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Journal of Southern Medical University logoLink to Journal of Southern Medical University
. 2020 Feb 20;40(2):177–182. [Article in Chinese] doi: 10.12122/j.issn.1673-4254.2020.02.07

肺音可以作为首诊慢阻肺严重程度的判断指标

Lung sounds can be used as an indicator for assessing severity of chronic obstructive pulmonary disease at the initial diagnosis

陈 仕锋 1,b, 黄 敏於 1,b, 彭 显如 1, 袁 亚飞 1, 黄 淑榆 1, 叶 艳梅 1, 赵 文驱 1, 李 博厚 1, 韩 慧姗 1, 杨 淑銮 1, 蔡 绍曦 1, 赵 海金 1,*
PMCID: PMC7086132  PMID: 32376545

Abstract

目的

探讨肺部听诊肺音对首诊慢性阻塞性肺疾病（慢阻肺）严重程度的判断价值。

方法

入选我院2016年5月~2019年5月临床首次确诊慢阻肺患者，根据肺部听诊情况把肺音区分为5组：呼吸音正常、呼吸音减弱、呼吸音减弱并喘鸣、呼吸音明显减弱、呼吸音明显减弱并喘鸣。基于GOLD指南和欧洲标准，区分慢阻肺和哮喘慢阻肺重叠（ACO）诊断，并进行肺功能分级。

结果

入组慢阻肺患者1046例，男性949例，女性97例，年龄62.6±8.71岁；根据GOLD标准，诊断为慢阻肺中度及以上占比88.1%，重度及以上占比为50.0%，进一步诊断ACO为347例，占33.2%。ANOVA分析肺音5组间在病程、用力呼气容积（FEV1）、FEV1占预计值百分比（FEV1%），FEV1/FVC、用力肺活量（FVC）、FVC占预计值百分比（FVC%）、mMRC均存在显著差别（P < 0.001），FENO未见显著差异（P=0.097）。ACO较单纯慢阻肺组表现更高比例的喘鸣（P < 0.001）。Spearman相关分析示：肺音与疾病严重程度、FEV1、FEV1%及FVC%显著相关（P < 0.001）。多元线性回归分析显示：病程、吸烟指数及肺音与疾病严重程度相关。

结论

肺音可以作为首诊慢阻肺严重程度判断指标，临床需加强识别。

Keywords: 慢阻肺, 肺部听诊, 肺音, 严重程度, 哮喘慢阻肺重叠

慢性阻塞性肺疾病（简称慢阻肺）严重危害人类健康，近期流行病学调查显示，中国总患病人数约9990万，已构成我国重大疾病负担[1]。慢阻肺诊断和评估主要依赖于症状、肺功能检查[2-3]。然而肺功能评估需要专业的仪器和严格的质控要求[4]，且临床必须选择合适的人群才能体现最佳的成本效应[5]。另一方面，症状往往不能客观反映疾病的真实情况，这使得慢阻肺的早期诊治存在明显滞后[6-8]。

肺部听诊是无创的，没有辐射，且病人基本不会出现不适的感觉，是呼吸系统疾病体格检查的重要组成部分。临床中由受过训练的专科医生进行听诊器听诊并获得明确的肺音，显然可以帮助临床医生诊断和评估病情[9-10]。早期研究发现胸部体检肺音减弱与气流阻塞存在关联[11]，2013年的研究显示胸部体格检查中出现叩诊过清音、肺音减弱及喘鸣显著增加气流阻塞的可能性[12]。但随着医学技术的进步，临床医生越来越依赖这些技术和实验室检查，而忽略病史采集和临床体检。慢阻肺指南中没有明确指出胸部体检可以用于更早地进行疾病筛查，这提示胸部体检对慢阻肺的诊治价值仍然有待进一步阐明。

研究发现，基于现代信号处理方法可以进行有效的肺音分析，帮助疾病筛查、诊断和管理[13-15]，也有助于鉴别阻塞性肺疾病及充血性心力衰竭[16-17]。然而，临床中尚未见有效的肺音评估工具推广，这大大阻碍了肺部听诊的感知价值和临床推广应用。有报道潮式呼吸时肺部喘鸣音可以区分哮喘不同严重程度[18]。台湾一项研究显示合并喘鸣表型的慢阻肺患者肺功能更低[19]。然而，目前尚未见通过听诊器判断肺音的强弱、喘鸣是否与慢阻肺表型、病情有关的报道。

本课题组前期发现慢阻肺及哮喘慢阻肺重叠（ACO）存在明显延迟诊断，且这与肺功能受损程度显著相关[20]。本研究重点探讨初诊慢阻肺患者肺音强弱及合并喘鸣与否，是否可以反映疾病的严重程度和延迟诊断、并区分疾病表型，从而进一步探索有利于慢阻肺早期诊断的方法。

1. 资料和方法

1.1. 研究对象

南方医院2016年5月~2019年5月符合以下入选/排除标准的临床首次确诊慢阻肺患者。

1.2. 疾病诊断及严重程度标准

1.2.1. 慢阻肺诊断标准基于GOLD指南

有或无呼吸困难、慢性咳嗽、咳痰症状；有吸烟、生物燃料、粉尘或其它风险因素暴露史；肺功能提示存在明显的气流受限（舒张后FEV1/FVC < 0.7）。

1.2.2. ACO诊断基于欧洲标准[21]

主要标准：年龄>40岁，持续的气流受限；10年以上的吸烟史或同等的生物燃料暴露；患者40岁之前曾患哮喘或吸入支气管舒张剂后第1秒用力呼气容积（FEV1）增加量>400 mL。次要标准：过敏史或过敏性鼻炎病史；随访过程中肺功能2次及以上FEV1可较基线增加≥ 200 mL，同时改善率≥ 12%；外周血嗜酸粒细胞≥ 0.3×109/L。满足3条主要标准和至少1条次要标准即可诊断。

1.2.3. 疾病严重程度按GOLD指南分为

轻度、中度、重度、极重度（GOLDⅠ~Ⅳ）。病例排除标准既往已确诊的慢性呼吸道疾病，如肺癌、肺转移瘤、间质性肺纤维化、支气管扩张症及重大心血管疾病，近1月明显的呼吸道感染，BMI>30[22]。

1.3. 研究设计及慢阻肺听诊、分组方法

本研究为南方医科大学南方医院单中心、回顾性研究。收集满足入选标准的患者的人口学特征；肺部听诊由具有高级职称副主任医师以上医师完成，听诊部位[15, 23]主要以双下肺听诊区（肩胛间区和肩胛下区），根据听诊情况区分为5组，包括呼吸音正常，呼吸音减弱，呼吸减弱并喘鸣，呼吸音明显减弱及呼吸音明显减弱并喘鸣。收集FEV1、FEV1占预计值百分比（FEV1%）、用力肺活量（FVC）、FVC占预计值百分比（FVC%）等数据；记录呼出气一氧化氮、胸片或CT检查等结果。

1.4. 统计学方法

应用SPSS 19.0统计软件进行数据分析。计量资料以均数±标准差描述，独立样本比较采用t检验。不服从正态分布的计量资料以中位数（四分位法），即中位数（25%，75%）描述，采用非参数检验（Mann-Whitney U检验、Kruskal-Wallis检验），多个样本比较采用ANOVA方差分析及卡方检验；肺音与疾病严重程度及肺功能（FEV1%、FVC%等）关系采用Spearman相关分析，疾病严重程度影响因素采用多元线性回归分析，P < 0.05表示差异有统计学意义。

2. 结果

2.1. 患者一般资料

入选慢阻肺患者1046例，男女分别为949例、97例，年龄62.6±8.71岁。根据GOLD标准，诊断慢阻肺中度及以上占比88.1%，重度以上占比为50.0%，其中40.6%为支气管舒张试验阳性，进一步诊断ACO为347例，占比33.2%（表 1）。

1.

患者的基本资料

Demographic characteristics and clinical data of patients with COPD and ACO

Index COPD(n=699) ACO (n=347) Total(1046)
ACO: Asthma chronic obstructive pulmonary disease overlap; FENO: Fractional exhaled nitric oxide; FEV1: Forced expiratory volume in one second; FVC: Forced vital capacity; GOLD: Global Initiative for Chronic Obstructive Lung Disease; BDT: Bronchodilation test.
Male (%) 665 (95.1%) 284 (81.8%) 949 (90.7%)
Age (year) 63.2±8.56 61.4±8.88 62.6±8.71
Disease course (year) 3 (0.5, 6) 3.0 (1, 9) 3 (1, 6)
Smoking history (pack-years) 39 (28, 50) 30 (1.8, 40) 30 (20, 45)
FENO (ppb) 17(11, 23) 34 (21, 55) 20 (13, 32)
FEV1 (L) 1.39±0.67 1.35±0.61 1.38±0.65
FVC (L) 2.84±0.85 2.81±0.85 2.83±0.85
FEV1/FVC (%) 47.5±13.1 47.3±11.7 47.5±12.6
FEV1% (%) 51.7±21.4 51.9±18.7 51.8±20.5
FVC% (%) 83.5±19.9 86.0±17.8 84.4±19.2
Mild (GOLD I) 85 39 124
Moderate (GOLD II) 270 129 399
Severe (GOLD III) 226 140 366
Very severe (GOLD IV) 118 39 157
≥Moderate 614 (87.8%) 308 (88.8%) 922 (88.1%)
≥Severe 344 (49.2%) 179 (51.6%) 523 (50.0%)
BDT (+) 208 (29.8%) 217 (62.5%) 425 (40.6%)
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2.2. 听诊不同肺音与疾病严重程度关系

ANOVA分析听诊5组间在病程、FEV1、FEV1%、FEV1/FVC、FVC%、FVC、mMRC均存在显著差别（P < 0.001），FENO未见显著差异（P=0.097）。组间比较发现，FEV1、FEV1%、FEV1/FVC、FVC%仅在明显减弱组与明显减弱并喘鸣组无差别，FVC在明显减弱组与明显减弱并喘鸣组之间及明显减弱组与减弱并喘鸣组之间无差别，其他组别均有显著差异（P < 0.05，表 2）。

2.

不同组别肺音与肺功能的比较

Comparison of pulmonary function among the 5 groups with different lung sounds

Pulmonary function tests Lung sounds P
Normal Weakened Weakened &
wheezing		 Apparent weakened Apparent weakened
& wheezing
mMRC: Modified Medical Research Council.
Number 121 251 240 223 211
FEV1 (L) 2.06±0.66 1.65±0.59 1.32±0.58 1.10±0.47 1.05±0.40 < 0.001
FEV1% (%) 73.4±18.5 60.9±18.2 52.8±19.6 41.6±16.1 41.0±13.8 < 0.001
FEV1/FVC (%) 58.4±10.2 52.1±11.6 48.5±11.9 42.1±12.0 42.3±9.95 < 0.001
FVC (L) 3.48±0.86 3.13±0.76 2.67±0.82 2.60±0.73 2.49±0.69 < 0.001
FVC% (%) 99.4±18.2 91.5±17.3 84.7±19.5 77.6±18.6 76.3±18.3 < 0.001
FENO (ppb) 20 (14, 32) 23 (15, 37) 18(12, 30) 20 (12, 29) 20 (13, 36) =0.097
mMRC 1 (0, 1) 1 (1, 2) 1 (1, 2) 2(1, 2) 1 (1, 2) < 0.001
Disease course (year) 1 (0.16, 3.75) 2 (0.35, 4.5) 3 (1, 5.5) 3.5 (2, 10) 4(2, 10) < 0.001
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2.3. 病程与肺音的关系。

所有纳入对象病程或延迟诊断时间为3（1，6）年，前期研究[20]证实延迟诊断时间为1年时，慢阻肺肺功能已出现显著差异，因此本研究按延迟诊断时间分组为 < 1年及≥ 1年，两组分别为257例和789例，结果显示，延迟诊断时间越长，肺音越弱，更多的合并喘鸣（χ2=69.8，P < 0.001，表 3）。

3.

肺音与性别、病程、年龄分层及诊断

Lung sounds analysis based on gender, disease course, age and diagnosis (COPD or ACO)

Index Lung sounds P
Normal Weakened Weakened & wheezing Apparent weakened Apparent weakened & wheezing
Gender (n)
  Male 110 (11.6%) 238 (25.1%) 204 (21.5%) 208(21.9%) 189(19.9%) 0.003
  Female 11 (11.3%) 13 (13.4%) 36 (37.1%) 15 (15.5%) 22 (22.7%)
Diagnosis (n)
  COPD 93 (13.3%) 185 (26.5%) 152(21.7%) 158 (22.6%) 111 (15.9%) < 0.001
  ACO 28(8.1%) (year) 66 (19.0%) 88 (25.4%) 65 (18.7%) 100 (28.8%)
Disease course(year)
  < 1 56 (21.8%) 84 (32.7%) 57 (22.2%) 26 (10.1%) 34 (13.2%) < 0.001
  ≥1 65 (8.2%) 167(21.2%) 183 (23.2%) 197 (25.0%) 177 (22.4%)
Age (year)
  ≤50 17(18.1%) 21 (22.3%) 17 (18.1%) 13 (13.8%) 26 (27.7%) 0.001
  50-60 46(14.6%) 82 (26.0%) 66(21.0%) 69 (21.9%) 52 (16.5%)
  60-70 49(11.1%) 106 (24.1%) 102(23.2%) 100 (22.7%) 83 (18.9%)
  > 70 9 (4.6%) 42 (21.3%) 55 (27.9%) 41 (20.8%) 50 (25.4%)
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2.4. 肺音在慢阻肺和ACO的比较

所有纳入研究对象中，单纯慢阻肺699例，ACO 347例，两组肺音存在显著差别（χ2=33.0，P < 0.001）。ACO表现更高比例的喘鸣（54.2%:37.6%，χ2=30.0，P= 0.001，表 2）。

2.5. 性别和年龄对肺音的影响

所有纳入对象男女性别比例为949:97，两组肺音存在显著差别（χ2=16.4，P=0.003），入组女性更多的为ACO（64.9%），表现更高比例喘鸣（男41.4%，女59.8%，χ2=12.1，P < 0.001）。按年龄分组[24]：≤ 50岁、50~60岁、60~70岁及>70岁组，组间肺音存在显著差异（χ2=30.0，P=0.001，表 2）。

2.6. 肺音与肺功能关系

本研究中肺音明显减弱伴或不伴喘鸣慢阻肺为41.5%，这与肺功能重度阻塞（FEV1% < 50%）比例（50.0%）接近，Spearman相关分析显示：肺音与FEV1%及FVC%显著相关（P均 < 0.001）；肺音与慢阻肺GOLD分级显著相关（P < 0.001），Spearman相关系数0.48。

2.7. 疾病严重程度的多因素分析

以FEV1%为因变量，把年龄、性别、吸烟指数、是否合并哮喘、病程、FENO及肺音作为自变量纳入多元线性回归分析。结果显示，性别、年龄、FENO及是否合并哮喘对FEV1%无显著影响（P>0.05），而吸烟指数，病程及肺音对FEV1%有显著影响（P < 0.05，表 4）。

4.

疾病严重程度FEV1%的影响因素

Factors affecting FEV1% of the patients

Parameter Regression coefficient SE t P
Constant 79.988 6.297 12.739 < 0.001
Gender -2.675 2.470 -1.083 0.279
Age (year) -0.023 0.085 -0.272 0.785
Smoking history (pack-years) -0.039 0.019 -2.056 0.040
COPD or ACO 3.006 1.862 1.615 0.107
Disease course -0.429 0.109 -3.932 < 0.001
FENO 0.018 0.036 0.496 0.620
Lung sounds -8.000 0.565 -14.164 < 0.001
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3. 讨论

本研究显示我院首诊慢阻肺患者中，听诊肺音异常为88.4%，肺音明显减弱加上明显减弱并喘鸣患者占比为41.5%，肺音异常可以反映疾病严重程度（FEV1%，FVC%）和延迟诊断时间。

听诊获得肺音是由于气流通过呼吸道和肺泡，产生湍流引起振动，发出声响，通过肺组织及胸壁传至体表的声音。肺部啰音指当空气通过含有分泌物的气管、支气管，或通过因痉挛或肿胀而狭窄的支气管时，听到一种附加的呼吸杂音，包括干啰音和湿啰音。临床往往重视啰音是否出现，出现部位，较少关注呼吸音强弱。2015年有学者报道[19]慢阻肺喘鸣表型占38.7%，并呈现更高的症状评分、更低的肺功能及过去一年更多的发作。本研究既关注肺音强弱，同时结合喘鸣分析，结果显示约88.4%表现为肺音减弱，其中43.1%同时伴有喘鸣，后者表现更低的肺功能和更高的mMRC评分，支持临床需要区分肺音表型，并重视肺音强弱的表现，以便更早地发现异常。

慢阻肺早期诊断是近年大家关注的热点问题[8, 12]。本研究显示肺音正常组与异常组在FEV1、FEV1%，FEV1/FVC、FVC%均存在显著差别，且肺音明显减弱与重度肺功能下降比例基本一致。肺音与反映症状指标mMRC也存在显著相关。同时多因素分析显示肺音是影响疾病严重程度的重要指标。支持肺音可能作为慢阻肺早期筛查的有效指标[25]。

前期研究发现延迟诊断时间可以反映疾病严重程度，病程越长，肺功能越低[20]。本研究进一步分析显示，病程越长，肺音更可能为明显降低伴或不伴喘鸣音。上述间接支持肺音可以帮助判断疾病严重程度。

慢阻肺与ACO在临床上不易鉴别，诊断更多的是基于临床综合评估[21, 26]。本研究发现慢阻肺和ACO在肺音上存在显著差异。ACO和慢阻肺均可表现呼吸音显著降低，但ACO表现更高比例的喘鸣。这可能与ACO临床症状更多，发作频率更高有关[27-28]，提示在首诊慢阻肺的患者，若病人合并喘鸣，临床需识别是否合并哮喘[29]。

肺音的研究是当前研究的热点，这主要是基于强度、时间及延续时间。近年来肺音研究已经从基于逻辑的算法向机器学习技术转变[30-31]。肺音的识别也可用来早期识别慢阻肺急性加重以及作为肺康复的评估指标[23, 32-33]。未来智能化的肺音检测及喘鸣分类将有利于慢性呼吸道疾病，尤其是重症阻塞性肺疾病的管理。

新型冠状病毒肺炎(新冠肺炎)以呼吸道和接触为主要传播方式，近期为阻断传播途径，慢阻肺诊断的金标准肺功能检查不能常规开展，这大大地阻碍了慢阻肺的早期诊治，因此，本研究为类似传染病流行期间慢阻肺、哮喘的早诊早治提供了新的理论依据。

本研究存在明显的局限性：首先，基于听诊器的肺音判断存在主观性，而且肺音的影响因素较多，比如肥胖可以明显影响肺音及肺功能[22]，本研究中排除了肥胖患者，同时尽量由同一研究者听诊判断。其次，本研究入组主要为男性患者，男女比9.78: 1，这与中国慢阻肺流调趋势一致，但有一定的差距，据2012~2015年全国慢阻肺调查数据显示[1]，70岁及以上男性的患病率为49.5%，女性为23.0%，男性为女性的2.15倍。本研究为回顾性资料分析，存在一定的误差，然而，我院临床诊疗中女性慢阻肺患者确实占比较少。第三，本研究基于初诊慢阻肺病人，在复诊病人中是否存在同样的结果，肺音是否发生改变、如何改变及与疾病预后的关联均需进一步阐明。

综上所述，本研究表明听诊肺音可以作为慢阻肺早期诊断、疾病严重程度的判断指标，值得临床推广及应用，同时肺音对于疾病的长期预后的影响值得深入研究。

Biographies

陈仕锋，在读硕士研究生，E-mail: 414262054@qq.com

黄敏於，在读硕士研究生，E-mail: 568368425@qq.com

Funding Statement

国家自然科学基金（81770033，81900027）；广东省科技计划项目（2017B020226006）；广州市科技计划项目（201804010069）

Supported by National Natural Science Foundation of China (81770033, 81900027)

Contributor Information

陈 仕锋 (Shifeng CHEN), Email: 414262054@qq.com.

黄 敏於 (Minyu HUANG), Email: 568368425@qq.com.

赵 海金 (Haijin ZHAO), Email: zhjin@smu.edu.cn.

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