Use of respiratory signal analysis to assess severity of Brachycephalic Obstructive Airway Syndrome (BOAS) in dogs

IF 2.3 2区 农林科学 Q1 VETERINARY SCIENCES
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Abstract

Brachycephalic Obstructive Airway Syndrome (BOAS) is a potentially life-threatening condition that can be challenging to diagnose and grade objectively. The aim of this study was to investigate the use of respiratory signal analysis to assess severity of BOAS in dogs. Hundred and seventeen client-owned dogs of brachycephalic and non-brachycephalic breeds were enrolled. Respiratory sounds were recorded using an electronic stethoscope before and after a 3-minute exercise test (ET). Dogs were assigned a BOAS severity grade (BOAS 0–3) using a validated respiratory functional grading scheme. Signal analysis techniques were used to identify seven sound variables. Analysis of variance (ANOVA) was used to investigate associations between variables and BOAS severity and receiver operating characteristic (ROC) curves to assess the diagnostic efficacy of each sound variable. For each sound variable, there was a significant association with BOAS grade. An increase in BOAS grade resulted in greater sound magnitude in the frequency spectrum (0–1000 Hz), and in a greater contribution of lower frequencies (170–260 Hz). The variable “Peak 1” had the best performance in predicting BOAS negative (BOAS 0 +1) versus BOAS positive dogs (BOAS 2 + 3) before the ET; area under the curve (AUC) = 76.6 % (95 % confidence interval 67.4–85.8 %), whereas the variable “Valley 1” had the highest predictive value after the ET; AUC = 87.8 % (95 % confidence interval 81.4–94.3 %). Respiratory signal analysis has good potential for assessing BOAS severity and could be valuable for clinicians in clinical decision processes and for breeders when selecting suitable breeding dogs.
利用呼吸信号分析评估犬类 "脑性气道阻塞综合征"(BOAS)的严重程度。
脑性气道阻塞综合征(BOAS)是一种可能危及生命的疾病,客观诊断和分级具有一定难度。本研究的目的是探讨如何利用呼吸信号分析来评估犬类呼吸道阻塞综合征的严重程度。研究人员招募了 177 只客户饲养的肱头犬和非肱头犬。在进行 3 分钟运动测试 (ET) 前后使用电子听诊器记录呼吸音。采用经过验证的呼吸功能分级方案对犬只进行 BOAS 严重程度分级(BOAS 0-3)。信号分析技术用于确定七个声音变量。方差分析 (ANOVA) 用于研究变量与 BOAS 严重程度之间的关联,接收器操作特征曲线 (ROC) 用于评估每个声音变量的诊断效果。每个声音变量都与 BOAS 分级有显著的关联。BOAS 分级的提高导致频谱(0-1000Hz)中的声音幅度增大,低频(170-260Hz)的贡献率增大。在 ET 之前,"峰值 1 "变量在预测 BOAS 阴性犬(BOAS 0 +1)和 BOAS 阳性犬(BOAS 2 +3)方面表现最佳;曲线下面积 (AUC) = 76.6%(95% 置信区间为 67.4 - 85.8%),而在 ET 之后,"谷值 1 "变量的预测价值最高;AUC = 87.8%(95% 置信区间为 81.4 - 94.3%)。呼吸信号分析在评估 BOAS 严重程度方面具有良好的潜力,对临床医生的临床决策过程和繁殖者选择合适的繁殖犬都很有价值。
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来源期刊
Veterinary journal
Veterinary journal 农林科学-兽医学
CiteScore
4.10
自引率
4.50%
发文量
79
审稿时长
40 days
期刊介绍: The Veterinary Journal (established 1875) publishes worldwide contributions on all aspects of veterinary science and its related subjects. It provides regular book reviews and a short communications section. The journal regularly commissions topical reviews and commentaries on features of major importance. Research areas include infectious diseases, applied biochemistry, parasitology, endocrinology, microbiology, immunology, pathology, pharmacology, physiology, molecular biology, immunogenetics, surgery, ophthalmology, dermatology and oncology.
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