Ni Kaiwen, Wang Sujie, Zhang Xinlei, Liu Shan, Zhang Ying, Li Yan, Wang Yiting, Yang Junchao
Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, Zhejiang, China.
Center of Clinical Evaluation, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, Zhejiang, China.
BMJ Open. 2025 Feb 22;15(2):e084721. doi: 10.1136/bmjopen-2024-084721.
OBJECTIVES: To compare physical information, such as age, sex, height, weight, body mass index (BMI) and pulmonary function test (PFT) results, between cough variant asthma (CVA) and chronic cough (CC) and establish a diagnostic model of CVA. DESIGN: A case-control study of patients with suspected CVA enrolled at The First Affiliated Hospital of Zhejiang Chinese Medical University. SETTING: One leader unit of the National Key Specialised Pulmonary Disease Cooperation Group in China. PARTICIPANTS: Enrolled 545 patients who underwent PFT and bronchial provocation tests. OUTCOME MEASURES: We obtained physical information and pulmonary test data and established the model using logistic regression analysis. The Hosmer-Lemeshow goodness-of-fit test, area under the receiver operating characteristic curve (AUC), calibration plot and decision curve analysis were used to evaluate this model. All data were analysed using SPSS V.27 and RStudio software. RESULTS: The CVA group had more female patients (%) (68.12% vs 51.48%, p value<0.001) and lower height (m) (1.61 (0.40) vs 1.65 (3.26), p value<0.001), weight (kg) (60 (56) vs 63 (85), p value<0.001) and BMI (kg/m) (22.59 (17.91) vs 23.28 (21.81), p value=0.016) than the CC group. Differences between CVA and CC in forced vital capacity (FVC) in percent predicted values (FVC% pred)(94.4 (57.3) vs 91.60 (94.10), p value=0.006), forced expiratory volume in 1 s/FVC (FEV1/FVC) (%) (84.65±6.82 vs 86.91±6.71, p value<0.001), peak expiratory flow in per cent predicted values (PEF% pred) (93.00 (81.10) vs 98.00 (108.00), p value=0.005), maximal mid-expiratory flow in percent predicted values (MMEF% pred) (74.50 (100.60) vs 90.85 (170.30), p value<0.001), forced expiratory flow (FEF) at 50% of FVC in per cent predicted values (FEF pred) (78.9(113.50) vs 93.10(169.80), p value<0.001) and FEF at 75% of FVC in per cent predicted values (FEF pred) (69.70 (137.60) vs 85.60 (225.80), p value<0.001) were significant. Patients with CVA were more in number compared with patients with CC at a lower degree (<65%) of MMEF% pred (32.37% vs 14.50%, p value<0.001), FEF pred (26.09% vs 13.02%, p value<0.001) and FEF pred (39.13% vs 23.67%, p value<0.001). FVC% pred, FEV1/FVC, BMI and MMEF% pred aided in establishing a model with an AUC of 0.733 (95% CI: 0.6829 to 0.7831). The model was tested using internal and external data (p value=0.2865 and p value=0.3197, respectively). CONCLUSION: BMI, FVC% pred, FEV1/FVC (%) and MMEF% pred were used to establish the diagnostic model. Our model potentially indicates CVA. TRIAL REGISTRATION NUMBER: NCT06199830.
目的：比较咳嗽变异性哮喘（CVA）和慢性咳嗽（CC）患者的年龄、性别、身高、体重、体重指数（BMI）等身体信息以及肺功能测试（PFT）结果，并建立CVA的诊断模型。 设计：对浙江中医药大学附属第一医院疑似CVA患者进行病例对照研究。 地点：中国国家重点专科肺病协作组牵头单位之一。 参与者：纳入545例行PFT和支气管激发试验的患者。 观察指标：获取身体信息和肺功能测试数据，采用逻辑回归分析建立模型。采用Hosmer-Lemeshow拟合优度检验、受试者操作特征曲线下面积（AUC）、校准图和决策曲线分析对该模型进行评估。所有数据均使用SPSS V.27和RStudio软件进行分析。 结果：CVA组女性患者比例更高（68.12%对51.48%，p值<0.001），身高更低（1.61（0.40）对1.65（3.26），p值<0.001），体重更低（60（56）对63（85），p值<0.001），BMI更低（22.59（17.91）对23.28（21.81），p值=0.016）。CVA组和CC组在预测值百分比的用力肺活量（FVC）（FVC%pred）（94.4（57.3）对91.60（94.10），p值=0.006）、第1秒用力呼气容积/用力肺活量（FEV1/FVC）（%）（84.65±6.82对86.91±6.71，p值<0.001）、预测值百分比的呼气峰值流速（PEF%pred）（93.00（81.10）对98.00（108.00），p值=0.005）、预测值百分比的最大呼气中期流速（MMEF%pred）（74.50（100.60）对90.85（170.30），p值<0.001）、FVC的50%时的用力呼气流量（FEF）预测值百分比（FEF pred）（78.9（113.50）对93.10（169.80），p值<0.001）以及FVC的75%时的FEF预测值百分比（FEF pred）（69.70（137.60）对85.60（225.80），p值<0.001）方面存在显著差异。在MMEF%pred、FEF pred和FEF pred较低水平（<65%）时，CVA患者数量多于CC患者（32.37%对14.50%，p值<0.001；26.09%对13.02%，p值<0.001；39.13%对23.67%，p值<0.001）。FVC%pred、FEV1/FVC、BMI和MMEF%pred有助于建立AUC为0.733（95%CI：0.6829至0.7831）的模型。该模型使用内部和外部数据进行检验（p值分别为0.2865和0.3197）。 结论：利用BMI、FVC%pred、FEV1/FVC（%）和MMEF%pred建立了诊断模型。我们的模型可能提示CVA。 试验注册号：NCT06_199830
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