Department of Cardiopulmonary Rehabilitation Science, Nagasaki University Graduate School of Biomedical Sciences, 1-7-1 Sakamoto, Nagasaki, 852-8520, Japan.
Department of Respiratory Medicine, Nagasaki University Graduate School of Biomedical Sciences, 1-7-1 Sakamoto, Nagasaki, 852-8501, Japan.
BMC Pulm Med. 2019 Aug 17;19(1):153. doi: 10.1186/s12890-019-0916-5.
Honeycombing on high-resolution computed tomography (HRCT) is a distinguishing feature of usual interstitial pneumonia and predictive of poor outcome in interstitial lung diseases (ILDs). Although fine crackles are common in ILD patients, the relationship between their acoustic features and honeycombing on HRCT has not been well characterized.
Lung sounds were digitally recorded from 71 patients with fine crackles and ILD findings on chest HRCT. Lung sounds were analyzed by fast Fourier analysis using a sound spectrometer (Easy-LSA; Fukuoka, Japan). The relationships between the acoustic features of fine crackles in inspiration phases (onset timing, number, frequency parameters, and time-expanded waveform parameters) and honeycombing in HRCT were investigated using multivariate logistic regression analysis.
On analysis, the presence of honeycombing on HRCT was independently associated with onset timing (early vs. not early period; odds ratios [OR] 10.407, 95% confidence interval [95% CI] 1.366-79.298, P = 0.024), F99 value (the percentile frequency below which 99% of the total signal power is accumulated) (unit Hz = 100; OR 5.953, 95% CI 1.221-28.317, P = 0.029), and number of fine crackles in the inspiratory phase (unit number = 5; OR 4.256, 95% CI 1.098-16.507, P = 0.036). In the receiver-operating characteristic curves for number of crackles and F99 value, the cutoff levels for predicting the presence of honeycombing on HRCT were calculated as 13.2 (area under the curve [AUC], 0.913; sensitivity, 95.8%; specificity, 75.6%) and 752 Hz (AUC, 0.911; sensitivity, 91.7%; specificity, 85.2%), respectively. The multivariate logistic regression analysis additionally using these cutoff values revealed an independent association of number of fine crackles in the inspiratory phase, F99 value, and onset timing with the presence of honeycombing (OR 33.907, 95% CI 2.576-446.337, P = 0.007; OR 19.397, 95% CI 2.311-162.813, P = 0.006; and OR 12.383, 95% CI 1.443-106.293, P = 0.022; respectively).
The acoustic properties of fine crackles distinguish the honeycombing from the non-honeycombing group. Furthermore, onset timing, number of crackles in the inspiratory phase, and F99 value of fine crackles were independently associated with the presence of honeycombing on HRCT. Thus, auscultation routinely performed in clinical settings combined with a respiratory sound analysis may be predictive of the presence of honeycombing on HRCT.
高分辨率计算机断层扫描(HRCT)上的蜂巢状改变是特发性间质性肺炎的特征性表现,并且与间质性肺疾病(ILD)的不良预后相关。虽然细湿啰音在ILD 患者中很常见,但它们的声学特征与 HRCT 上的蜂巢状改变之间的关系尚未得到很好的描述。
从 71 名有细湿啰音和胸部 HRCT ILD 表现的患者中数字记录肺部声音。使用声音频谱仪(Easy-LSA;日本福冈)通过快速傅里叶分析对肺部声音进行分析。使用多元逻辑回归分析研究吸气阶段细湿啰音的声学特征(起始时间、数量、频率参数和时间扩展波形参数)与 HRCT 上的蜂巢状改变之间的关系。
在分析中,HRCT 上存在蜂巢状改变与起始时间(早期与非早期)独立相关(优势比[OR]10.407,95%置信区间[95%CI]1.366-79.298,P=0.024)、F99 值(低于该值 99%的总信号功率累积的百分位数频率,单位 Hz=100)(OR 5.953,95%CI 1.221-28.317,P=0.029)和吸气阶段细湿啰音的数量(单位数量=5)(OR 4.256,95%CI 1.098-16.507,P=0.036)。在数量和 F99 值的受试者工作特征曲线中,预测 HRCT 上存在蜂巢状改变的截断值分别计算为 13.2(曲线下面积[AUC],0.913;敏感性,95.8%;特异性,75.6%)和 752 Hz(AUC,0.911;敏感性,91.7%;特异性,85.2%)。使用这些截断值的多元逻辑回归分析还表明,吸气阶段细湿啰音的数量、F99 值和起始时间与蜂巢状改变的存在独立相关(OR 33.907,95%CI 2.576-446.337,P=0.007;OR 19.397,95%CI 2.311-162.813,P=0.006;和 OR 12.383,95%CI 1.443-106.293,P=0.022;分别)。
细湿啰音的声学特性可将蜂巢状改变与非蜂巢状改变区分开来。此外,起始时间、吸气阶段细湿啰音的数量和 F99 值与 HRCT 上的蜂巢状改变独立相关。因此,在临床环境中常规进行听诊并结合呼吸音分析可能有助于预测 HRCT 上的蜂巢状改变。