Clinical Burden of Chronic Obstructive Pulmonary Disease in Patients with Suboptimal Peak Inspiratory Flow

Ohar, Jill A.; Mahler, Donald A.; Davis, Gabrielle N.; Lombardi, David A.; Moran, Edmund J.; Crater, Glenn D.

doi:https://doi.org/10.1155/2024/8034923

Canadian Respiratory Journal

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Abstract Introduction Materials and Methods Results Discussion Data Availability Disclosure Conflicts of Interest Authors’ Contributions Acknowledgments Supplementary Materials References Copyright Related Articles

Research Article | Open Access

Volume 2024 | Article ID 8034923 | https://doi.org/10.1155/2024/8034923

Clinical Burden of Chronic Obstructive Pulmonary Disease in Patients with Suboptimal Peak Inspiratory Flow

Jill A. Ohar,¹Donald A. Mahler,^2,3Gabrielle N. Davis,⁴David A. Lombardi,⁴Edmund J. Moran,⁴and Glenn D. Crater⁴

Academic Editor: Jaber Alqahtani

Received11 Jan 2022

Revised09 Feb 2024

Accepted22 Feb 2024

Published22 Mar 2024

Abstract

Introduction. Many patients with chronic obstructive pulmonary disease (COPD) may derive inadequate benefit from dry powder inhalers (DPIs) because of suboptimal peak inspiratory flow (sPIF). Objectives. To assess the clinical burden of COPD by characterizing the clinical characteristics of participants with sPIF against medium-low resistance DPIs versus those with optimal PIF (oPIF) from two phase 3 clinical trials. Methods. Baseline data were collected from two randomized, controlled, phase 3 trials (NCT03095456; NCT02518139) in participants with moderate-to-severe COPD. oPIF (60 L/min) against the medium-low resistance DPIs was used as the threshold for defining the PIF subgroups (<60 L/min (sPIF) vs ≥60 L/min (oPIF)). Results. Most participants included in this analysis were White (92%) and male (63%); the mean (range) age was 65 (43–87) years. Participants with sPIF had significantly greater dyspnea than those with oPIF as measured using the modified Medical Research Council scoring (mean (95% CI): 2.1 (2.0–2.2) vs 1.6 (1.4–1.7); ) and baseline dyspnea index (mean (95% CI): 5.1 (4.9–5.4) vs 6.1 (5.8–6.3); ). Based on COPD Assessment Test scores, participants with sPIF had a higher COPD symptom burden than those with oPIF (mean (95% CI): 21.5 (19.7–23.3) vs 19.5 (18.6–20.4); 5). Conclusion. In these trials, participants with COPD who had sPIF against the medium-low resistance DPIs had more dyspnea and worse health status than those with oPIF. These results demonstrate that sPIF is associated with a higher clinical burden as measured by patient-reported outcomes.

1. Introduction

Treatment with inhaled bronchodilators is the foundation of pharmacologic management of symptoms in patients with chronic obstructive pulmonary disease (COPD) [1]. Dry powder inhalers (DPIs), pressurized metered-dose inhalers (pMDIs), soft mist inhalers (SMIs), and nebulizers are the most commonly prescribed inhalation devices for the delivery of bronchodilators [2]. Each device requires a unique inhalation technique for optimal delivery of medication to the lower respiratory tract [3]. For optimal use of DPIs, patients must be able to generate sufficient peak inspiratory flow (PIF) against the internal resistance of the device to disaggregate powdered drugs into fine particles for lung deposition [4]. However, many patients with COPD have a suboptimal PIF (sPIF) and may not derive optimal benefit from DPIs. In observational studies, sPIF was observed in 19%–78% of stable outpatients with COPD and 32%–52% of inpatients before hospital discharge after treatment for COPD exacerbation [5–10].

sPIF in patients with COPD is associated with female sex, older age, shorter height, and lung function impairment [5–7, 9–11]. Low forced vital capacity (FVC) percent predicted and inspiratory capacity (IC) percent predicted are independent predictors of sPIF [9]. Despite several studies demonstrating the effects of age, sex, lung function parameters, and device resistance on PIF in patients with COPD, little is known about the association between inspiratory flow and severity of dyspnea and respiratory health status. In this analysis, we assessed the demographics and baseline clinical characteristics of participants with moderate-to-severe COPD from two randomized, controlled phase 3 trials of revefenacin [12–14] according to their PIF status (sPIF vs optimal PIF (oPIF)) to compare the population differences between the PIF subgroups. We used the optimal PIF of medium-low resistance DPIs such as Diskus®, Diskhaler®, and Ellipta® (60 L/min) [15, 16] as a cut-off value for these analyses because medium-low resistance has been used most frequently for reporting the prevalence of sPIF [7–10, 17, 18] and a PIF of ≥60 L/min is generally considered optimal for most DPI devices [7, 9, 17, 19].

2. Materials and Methods

2.1. Trial Design

Demographics and baseline clinical characteristics of participants with COPD were pooled from two randomized, controlled phase 3 trials, 0128 and 0149; both have been described previously [12–14]. In brief, trial 0128 (NCT02518139) was a 52-week, tiotropium-controlled, parallel-group phase 3 safety trial evaluating the safety and tolerability of revefenacin for nebulization in participants with moderate-to-very severe COPD [12, 13]. Trial 0149 (NCT03095456) was a 28-day, double-blind, double-dummy, parallel-group phase 3b trial comparing the effect of once-daily revefenacin for nebulization administered via the PARI LC® Sprint jet nebulizer with tiotropium administered via HandiHaler® on lung function in participants with moderate-to-very severe COPD and a PIF of <60 L/min against the medium-low resistance DPIs [14]. The trials were conducted in accordance with the principles of the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use guidelines for good clinical practice and the code of ethics of the World Medical Association’s Declaration of Helsinki, and all patients provided written informed consent.

2.2. Participants

Both trials enrolled participants diagnosed with moderate-to-severe COPD. Eligible participants had a smoking history of ≥10 packs per year, a postipratropium forced expiratory volume in 1 second (FEV₁) to a FVC ratio of <0.7 at screening, and a postipratropium FEV₁ of <80% of predicted normal and >700 mL at screening in trial 0128 and >400 mL in trial 0149. In addition, participants in trial 0149 had a PIF of <60 L/min.

2.3. PIF and Pulmonary Function Measurements

Baseline PIF was measured using the In-Check™ DIAL device (Alliance Tech Medical, Inc.) set to medium-low resistance DPI (R-2) and high resistance DPI (R-5) in trial 0149 and to R-5 alone in trial 0128. To measure PIF, participants were instructed to exhale completely, place the mouthpiece of the device into their mouths, and inhale as forcefully and deeply as possible. Participants repeated the PIF maneuver three times after adequate rest and recovery from each effort, with their PIF measurement reflecting the highest recorded value. Measurements were conducted at zero resistance followed by R-5 resistance in trial 0128 and at R-2 resistance followed by R-5 resistance in trial 0149. Data from trial 0149 were used to develop an algorithm to predictively correlate resistance in the R-2 device to resistance in the R-5 device. These values were then used to define PIF against the R-2 device in trial 0128. The methodology used to define the correlation between PIF against the R-2 device and PIF against the R-5 device has been described previously [20]. On the basis of this correlation analysis, a PIF value of 40 L/min against the R-5 device is approximately equivalent to a PIF of 60 L/min against the R-2 device [20]. oPIF against the resistance of the R-2 device was defined as >60 L/min, and sPIF was defined as ≤60 L/min.

Baseline lung function was evaluated by spirometric measurements of FEV₁ and FVC. The distribution of the Global Initiative for Chronic Obstructive Lung Disease (GOLD) airflow limitation categories (GOLD 1, FEV₁ ≥80% predicted (mild airflow obstruction); GOLD 2, FEV₁ 50%–79% predicted (moderate airflow obstruction); GOLD 3, FEV₁ 30%–49% predicted (severe airflow obstruction); and GOLD 4, FEV₁ <30% predicted (very severe airflow obstruction)) between the PIF subgroups was also assessed.

2.4. Patient-Reported Outcomes

Dyspnea at baseline was assessed using the modified Medical Research Council (mMRC) dyspnea scale and the baseline dyspnea index (BDI) using standard methods [21–24]. A higher score on the mMRC scale and a lower score on the BDI represented greater dyspnea. An mMRC score of ≥2 was used as a threshold for distinguishing participants with more dyspnea from those with less dyspnea [1].

Participants’ quality of life was assessed using the COPD Assessment Test (CAT) and St. George’s Respiratory Questionnaire (SGRQ) [25, 26]. Participants with a CAT score of ≥10 were categorized as symptomatic [27] and those with a CAT score of ≥20 [1] as having more severe COPD symptoms. Participants with an SGRQ score of ≥40 were considered to have severe COPD symptoms [28]. CAT and SGRQ scores are reported only for trial 0128, as they were not assessed during trial 0149.

2.5. Statistical Analysis

Baseline characteristics, such as age, time since COPD diagnosis, smoking duration, height, weight, body mass index (BMI), PIF, percent predicted FEV₁ and FVC, SGRQ score, and CAT score, are reported as mean values with 95% confidence intervals. Differences between the oPIF and sPIF subgroups were compared using a two-sample t-test.

3. Results

3.1. Baseline Demographics and Clinical Characteristics

Of the total number of participants enrolled, PIF data were available for 525 participants (actual measurements from 206 participants enrolled in trial 0149 and derived values for participants enrolled in trial 0128). Of these participants, 273 (52.0%) had sPIF (mean (95% CI): 44.6 L/min (43.4–45.8 L/min)), and 252 (48.0%) had oPIF (96.7 L/min (94.2–99.1 L/min)). Baseline characteristics of participants with optimal and suboptimal PIF against the medium-low resistance DPIs are presented in Table 1. Most participants were White and male; the mean age was 65 years. The number of participants who smoked, used concomitant inhaled corticosteroids/long-acting β-agonist combination treatment, and had at least one exacerbation in the year prior to trial initiation were consistent between the two subgroups. There were significant differences in height, weight, BMI, COPD duration, and smoking history between participants with oPIF and those with sPIF against medium-low resistance DPIs. Participants with sPIF were shorter and had a lower weight and BMI than participants with oPIF. They also had a longer COPD duration and smoking history than participants with oPIF.

3.2. Dyspnea Measures

Participants with sPIF had significantly greater dyspnea than participants with oPIF based on the mMRC score (; Figure 1(a); Table 2) and BDI (; Figure 1(b); Table 2). A significantly greater number of participants with sPIF than with oPIF had a mMRC of ≥2 (severe dyspnea; 70.0% vs 48.8%; ) and used supplemental oxygen (22.3% vs 9.5%; ).

(a)

(b)

3.3. Quality-of-Life Assessments

Of the 318 participants (250 participants with oPIF and 68 with sPIF) in trial 0128 with CAT and SGRQ scores, 64 participants (94.1%) with sPIF and 227 (90.8%) with oPIF had CAT scores of ≥10 (symptomatic). Forty-three participants (63.2%) with sPIF and 132 participants with oPIF (52.8%) had CAT scores of ≥20 (highly symptomatic). SGRQ scores of ≥40 were reported in 53 participants (77.9%) with sPIF and 180 participants (72.0%) with oPIF.

Participants with sPIF had a significantly higher COPD symptom burden than participants with oPIF on the basis of CAT’s total score (; Figure 2; Table 2). There was no significant difference in SGRQ scores between participants with sPIF and those with oPIF (; Table 2).

3.4. Pulmonary Function Test

More participants with sPIF (41.4%) had very severe airflow obstruction (FEV₁ <30% predicted) than participants with oPIF (6.0%; Figure S1). In comparison with oPIF, participants with sPIF had a significantly lower postipratropium percent predicted FEV₁ (; Figure 3(a); Table 2) and FVC (; Figure 3(b); Table 2).

(a)

(b)

4. Discussion

In this analysis of data from two phase 3 trials of participants with moderate-to-very severe COPD, we have demonstrated that sPIF in patients with COPD is associated with high levels of dyspnea. Chronic dyspnea is one of the most common symptoms of COPD [1] and may be caused by a variety of mechanisms, including increased ventilatory demand, dynamic airway compression, lung hyperinflation, and respiratory muscle weakness [29]. PIF is determined by the patient’s inspiratory effort and the strength of the inspiratory muscles [30]. Janssens and colleagues have demonstrated a significant correlation between PIF and both the inspiratory and expiratory mouth pressures, measures of respiratory muscle strength [7]. Respiratory muscle function is often compromised in COPD because of lung hyperinflation, hypoxemia, and muscle wasting [8]. Lung hyperinflation can affect PIF by shortening the vertical muscle fibers of the diaphragm, which in turn reduces the inspiratory muscle strength, and by adding an elastic load that must be overcome during inspiration [30, 31]. In addition, weight loss caused by poor nutrition and muscle wasting in patients with COPD can also lead to lower respiratory and peripheral muscle strength [32–35], resulting in dyspnea and sPIF. Thus, reduced inspiratory muscle strength in patients with COPD may be the common mechanism responsible for sPIF and dyspnea.

In addition to experiencing greater dyspnea, participants with sPIF against the medium-low resistance DPIs also had a higher perceived symptom burden than those with oPIF, on the basis of participants’ CAT scores. More participants with sPIF than with oPIF reported SGRQ and CAT scores above the threshold for the disease with uncontrolled symptoms. SGRQ and CAT provide a comprehensive assessment of the COPD-specific health status of patients [1]; therefore, a significant difference between CAT scores in participants with sPIF and those with oPIF suggests that suboptimal PIF may be generally associated with poor health status. A significant association between sPIF and high levels of dyspnea and poor COPD-related health status was also recently demonstrated in outpatients with stable moderate-to-very severe COPD and sPIF [36].

In this analysis, participants with sPIF had a significantly lower FEV₁ percent predicted and FVC percent predicted than did participants with oPIF. Other studies have not shown a consistent difference in spirometric measurements (FEV₁ and FEV₁ percent predicted) between participants with sPIF and oPIF [7–9, 11, 37], although Price and colleagues reported a weak correlation between FEV₁ and PIF among participants who were discharged after hospitalization for a COPD exacerbation in a small retrospective observational study [38]. Results of another observational study that included 213 participants with advanced COPD also demonstrated significantly lower values for FVC percent predicted and IC percent predicted, but not for FEV₁ percent predicted, in participants with sPIF (defined as <60 L/min against the simulated resistance of Diskus, a medium-low DPI) when compared with those with oPIF (≥60 L/min against the simulated resistance of Diskus) [9]. The differences in FVC percent predicted and IC percent predicted could be due to the greater air trapping and hyperinflation. Alternatively, lower lung volumes may be a result of lower inspiratory effort [9].

This analysis demonstrated that participants with sPIF had a significantly lower height, weight, and BMI, longer smoking history and COPD duration, and significantly more severe airflow obstruction than participants with oPIF. Previous studies have also shown that characteristics such as age, sex (female), and markers of hyperinflation are consistently associated with the presence of sPIF [5–7, 9, 11, 39]. Height and measures of lung function such as FVC percent predicted and IC percent predicted may also be associated with the presence of sPIF [5, 9]. Thus, our results generally support the published data.

This analysis has some limitations. In trial 0149, only participants with sPIF were enrolled; therefore, an estimate of prevalence cannot be provided from this analysis. Data for CAT and SGRQ tests were collected only in trial 0128, leading to considerably fewer participants in the sPIF subgroup than in the oPIF subgroup. Peak inspiratory flow against the resistance of medium-low resistance DPIs was measured only in trial 0149; in trial 0128, these values were estimated using the predictive model correlating PIF with medium-low resistance DPIs and high resistance DPIs from trial 0149 [20]. An additional limitation is that the trials on which this analysis was based had strict inclusion and exclusion criteria (e.g., exclusion of patients with significant comorbid pulmonary conditions or, for trial 0128, elevated cardiovascular risk) [12–14], and the trial population may therefore not be representative of the real-world population with COPD.

According to the 2024 GOLD strategy report, an individualized assessment of each patient’s symptoms and future risk of exacerbations should be made before prescribing treatment for COPD [1]. Discovering measures and approaches that predict readmission following COPD exacerbation is needed to improve patient health. One study found that reduced PIF rate at discharge, a higher CAT score at discharge, frailty, and previous exacerbations were associated with hospital readmissions in patients with COPD [40]. Although some studies have found no relationship between the likelihood of readmission and the presence of sPIF among participants hospitalized for an acute COPD exacerbation [10, 41], others have shown that participants with sPIF are at increased risk of readmission [8]. Specifically, one retrospective analysis of patients who were hospitalized for acute exacerbation of COPD found that sPIF was common in these patients and sPIF predicted all-cause and COPD readmissions [8]. Participants with sPIF may not receive an adequate dose of bronchodilators through DPI devices to ameliorate their symptoms; therefore, in addition to measuring airflow limitation by spirometry, a healthcare provider should consider measuring PIF against the simulated resistance of the DPI before prescribing treatment. It has been suggested that PIF measured against the simulated resistance of a specific DPI may be used as a biomarker to identify patients who are likely or not likely to benefit from the DPI [42, 43]. In addition, characteristics such as sex, age, height, weight, BMI, and the markers of hyperinflation may also have utility in identifying participants at a higher risk for sPIF. Patients with sPIF are less likely to have a favorable response to DPIs and may be candidates for bronchodilator therapies administered via delivery systems that require low inspiratory efforts, such as pMDI, SMIs, and nebulizers.

In conclusion, in this analysis of pooled data from two phase 3 clinical trials, participants with COPD who had sPIF had significantly more dyspnea and worse health status than patients with oPIF, suggesting that sPIF is associated with a higher clinical burden than oPIF.

Data Availability

Summary data for all analyses described in this paper are presented within the results of the submitted manuscript, including in tables/graphs. To protect the privacy of trial participants and investigators, Theravance Biopharma (and its affiliates) does not share individual deidentified participant data or other relevant trial documents.

Disclosure

Gabrielle N. Davis, David A. Lombardi, and Glenn D. Crater were employees of Theravance Biopharma US, Inc., at the time, and this analysis was conducted. The current affiliation for GDC is Clinical Pharma Specialist Consultants, LLC, Raleigh, NC, USA.

Conflicts of Interest

JAO has served on advisory boards for AstraZeneca, Boehringer Ingelheim, GSK, Mylan Inc., Reckitt Benckiser, Sunovion Pharmaceuticals, and Theravance Biopharma US, Inc. DAM has served on advisory boards for AstraZeneca, Boehringer Ingelheim, GSK, Mylan, Sunovion Pharmaceuticals, Theravance Biopharma US, Inc., and Teva and is on the speaker’s bureau for AstraZeneca, Boehringer Ingelheim, and Teva Pharmaceuticals. GND is an employee of Theravance Biopharma US, Inc. DAL is a contract employee of Theravance Biopharma US, Inc. EJM is an employee of Theravance Biopharma US, Inc. GDC was an employee of Theravance Biopharma US, Inc., at the time these trials were conducted.

Authors’ Contributions

JAO, DAM, EJM, and GDC contributed to trial design and conduct, data collection, and data interpretation and reviewed the manuscript critically. GND reviewed the manuscript critically for important intellectual content. DAL contributed to data analysis and interpretation and reviewed the manuscript critically. All authors provided final approval of the submitted manuscript.

Acknowledgments

Medical writing support for the development of this manuscript, under the direction of the authors, was provided by Ritu Pathak, PhD, of Ashfield MedComms (Middletown, CT, USA), an Ashfield Health company, and editorial support was provided by Frederique H. Evans, MBS, of Ashfield MedComms and Hilary Durbano, PhD, of AlphaBioCom, a Red Nucleus company, and funded by Theravance Biopharma US, Inc. (South San Francisco, CA, USA), and Mylan Inc., a Viatris company (Canonsburg, PA, USA). This analysis was funded by Theravance Biopharma Ireland Limited (Dublin, Ireland). Mylan Inc., a Viatris company (Canonsburg, PA, USA), and Theravance Biopharma US, Inc. (South San Francisco, CA, USA), funded the medical writing/editorial support.

Supplementary Materials

Figure S1: distribution of airflow limitation categories in patients with oPIF and sPIF against the simulated medium-low resistance in trials 0128 and 0149. Graphical Abstract: In this analysis of data from two phase 3 clinical trials of participants with moderate-to-very severe chronic obstructive pulmonary disease (COPD), the authors demonstrated that suboptimal peak inspiratory flow (sPIF) is associated with significantly more dyspnea as measured by the (A) modified Medical Research Council (mMRC) scoring and (B) the baseline dyspnea index (BDI). Compared with optimal PIF (oPIF), sPIF was associated with a higher clinical burden of COPD. (Supplementary Materials)

References

Global Initiative for Chronic Obstructive Lung Disease, “Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: 2024 report,” https://goldcopd.org/2024-gold-report/.
View at: Google Scholar
D. P. Tashkin, “A review of nebulized drug delivery in COPD,” International Journal of Chronic Obstructive Pulmonary Disease, vol. 11, pp. 2585–2596, 2016.
View at: Publisher Site | Google Scholar
P. Rogliani, L. Calzetta, A. Coppola et al., “Optimizing drug delivery in COPD: the role of inhaler devices,” Respiratory Medicine, vol. 124, pp. 6–14, 2017.
View at: Publisher Site | Google Scholar
P. Muralidharan, D. Hayes Jr., and H. M. Mansour, “Dry powder inhalers in COPD, lung inflammation and pulmonary infections,” Expert Opinion on Drug Delivery, vol. 12, no. 6, pp. 947–962, 2015.
View at: Publisher Site | Google Scholar
A. G. Duarte, L. Tung, W. Zhang, E. S. Hsu, Y. F. Kuo, and G. Sharma, “Spirometry measurement of peak inspiratory flow identifies suboptimal use of dry powder inhalers in ambulatory patients with COPD,” Chronic Obstructive Pulmonary Diseases: Journal of the COPD Foundation, vol. 6, no. 3, pp. 246–255, 2019.
View at: Publisher Site | Google Scholar
S. Ghosh, R. A. Pleasants, J. A. Ohar, J. F. Donohue, and M. B. Drummond, “Prevalence and factors associated with suboptimal peak inspiratory flow rates in COPD,” International Journal of Chronic Obstructive Pulmonary Disease, vol. 14, pp. 585–595, 2019.
View at: Publisher Site | Google Scholar
W. Janssens, P. VandenBrande, E. Hardeman et al., “Inspiratory flow rates at different levels of resistance in elderly COPD patients,” European Respiratory Journal, vol. 31, no. 1, pp. 78–83, 2008.
View at: Publisher Site | Google Scholar
C. H. Loh, S. P. Peters, T. M. Lovings, and J. A. Ohar, “Suboptimal inspiratory flow rates are associated with chronic obstructive pulmonary disease and all-cause readmissions,” Annals of the American Thoracic Society, vol. 14, no. 8, pp. 1305–1311, 2017.
View at: Publisher Site | Google Scholar
D. A. Mahler, L. A. Waterman, and A. H. Gifford, “Prevalence and COPD phenotype for a suboptimal peak inspiratory flow rate against the simulated resistance of the Diskus^® dry powder inhaler,” Journal of Aerosol Medicine and Pulmonary Drug Delivery, vol. 26, no. 3, pp. 174–179, 2013.
View at: Publisher Site | Google Scholar
G. Sharma, D. A. Mahler, V. M. Mayorga, K. L. Deering, O. Harshaw, and V. Ganapathy, “Prevalence of low peak inspiratory flow rate at discharge in patients hospitalized for COPD exacerbation,” Chronic Obstructive Pulmonary Diseases: Journal of the COPD Foundation, vol. 4, no. 3, pp. 217–224, 2017.
View at: Publisher Site | Google Scholar
L. P. Malmberg, P. Rytilä, P. Happonen, and T. Haahtela, “Inspiratory flows through dry powder inhaler in chronic obstructive pulmonary disease: age and gender rather than severity matters,” International Journal of Chronic Obstructive Pulmonary Disease, vol. 5, pp. 257–262, 2010.
View at: Publisher Site | Google Scholar
J. F. Donohue, E. Kerwin, S. Sethi et al., “Revefenacin, a once-daily, lung-selective, long-acting muscarinic antagonist for nebulized therapy: safety and tolerability results of a 52-week phase 3 trial in moderate to very severe chronic obstructive pulmonary disease,” Respiratory Medicine, vol. 153, pp. 38–43, 2019.
View at: Publisher Site | Google Scholar
J. F. Donohue, E. Kerwin, S. Sethi et al., “Maintained therapeutic effect of revefenacin over 52 weeks in moderate to very severe chronic obstructive pulmonary disease (COPD),” Respiratory Research, vol. 20, no. 1, p. 241, 2019.
View at: Publisher Site | Google Scholar
D. A. Mahler, J. A. Ohar, C. N. Barnes, E. J. Moran, S. Pendyala, and G. D. Crater, “Nebulized versus dry powder long-acting muscarinic antagonist bronchodilators in patients with COPD and suboptimal peak inspiratory flow rate,” Chronic Obstructive Pulmonary Diseases: Journal of the COPD Foundation, vol. 6, no. 4, pp. 321–331, 2019.
View at: Publisher Site | Google Scholar
S. Ghosh, J. A. Ohar, and M. B. Drummond, “Peak inspiratory flow rate in chronic obstructive pulmonary disease: implications for dry powder inhalers,” Journal of Aerosol Medicine and Pulmonary Drug Delivery, vol. 30, no. 6, pp. 381–387, 2017.
View at: Publisher Site | Google Scholar
M. J. Sanders, “Guiding inspiratory flow: development of the in-check DIAL G16, a tool for improving inhaler technique,” Pulmonary Medicine, vol. 2017, pp. 1–7, 2017.
View at: Publisher Site | Google Scholar
R. A. Al-Showair, W. Y. Tarsin, K. H. Assi, S. B. Pearson, and H. Chrystyn, “Can all patients with COPD use the correct inhalation flow with all inhalers and does training help?” Respiratory Medicine, vol. 101, no. 11, pp. 2395–2401, 2007.
View at: Publisher Site | Google Scholar
D. A. Mahler, L. A. Waterman, J. Ward, and A. H. Gifford, “Comparison of dry powder versus nebulized beta-agonist in patients with COPD who have suboptimal peak inspiratory flow rate,” Journal of Aerosol Medicine and Pulmonary Drug Delivery, vol. 27, no. 2, pp. 103–109, 2014.
View at: Publisher Site | Google Scholar
P. J. Atkins, “Dry powder inhalers: an overview,” Respiratory Care, vol. 50, no. 10, pp. 1304–1312, 2005.
View at: Google Scholar
C. N. Barnes, D. A. Mahler, J. A. Ohar, D. A. Lombardi, and G. D. Crater, “Peak inspiratory flows: defining repeatability limits and a predictive equation for different inhalers,” Chest, vol. 158, no. 4, pp. 1413–1419, 2020.
View at: Publisher Site | Google Scholar
E. G. Eakin, D. E. Sassi-Dambron, A. L. Ries, and R. M. Kaplan, “Reliability and validity of dyspnea measures in patients with obstructive lung disease,” International Journal of Behavioral Medicine, vol. 2, no. 2, pp. 118–134, 1995.
View at: Publisher Site | Google Scholar
T. Hajiro, K. Nishimura, M. Tsukino, A. Ikeda, H. Koyama, and T. Izumi, “Analysis of clinical methods used to evaluate dyspnea in patients with chronic obstructive pulmonary disease,” American Journal of Respiratory and Critical Care Medicine, vol. 158, no. 4, pp. 1185–1189, 1998.
View at: Publisher Site | Google Scholar
D. A. Mahler, D. H. Weinberg, C. K. Wells, and A. R. Feinstein, “The measuremet of dyspnea: Contents, interobserver agreement, and physiologic correlates of two new clinical indexes,” Chest, vol. 85, no. 6, pp. 751–758, 1984.
View at: Publisher Site | Google Scholar
D. A. Mahler and C. K. Wells, “Evaluation of clinical methods for rating dyspnea,” Chest, vol. 93, no. 3, pp. 580–586, 1988.
View at: Publisher Site | Google Scholar
P. W. Jones, G. Harding, P. Berry, I. Wiklund, W. H. Chen, and N. Kline Leidy, “Development and first validation of the COPD Assessment Test,” European Respiratory Journal, vol. 34, no. 3, pp. 648–654, 2009.
View at: Publisher Site | Google Scholar
P. W. Jones, F. H. Quirk, and C. M. Baveystock, “The St George's Respiratory Questionnaire,” Respiratory Medicine, vol. 85, pp. 25–31, 1991.
View at: Publisher Site | Google Scholar
P. W. Jones, M. Tabberer, and W. H. Chen, “Creating scenarios of the impact of COPD and their relationship to COPD Assessment Test (CAT™) scores,” Bone Marrow Concentrate Pulmonary Medicine, vol. 11, no. 1, p. 42, 2011.
View at: Publisher Site | Google Scholar
Y. S. Jo, S. Park, D. K. Kim, C. G. Yoo, and C. H. Lee, “The cutoff point of clinical chronic obstructive pulmonary disease questionnaire for more symptomatic patients,” Bone Marrow Concentrate Pulmonary Medicine, vol. 18, no. 1, p. 38, 2018.
View at: Publisher Site | Google Scholar
D. A. Mahler, “Mechanisms and measurement of dyspnea in chronic obstructive pulmonary disease,” Proceedings of the American Thoracic Society, vol. 3, no. 3, pp. 234–238, 2006.
View at: Publisher Site | Google Scholar
D. A. Mahler, “Peak inspiratory flow rate as a criterion for dry powder inhaler use in chronic obstructive pulmonary disease,” Annals of the American Thoracic Society, vol. 14, no. 7, pp. 1103–1107, 2017.
View at: Publisher Site | Google Scholar
D. E. O'Donnell, R. B. Banzett, V. Carrieri-Kohlman et al., “Pathophysiology of dyspnea in chronic obstructive pulmonary disease: a roundtable,” Proceedings of the American Thoracic Society, vol. 4, no. 2, pp. 145–168, 2007.
View at: Publisher Site | Google Scholar
K. Gray-Donald, L. Gibbons, S. H. Shapiro, P. T. Macklem, and J. G. Martin, “Nutritional status and mortality in chronic obstructive pulmonary disease,” American Journal of Respiratory and Critical Care Medicine, vol. 153, no. 3, pp. 961–966, 1996.
View at: Publisher Site | Google Scholar
Y. Nishimura, M. Tsutsumi, H. Nakata, T. Tsunenari, H. Maeda, and M. Yokoyama, “Relationship between respiratory muscle strength and lean body mass in men with COPD,” Chest, vol. 107, no. 5, pp. 1232–1236, 1995.
View at: Publisher Site | Google Scholar
K. Sugawara, H. Takahashi, C. Kasai et al., “Effects of nutritional supplementation combined with low-intensity exercise in malnourished patients with COPD,” Respiratory Medicine, vol. 104, no. 12, pp. 1883–1889, 2010.
View at: Publisher Site | Google Scholar
D. O. Wilson, R. M. Rogers, E. C. Wright, and N. R. Anthonisen, “Body weight in chronic obstructive pulmonary disease. The national institutes of health intermittent positive-pressure breathing trial,” American Review of Respiratory Disease, vol. 139, no. 6, pp. 1435–1438, 1989.
View at: Publisher Site | Google Scholar
D. A. Mahler, X. Niu, K. L. Deering, and C. Dembek, “Prospective evaluation of exacerbations associated with suboptimal peak inspiratory flow among stable outpatients with COPD,” International Journal of Chronic Obstructive Pulmonary Disease, vol. 17, pp. 559–568, 2022.
View at: Publisher Site | Google Scholar
J. van der Palen, “Peak inspiratory flow through Diskus and Turbuhaler, measured by means of a peak inspiratory flow meter (In-Check DIAL®),” Respiratory Medicine, vol. 97, no. 3, pp. 285–289, 2003.
View at: Publisher Site | Google Scholar
D. B. Price, S. Yang, S. Wan Yau Ming et al., “Physiological predictors of peak inspiRatory flow using Observed lung function resultS (POROS): evaluation at discharge among patients hospitalized for a COPD exacerbation,” International Journal of Chronic Obstructive Pulmonary Disease, vol. 13, pp. 3937–3946, 2018.
View at: Publisher Site | Google Scholar
D. A. Mahler, “The role of inspiratory flow in selection and use of inhaled therapy for patients with chronic obstructive pulmonary disease,” Respiratory Medicine, vol. 161, Article ID 105857, 2020.
View at: Publisher Site | Google Scholar
J. S. Alqahtani, Y. S. Aldabayan, A. M. Aldhahir, A. M. Al Rajeh, S. Mandal, and J. R. Hurst, “Predictors of 30- and 90-day COPD exacerbation readmission: a prospective cohort study,” International Journal of Chronic Obstructive Pulmonary Disease, vol. 16, pp. 2769–2781, 2021.
View at: Publisher Site | Google Scholar
A. Samarghandi, O. C. Ioachimescu, and R. Qayyum, “Association between peak inspiratory flow rate and hand grip muscle strength in hospitalized patients with acute exacerbation of chronic obstructive pulmonary disease,” Public Library of Science One, vol. 15, no. 1, Article ID e0227737, 2020.
View at: Publisher Site | Google Scholar
D. A. Mahler, “Peak inspiratory flow rate: an emerging biomarker in chronic obstructive pulmonary disease,” American Journal of Respiratory and Critical Care Medicine, vol. 199, no. 12, pp. 1577–1579, 2019.
View at: Publisher Site | Google Scholar
D. A. Mahler and D. M. G. Halpin, “Peak inspiratory flow as a predictive therapeutic biomarker in COPD,” Chest, vol. 160, no. 2, pp. 491–498, 2021.
View at: Publisher Site | Google Scholar

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Copyright © 2024 Jill A. Ohar et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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