Correlation between Hand Grip Strength and Peak Inspiratory Flow Rate in Patients with Stable Chronic Obstructive Pulmonary Disease
by
, , and
Apisara Suriyakul
1,
Narongkorn Saiphoklang
1,2,*
,
Igor Barjaktarevic
2 and
Christopher B. Cooper
2,3 1
Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Faculty of Medicine, Thammasat University, Pathum Thani 12120, Thailand
2
Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
3
Airway & Exercise Physiology Research Laboratory, Department of Physiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
*
Author to whom correspondence should be addressed.
Diagnostics 2022, 12(12), 3050; https://doi.org/10.3390/diagnostics12123050
Submission received: 8 November 2022
/
Revised: 2 December 2022
/
Accepted: 3 December 2022
/
Published: 5 December 2022
(This article belongs to the Section Point-of-Care Diagnostics and Devices)
Abstract
:Optimal peak inspiratory flow rate (PIFR) is required for effective drug delivery to distal airways when using dry powder inhalers (DPIs). This study aimed to examine the association between PIFR and hand grip strength (HGS) in stable COPD patients. A cross-sectional study was conducted. PIFR was measured using the In-check DIAL to assess for Accuhaler and Turbuhaler DPIs. HGS was measured using a handheld dynamometer. A PIFR of <60 L/min was considered suboptimal PIFR. Demographics, clinical data, and spirometric data were collected and compared. Eighty-one patients (86% men) were included. Mean age was 73.3 ± 8.9 years. FEV1 was 65.3 ± 23.7%. The prevalence of suboptimal PIFR was 38% and 59% for Accuhaler and Turbuhaler, respectively. HGS in the suboptimal PIFR group was lower than in the optimal PIFR group for Accuhaler (22.8 ± 4.7 vs. 33.2 ± 6.9 kg, p < 0.001) and for Turbuhaler (25.3 ± 6.4 vs. 35.1 ± 6.3 kg, p < 0.001). The equation for predicted Accuhaler PIFR (L/min) was −30.340 + (0.274 × hand grip strength) − (0.206 × age) + (0.219 × height) + (1.019 × FVC). The equation for predicted Turbuhaler PIFR (L/min) was 56.196 + (0.321 × hand grip strength) − (0.196 × female) − (0.224 × age) + (0.304 × FVC). The best cutoff values of HGS for predicting optimal PIFR in Accuhaler and Turbuhaler were 26.8 kg (with 82% sensitivity and 84% specificity) and 31.9 kg (with 79% sensitivity and 90% specificity), respectively. In conclusion, HGS correlated with PIFR in patients with clinically stable COPD, especially in the group with pronounced symptoms without frequent exacerbations. HGS threshold values associated with suboptimal PIFR were identified. HGS may be used as an alternative tool to assess an optimal inspiratory force for DPIs.
1. Introduction
Chronic obstructive pulmonary disease (COPD) is a common cause of death worldwide, and the prevalence of the disease continues to rise [1,2]. The goals for treatment of stable COPD are to reduce current symptoms and future risks of exacerbation. Pharmacological therapy for COPD is used to reduce symptoms, reduce the frequency and severity of exacerbations, and improve exercise tolerance and health status [1]. Inhaled drugs are the cornerstone for prevention of COPD exacerbation and hospitalization [2]; they provide better pulmonary bioavailability, lower dose requirement, and less systemic toxicities than the oral or injectable drugs [3,4]. Aerosol delivery is a function of the dose deposited at the appropriate site in the lung. Aerosol deposition is affected by the amount of aerosol produced and the particle characteristics, ventilatory pattern, and airway anatomy and geometry. One of the factors associated with the ventilatory pattern that affects aerosol deposition is the inspiratory flow rate [5]. Therefore, patient inhaler techniques must be taken into account when assessing distal lung deposition.
Generation of an effective peak inspiratory flow rate (PIFR) is thought to be necessary for optimal drug delivery to distal airways and lung parenchyma, and better clinical outcomes in those on inhaler therapy, especially dry powder inhaler (DPI) users [6,7,8]. Using an arbitrary threshold of 60 L/min, several previous studies have investigated suboptimal PIFR in COPD patients with or without exacerbation, revealing an incidence of suboptimal PIFR of 30–60% [3,9,10,11], which might lead to the failure of COPD treatment. Some studies that also investigated factors associated with suboptimal PIFR showed that female sex was an associated factor [9,10,11].
Generation of inspiratory flow is dependent on thoracic geometry and inspiratory muscle force [12,13]. Thus, reduced muscular strength in general may be a problem for DPI use. Muscular strength assessed by hand grip strength (HGS) is a simple measure of upper limb muscle function and is associated with mortality in the general population and in COPD patients [14,15,16]. HGS is significantly associated with Accuhaler PIFR in hospitalized COPD patients with acute exacerbation [3].
Although PIFR directly assesses a patient’s ability to use inhalation therapy, it may not always be fully informative, especially in situations such as acute exacerbations of COPD, where differentiating acute PIFR compromise from chronic inability to properly use inhalers may not be possible. Thus, additional assessment tools that could help assess the potential of the respiratory system to create enough negative inspiratory pressures for adequate lung drug deposition are needed. We hypothesized that HGS values correlate with PIFR, allowing this test to be used as an alternative tool to assess an optimal inspiratory force for DPIs in patients with clinically stable COPD.
2. Materials and Methods
2.1. Study Design
A cross-sectional study was conducted at a medical outpatient department of Thammasat University Hospital, Thailand, between January 2021 and December 2021. COPD patients aged 40 years or older whose diagnoses were confirmed with spirometry (post-bronchodilator force expiratory volume in 1 second (FEV1)/forced vital capacity (FVC) < 70%) were included. Exclusion criteria were history of COPD exacerbation within 3 months, history of oral or intravenous corticosteroid treatment within 6 weeks, inability to use the devices for PIFR and HGS assessment, patients with tracheostomy, and patients requiring invasive or noninvasive mechanical ventilation. Demographics, smoking history, comorbidities, respiratory symptoms assessed by the modified Medical Research Council (mMRC) dyspnea scale [17] and COPD Assessment Test (CAT) [18], and spirometry data, including FEV1 and FVC, were collected.
2.2. Procedures
PIFR was measured by In-check DIAL® (Clement Clarke International Ltd., Harlow, UK). In-Check DIAL® was developed to assess PIFR [19,20]. The resistance of this device was set to simulate the Accuhaler and Turbuhaler DPIs because these DPI devices were commonly used by our COPD patients. A patient performed three consecutive measurements of PIFR for each DPI. All patients were instructed to inhale as fast as possible after a complete exhalation in a sitting position with one-minute breaks between attempts [3]. The maximal value of PIFR from three consecutive measurements of each DPI was recorded for the final analysis. The optimal PIFR for Accuhaler and Turbuhaler was ≥ 60 L/min [8]. A PIFR < 60 L/min was considered as suboptimal PIFR.
HGS was measured by Jamar® hand dynamometer (Asimow Engineering Co., CA, USA) and was reported in kilograms. A patient performed the test at rest in sitting position with the dominant hand unsupported, the wrist in neutral position, the elbow at 90° flexion, and the shoulder in adduction. All patients were instructed to squeeze the hand dynamometer as hard as possible for 3–5 s. The test was performed with three attempts, with one-minute breaks between attempts [3]. The maximal value of the three efforts was recorded for the final analysis.
2.3. Outcomes
The primary outcome was the correlation between HGS and PIFR in clinically stable COPD patients. The secondary outcomes were prevalence of suboptimal PIFR for Accuhaler and Turbuhaler in these patients and the best cutoff value of HGS to predict optimal PIFR.
2.4. Statistical Analysis
In a previous study [3], hospitalized COPD patients with suboptimal PIFR had lower HGS (24.2 ± 11.1 vs. 30.9±10.9 kg) compared to those with optimal PIFR. We hypothesized that our study would find a difference in HGS between the suboptimal and the optimal PIFR groups similar to that study [3]. The sample size was calculated for a 2-sample means test using 80% power and 5% type I error. Thus, the calculated sample size would be 88.
Descriptive statistics are presented as number (%) and mean ± standard deviation. The chi-squared test was used to compare categorical variables between the suboptimal and optimal PIFR groups. Student’s t-test was used to compare continuous variables between the two groups. Pearson correlation was used to determine the correlation between HGS and PIFR. To determine the set of variables associated with PIFR, we used the linear regression model with PIFR set as the dependent variable. All independent variables—age, sex, height, respiratory rate, FEV1, FVC, CAT and mMRC scores, HGS, and short-acting bronchodilator (SABD) dose—were entered into the regression model, followed by backward selection using a p-value cutoff of 0.1. We report the regression coefficients, their 95% confidence interval, and corresponding p-values. Variables with p-value < 0.05 were considered statistically associated with PIFR. Using the regression coefficients and the intercept, predicted PIFR for a patient could be calculated from the following equation, where V was the covariate, β was the regression coefficient, and I was the number of variables:
Predicted PIFR = intercept + V1β1 + V2β2 + Viβi
The Receiver Operator Characteristic (ROC) curve was used to determine the best HGS cutoff value to predict the optimal PIFR. A two-sides p-value < 0.05 was considered statistically significant. Statistical analyses were performed using SPSS version 25.0 software (IBM corp., Armonk, NY, USA).
3. Results
3.1. Participants
Ninety patients with clinically stable COPD were screened. Eighty-one patients were included in the study (Figure 1). Eighty-six percent were men. Mean age was 73.3 ± 8.9 years. Most patients were classified as COPD Grade A or B, as well as the Global Initiative for Chronic Obstructive Lung Disease (GOLD) spirometric stage 1. Mean post-bronchodilator FEV1/FVC was 58 ± 11%. Mean maximal PIFR was 63.7 ± 18.9 L/min and 52.4 ± 15.4 L/min for Accuhaler and Turbuhaler, respectively. Mean HGS was 29.2 ± 8.0 kg (Table 1).
3.2. Prevalence of Suboptimal PIFR
The prevalence of suboptimal PIFR, defined as PIFR <60 L/min, was 38% and 59% for Accuhaler and Turbuhaler, respectively (Table 2). Differences in PIFR and other variables between the optimal and the suboptimal PIFR groups for Accuhaler and Turbuhaler are shown in Table 2. The suboptimal groups had significantly lower PIFR than the optimal groups for Accuhaler and Turbuhaler (Table 2).
When compared to the optimal PIFR groups, the suboptimal Accuhaler and Turbuhaler groups had had more females, older patients, higher breathing frequency, more inhaled SABD use, higher CAT and mMRC scores, lower HGS, shorter height, and worse pulmonary function (Table 2).
3.3. Association between HGS and PIFR
HGS showed highly significant positive correlation with Accuhaler PIFR (r = 0.591, p < 0.001) and also with Turbuhaler PIFR (r = 0.614, p < 0.001) (Table 3).
3.4. HGS Cutoff Value for Predicting Optimal Accuhaler and Turbuhaler PIFR
The area under the ROC curve of 0.892 (95% CI; 0.824–0.961, p < 0.001) for the best cutoff value of HGS for Accuhaler was 26.8 kg, with 82% sensitivity and 84% specificity (Table 4, Figure 4). The best cutoff value of HGS for Turbuhaler was 31.9 kg, with the area under the ROC curve of 0.862 (95% CI; 0.779–0.945, p < 0.001), 79% sensitivity, and 90% specificity (Table 5, Figure 5).
4. Discussion
Based on the results of our study, there is a significant positive correlation between HGS and PIFR for Accuhaler and Turbuhaler DPIs, suggesting HGS may be a valuable test in the assessment of the ability of the respiratory system to create adequate PIFR. To our knowledge, this is the first study to identify a HGS cutoff value for predicting the adequacy of PIFR in patients with clinically stable COPD.
A previous study of 44 clinically stable patients with COPD by Tsuburai et al. [21] showed a significant positive correlation between HGS and PIFR for Accuhaler (r = 0.326, p = 0.031) and Turbuhaler (r = 0.328, p = 0.030). This study differed from our study in that the investigators did not derive a prediction equation for PIFR considering both genders and all stages of disease, and they did not identify a HGS cutoff value associated with suboptimal PIFR. Our study showed higher correlation coefficients of HGS and PIFR (r = 0.591 for Accuhaler and r = 0.614 for Turbuhaler) than the study by Tsuburai [21], probably because of the larger number of patients in our study and the broader range of HGS. A study of 75 hospitalized patients with acute exacerbation of COPD by Samarghandi et al. [3] demonstrated that HGS was significantly correlated with Accuhaler PIFR. Patients in the suboptimal PIFR group had significantly lower HGS than those in the optimal PIFR group, and each kilogram increase in HGS was associated with a 0.5 L/min increase in PIFR [3]. Similarly, our study demonstrated that the suboptimal PIFR group had significantly lower HGS than the optimal group in both Accuhaler and Turbuhaler. These results suggest that HGS might be able to predict the efficiency of inhaled drug delivery in COPD patients with or without exacerbation.
There were variations of the prevalence of suboptimal PIFR in patients with COPD in previous studies. The study by Sharma et al. revealed that the proportion of suboptimal PIFR for Accuhaler DPI was 32% at discharge following hospitalization for exacerbation of COPD, but this group had no difference in incidence of all-cause rehospitalization up to 180 days compared to the optimal PIFR group [9]. A study by Ghosh et al. showed that 40% of outpatients with COPD were unfit to use prescribed inhalers. Suboptimal PIFR (PIFR < 60 L/min) was 44% of low-medium resistance DPIs (mimicking Accuhaler and Ellipta), and PIFR < 30 L/min was 32% of high resistance DPI (mimicking Handihaler) [10]. A study by Harb et al. [11] showed that the prevalence of suboptimal PIFR of any resistance representative of a specific inhaler was 44.44% in COPD patients before hospital discharge. In our study, the prevalence of suboptimal PIFR was 38.3% in Accuhaler and 59.3% in Turbuhaler. All of these studies suggest that many COPD patients might not generate adequate inspiratory force to overcome prescribed DPI resistance. These results suggest that Accuhaler might be more suitable for certain COPD patients than Turbuhaler, because the prevalence of suboptimal PIFR for Accuhaler was lower than that for Tubuhaler. Interestingly, a previous study also demonstrated that the mean peak inspiratory flow for Accuhaler was significantly higher than it was for Turbuhaler [22].
Several previous studies demonstrated that one factor associated with suboptimal PIFR was female sex [9,10,11]. A literature review by Ghosh et al. [8] found that female sex and older age were factors associated with lower PIFR. In concordance with these findings, our results showed that suboptimal PIFR was more common in female than in male patients, and the mean PIFR was significantly lower in female patients than it was in men. Furthermore, we found that other factors associated with suboptimal PIFR were older age, higher breathing frequency, more inhaled SABD use, higher CAT and mMRC scores, lower HGS, shorter height, and lower pulmonary function. These indicate that a higher symptom burden affects patients’ health status and their inspiratory force, leading to suboptimal PIFR.
Our study revealed that the best HGS cutoff values for predicting optimal PIFR for Accuhaler and Turbuhaler had a large area under the ROC curve with high sensitivity and specificity. Therefore, HGS might be applied as a predictive tool for optimal PIFR in clinically stable COPD patients being considered for prescription of Accuhaler and Turbuhaler devices. Moreover, these cutoff values might be particularly useful in elderly patients with COPD because the mean age of participants in our study was 73 years. Older patients with stable COPD were commonly found in several observational studies, including a study in the same research center by Saiphoklang N et al. [23]. A previous study by Fronhofen et al. [24] demonstrated that a threshold HGS value of 10 kg could predict inspiratory flow achievement for Turbuhaler in hospitalized elderly patients with COPD. In contrast to that previous study, our study found that the cutoff value of HGS was higher (31.9 kg) for predicting the optimal Turbuhaler PIFR. The reason may be because the participants in the study by Fronhofen et al. [24] were even older than those in our study (mean age of 81 versus 73 years). Although patient settings may differ, our study suggests that HGS can be applied to all elderly patients with COPD by using the predicting equations. However, assessment of HGS may be limited by the cooperation of patients and by hand abnormalities.
Furthermore, HGS could also predict clinical outcomes in mechanically ventilated patients, including COPD patients (12% of the study subjects) [25]. A previous study by Strandkvist VJ et al. revealed that COPD patients with heart disease had lower HGS than those without heart disease [26]. These patients with GOLD spirometric stages 3–4 (severe to very severe airflow limitation) had lower HGS than those without COPD [26].
Clinical applications are proposed from our study. HGS might be a reasonable alternative to PIFR to evaluate the adequacy of inspiratory force for DPIs. The best cutoff values of HGS for optimal Accuhaler and Turbuhaler PIFR are 26.8 kg and 31.9 kg, respectively.
This study has certain limitations. Firstly, a small sample size of the population was used in this study. Therefore, some explanatory variables for calculated PIFR using regression coefficients might not be as precise as indicated by the wide 95% CI, and some variables might be outliers in the outcome variables. Moreover, only 8% of participants were COPD group D; thus, it might not have a realistic association between HGS and PIFR in clinical practice. Secondly, the study was conducted in a single research center in Thailand; the results might not be applicable to other ethnicities or countries. Thirdly, the participants were clinically stable COPD patients without acute exacerbation, and none of them used home oxygen therapy or home invasive or non-invasive mechanical ventilation. Therefore, the cutoff value of HGS might not be applicable for more severe and debilitated COPD patients, such as those with exacerbations, home oxygen therapy or mechanical ventilation, and other obstructive lung diseases. Fourthly, there were 14% women in this study, which may be a substantial under-representation of women with COPD that could impact generalizability. We did not collect data on patients with sarcopenia or data on muscle mass and function, e.g., walking speed. Therefore, we could not speculate that gender differences independently influence PIFR, especially in female Turbuhaler DPI users. Lastly, these cutoff values were analyzed to apply only to patients who potentially use Accuhaler and Turbuhaler. Consequently, they may not apply to patients who use other types of inhaler devices. A future study is required to determine the correlation between HGS and optimal PIFR for other types of inhaler devices and in different settings of patients, such as stable COPD patients with home oxygen therapy or mechanical ventilation, or patients with other pulmonary diseases.
5. Conclusions
HGS was positively correlated with Accuhaler and Turbuhaler PIFR in clinically stable COPD patients, especially in the group with pronounced symptoms without frequent exacerbations. The prevalence of suboptimal PIFR, defined by PIFR < 60 L/min, was found in up to half of the patients. The suboptimal group had more females, older patients, higher breathing frequency, more inhaled SABD users and doses, higher CAT and mMRC scores, lower HGS, shorter height, and worse pulmonary function compared to the optimal groups. HGS may be a predictive tool for determining the efficacy of inhaler drug delivery.
Author Contributions
Conceptualization, A.S., N.S., I.B. and C.B.C.; Methodology, A.S. and N.S.; Software, N.S.; Validation, A.S. and N.S.; Formal Analysis, A.S. and N.S.; Investigation, A.S.; Resources, N.S.; Data Curation, N.S.; Writing—Original Draft Preparation, A.S.; Writing—Review & Editing, N.S., I.B. and C.B.C.; Visualization, I.B.; Supervision, C.B.C.; Project Administration, N.S.; Funding Acquisition, A.S. All authors have read and agreed to the published version of the manuscript.
Funding
Financial support was provided by the Faculty of Medicine, Thammasat University, Thailand.
Institutional Review Board Statement
The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Human Research Ethics Committee of Thammasat University (Medicine) (IRB No. MTU-EC-IM-0-239/63, COA No. 013/2021 on 18 January 2021).
Informed Consent Statement
Informed consent was obtained from all subjects involved in the study.
Data Availability Statement
The data supporting the results of this study are available within the article.
Acknowledgments
The authors would like to thank Michael Jan Everts, Faculty of Medicine, Thammasat University, for proofreading this manuscript. This work was supported by Thammasat University Research Unit in Allergy and Respiratory Medicine, Thailand.
Conflicts of Interest
The authors declare no conflict of interest.
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Figure 1.
Flowchart of COPD patient recruitment to the study.
Figure 2.
Linear regression analysis showed the correlation between peak inspiratory flow rate (PIFR) for Accuhaler and hand grip strength (HGS) adjusted by age, height, forced vital capacity (FVC), and short-acting bronchodilator dose. Predicted Accuhaler PIFR (L/min) = −30.340 + (0.274 × hand grip strength) − (0.206 × age) + (0.219 × height) + (1.019 × FVC).
Figure 2.
Linear regression analysis showed the correlation between peak inspiratory flow rate (PIFR) for Accuhaler and hand grip strength (HGS) adjusted by age, height, forced vital capacity (FVC), and short-acting bronchodilator dose. Predicted Accuhaler PIFR (L/min) = −30.340 + (0.274 × hand grip strength) − (0.206 × age) + (0.219 × height) + (1.019 × FVC).
Figure 3.
Linear regression analysis showed the correlation between peak inspiratory flow rate (PIFR) for Turbuhaler and hand grip strength (HGS) adjusted by gender, age, forced vital capacity (FVC), and short-acting bronchodilator dose. Predicted Turbuhaler PIFR (L/min) = 56.196 + (0.321 × hand grip strength) − (0.196 × female) − (0.224 × age) + (0.304 × FVC).
Figure 3.
Linear regression analysis showed the correlation between peak inspiratory flow rate (PIFR) for Turbuhaler and hand grip strength (HGS) adjusted by gender, age, forced vital capacity (FVC), and short-acting bronchodilator dose. Predicted Turbuhaler PIFR (L/min) = 56.196 + (0.321 × hand grip strength) − (0.196 × female) − (0.224 × age) + (0.304 × FVC).
Figure 4.
The receiver operating characteristic (ROC) plot of handgrip strength (HGS) and peak inspiratory flow rate (PIFR) for Accuhaler. The best cutoff value of HGS for Accuhaler is 26.8 kg, with the area under the ROC curve of 0.892 (95% CI; 0.824–0.961, p <0.001), 82% sensitivity, and 84% specificity.
Figure 4.
The receiver operating characteristic (ROC) plot of handgrip strength (HGS) and peak inspiratory flow rate (PIFR) for Accuhaler. The best cutoff value of HGS for Accuhaler is 26.8 kg, with the area under the ROC curve of 0.892 (95% CI; 0.824–0.961, p <0.001), 82% sensitivity, and 84% specificity.
Figure 5.
The receiver operating characteristic (ROC) plot of handgrip strength (HGS) and peak inspiratory flow rate (PIFR) for Turbuhaler. The best cutoff value of HGS for Turbuhaler is 31.9 kg, with the area under the ROC curve of 0.862 (95% CI; 0.779–0.945, p < 0.001), 79% sensitivity, and 90% specificity.
Figure 5.
The receiver operating characteristic (ROC) plot of handgrip strength (HGS) and peak inspiratory flow rate (PIFR) for Turbuhaler. The best cutoff value of HGS for Turbuhaler is 31.9 kg, with the area under the ROC curve of 0.862 (95% CI; 0.779–0.945, p < 0.001), 79% sensitivity, and 90% specificity.
Table 1.
Baseline characteristics of clinically stable patients with chronic obstructive pulmonary disease.
Table 1.
Baseline characteristics of clinically stable patients with chronic obstructive pulmonary disease.
| Characteristic | N = 81 |
|---|---|
| Age, years | 73.3 ± 8.9 |
| Male | 70 (86.4) |
| Body mass index, kg/m2 | 22.5 ± 3.9 |
| Active smoking | 4 (4.9) |
| Smoking, pack-years | 24.9 ± 22.2 |
| Dominant right hand | 72 (88.9) |
| Maximal Accuhaler PIFR, L/min | 63.7 ± 18.9 |
| Maximal Turbuhaler PIFR, L/min | 52.4 ± 15.4 |
| HGS, kg | 29.2 ± 8.0 |
| Comorbidity | |
| Hypertension | 44 (54.3) |
| Diabetes mellitus | 21 (25.9) |
| Coronary artery disease | 14 (17.3) |
| Congestive heart failure | 5 (6.2) |
| Atrial fibrillation | 4 (4.9) |
| Lung cancer | 4 (4.9) |
| Bronchiectasis | 4 (4.9) |
| Depression | 1 (1.2) |
| Vital sign | |
| Systolic blood pressure, mmHg | 128.4 ± 21.5 |
| Diastolic blood pressure, mmHg | 71.3 ± 12.2 |
| Pulse rate, beats/min | 81.4 ± 14.3 |
| Breathing frequency, breaths/min | 17.0 ± 1.9 |
| Oxygen saturation, % | 96.4 ± 4.0 |
| Spirometric data | |
| Post-BD FEV1, L | 1.45 ± 0.60 |
| Post-BD FEV1, % predicted | 65.3 ± 23.7 |
| Post-BD FVC, L | 2.50 ± 0.90 |
| Post-BD FVC, % predicted | 82.7 ± 22.1 |
| Post-BD FEV1/FVC, % | 58.0 ± 11.0 |
| Functional performance | |
| CAT scores | 9.5 ± 6.5 |
| CAT score < 10 | 40 (49.4) |
| CAT score ≥ 10 | 41 (50.6) |
| mMRC scores | 1.5 ± 1.1 |
| mMRC < 2 | 42 (51.9) |
| mMRC ≥ 2 | 39 (48.1) |
| Spirometry grading | |
| 1 | 32 (39.5) |
| 2 | 24 (29.6) |
| 3 | 16 (19.8) |
| 4 | 9 (11.1) |
| GOLD classification | |
| A | 35 (43.2) |
| B | 39 (48.1) |
| C | 0 (0) |
| D | 7 (8.6) |
| Medication | |
| SABD | 25 (30.9) |
| SABD dose, puffs/day | 1.0 ± 1.9 |
| Inhaled LABA | 2 (2.5) |
| LAMA | 43 (53.1) |
| LABA/LAMA | 21 (25.9) |
| ICS/LABA | 36 (44.4) |
| Xanthine | 30 (37.0) |
| Phosphodieterase-4 inhibitor | 4 (4.9) |
| Oral beta-2 agonist | 5 (6.2) |
| Azithromycin | 5 (6.2) |
| Acetylcysteine | 36 (44.4) |
Data shown n (%) or mean ± SD BD = bronchodilator, CAT = COPD assessment test, FEV1 = force expiratory volume in 1 s, FVC = forced vital capacity, GOLD = Global Initiative for Obstructive Lung Disease, HGS = hand grip strength, ICS = inhaled corticosteroid, LABA = long-acting beta2 agonist, LAMA = long-acting muscarinic antagonist, mMRC = modified Medical Research Council, PIFR = peak inspiratory flow rate, SABD = short-acting bronchodilator.
Table 2.
Comparison of characteristics of clinically stable COPD patients between the optimal and the suboptimal PIFR groups for Accuhaler and Turbuhaler.
Table 2.
Comparison of characteristics of clinically stable COPD patients between the optimal and the suboptimal PIFR groups for Accuhaler and Turbuhaler.
| Variable | Accuhaler | Turbuhaler | ||||
|---|---|---|---|---|---|---|
| Optimal | Suboptimal | p-Value | Optimal | Suboptimal | p-Value | |
| Patients | 50 (61.7) | 31 (38.3) | NA | 33 (40.7) | 48 (59.3) | NA |
| Maximal PIFR, L/min | 75.6 ± 12.3 | 44.7 ± 9.9 | <0.001 | 67.4 ± 6.7 | 42.2 ± 10.5 | <0.001 |
| HGS, kg | 33.2 ± 6.9 | 22.8 ± 4.7 | <0.001 | 35.1 ± 6.3 | 25.3 ± 6.4 | <0.001 |
| Gender | ||||||
| Male | 49 (98.0) | 21 (67.7) | <0.001 | 33 (100) | 37 (77.1) | 0.002 |
| Female | 1 (2.0) | 10 (32.3) | 0 (0) | 11 (22.9) | ||
| Age, years | 70.0 ± 8.8 | 78.7 ± 6.2 | <0.001 | 67.9 ± 8.6 | 77.0 ± 7.2 | <0.001 |
| Height, cm | 165.7 ± 6.1 | 158.6 ± 8.0 | <0.001 | 166.8 ± 5.1 | 160.4 ± 8.1 | <0.001 |
| BMI, kg/m2 | 22.8 ± 4.0 | 22.0 ± 3.7 | 0.372 | 23.2 ± 3.6 | 22.1 ± 4.0 | 0.190 |
| Active smoking | 3 (6.0) | 1 (3.2) | 1.000 | 2 (6.1) | 2 (4.2) | 1.000 |
| Smoking, pack-years | 27.5 ± 22.4 | 20.7 ± 21.7 | 0.182 | 25.3 ± 18.1 | 24.6 ± 24.9 | 0.892 |
| Dominant hand | ||||||
| Left | 7 (14.0) | 2 (6.5) | 0.471 | 3 (9.1) | 6 (12.5) | 0.731 |
| Right | 43 (86.0) | 29 (93.5) | 30 (90.9) | 42 (87.5) | ||
| Comorbidity | ||||||
| Diabetes mellitus | 12 (24.0) | 9 (29.0) | 0.615 | 8 (24.2) | 13 (27.1) | 0.774 |
| Hypertension | 28 (56.0) | 16 (51.6) | 0.700 | 21 (63.6) | 23 (47.9) | 0.163 |
| Coronary artery disease | 6 (12.0) | 8 (25.8) | 0.110 | 5 (15.2) | 9 (18.8) | 0.674 |
| Congestive heart failure | 3 (6.0) | 2 (6.5) | 1.000 | 2 (6.1) | 3 (6.3) | 1.000 |
| Atrial fibrillation | 2 (4.0) | 2 (6.5) | 0.635 | 2 (6.1) | 2 (4.2) | 1.000 |
| Lung cancer | 1 (2.0) | 3 (9.7) | 0.154 | 1 (3.0) | 3 (6.3) | 0.642 |
| Bronchiectasis | 1 (2.0) | 3 (9.7) | 0.154 | 0 (0.0) | 4 (8.3) | 0.142 |
| Vital sign | ||||||
| SBP, mmHg | 126.9 ± 24.4 | 130.9 ± 15.9 | 0.424 | 130.2 ± 16.0 | 127.3 ± 24.7 | 0.554 |
| DBP, mmHg | 73.0 ± 12.9 | 68.8 ± 10.7 | 0.126 | 74.6 ± 13.0 | 69.1 ± 11.2 | 0.044 |
| Pulse rate, beats/min | 81.3 ± 14.2 | 81.6 ± 14.6 | 0.932 | 80.7 ± 13.2 | 81.9 ± 15.1 | 0.713 |
| Respiratory rate, breaths/min | 16.6±1.8 | 17.6±1.96 | 0.031 | 16.3±1.6 | 17.4±2.0 | 0.007 |
| SpO2, % | 96.6±4.5 | 96.1±3.1 | 0.578 | 96.3±5.3 | 96.5±2.8 | 0.837 |
| Spirometry data | ||||||
| Post-BD FEV1, % | 70.6 ± 20.2 | 56.8 ± 26.6 | 0.010 | 75.2 ± 18.4 | 58.5 ± 24.7 | 0.001 |
| Post-BD FVC, % | 88.5 ± 18.1 | 73.4 ± 24.9 | 0.002 | 93.0 ± 17.9 | 75.7 ± 22.0 | <0.001 |
| Spirometric grade 3 and 4 | 12 (24.0) | 13 (16.0) | 0.089 | 5 (15.2) | 20 (41.7) | 0.011 |
| Functional performance | ||||||
| CAT scores | 7.8 ± 6.3 | 12.2 ± 6.0 | 0.003 | 5.5 ± 5.1 | 12.2 ± 5.9 | <0.001 |
| CAT ≥ 10 | 20 (40.0) | 21 (67.7) | 0.015 | 7 (21.2) | 34 (70.8) | <0.001 |
| mMRC scores | 1.2 ± 1.1 | 2.0 ± 0.9 | 0.001 | 0.9 ± 0.9 | 2.0 ± 1.0 | <0.001 |
| mMRC ≥ 2 | 17 (34.0) | 22 (71.0) | 0.001 | 6 (18.2) | 33 (68.8) | <0.001 |
| GOLD group D | 5 (10.0) | 2 (6.5) | 0.702 | 1 (3.0) | 6 (12.5) | 0.136 |
| Medication | ||||||
| SABD | 11 (22.0) | 14 (45.2) | 0.028 | 5 (15.2) | 20 (41.7) | 0.014 |
| SABD dose, puffs/day | 0.6±1.3 | 1.6±2.6 | 0.043 | 0.4±1.1 | 1.4±2.3 | 0.008 |
| LABA | 1 (2.0) | 1 (3.2) | 1.000 | 1 (3.0) | 1 (2.1) | 1.000 |
| LAMA | 28 (56.0) | 15 (48.4) | 0.505 | 15 (45.5) | 28 (58.3) | 0.254 |
| LABA/LAMA | 10 (20.0) | 11 (35.5) | 0.122 | 10 (30.3) | 11 (22.9) | 0.456 |
| ICS/LABA | 25 (50.0) | 11 (35.5) | 0.201 | 14 (42.4) | 22 (45.8) | 0.762 |
| Theophylline | 10 (20.0) | 5 (16.1) | 0.663 | 6 (18.2) | 9 (18.8) | 0.948 |
| Doxophylline | 7 (14.0) | 8 (25.8) | 0.184 | 7 (21.2) | 8 (16.7) | 0.605 |
| Roflumilast | 3 (6.0) | 1 (3.2) | 1.000 | 1 (3.0) | 3 (6.3) | 0.642 |
| Oral beta2 agonist | 3 (6.0) | 2 (6.5) | 1.000 | 2 (6.1) | 3 (6.3) | 1.000 |
| Azithromycin | 2 (4.0) | 3 (9.7) | 0.366 | 0 (0) | 5 (10.4) | 0.076 |
| Acetylcysteine | 18 (36.0) | 18 (58.1) | 0.052 | 11 (33.3) | 25 (52.1) | 0.095 |
Data shown n (%) or mean ± SD BD = bronchodilator, BMI = body mass index, CAT = COPD assessment test, DBP = diastolic blood pressure, FEV1 = force expiratory volume in 1 s, FVC = forced vital capacity, GOLD = Global Initiative for Obstructive Lung Disease, HGS = hand grip strength, ICS = inhaled corticosteroid, LABA = long-acting beta2 agonist, LAMA = long-acting muscarinic antagonist, mMRC = modified Medical Research Council, PIFR = peak inspiratory flow rate, SABD = short-acting bronchodilator, SBP = systolic blood pressure, SpO2 = oxygen saturation.
Table 3.
Correlation between handgrip strength with peak inspiratory flow rate for Accuhaler and Turbuhaler.
Table 3.
Correlation between handgrip strength with peak inspiratory flow rate for Accuhaler and Turbuhaler.
| Factor | Correlation Coefficient | p-Value |
|---|---|---|
| Accuhaler PIFR | 0.591 | <0.001 |
| Turbuhaler PIFR | 0.614 | <0.001 |
PIFR = peak inspiratory flow rate.
Table 4.
Linear regression analysis for peak inspiratory flow rate and hand grip strength adjusted by age, gender, height, forced vital capacity, and short-acting bronchodilator dose.
Table 4.
Linear regression analysis for peak inspiratory flow rate and hand grip strength adjusted by age, gender, height, forced vital capacity, and short-acting bronchodilator dose.
| Variables | Regression Coefficients | 95% CI of Coefficients | p-Value |
|---|---|---|---|
| Accuhaler | |||
| Intercept | −30.340 | −114.534, 53.853 | 0.475 |
| Hand grip strength, kg | 0.274 | 0.132, 1.170 | 0.015 |
| Age, years | −0.206 | −0.847, −0.025 | 0.038 |
| Height, cm | 0.219 | 0.063, 1.019 | 0.027 |
| Forced vital capacity, % | 1.019 | 0.099, 0.389 | 0.001 |
| Short-acting bronchodilator dose, puffs/day | −0.148 | −3.118, 0.238 | 0.091 |
| Turbuhaler | |||
| Intercept | 56.196 | 20.715, 91.676 | 0.002 |
| Hand grip strength, kg | 0.321 | 0.210, 1.032 | 0.004 |
| Female | −0.196 | −16.718, −0.850 | 0.030 |
| Age, years | −0.224 | −0.717, −0.058 | 0.022 |
| Forced vital capacity, % | 0.304 | 0.096, 0.330 | 0.001 |
| Short-acting bronchodilator dose, puffs/day | −0.147 | −2.508, 0.173 | 0.087 |
Table 5.
Cutoff values of hand grip strength for predicting optimal Accuhaler and Turbuhaler PIFR.
| Variable | Cutoff Value | AUC | 95% CI | Sensitivity (%) | Specificity (%) | PPV (%) | NPV (%) | p-Value |
|---|---|---|---|---|---|---|---|---|
| HGS for Accuhaler, kg | 26.75 | 0.892 | 0.824–0.961 | 82.00 | 83.90 | 89.14 | 74.32 | <0.001 |
| HGS for Turbuhaler, kg | 31.90 | 0.862 | 0.779–0.945 | 78.80 | 89.60 | 83.87 | 86.03 | <0.001 |
AUC = area under the ROC curve, CI = confidence interval, HGS = hand grip strength, NPV = negative predictive values, PIFR = peak inspiratory flow rate, PPV = positive predictive values.
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Suriyakul, A.; Saiphoklang, N.; Barjaktarevic, I.; Cooper, C.B. Correlation between Hand Grip Strength and Peak Inspiratory Flow Rate in Patients with Stable Chronic Obstructive Pulmonary Disease. Diagnostics 2022, 12, 3050. https://doi.org/10.3390/diagnostics12123050
AMA Style
Suriyakul A, Saiphoklang N, Barjaktarevic I, Cooper CB. Correlation between Hand Grip Strength and Peak Inspiratory Flow Rate in Patients with Stable Chronic Obstructive Pulmonary Disease. Diagnostics. 2022; 12(12):3050. https://doi.org/10.3390/diagnostics12123050
Chicago/Turabian StyleSuriyakul, Apisara, Narongkorn Saiphoklang, Igor Barjaktarevic, and Christopher B. Cooper. 2022. "Correlation between Hand Grip Strength and Peak Inspiratory Flow Rate in Patients with Stable Chronic Obstructive Pulmonary Disease" Diagnostics 12, no. 12: 3050. https://doi.org/10.3390/diagnostics12123050
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