Enayat Javad, Mahdaviani Sayed Alireza, Rekabi Mahsa, Ghaini Mehdi, Eslamian Golnaz, Fallahi Mazdak, Ghazvineh Sepideh, Sharifinejad Niusha, Raoufy Mohammad Reza, Velayati Ali Akbar
Immunology and Allergy Department, Mofid Children's Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Pediatric Respiratory Diseases Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Respir Physiol Neurobiol. 2022 Jun;300:103873. doi: 10.1016/j.resp.2022.103873. Epub 2022 Feb 22.
The intensity of respiratory symptoms and expiratory airflow limitations in asthma fluctuate over time. Some studies have reported variable complexity of the respiratory patterns in asthmatic patients. Thus, we conducted a novel study to assess the correlation between asthma severity and breathing pattern dynamics in newly-diagnosed asthmatic patients.
A total of 20 newly-diagnosed asthmatic patients (7 male, 13 female) and 20 healthy cases (11 male, 9 female) were included. The respiratory patterns of all participants and the asthma severity for asthmatic patients were measured using a spirometer (before and after a bronchodilator exposure) and airflow recorder, respectively. The peak-to-peak intervals and the amplitude of peaks were considered as the inter-breath interval (IBI) and lung volume (LV) series. The Detrended Fluctuation Analysis (DFA), Sample Entropy (SampEn), Multi-scale Entropy (MSE), short-term (SD1) and long-term (SD2) variability, and IBI and LV Cross-Sample Entropy of the respiratory pattern dynamics were calculated using MATLAB (Mathwork, USA).
Asthma patients showed notable increase in the average of sample entropy in both IBI and LV parameters (p = 0.025 and p = 0.018, respectively) and also decreased synchronization between IBI and LV (p = 0.042). The multi-scale sample entropy of both IBI and LV was significantly higher in asthmatic patients (p < 0.05). Furthermore, SD1 and SD2 were higher in the patients with asthma (p < 0.05). Significant correlations were detected between spirometric (forced expiratory flow (FEF) change, pre FEF, pre forced expiratory volume in one second (FEV1) / forced vital capacity (FVC), FVC change) and respiratory pattern (mean-IBI, mean-LV, mean-respiratory rate (RR), coefficient of variation (CV)-IBI, CV-LV, cross-sample entropy) parameters (p < 0.05). Furthermore, we identified a negative correlation between CV of IBI and asthma severity (r = -0.52, p = 0.021).
Here, we took a novel approach and observed increased irregularity (more complexity) in the breathing pattern of patients newly-diagnosed with asthma. Remarkable correlations were detected between breathing complexity markers and spirometric indices along with disease severity in asthmatic patients. Thus, our data suggests respiratory pattern indices could be utilized as an indicator of asthma and its severity. However, more clinical data are required to support this conclusion.
哮喘患者的呼吸道症状强度和呼气气流受限情况会随时间波动。一些研究报告了哮喘患者呼吸模式的复杂程度各异。因此，我们开展了一项新研究，以评估新诊断哮喘患者的哮喘严重程度与呼吸模式动态之间的相关性。
共纳入20例新诊断的哮喘患者（7例男性，13例女性）和20例健康对照者（11例男性，9例女性）。分别使用肺活量计（支气管扩张剂暴露前后）和气流记录仪测量所有参与者的呼吸模式以及哮喘患者的哮喘严重程度。峰峰值间隔和峰值幅度分别被视为呼吸间隔（IBI）和肺容积（LV）序列。使用MATLAB（美国Mathwork公司）计算呼吸模式动态的去趋势波动分析（DFA）、样本熵（SampEn）、多尺度熵（MSE）、短期（SD1）和长期（SD2）变异性以及IBI与LV交叉样本熵。
哮喘患者的IBI和LV参数样本熵平均值均显著增加（分别为p = 0.025和p = 0.018），且IBI与LV之间的同步性降低（p = 0.042）。哮喘患者的IBI和LV多尺度样本熵均显著更高（p < 0.05）。此外，哮喘患者的SD1和SD2更高（p < 0.05）。在肺活量测定参数（用力呼气流量（FEF）变化、FEF前值、一秒用力呼气容积（FEV1）/用力肺活量（FVC）、FVC变化）与呼吸模式参数（平均IBI、平均LV、平均呼吸频率（RR）、变异系数（CV）-IBI、CV-LV、交叉样本熵）之间检测到显著相关性（p < 0.05）。此外，我们发现IBI的CV与哮喘严重程度呈负相关（r = -0.52，p = 0.021）。
在此，我们采用了一种新方法，观察到新诊断哮喘患者的呼吸模式不规则性增加（更复杂）。在哮喘患者中，呼吸复杂性标志物与肺活量测定指标以及疾病严重程度之间检测到显著相关性。因此，我们的数据表明呼吸模式指标可作为哮喘及其严重程度的指标。然而，需要更多临床数据来支持这一结论。
