- Research
- Open access
- Published:
MRI-assessed diaphragmatic function can predict frequent acute exacerbation of COPD: a prospective observational study based on telehealth-based monitoring system
BMC Pulmonary Medicine volume 22, Article number: 438 (2022)
Abstract
Background
Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) have considerably high mortality and re-hospitalisation rate. Diaphragmatic dysfunction (DD) is common in COPD patients. However, whether diaphragmatic dysfunction is related to acute exacerbation is yet to be elucidated. This study aimed to evaluate the diaphragm function by magnetic resonance imaging (MRI) in COPD patients and assess whether the impact of DD may help predict AECOPD.
Methods
20 healthy adult volunteers and 80 COPD patients were enrolled. The diaphragms function parameters were accessed by MRI. Patients were guided to start self-management by the Telehealth-based monitoring system following the enrolment. Events of acute exacerbation of COPD were recorded by the system and confirmed by healthcare providers. Binary univariate and multivariate logistic regression analyses were performed to investigate the factors associated with the frequency of AECOPD. Receiver operating characteristic (ROC) curves were further used to assess the value of prediction indexes.
Results
Fifty-nine COPD patients completed a one-year follow-up based on the Telehealth-based monitoring system. The clinical outcomes showed that the diaphragm function parameters at the end of maximal breathing were lower in the COPD group than in the healthy control group (P < 0.05). ANOVA showed significant differences among Global Initiative for Chronic Obstructive Lung Disease (GOLD) stages for diaphragm function parameters, including chest wall motion, lung area, upper-lower diameter, and the diaphragm thickening fraction at the end of maximal breathing (P < 0.05). Moreover, significant differences in diaphragm function parameters were observed between patients with infrequent AECOPD (n = 28) and frequent AECOPD (n = 31) based on the frequency of AECOPD (P < 0.05). The diaphragm thickening fraction and the chest wall motion were associated with AECOPD after adjusting for age, sex, BMI, and lung functions, and the combination of predictions showed better accuracy in predicting the frequency of AECOPD.
Conclusions
In COPD patients, diaphragm function parameters correlate with the severity of airflow limitation. The diaphragm thickening fraction and the chest wall motion were associated with the frequency of AECOPD and can predict it.
Background
Chronic Obstructive Pulmonary Disease (COPD) is characterised by persistent respiratory symptoms and airflow limitation. Its worldwide prevalence is increasing, and it is estimated that COPD will be the fourth leading cause of death by 2030 [1]. Acute exacerbation of COPD (AECOPD) plays a central role in the natural history of the disease, and its effect should not be underestimated. An exacerbation of COPD is defined as an acute worsening of respiratory symptoms that results in additional therapy [2]. AECOPD are complicate complications usually associated with increased airway inflammation, mucus production, and gas trapping, which contribute to increased dyspnoea, cough, and wheeze. Some patients are particularly susceptible to exacerbations and show worse health status, considerably decreased pulmonary function, exacerbated disease progression than those who have infrequent exacerbations, leading to severe morbidity and mortality [3]。 The COPD phenotype reflects the differences between individuals with COPD [4]. The exacerbation rates vary significantly among patients and during follow-up [5, 6]. Clinically, patients with frequent acute exacerbation have a higher hospitalisation rate than those with infrequent exacerbation. In addition, the acuteness of disease progresses further increases the difficulty in treatment of this disease [7]. The predictor of frequent exacerbations (defined as two or more exacerbations per year) is a history of previously treated events [5], and deteriorating airflow limitation is associated with an increasing prevalence of AECOPD [8]. Moreover, skeletal muscle, including diaphragm weakness, is associated with an increased risk for more severe exacerbations and increased hospital re-admission rate after an exacerbation [9].
The diaphragm is the most important respiratory muscle and plays a significant role in maintaining ventilation [10]. Diaphragmatic dysfunction occurs in patients with COPD, and changes in diaphragm structure are closely related to diaphragmatic dysfunction (DD) [11,12,13,14]. Patients with COPD have difficulty inhaling as the diaphragm, the agonist muscle of respiration, becomes short and flat [15]. This reduced respiratory function can be compensated by the increased activities of the respiratory synergistic muscles. Previous studies revealed the relationship between DD and high mortality, poor effect of treatments, and prolonged hospital stay in patients with AECOPD. During severe AECOPD, progressive development of dynamic hyperinflation causes a change in the geometry of the chest wall and diaphragm, with impaired lung function [16, 17]. Another study confirmed that lower diaphragm thickening fraction (DTFmax) and diaphragm excursion (DEmax) during maximal deep breathing were associated with AECOPD, which leads to the possibility of an imaging biomarker distinguishing AECOPD from stable status [18]. The impairment of diaphragmatic function may be one of the main pathophysiological mechanisms in COPD that seems related to acute exacerbation [19]. However, little is known about the role of the diaphragm on COPD exacerbation, and whether DD is a predictor of AECOPD needs to be further investigated.
COPD exacerbations are usually identified based on an increase in various symptoms, including increased breathlessness and sputum production [20]. It has been known that the objective markers for exacerbations such as the use of antibiotics and steroids and whether the patient was admitted to the hospital or attended accident and emergency [1].However, it is estimated that less than a third of deteriorations are reported. Unreported exacerbations may not be serious enough to warrant an emergency visit or hospitalization [21]. Consequently, patient education on when to seek medical attention for exacerbations is of vital importance. In the management of COPD, some researchers have argued that remote monitoring may be a promising alternative or an adjunct to traditional health care services [22]. In recent years, many studies have established risk factors prediction models for acute exacerbation in COPD patients through different monitoring methods, including electronic medical records [23], questionnaire diary records [24], or telephone follow-up [25]. Nonetheless, it is not always possible to monitor all exacerbations in terms of frequency, severity, and likely causes. Due to these factors, acute exacerbations of COPD patients are underestimated, which renders that a dynamically monitoring COPD symptoms is crucial. In a recently published study, we designed and validated a feasible Telehealth-based monitoring system to monitor COPD acute exacerbations [26]. Under this system, acute exacerbation events were detected and further confirmed by a health care provider. Therefore, in this study, we hypothesize that diaphragmatic dysfunction in COPD patients and the diaphragm function worsens in frequent AECOPD patients compared with infrequent AECOPD patients, DD thus can help predict patients likely with frequency of AECOPD.
Materials and methods
Study design and data collection
This prospective observational study was conducted at the General Hospital of Ningxia Medical University from September 2017 to December 2019 and approved by the Ethics Committee for the Conduct of Human Research at the General Hospital of Ningxia Medical University (No.2017–206). All participants provided written informed consent after being informed of the possible risks of the study.
Study population
The patients in the COPD group were recruited from outpatient clinics of the General Hospital of Ningxia Medical University. The inclusion criteria of enrolment included (1) age ≥ 40 years; (2) confirmed diagnosis of COPD according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) guideline [1]; (3) stable phase of COPD, no visit to the outpatient or emergency department, and no hospitalisation for AECOPD during the past 12 weeks; (4) access to a smartphone. The exclusion criteria were any of the following: the presence of pulmonary diseases besides COPD, such as pleural effusion, pneumothorax, phrenic nerve palsy, and interstitial lung disease; neuromuscular disease or chest wall deformities; metallic materials in the body; claustrophobia; patients with mental disorders; and participation in other studies.
Healthy volunteers (healthy control group)
A total of 20 healthy volunteers were recruited from the local community, all of whom were non-smokers and whose pulmonary function tests were normal. The healthy control group was selected based on the following criteria: (1) age was older than 20 without any evidence or history of pulmonary disease; (2) no noticeable emphysematous changes on chest radiographs; (3) no previous thoracic surgery; (4) voluntary participation.
The exclusion criteria were any of the following: (1) individuals with a history of thoracic/upper abdominal surgery; (2) known diaphragmatic paralysis; (3) chronic neuromuscular disease; (4) significant underlying respiratory diseases; (5) claustrophobia; (6) subjects with mental disorders.
The baseline data of participants, including demographic information, were recorded. Patients were instructed how to install and use the App and started self-management of the Telehealth-based monitoring system.
Monitoring acute exacerbation of COPD
The acute exacerbation events monitoring system was developed in collaboration with Zhejiang University, as described in our previously published paper [26]. The system was used for acute exacerbation risk evaluation, disease management, risk warning, follow-up, and compliance management. The online diary allows patients to track their discomfort symptoms through the app, while the workstation automatically detects acute exacerbations. Definition of acute exacerbations based on a previously validated criterion [27]. The system automatically detected a suspected exacerbation if the summed symptom score was > 6 points for ≥ 2 consecutive days. The detection required further confirmation by health care providers to ensure whether the symptoms were caused by COPD. The Telehealth-based monitoring system is illustrated below in Fig. 1.
MRI acquisition and analysis
MRI examinations were performed with subjects lying supine inside a 3.0 T MRI system (Philips Medical Systems, Ingenia, The Netherlands) with a 64-channel coil. We used the sequence of 3D mDIXON SENSE. The field of view (FOV) was 360 mm, the matrix size was 512*512, and the image slice thickness was 3 mm. Images of the lung and diaphragm were obtained during eupnoea and at the end of maximal breathing. In order to ensure that all breathing maneuvers are executed correctly, and with maximum effort, all breathing maneuvers are trained and instructed by an experienced physician before and during the MRI, and they did not use sedatives or contrast agents. To ensure get the best results for the MRI examination, the patients were advised to breathe deeply twice under the coil before the images were scanned. Each participant was encouraged to repeatedly breathe deeply and regularly for five to seven respiratory cycles from maximal inspiration to maximal expiration with as much effort as possible [28]. The acquisition time of a patient is approximately 20 to 25 min. Subjects were instructed to breathe in and out deeply upon hearing the scanner alarming(respiratory rate of 6 to 8 breaths per minute). Furthermore, the rest of the time, their respiratory rate of breath is normal. Images were checked by the radiologist and reacquired during the same imaging session if unsatisfactory. During MRI examination, 13 patients were unable to tolerate in a supine position (2 had dizziness, 5 had pain in the limbs, 2 had dyspnea, and 4 had claustrophobia) in our study. We paused the process immediately and allow the patient to relax. All of them felt better and continue to acquire images after the rest. If the patient refuses collection, we will terminate the acquisition, and the patient will withdraw from the study. According to previous studies, the parameters of diaphragmatic dome factor [29], diaphragmatic displacement [30], chest wall motion [28], lung area, and diaphragm thickness were ascertained and manually measured by two or more experienced radiologists at the end of maximal breathing. The measuring method is shown in Fig. 2, and the diaphragm function data are showed in Additional file 1.
Statistical analysis
The data were analysed by SPSS version 26.0 for Windows (IBM Corp, Armonk, New York, USA). Continuous variables with normal distribution were presented as mean ± standard deviation (SD). Non-normally distributed variables were reported as median (inter-quartile range). A t-test was used to compare variables between the COPD group and healthy control group, as well as between the frequent AECOPD group and the infrequent AECOPD group. ANOVA was used to analyse the within-group difference between the GOLD stage. Pearson or Spearman correlation test was applied to assess the relationship of variables. Univariate and multivariable logistic regression analyses were applied to identify the risk factors for the frequency of AECOPD. The area under the receiver operating characteristic curve (ROC) was applied to compare the prediction ability for the frequency of AECOPD risk according to diaphragm function parameters with different thresholds. A value of P < 0.05 was considered significant in all statistical analyses.
Results
Demographics of study subject
Of the 80 COPD patients, of which 59 patients had one year of complete prospective follow-up data, 21 were excluded for various reasons, including three patients were excluded due to diaphragmatic paralysis, five patients with metallic materials in the body, four with claustrophobia, and one participating in other studies. However, after one year of follow-up, eight patients withdrew from the study, including three died, two moved to other cities, and three failed to use smartphones properly. The COPD group included 56 males and 3 females with a mean age of 60.37 ± 6.56 years; 9 COPD patients were categorized as mild, 19 as moderate, 18 as severe, and 13 as very severe based on respiratory function. A total of 20 healthy controls were enrolled, and 16 completed the study; the mean age was 58.75 ± 7.10, including 11 males and 5 females. Gender, smoking status, and the pulmonary function parameters, FEV1, FEV1% predict, FVC, FVC% predict, and FEV1/FVC% were significantly different between the COPD group and healthy controls (P < 0.05). Patient demographics and clinical characteristics are shown in Table 1.
Comparison of diaphragm function between COPD group and healthy group
The parameters of diaphragm function were measured by MRI on the right side, both during the end of maximal inspiration and expiration. The diaphragmatic dome factor was lower in the COPD group than in the healthy control group at the end of maximal breathing (1.15 ± 0.09 vs. 1.27 ± 0.11, P < 0.001; 1.24 ± 0.13 vs. 1.32 ± 0.07, P = 0.02). Anterior–posterior diameter of the chest at the end of maximal inspiration and expiration was higher in the COPD group than in the healthy control group (18.78 ± 1.61 vs. 17.20 ± 1.98, P = 0.001; 16.88 ± 1.64 vs. 14.31 ± 1.92, P < 0.001). Chest wall motion was lower in the COPD group than in the healthy control group (1.91 ± 1.07 vs. 2.89 ± 1.38, P = 0.003). Lung area at the end of maximal inspiration and expiration were higher in the COPD group than in the healthy control group (355.25 ± 43.62 vs. 305.59 ± 42.17, 244.28 ± 52.94 vs. 172.28 ± 29.04, respectively, P < 0.001). The change of lung area was lower in the COPD group than in the healthy control group (110.98 ± 40.30 vs. 133.31 ± 36.50, P = 0.049). Upper-lower diameter of the lung at the end of maximal inspiration and expiration was higher in patients with COPD than in the healthy subjects (23.15 ± 2.48 vs. 21.80 ± 1.49, P = 0.009, 19.00 ± 3.49 vs. 14.56 ± 1.45, P < 0.001). Diaphragmatic displacement was lower in the COPD group than in the healthy control group (4.16 ± 2.02 vs. 7.24 ± 1.26, P < 0.001). Diaphragm thickness at the end of maximal inspiration and the diaphragm thickening fraction (DTF) were lower in the COPD group than in the healthy control group (6.04 ± 1.07 vs. 7.04 ± 1.02, P = 0.001; 29.08 ± 16.57 vs. 66.11 ± 22.46, P < 0.001). The diaphragm thickness at the end of maximal expiration was thicker in the COPD group than in the control group (4.69 ± 0.61 vs. 4.30 ± 0.78, P = 0.038). The results presented in Table 2. Figure 3 clearly shows the diaphragm morphology and lung hyperinflation at the end of maximal inspiration and expiration in a healthy control and a COPD patient.
Comparison of diaphragm function between COPD subgroup
COPD patients were classified into four groups according to the GOLD stages: 9 (15.25%) patients in the mild group, 19 (32.20%) in the moderate group, 18 (30.51%) in the severe group, and 13 (22.03%) in the very severe group, respectively. As presented in Table 3 (at the end of the document text file), ANOVA analysis revealed significant differences in the lung area, the upper-lower diameter at the end of maximal inspiration and expiration, the chest wall motion, the change of lung area, the diaphragmatic displacement, the diaphragm thickening fraction, and the diaphragm thickness at the end of maximal inspiration, among patients with different severity of COPD (all P < 0.05). But no statistical differences were found in the diaphragmatic dome factor, the anterior–posterior diameter of the chest at the end of maximal inspiration and expiration, and diaphragm thickness at the end of maximal expiration among four groups (all P > 0.05). Figure 4 clearly showed the diaphragm morphology and the lung hyperinflation in COPD patients with varied severities. As the respiratory function deteriorates, the diaphragm morphology appeared to be more flattened, and lung hyperinflation becomes more pronounced.
Correlation analysis
Correlation matrix analyses were conducted between clinical parameters and diaphragm function data. The results were presented in Fig. 5 and Additional file 2. A matrix graph of Pearson's correlation analysis indicated that the diaphragm function parameters (the chest wall motion, change of lung area, diaphragmatic displacement, and diaphragm thickening fraction) are closely related to pulmonary function parameters (FEV1, FEV1% pred, FVC, and FVC% pred) and 6MWD with a correlation coefficient between 0.4 and 0.8. In addition, a negative degree of correlation was observed among the diaphragm function parameters, the frequency of AECOD, and CAT scores (correlation coefficient between 0.3 and 0.7).
Monitoring of AECOPD
Under the Telehealth-based monitoring system, a total of 719 warning events were recorded. Among them, 136 warning events resulted from acute exacerbations of COPD following a one-year follow-up period. Twenty-eight patients experienced acute exacerbations of less than two episodes, including nine patients who did not experience any acute exacerbation. Thirty-one patients experienced two or more exacerbations in the one-year follow-up. The average frequency of acute exacerbation was 2.31 times. As shown in Fig. 6, the frequency of acute exacerbations in COPD patients increased with GOLD grade and it is significantly higher for patients with GOLD 3 (severe) and GOLD 4 (very severe). After one year of follow-up, among GOLD I patients, one patient had episode two acute exacerbations, with a median of 0, IQR (0–1); among GOLD II patients, eight patients had episode two acute exacerbations, with a median of 1, IQR (1, 2); nine patients had two or more acute exacerbations in GOLD III, with a median of 2, IQR (1,4); Remarkably, all the patients had experienced at least two acute exacerbations in the GOLD IV group, the median was 5, IQR (4,6).
Comparison of diaphragm function between frequent AECOPD group and infrequent AECOPD group
According to the frequency of AECOPD, patients with COPD were divided into two groups: the infrequent AECOPD group (defined as less than two episodes of exacerbation per year) and the frequent AECOPD group (defined as two or more exacerbations per year). Compared with the infrequent AECOPD group, the frequent AECOPD group showed older age (P = 0.005), lower BMI (P = 0.001), higher CAT scores (P = 0.001), more severe mMRC (P = 0.001), a shorter distance of 6MWD (P < 0.001), worse lung function (EFV1:P < 0.001; FEV1% pred: P < 0.001; FVC: P = 0.007; FVC% pred: P = 0.001), lower chest wall motion (P = 0.002), lower change of lung area (P = 0.003), lower diaphragmatic displacement (P < 0.001), and significantly decreased diaphragm thickening fraction (P < 0.001). No significant differences were found in the diaphragmatic dome factor at the end of maximal inspiration and expiration. Overall, these results suggest that patients in the frequent AECOPD group had worse pulmonary function status, health status, and diaphragm function than those in the infrequent AECOPD group (Table 4).
Predictive factors for frequency of AECOPD during a one-year follow-up in patients with COPD
Variables with statistical significance in comparisons of the clinical parameters and diaphragm function data were included in the multivariate logistic regression analysis. The backward stepwise regression method was adopted. Eleven variables were significantly associated with an increased risk of frequent AECOPD on univariate analysis and entered in the logistic regression model: age (P = 0.009), BMI (P = 0.003), CAT scores (P = 0.003), 6MWD.
(P = 0.001), FEV1% pred (P < 0.001), FVC% pred (P = 0.004), FEV1/FVC% (P = 0.007), chest wall motion (P = 0.032), the change of the lung area (P = 0.007), diaphragmatic displacement (P < 0.001), and the diaphragm thickening fraction (P < 0.001). After further adjusting for covariates, the chest wall motion (OR 0.37, 95% CI: 0.15–0.92, P = 0.032) and the diaphragm thickening fraction (OR: 0.89, 95% CI 0.83–0.94, P < 0.001) were independently associated with the frequency of AECOPD (Table 5).
ROC analysis of the predictive values of the simple and combined index
A ROC analysis was conducted to determine the optimal cut-off values of the diaphragm function to predict the frequency of AECOPD. The sensitivity, specificity, optimal threshold, and AUC for the chest wall motion, change of lung area, diaphragmatic displacement, and diaphragm thickening fraction were shown in the Additional file 3; corresponding ROC curves are presented in Fig. 7. As far as predicted ability was concerned, all four parameters were suitable, but the diaphragm thickening fraction has the largest AUC 0.869 (95% CI: 0.722–0.965). Moreover, the optimal cut-off value was 29.51% at a sensitivity of 78.6%, a specificity of 93.5%. We also found that the diaphragm thickening fraction was combined with chest wall motion to predict the frequency of AECOPD has the best AUC, sensitivity and specificity index (AUC = 0.901, 95% CI: 0.822–0.980, P < 0.0001, sensitivity: 90.3%, specificity: 85.7%) (Fig. 8).
Discussion
In this study with small size of sample, we investigated whether the diaphragm parameters could predict the acute exacerbations of AECOPD. Results showed that the diaphragmatic functional parameters, especially chest wall motion and diaphragmatic thickening fraction. These indexes could be used for the prediction of the frequency of acute exacerbation of AECOPD, and could further be potential risk factors for clinically stable COPD patients that occur the frequency of acute exacerbations in the future. To our knowledge, we first proved evidence that the inclusion of DD can aid to predict AECOPD. This study thus also offers a novel index to make up a lack of other conventional indicators in predicting AECOPD in COPD patients.
Our results show that in the frequent AECOPD group, patients have severe airflow limitation (FEV1%pred:40.44 ± 18.11), low exercise capacity (6MWD:399.19 ± 109.46 m), and moderate disease burden (CAT score: 16.42 ± 7.55) and poor diaphragm dysfunction (seen in Table 4). Therefore, for patients who are at risk of frequent acute exacerbations, we suggested to undergo the diaphragmatic MRI to clarify the functional status of the diaphragm and provide targeted interventions to prevent acute exacerbation events. However, considering the cost-effectiveness, the clinically indicated patients with low disease burden, breathlessness, good exercise capacity, and moderate disease (FEV1 over 50) may not request to complete the MRI unless the patient who is particularly susceptible to exacerbations risks.
In addition, we also explored the relationship between diaphragmatic function parameters, disease severity, and acute exacerbation frequency. The results of our study showed that diaphragm dysfunction occurs in COPD patients, mainly characterized by a short and flat diaphragm dome, hyperinflation of the lung area, chest wall motion, diaphragmatic displacement, and the diaphragm thickening fraction, which were positively correlated with COPD severity.
To our knowledge, COPD is a heterogeneous disorder characterized by small airway dysfunction and parenchymal and vascular destruction [31]. Extensive studies on COPD patients have demonstrated that expiratory flow limitation is the hallmark of COPD, and its most important consequence is lung hyperinflation [32, 33]. Dynamic hyperinflation also impairs diaphragmatic function, shortening the diaphragm to a suboptimal length, decrease the diaphragmatic curvature, and reduce the area of apposition of the costal diaphragm with the chest wall, which gradually affects the motion of the chest wall and diaphragm, leading to the reduced motion; dyspnoea and respiratory drive are increased as a result [34,35,36]. Additionally, COPD patients reduce their activities of daily living to prevent dyspnoea, which negatively affects their quality of life. As previously reported, our study observed a similar loss of diaphragmatic displacement, shortened diaphragm, and decreased diaphragmatic curvature in COPD patients [30, 37,38,39]. Further, we detected statistically significant differences between the grade groups according to the severity of COPD in MRI-assessed diaphragmatic displacement. Correlation analysis demonstrated that the diaphragmatic displacement was positively correlated with airflow limitation and exercise tolerance; it was negatively correlated with COPD's quality of life (CAT scores). These data imply that hyperinflation of the lung not only increases the lung area and alters the chest wall and diaphragmatic motion but also puts the respiratory muscles at a mechanical disadvantage [38, 40, 41].
We also observed that the MRI-assessed diaphragm thickness at the end of maximal breathing and the diaphragm thickening fraction in the COPD group were lower than in healthy controls; Our data showed that the diaphragm thickening fraction was positively correlated with airflow limitation and exercise tolerance; it was negatively correlated with the quality of life in COPD patients. These data imply that the diaphragm morphology becomes short and flat in COPD patients. The progression of the disease causes the shortening of diaphragm fibbers and decreases the resting diaphragm muscle length. Structural changes would cause the diaphragms to flatten and thin [42, 43]. Elsawy reported that DTF was significantly decreased in the COPD group and decreased with the increasing severity of COPD [44]. The result is consistent with the work of Kazuki Okura, who reported that the thickness of the diaphragm at total lung capacity or the end of maximal inspiration and the change ratio of the diaphragm thickness (ΔTdi%) were significantly lower in the COPD group [45], and also in agreement with Smargiassi et al., who reported progressive reductions in diaphragm thickness and thickening as air trapping increased [46]. Moreover, Rittayamai also proved that compared to controls, patients with COPD had poorer diaphragm function as measured by TFdi-max and DE-max COPD patients, and diaphragm dysfunction was positively correlated with the severity of airflow obstruction [47].
It is well known that acute exacerbations were the most significant adverse events of COPD, punctuating the natural history, which is associated with significant mortality as well as health and socioeconomic burden [48, 49]. Acute exacerbations of COPD are characterized clinically by symptoms of worsening dyspnoea, cough, sputum production, and sputum purulence, as well as by worsening airflow obstruction [48]. It was reported that 22%–40% of COPD patients experience at least one moderate or severe exacerbation yearly, while 9%–16% experience more than one [50, 51]. At the population level, approximately 20% of GOLD 2 (moderate airflow limitation) patients may experience frequent exacerbations requiring treatment with antibiotics and/or systemic corticosteroids [5]. The risk of exacerbations is significantly higher for patients with GOLD 3 (severe) and GOLD 4 (very severe), which corresponds with our results.
Although exacerbations become more frequent and more severe as COPD progresses, the rate at which they occur appears to reflect an independent susceptibility phenotype. The susceptibility and frequent occurrence of COPD are based on multiple factors, such as low FEV1 [52, 53], poor nutritional status [54], and weak muscles [55]. However, the most crucial determinant of frequent exacerbations is a history of exacerbation [5]. The early identification of exacerbations is of utmost importance. Less than one-third of exacerbations are estimated to be reported [21], because most exacerbations are recorded in outpatient and emergency departments during patient admittance. Furthermore, if acute exacerbations with gradual or mild onset occur at home or treatment works well enough to relieve the symptoms, patients will not visit the hospital, and the exacerbation events might not be recorded. Therefore, early detection and appropriate management of AECOPD patients are essential. However, the early detection of acute exacerbations requires considerable human resources, and caregivers face difficulties in personalizing follow-up management. A major step forward is the current availability of mobile health tools and remote monitoring platforms [56]. Which can provide daily symptoms and enable the dynamic collection of data characterizing a patient's physiological and clinical status. Based on this advantage, we also designed a Telehealth-based monitoring system according to Aaron's modeling. We monitored the occurrence of acute exacerbations in out-of-hospital and demonstrated the feasibility of monitoring acute exacerbations with the Telehealth-based monitoring system [26]. We recorded events of exacerbation during the one-year follow-up and explored the relationship between the frequency of acute exacerbation and diaphragm function.
The diaphragm muscle is essential in respiration, and it is vital to understand lung exercise physiology and the mechanics of COPD [57]. The previous study focused on DD in the acute exacerbation of COPD. It was reported that patients with AECOPD have a higher rate of diaphragmatic dysfunction (DD) than healthy controls, which has been ascribed to hyperinflation-induced diaphragm shortening, placing the diaphragm and respiratory muscle at a mechanical disadvantage, which ultimately causes diaphragmatic weakness and a marked increase in the rate of non-invasive ventilation (NIV) [17]. A further study showed that DD has high specificity and sensitivity in predicting NIV failure in AECOPD patients and may help identify patients who need NIV therapy and be helpful in predicting the duration of hospitalization [58]. A recent study explored the value of diaphragm markers for distinguishing AECOPD from stable COPD [18], which is a step forward in the role of the diaphragm between AECOPD.
The diaphragm, which is the main inspiratory muscle, generates a craniocaudal movement of its dome during contraction [59]. The two striking features in COPD, air trapping and lung hyperinflation, impair the function of the diaphragm, shortening its operating length and changing the mechanical linkage between its various parts, thereby placing it at a mechanical disadvantage [60]. Diaphragmatic dysfunction affects the disease's severity and is related to high mortality, poor treatment effects, and prolonged hospital stay in patients with AECOPD, providing a potential target for therapeutic intervention [61]. The main treatment approaches for COPD are pharmacological therapy and pulmonary rehabilitation. Early pulmonary rehabilitation could improve functional exercise capacity, and health-related quality of life has been demonstrated across all grades of COPD severity [62]. In this study, we demonstrate that the DD could predict AECOPD. If the patients' DD parameters were lower than the optimal cut-off value, we should consider who had a high risk of AECOPD and give target diaphragm rehabilitation. Early targeted diaphragm rehabilitation therapy for patients with a high risk of acute exacerbation may help reduce the number of acute exacerbations and hospitalization.
This study had several limitations. First, this study was conducted at a single centre with a relatively small number of participants; therefore, comparative studies involving a larger number of patients are necessary. Second, in our study, the inclusion criteria required patients to have been exacerbation-free for 12Â weeks, considering patients MRI examination cooperation. For COPD patients with poor general conditions and lung function, it is difficult to tolerate magnetic resonance examination. However, we may miss some patients with particularly high exacerbation rates, most of them are being in poor lung function. Third, most of the COPD patients in our study are male. The cause of the male-predominant in the cohort may be related to the epidemiology of COPD. According to previously study, the function of the diaphragm differs between men and women. In normoxia, the magnitude of diaphragm fatigue was equivalent in men and women. Conversely, greater fatigue was expressed in female diaphragm compared to males during equivalent cumulative work in hypoxia [63]. Archiza et al. study also showed the greater vulnerability of the female diaphragm to fatigue during hypoxia [64]. Unfortunately, our male-predominant cohort, may not be generalizable to all COPD patients. It required further study. Finally, our study focused on the relationship between diaphragmatic function parameters and the acute exacerbation of COPD. Diaphragmatic function parameters and acute exacerbations were collected primarily during data acquisition, while the relationship between tidal volumes (VT), inspiratory capacity (IC) and diaphragmatic function was ignored. As for SNIP, our hospital cannot measure it at present. We were unable to analyze the correlation between these measures and MRI indices. The future study will investigate the relationship between IC, VT, and diaphragm function.
Conclusion
Diaphragmatic dysfunction in patients with COPD is characterised by decreased diaphragmatic displacement, chest wall motion, diaphragmatic dome factor, change of lung area and diaphragm thickening fraction, and increased lung hyperinflation. Moreover, it positively correlated with COPD severity. Additionally, the results of multivariate analysis in this study revealed that the chest wall motion and the diaphragm thickening fraction might predict the frequency of acute exacerbations of COPD.
Availability of data and materials
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
Abbreviations
- COPD:
-
Chronic obstructive pulmonary disease
- AECOPD:
-
Acute exacerbation of COPD
- GOLD:
-
Global initiative for chronic obstructive lung disease
- ROC:
-
Receiver operating characteristics curve
- DD:
-
Diaphragmatic dysfunction
- BMI:
-
Body mass index
- CAT:
-
Chronic obstructive pulmonary disease assessment test
- 6MWT:
-
6-Min walk test
- FEV1:
-
Forced expiratory volume at 1Â s
- FVC:
-
Forced vital capacity
- AUC:
-
Area under the curve
- NIV:
-
Non-invasive ventilation
- mMRC:
-
Modified Medical Research Council
- OR:
-
Odds ratio
- CI:
-
Confidence interval
References
Global Initiative for Chronic Obstructive Lung Disease (GOLD). Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease. 2021;1–164.1. https://goldcopd.org/wp-content/uploads/2020/11/GOLD-REPORT-2021-v1.1-25Nov20_WMV.pdf. Accessed 3 Mar 2021.
Oliveira AS, Munhá J, Bugalho A, et al. Identification and assessment of COPD exacerbations. Pulmonology. 2017;24:42–7.
Wedzicha JA, Seemungal TA. COPD exacerbations: defining their cause and prevention. Lancet. 2007;370(9589):786–96.
Blasi F, Chalmers JD, Aliberti S. COPD and bronchiectasis: phenotype, endotype or co-morbidity? COPD. 2014;11(6):603–4.
Hurst JR, Vestbo J, Anzueto A, et al. Susceptibility to exacerbation in chronic obstructive pulmonary disease. N Engl J Med. 2010;363(12):1128–38.
Han MK, Quibrera PM, Carretta EE, et al. Frequency of exacerbations in patients with chronic obstructive pulmonary disease: an analysis of the SPIROMICS cohort. Lancet Respir Med. 2017;5(8):619–26.
Cao Z, Ong KC, Eng P, et al. Frequent hospital readmissions for acute exacerbation of COPD and their associated factors. Respirology. 2006;11(2):188–95.
Müllerova H, Maselli DJ, Locantore N, et al. Hospitalized exacerbations of COPD: risk factors and outcomes in the ECLIPSE cohort. Chest. 2015;147(4):999–1007.
Hogea SP, Tudorache E, Fildan AP, et al. Risk factors of chronic obstructive pulmonary disease exacerbations. Clin Respir J. 2020;14(3):183–97.
Ratnovsky A, Elad D. Anatomical model of the human trunk for analysis of respiratory muscles mechanics. Respir Physiol Neurobiol. 2005;148(3):245–62.
Testa A, Soldati G, Giannuzzi R, et al. Ultrasound M-mode assessment of diaphragmatic kinetics by anterior transverse scanning in healthy subjects. Ultrasound Med Biol. 2011;37(1):44–52.
Laveneziana P, Albuquerque A, Aliverti A, et al. ERS statement on respiratory muscle testing at rest and during exercise. Eur Respir J. 2019;53:6.
Boussuges A, Gole Y, Blanc P. Diaphragmatic motion studied by m-mode ultrasonography: methods, reproducibility, and normal values. Chest. 2009;135(2):391–400.
Boon AJ, Ogorman C. Ultrasound in the assessment of respiration. J Clin Neurophysiol. 2016;33(2):112–9.
Calverley PM, Koulouris NG. Flow limitation and dynamic hyperinflation: key concepts in modern respiratory physiology. Eur Respir J. 2005;25(1):186–99.
Lim SY, Lim G, Lee YJ, et al. Ultrasound assessment of diaphragmatic function during acute exacerbation of chronic obstructive pulmonary disease: a pilot study. Int J Chron Obstruct Pulmon Dis. 2019;14:2479–84.
Antenora F, Fantini R, Iattoni A, et al. Prevalence and outcomes of diaphragmatic dysfunction assessed by ultrasound technology during acute exacerbation of COPD: a pilot study. Respirology. 2017;22(2):338–44.
An TJ, Yoo YJ, Lim JU, et al. Diaphragm ultrasound is an imaging biomarker that distinguishes exacerbation status from stable chronic obstructive pulmonary disease. Int J Chron Obstruct Pulmon Dis. 2022;17:3–12.
Vostatek P, Novák D, Rychnovský T, et al. Diaphragm postural function analysis using magnetic resonance imaging. PLoS ONE. 2013;8(3):e56724.
Sapey E, Bafadhel M, Bolton CE, et al. Building toolkits for COPD exacerbations: lessons from the past and present. Thorax. 2019;74(9):898–905.
Langsetmo L, Platt RW, Ernst P, et al. Underreporting exacerbation of chronic obstructive pulmonary disease in a longitudinal cohort. Am J Respir Crit Care Med. 2008;177(4):396–401.
Sanchez-Morillo D, Fernandez-Granero MA, Leon-Jimenez A. Use of predictive algorithms in-home monitoring of chronic obstructive pulmonary disease and asthma: a systematic review. Chron Respir Dis. 2016;13(3):264–83.
Goto T, Camargo CA, Faridi MK, et al. Machine learning approaches for predicting disposition of asthma and COPD exacerbations in the ED. Am J Emerg Med. 2018;36(9):1650–4.
Shah SA, Velardo C, Farmer A, et al. Exacerbations in chronic obstructive pulmonary disease: identification and prediction using a digital health system. J Med Internet Res. 2017;19(3): e69.
Deng L, Yan J, Xu H, et al. Prediction of exacerbation frequency of AECOPD based on next-generation sequencing and its relationship with imbalance of lung and gut microbiota: a protocol of a prospective cohort study. BMJ Open. 2021;11(9): e047202.
Deng N, Chen J, Liu Y, et al. Using mobile health technology to deliver a community-based closed-loop management system for chronic obstructive pulmonary disease patients in remote areas of china: development and prospective observational study. JMIR Mhealth Uhealth. 2020;8(11): e15978.
Aaron SD, Donaldson GC, Whitmore GA, et al. Time course and pattern of COPD exacerbation onset. Thorax. 2012;67(3):238–43.
Suga K, Tsukuda T, Awaya H, et al. Impaired respiratory mechanics in pulmonary emphysema: evaluation with dynamic breathing MRI. J Magn Reson Imaging. 1999;10(4):510–20.
Iwasawa T, Takahashi H, Ogura T, et al. Influence of the distribution of emphysema on diaphragmatic motion in patients with chronic obstructive pulmonary disease. Jpn J Radiol. 2011;29(4):256–64.
Iwasawa T, Kagei S, Gotoh T, et al. Magnetic resonance analysis of abnormal diaphragmatic motion in patients with emphysema. Eur Respir J. 2002;19(2):225–31.
Hogg JC, Timens W. The pathology of chronic obstructive pulmonary disease. Annu Rev Pathol. 2009;4:435–59.
Elbehairy AF, Ciavaglia CE, Webb KA, et al. Pulmonary gas exchange abnormalities in mild chronic obstructive pulmonary disease. Implications for dyspnea and exercise intolerance. Am J Respir Crit Care Med. 2015;191(12):1384–94.
Ofir D, Laveneziana P, Webb KA, et al. Mechanisms of dyspnea during cycle exercise in symptomatic patients with GOLD stage I chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2008;177(6):622–9.
Laghi F, Tobin MJ. Disorders of the respiratory muscles. Am J Respir Crit Care Med. 2003;168(1):10–48.
Gagnon P, Guenette JA, Langer D, et al. Pathogenesis of hyperinflation in chronic obstructive pulmonary disease. Int J Chron Obstruct Pulmon Dis. 2014;9:187–201.
Criner G, Cordova FC, Leyenson V, et al. Effect of lung volume reduction surgery on diaphragm strength. Am J Respir Crit Care Med. 1998;157(5 Pt 1):1578–85.
Yamaguti WP, Claudino RC, Neto AP, et al. Diaphragmatic breathing training program improves abdominal motion during natural breathing in patients with chronic obstructive pulmonary disease: a randomized controlled trial. Arch Phys Med Rehabil. 2012;93(4):571–7.
Paulin E, Yamaguti WP, Chammas MC, et al. Influence of diaphragmatic mobility on exercise tolerance and dyspnea in patients with COPD. Respir Med. 2007;101(10):2113–8.
Dos Santos Yamaguti WP, Paulin E, Shibao S, et al. Air trapping: the major factor limiting diaphragm mobility in chronic obstructive pulmonary disease patients. Respirology. 2008;13(1):138–44.
Shiraishi M, Higashimoto Y, Sugiya R, et al. Diaphragmatic excursion correlates with exercise capacity and dynamic hyperinflation in COPD patients. ERJ Open Res. 2020;6:4.
Davachi B, Lari SM, Attaran D, et al. The relationship between diaphragmatic movements in sonographic assessment and disease severity in patients with stable chronic obstructive pulmonary disease (COPD). J Cardio Thor Med. 2014;2(3):187–92.
Steele RH, Heard BE. Size of the diaphragm in chronic bronchitis. Thorax. 1973;28(1):55–60.
Ogan N, Aydemir YTEV, et al. Diaphragmatic thickness in chronic obstructive lung disease and relationship with clinical severity parameters. Turk J Med Sci. 2019;49(4):1073–8.
Elsawy SB. Impact of chronic obstructive pulmonary disease severity on diaphragm muscle thickness. Egypt J Chest Dis Tuberculosis. 2017;66(4):587–92.
Okura K, Iwakura M, Shibata K, et al. Diaphragm thickening assessed by ultrasonography is lower than healthy adults in patients with chronic obstructive pulmonary disease. Clin Respir J. 2020;14(6):521–6.
Smargiassi A, Inchingolo R, Tagliaboschi L, et al. Ultrasonographic assessment of the diaphragm in chronic obstructive pulmonary disease patients: relationships with pulmonary function and the influence of body composition—a pilot study. Respiration. 2014;87(5):364–71.
Rittayamai N, Chuaychoo B, Tscheikuna J, et al. Ultrasound evaluation of diaphragm force reserve in patients with chronic obstructive pulmonary disease. Ann Am Thorac Soc. 2020;17(10):1222–30.
Vogelmeier CF, Criner GJ, MartÃnez FJ, et al. Global strategy for the diagnosis, management, and prevention of chronic obstructive lung disease 2017 report: GOLD executive summary. Arch Bronconeumol. 2017;53(3):128–49.
Wedzicha JA, Wilkinson T. Impact of chronic obstructive pulmonary disease exacerbations on patients and payers. Proc Am Thorac Soc. 2006;3(3):218–21.
Gayle A, Dickinson S, Morris K, et al. What is the impact of GOLD 2017 recommendations in primary care? A descriptive study of patient classifications, treatment burden and costs. Int J Chron Obstruct Pulmon Dis. 2018;13:3485–92.
Hastie AT, Martinez FJ, Curtis JL, et al. Association of sputum and blood eosinophil concentrations with clinical measures of COPD severity: an analysis of the SPIROMICS cohort. Lancet Respir Med. 2017;5(12):956–67.
Garcia-Aymerich J, Monsó E, Marrades RM, et al. Risk factors for hospitalization for a chronic obstructive pulmonary disease exacerbation. EFRAM study. Am J Respir Crit Care Med. 2001;164(6):1002–7.
Garcia-Aymerich J, Farrero E, Félez MA, et al. Risk factors of readmission to hospital for a COPD exacerbation: a prospective study. Thorax. 2003;58(2):100–5.
Pouw EM, Ten Velde GP, Croonen BH, et al. Early non-elective readmission for chronic obstructive pulmonary disease is associated with weight loss. Clin Nutr. 2000;19(2):95–9.
Decramer M, Gosselink R, Troosters T, et al. Muscle weakness is related to utilization of health care resources in COPD patients. Eur Respir J. 1997;10(2):417–23.
Donner CF, Zuwallack R, Nici L. The role of telemedicine in extending and enhancing medical management of the patient with chronic obstructive pulmonary disease. Medicina (Kaunas). 2021;57(7):726.
Zambon M, Greco M, Bocchino S, et al. assessment of diaphragmatic dysfunction in the critically ill patient with ultrasound: a systematic review. Intensive Care Med. 2017;43(1):29–38.
Kocyigit H, Gunalp M, Genc S, et al. Diaphragm dysfunction detected with ultrasound to predict noninvasive mechanical ventilation failure: a prospective cohort study. Am J Emerg Med. 2021;45:202–7.
Reid W, Dechman G. Considerations when testing and training the respiratory muscles. Phys Ther. 1995;75(11):971–82.
Similowski T, Yan S, Gauthier A, et al. Contractile properties of the human diaphragm during chronic hyperinflation. N Engl J Med. 1991;325(13):917–23.
Vogelmeier C, Criner G, Martinez F, et al. Global strategy for the diagnosis, management and prevention of chronic obstructive lung disease 2017 report: GOLD executive summary. Respirology. 2017;22(3):575–601.
Sahin H, Naz I, Varol Y, et al. Is a pulmonary rehabilitation program effective in COPD patients with chronic hypercapnic failure? Expert Rev Respir Med. 2016;10(5):593–8.
Geary C, Welch J, Mcdonald M, et al. Diaphragm fatigue and inspiratory muscle metaboreflex in men and women matched for absolute diaphragmatic work during pressure-threshold loading. J Physiol. 2019;597(18):4797–808.
Archiza B, Reinhard P, Welch J, et al. Sex differences in diaphragmatic fatigue: effects of hypoxia during inspiratory loading. J Physiol. 2021;599(4):1319–33.
Acknowledgements
Not applicable.
Funding
This study was supported by a grant from the National Natural Science Foundation of China (Nos. 81760004); grants from the Key research and development program of Ningxia hui autonomous (2018BEG03035); the Open Project of State Key Laboratory of Respiratory Disease (SKLRD-OP-201903); a grant from the Natural Science Foundation of Ningxia (Nos. 2020AAC02001).
Author information
Authors and Affiliations
Contributions
JC conceived and designed the clinical trial. SSW and RL analyzed the data and drafted the manuscript. FXW assisted with data analysis. ZZP measured the parameters. HT, XHW, JLM, and YTZ recruited the patients. ND designed the Telehealth-based monitoring system. JC and ND interpreted data and critically revised the manuscript. All authors read and approved the final version of the manuscript.
Corresponding authors
Ethics declarations
Ethics approval and consent to participate
The study was approved by the Ethics Committee for the Conduct of Human Research at the General Hospital of Ningxia Medical University (2017-209). All participants were informed of the possible risks of the study and gave written informed consent. All methods were carried out in accordance with relevant guidelines and regulations.
Consent for publication
All the authors consent for publication of this manuscript.
Competing interests
The authors declare that they have no conflicting interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Shuoshuo Wei, Rong Lu and Zhengping Zhang: Co-authors
Supplementary Information
Additional file 1
. COPD patients clinical and diaphragm function parameters
Additional file 2
. Correlation between diaphragm function and clinical parameters in the COPD group
Additional file 3
. Receiver operating characteristic curves of diaphragm function indices predicting the frequency of AECOPD
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
About this article
Cite this article
Wei, S., Lu, R., Zhang, Z. et al. MRI-assessed diaphragmatic function can predict frequent acute exacerbation of COPD: a prospective observational study based on telehealth-based monitoring system. BMC Pulm Med 22, 438 (2022). https://doi.org/10.1186/s12890-022-02254-x
Received:
Accepted:
Published:
DOI: https://doi.org/10.1186/s12890-022-02254-x