Department of Clinical Sciences, Swedish University of Agricultural Sciences, P.O. Box 7054, Uppsala 750 07, Sweden.
Department of Physics, Chalmers University of Technology, Gothenburg 412 96, Sweden.
Vet J. 2024 Dec;308:106261. doi: 10.1016/j.tvjl.2024.106261. Epub 2024 Oct 28.
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.
短头气道阻塞综合征（BOAS）是一种潜在危及生命的疾病，可能难以客观诊断和分级。本研究旨在探讨呼吸信号分析在评估犬 BOAS 严重程度中的应用。共纳入 117 只短头和非短头品种的患犬。使用电子听诊器在 3 分钟运动试验（ET）前后记录呼吸音。使用经过验证的呼吸功能分级方案对犬进行 BOAS 严重程度分级（BOAS 0-3）。使用信号分析技术识别了七个声音变量。方差分析（ANOVA）用于研究变量与 BOAS 严重程度之间的关联，接收者操作特征（ROC）曲线用于评估每个声音变量的诊断效能。对于每个声音变量，与 BOAS 分级均有显著关联。BOAS 分级增加导致频谱（0-1000 Hz）中的声音幅度增大，低频（170-260 Hz）的贡献增加。在 ET 前，变量“峰 1”在预测 BOAS 阴性（BOAS 0+1）与 BOAS 阳性犬（BOAS 2+3）方面表现最佳；曲线下面积（AUC）为 76.6%（95%置信区间 67.4-85.8%），而在 ET 后，变量“谷 1”具有最高的预测价值；AUC = 87.8%（95%置信区间 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应用潜力，对于临床医生在临床决策过程中以及在选择合适的繁殖犬时，对于繁殖者来说都具有重要价值。
