Functional Predictors Discriminating Asthma-COPD Overlap (ACO) from Chronic Obstructive Pulmonary Disease (COPD)

Response to Biologic Therapy in Patients with Asthma and Reduced Pulmonary Diffusion Capacity

INTRODUCTION

Asthma patients with a smoking history are usually excluded from asthma trials to exclude smoking-related comorbidities like chronic obstructive pulmonary disease (COPD). Therefore, little is known about the efficacy of biologic therapy in asthma patients with reduced diffusing capacity of the lungs for carbon monoxide (DLCO).

METHODS

This study aimed to assess the response to biologic therapy in asthma patients with reduced DLCO. A total of 77 consecutive patients undergoing biologic therapy in a routine clinical setting were included in the analysis and divided into three groups: DLCO ≥60%, DLCO <60% and <10 pack-years, and DLCO <60% and ≥10 pack-years = asthma and COPD comorbidity. Follow-up evaluations were conducted after a minimum of 6 months of therapy.

RESULTS

After 34.0 ± 10.2 weeks, comparable therapeutic responses were observed between the three groups. There were no differences between the groups in terms of reduction in the annual acute exacerbation rate (AE median -3 [25th percentile -5; 75th percentile -1] vs. -6.1 [-11.3;-2.2] vs. -3 [-6;-2], p = 0.067), oral corticosteroid (OCS) doses (-5 [-10;0] vs. -1 [-7.5;0] vs. -7.5 [-10;-4] mg, p = 0.136), improvement in Asthma Control Test (ACT) scores (4 [0;9.3] vs. 3 [-1;6] vs. 4 [3;10], p = 0.276) or forced expiratory volume in 1 s (FEV1) improvement (5.5 [-2;21.5] vs. 0.5 [-2.8;9.3] vs. 5 [0;16] % predicted, p = 0.328). Linear regression analysis revealed no significant correlation between DLCO levels and changes in OCS dosage or AE rate, nor between DLCO and improvements in ACT scores or FEV1. Notably, a smaller proportion of patients exhibited a reduced transfer coefficient (DLCO/VA) (n = 13, 16.9%). This parameter did not significantly impact therapy response either.

CONCLUSION

Our findings suggest that biologic therapy can effectively manage asthma irrespective of DLCO measurements. Thus, reduced DLCO values should not preclude thorough asthma diagnosis and treatment. Further investigation into the utility of DLCO/VA assessment in this context is warranted.

Predicting parameters of airway dynamics generated from inspiratory and expiratory plethysmographic airway loops, differentiating subtypes of chronic obstructive diseases

BACKGROUND

The plethysmographic shift volume-flow loop (sRaw-loop) measured during tidal breathing allows the determination of several lung function parameters such as the effective specific airway resistance (sReff), calculated from the ratio of the integral of the resistive aerodynamic specific work of breathing (sWOB) and the integral of the corresponding flow-volume loop. However, computing the inspiratory and expiratory areas of the sRaw-loop separately permits the determination of further parameters of airway dynamics. Therefore, we aimed to define the discriminating diagnostic power of the inspiratory and expiratory sWOB (sWOBin, sWOBex), as well as of the inspiratory and expiratory sReff (sReff IN and sReff EX), for discriminating different functional phenotypes of chronic obstructive lung diseases.

METHODS

Reference equations were obtained from measurement of different databases, incorporating 194 healthy subjects (35 children and 159 adults), and applied to a collective of 294 patients with chronic lung diseases (16 children with asthma, aged 6-16 years, and 278 adults, aged 17-92 years). For all measurements, the same type of plethysmograph was used (Jaeger Würzburg, Germany).

RESULTS

By multilinear modelling, reference equations of sWOBin, sWOBex, sReff IN and sReff EX were derived. Apart from anthropometric indices, additional parameters such as tidal volume (VT), the respiratory drive (P0.1), measured by means of a mouth occlusion pressure measurement 100 ms after inspiration and the mean inspiratory flow (VT/TI) were found to be informative. The statistical approach to define reference equations for parameters of airway dynamics reveals the interrelationship between covariants of the actual breathing pattern and the control of breathing.

CONCLUSIONS

We discovered that sWOBin, sWOBex, sReff IN and sReff EX are new discriminating target parameters, that differentiate much better between chronic obstructive diseases and their subtypes, especially between chronic obstructive pulmonary disease (COPD) and asthma-COPD overlap (ACO), thus strengthening the concept of precision medicine.

Assessment of functional diversities in patients with Asthma, COPD, Asthma-COPD overlap, and Cystic Fibrosis (CF)

The objectives of the present study were to evaluate the discriminating power of spirometric and plethysmographic lung function parameters to differenciate the diagnosis of asthma, ACO, COPD, and to define functional characteristics for more precise classification of obstructive lung diseases. From the databases of 4 centers, a total of 756 lung function tests (194 healthy subjects, 175 with asthma, 71 with ACO, 78 with COPD and 238 with CF) were collected, and gradients among combinations of target parameters from spirometry (forced expiratory volume one second: FEV1; FEV1/forced vital capacity: FEV1/FVC; forced expiratory flow between 25-75% FVC: FEF25-75), and plethysmography (effective, resistive airway resistance: sReff; aerodynamic work of breathing at rest: sWOB), separately for in- and expiration (sReffIN, sReffEX, sWOBin, sWOBex) as well as static lung volumes (total lung capacity: TLC; functional residual capacity: FRCpleth; residual volume: RV), the control of breathing (mouth occlusion pressure: P0.1; mean inspiratory flow: VT/TI; the inspiratory to total time ratio: TI/Ttot) and the inspiratory impedance (Zinpleth = P0.1/VT/TI) were explored. Linear discriminant analyses (LDA) were applied to identify discriminant functions and classification rules using recursive partitioning decision trees. LDA showed a high classification accuracy (sensitivity and specificity > 90%) for healthy subjects, COPD and CF. The accuracy dropped for asthma (~70%) and even more for ACO (~60%). The decision tree revealed that P0.1, sRtot, and VT/TI differentiate most between healthy and asthma (68.9%), COPD (82.1%), and CF (60.6%). Moreover, using sWOBex and Zinpleth ACO can be discriminated from asthma and COPD (60%). Thus, the functional complexity of obstructive lung diseases can be understood, if specific spirometric and plethysmographic parameters are used. Moreover, the newly described parameters of airway dynamics and the central control of breathing including Zinpleth may well serve as promising functional marker in the field of precision medicine.

The value of bronchodilator response in FEV1 and FeNO for differentiating between chronic respiratory diseases: an observational study

BACKGROUND

There is no uniform standard for a strongly positive bronchodilation test (BDT) result. In addition, the role of bronchodilator response in differentiating between asthma, chronic obstructive pulmonary disease (COPD), and asthma-COPD overlap (ACO) in patients with a positive BDT result is unclear. We explored a simplified standard of a strongly positive BDT result and whether bronchodilator response combined with fractional exhaled nitric oxide (FeNO) can differentiate between asthma, COPD, and ACO in patients with a positive BDT result.

METHODS

Three standards of a strongly positive BDT result, which were, respectively, defined as post-bronchodilator forced expiratory volume in 1-s responses (ΔFEV1) increasing by at least 400 mL + 15% (standard I), 400 mL (standard II), or 15% (standard III), were analyzed in asthma, COPD, and ACO patients with a positive BDT result. Receiver operating characteristic curves were used to determine the optimal values of ΔFEV1 and FeNO. Finally, the accuracy of prediction was verified by a validation study.

RESULTS

The rates of a strongly positive BDT result and the characteristics between standards I and II were consistent; however, those for standard III was different. ΔFEV1 ≥ 345 mL could predict ACO diagnosis in COPD patients with a positive BDT result (area under the curve [AUC]: 0.881; 95% confidence interval [CI] 0.83-0.94), with a sensitivity and specificity of 90.0% and 91.2%, respectively, in the validation study. When ΔFEV1 was < 315 mL combined with FeNO < 28.5 parts per billion, patients with a positive BDT result were more likely to have pure COPD (AUC: 0.774; 95% CI 0.72-0.83).

CONCLUSION

The simplified standard II can replace standard I. ΔFEV1 and FeNO are helpful in differentiating between asthma, COPD, and ACO in patients with a positive BDT result.