogs diagnosed between 2020 and 2021 were prospectively investigated to validate the efficacy (Group V; GV). Prediction models were created using two methods: manual (Model 1) and LASSO logistic regression analysis (Model 2). In total, 103 and 86 dogs were included in GM and GV, respectively. In Model 1, the sensitivity and specificity of neoplasia (NP) and sino-nasal aspergillosis (SNA) were 0.88 and 0.81 in GM and 0.92 and 0.78 in GV, respectively. Those of non-infectious rhinitis (NIR) and rhinitis secondary to dental disease (DD) were 0.78 and 0.88 in GM and 0.64 and 0.80 in GV, respectively. In Model 2, the sensitivity and specificity of NP and SNA were 0.93 and 1 in GM and 0.93 and 0.75 in GV, respectively. Those of NIR and DD were 0.96 and 0.89 in GM and 0.80 and 0.79 in GV, respectively. This study suggest that it is possible to create a prediction model using less-invasive examinations. Utilizing these predictive models may lead to appropriate general anesthesia examinations and treatment referrals.
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Search related cases →Original publication on PubMed: https://pubmed.ncbi.nlm.nih.gov/37661430/