Small-Airway Function Variables in Spirometry, Fractional Exhaled Nitric Oxide, and Circulating Eosinophils Predicted Airway Hyperresponsiveness in Patients with Mild Asthma

Plain Language Summary

Why was the study done?

Patients with variable symptoms suggestive of asthma but with normal forced expiratory volume in 1 second (FEV₁, reflect large airway function) often fail to be diagnosed without a bronchial provocation test. But the test is expensive, time-consuming, risky, and not readily available in all clinical settings. Thus, we investigated the predictive values of simple and convenient tests, such as spirometry, fractional exhaled nitric oxide (FENO), and circulating eosinophils for predicting airway hyperresponsiveness (AHR) in patients with mild asthma.

What did the researchers do and find?

We performed a retrospective cross-section study, enrolled 692 patients with normal large airway function (FEV₁≥80% predicted) and recurrent dyspnea, cough, wheeze, or chest tightness. We find that small airway dysfunction (FEF_25%-75% and FEF_50%) do exist in patients with normal FEV₁ values but had typical asthmatic symptoms, and FEF_25%-75% < 84.4%, or FEF_50% < 76.8% combine with FENO > 41 ppb predict positive AHR. FEFs combined with circulating eosinophils also can predict AHR effectively in patients with chest tightness.

What do these results mean?

Combining FEF_50% or FEF_25%-75% with FENO could forego bronchial provocation test to support asthma diagnosis in patients with normal FEV₁ and symptoms suggestive of asthma. FEF_25%-75%, FEF_50%, or FEF_25%-75% together with circulating eosinophils also can potentially suggest asthma in patients with chest tightness.

Introduction

Bronchial asthma (asthma) is a chronic inflammatory airway disease affecting the entire bronchial tree from the large to the small airways (<2 mm diameter).¹ The diagnosis is based on recurrent symptoms of dyspnea, cough, wheeze, and chest tightness, as well as reversible airway limitation or airway hyperresponsiveness (AHR).² Mild asthma is the most common type of asthma, often has normal forced expiratory volume in 1 second (FEV₁), representing 50% to 75% of all asthma patients.³ In nearly 90% of the asthmatic patients with normal FEV₁ in china (Min Zhang, unpublished paper), the bronchial dilation test is negative, so the bronchial provocation test (BPT) is important for confirming or excluding asthma after detailed medical history and physical examination. However, many hospitals do not perform provocation tests because they are expensive, time-consuming, and entail a risk of severe bronchospasm.² What’s more, pulmonary function testing should be performed in patients with chronic airway disease during COVID-19 pandemic only if it is needed to guide management and limited to the necessary tests, which usually do not include BPT. Therefore, additional ways are needed to predict AHR safely and to detect patients with mild asthma as early as possible in order to relieve their symptoms and prevent the development of chronic inflammation and airway remodeling.

Small-airway dysfunction exists in mild asthma patient, evaluated by a variety of spirometry and IOS measurements. Lower forced expiratory flow between 25% and 75% (FEF_25%-75%) and Forced expiratory flow at 50% of forced vital capacity (FEF_50%) are widely used for assessing small-airway function.^4–6 Impulse oscillometry (IOS) may also reliably reflect small-airway function and predict clinical asthma outcomes and AHR.^7–11 FEV in 3 seconds (FEV₃)/FVC is influenced by the airflow velocity in both the central and peripheral airway, normally 95% or greater in adults.^12–14

In our previous study of patients with chronic cough, patients with AHR had higher fractional exhaled nitric oxide (FENO), a higher percentage of eosinophils in blood (EOS%), and FEF_25%-75% than patients without AHR.⁴ The combination of FEF_25%-75% and FENO increased the area under the curve (AUC) for AHR diagnosis substantially compared with FENO alone. In that study, FEF_25%-75% < 78.5% and FENO > 43 ppb strongly predicted positive AHR in Chinese patients with chronic cough.

In the current study, we used FEFs, peripheral airway resistance as the difference between 5 and 20 Hz (R5-R20), reactance at 5 Hz (X5), resonant frequency (Fres), FEV₃/FVC, FENO, EOS, and EOS% alone or in combination, as potential predictive variables for the presence of AHR, and extended our cross-sectional study to patients with FEV₁ ≥ 80% predicted who had more than 1 typical symptom of asthma, such as variable cough, dyspnea, wheeze, and chest tightness, to confirm the predictive value of small-airway function tests, FENO, and EOS for AHR.

Patients and Methods

Study Design and Subject Selection

A retrospective cross-section of diagnostic data was collected at the initial visit of adult patients with recurrent variable symptoms of dyspnea, cough, wheeze, or chest tightness of at least 8 weeks’ duration who were referred to the Pulmonary Outpatient Clinic of Shanghai General Hospital (China). The patients had to undergo a peripheral blood test, spirometry,¹⁵ FENO measurement (NIOX MINO, Aerocrine AB, Solna, Sweden),¹¹ methacholine challenge testing (MCT), and high-resolution computerized tomography (HRCT, GE Medical System; slice thickness 0.625 mm) from September 2016 to January 2020. The MCT was performed with a Jaeger APS Pro system using a Medic-Aid sidestream nebulizer and doubling doses of methacholine (0.015 to 0.48 mg) following the American Thoracic Society/European Respiratory Society recommendations.¹⁶

Additional inclusion criteria were age 18–75 years, normal HRCT results, and predicted FEV₁ of 80% or greater with spirometric measurement.

Subjects were excluded if they had had respiratory tract infections in the past 8 weeks; peripheral blood test indicating abnormal hemoglobin, platelets, or neutrophils; use of montelukast, long-acting β₂-agonists, theophylline, anticholinergic agents, or an inhaled or oral corticosteroid in the previous 4 weeks; or having concomitant severe systemic diseases. Patients who had more than a 10 pack-year smoking history, who currently smoked, or who had quit less than 2 years earlier were also excluded.

Descriptive characteristics, clinical history, results of MCT, spirometry, FENO measurement, IOS (Jaeger Co, Hochberg, Germany),¹⁷ EOS, and EOS% were reviewed and analyzed. The FVC, FEV₁, peak expiratory flow (PEF), FEF_25%, FEF_50%, FEF_75%, and FEF_25%-75% were expressed as percentages of predicted values. FEV₁/FVC and FEV₃/FVC were expressed as the ratios of the absolute values of the variables. Small-airway dysfunction was identified if 2 of FEF_50%, FEF_75%, and FEF_25%-75% were lower than 80%. IOS variables R5, R20, R5-R20, X5, and Fres were also collected. For bronchial provocation tests, the provocative dose causing a 20% fall in FEV₁ (PD₂₀) was recorded, and AHR was defined as present if PD₂₀ was ≤0.48 mg. Associations of PD₂₀ and FENO, EOS, EOS%, FEFs, and IOS were analyzed in patients positive for AHR.

The ethics committee of Shanghai General Hospital, Shanghai Jiao Tong University, approved the protocol. It is a retrospective cross-section study, and the exemption of informed consent will not affect subjects’ rights and welfare. Besides, it is very difficult to contact all the patients of our study. Thus, a waiver of informed consent was given for our study (number 2017KY159). We confirm that the patient data was maintained with confidentiality and complied with the Declaration of Helsinki.

Statistical Analysis

Analyses were performed with SPSS software version 19.0 (SPSS Inc, Chicago, Illinois, USA), except for the ROC contrast estimation, ROC contrast test, and 80/20 split-sample cross-validation, which were performed with SAS Proc LOGISTIC version 9.4 (SAS Institute Inc., Cary, NC, USA). Normality of distribution was checked with the Kolmogorov–Smirnov test. Normally distributed data were expressed as mean ± standard deviation (SD) or 95% Confidence interval (CI). Non-normally distributed data were expressed as median and interquartile ranges (IQR). The coefficient of variance (CV) was calculated. Fisher’s exact test (discontinuous variables) and the Mann–Whitney test (continuous variables) were performed for the intergroup comparisons. The association between different variables was decided by Spearman correlation.

The prediction performance of each variable was measured as the AUC of the ROC. Furthermore, a multiple logistic model of the 2 variables was fitted, and the resultant AUC of this multiple logistic model was used as a measure of the joint prediction performance. Chi-square test proposed by DeLong et al was used to determine whether the multiple logistic model would significantly improve the prediction performance, defined as the AUC, relative to the marginal models.¹⁸

MCT was used as the gold standard for defining AHR. The optimal value of the single measurement giving the highest sum of AHR diagnostic sensitivity and specificity was used as a cut-off value.^19,20 Positive predictive values (PPV), negative predictive values (NPV), and percentages correctly classified (PCC) were calculated for each cut-off value.^21,22 The corresponding odds ratios, CI, and P values were also calculated.

Continuous variables were converted to dichotomous-state variables on the basis of the cut-off values. Subsequently, ROC curves were determined for the joint models with the dichotomous-state variables.

We constructed and examined all models to predict AHR with repeated five-fold cross-validation (5 repeats). The average AUC of 5 different cross-validation models and the whole-model AUC using the entire data set were calculated. The Error Rate equals abs (Average AUC – Whole Model AUC)/(Whole Model AUC). Accurate classification was also calculated for the test subset.

The threshold for statistical significance for all analyses was set at P < 0.05.

Results

Baseline Characteristics

Clinical data from 692 adults were ultimately included. Bronchial provocation tests were positive in 170 patients and negative in 522 patients. Baseline demographics categorized by bronchial provocation test positivity are shown in Table 1. There were no significant differences in age, sex, smoking history, or body mass index (BMI) between the 2 groups. Subjects with AHR had lower percent predicted of FEV₁, FEV₁/FVC, FEV₃/FVC, and PEF (P ≤ 0.001 for all). All FEF values, alone or as ratios of their predicted value to that of FEV₁ predicted value, were significantly lower in subjects with AHR than without AHR (P < 0.001 for all).

Table 1 Demographic Data, Spirometric Variables, IOS Variables, and Values for FENO and Peripheral Eosinophils of Patients with Positive or Negative Bronchial Provocation Tests

Significantly higher levels of R5-R20 and Fres, lower level of X5 were observed in the AHR group (P < 0.001 for all). Furthermore, FENO values, EOS, and EOS% were also dramatically higher in the AHR group (P < 0.001 for all) (Table 1).

Mean values of FEV₁, FEV₁/FVC, FEF_50%, FEF_75% and FEF_25%-75% were lower in subjects with chest tightness (n = 75) compared with those without this symptom (P = 0.009, 0.008, 0.003, <0.001, and 0.002, respectively). FENO values, EOS, and EOS% values were higher in subjects with than without chest tightness (P = 0.012, 0.001, 0.003, and 0.014, respectively).

Correlation Between PD₂₀ and FENO, EOS, EOS%, FEFs, and R5-R20

FENO, FEF_25%, FEF_50%, FEF_75%, and FEF_25%-75% were weakly correlated with PD₂₀ by Spearman analysis (Table 2). No significant correlation between PD₂₀ and FEV₃/FVC, EOS, EOS%, R5-R20, X5, or Fres was found. There was no difference in PD₂₀ between subjects with chest tightness (0.065; IQR, 0.220) and those without this symptom (0.089; IQR, 0.349; P = 0.764).

Table 2 Spearman Correlation Between PD20 and Other Variables in BPT+ Subjects

Diagnostic Accuracy of Single Variables Used for Predicting AHR

We created ROC curves to evaluate the ability of many of the variables to predict positive AHR. In the spirometry measurements, the 2 largest AUCs for a positive AHR diagnosis were 0.763 for FEF_25%-75% and 0.762 for FEF_50% (Table 3). The FEV₁/FVC, EOS%, EOS, and IOS measurements did not give high AUCs for positive AHR diagnosis.

Table 3 Optimal Cut-Off Values and Other Measures of Usefulness for Predicting Airway Hyperresponsiveness

In patients with chest tightness, the AUCs for positive AHR diagnosis were FEF_50% 0.751 (95% CI, 0.637–0.864), FEF_75% 0.812 (95% CI, 0.708–0.916), FEF_25%-75% 0.763 (95% CI, 0.651–0.875), FENO 0.731 (95% CI, 0.607–0.855), and EOS 0.706 (95% CI, 0.580–0.832).

Diagnostic Accuracy of Small-Airway Function Variables Combined with FENO and Internal Cross-Validation of the Final Models

To determine whether combining measurements would improve AHR prediction, we repeated the ROC analyses for spirometry measurements combined with FENO. The AUC of FEF_50% combined with FENO was 0.845 (95% CI, 0.812–0.878), which was significantly higher than the AUC of univariate FEF_50% (P < 0.001) (Table 4 and Figure 1). Similarly, the other spirometry measurements also had higher AUCs when combined with FENO than they did alone (Table 4) NPV was ≥85.45% for all of the combinations.

Table 4 Predictive Values of the Combination of Different Variables with FENO in Predicting Airway Hyperresponsiveness

Figure 1 ROC curves for the models of FEFs combined with FENO for predicting positive bronchial provocation tests (n = 692). (A) FEF50% combined with FENO. AUCModel = 0.845 (95% CI, 0.812–0.878); AUCFENO = 0.748 (95% CI, 0.702–0.793; P < 0.001, compared with the model); AUCFEF50% = 0.762 (95% CI, 0.721–0.803; P < 0.001, compared with the model). (B) FEF75% combined with FENO. AUCModel = 0.824 (95% CI, 0.788–0.859); AUCFENO =0.748 (95% CI, 0.702–0.793; P < 0.001, compared with the model); AUCFEF75% = 0.745 (95% CI, 0.703–0.786; P < 0.001, compared with the model). (C) FEF25%-75% combined with FENO. AUCModel = 0.844 (95% CI, 0.811–0.876); AUCFENO =0.748 (95% CI, 0.702–0.793; P < 0.001, compared with the model); AUCFEF25%-75% = 0.763 (95% CI, 0.723–0.802; P < 0.001, compared with the model). (D) FEV1/FVC combined with FENO. AUCModel = 0.807 (95% CI, 0.769–0.844); AUCFENO = 0.748 (95% CI, 0.702–0.793; P = 0.001, compared with the model); AUCFEV1/FVC = 0.713 (95% CI, 0.669–0.758). Abbreviations: ROC, receiver operating characteristic; FEF, forced expiratory flow; FENO, fractional exhaled nitric oxide; AUC, area under the curve; FEV1, forced expiratory volume in 1 second; FVC, forced vital capacity; FEF50%, FEF at 50% of FVC; FEF75%, FEF at 75% of FVC; FEF25%-75%, FEF at 25% to 75% of FVC.

We then transformed the continuous variables into binary variables according to the cut-off values shown in the tables and reanalyzed the mentioned above ROC curves. The AUCs of FEF_50% and FEF_25%-75% combined with FENO remained high (Figure 2).

Figure 2 ROC curves of dichotomous state variables of the models of FEFs combined with FENO in predicting positive bronchial provocation tests (n = 692). (A) FEF50% combined with FENO. AUCModel = 0.809 (95% CI, 0.786–0.878); (B) FEF75% combined with FENO. AUCModel = 0.797 (95% CI, 0.768–0.866); (C) FEF25%–75% combined with FENO. AUCModel = 0.802 (95% CI, 0.780–0.874). Abbreviations: ROC, receiver operating characteristic; FEF, forced expiratory flow; FENO, fractional exhaled nitric oxide; AUC, area under the curve; FEV1, forced expiratory volume in 1 second; FVC, forced vital capacity; FEF50%, FEF at 50% of FVC; FEF75%, FEF at 75% of FVC; FEF25%-75%, FEF at 25% to 75% of FVC.

In patients with chest tightness, the AUCs of FEF_50%, FEF_75%, and FEF_25%-75% combined with FENO were 0.880 (95% CI, 0.806–0.954), 0.892 (95% CI, 0.812–0.972), and 0.884 (95% CI, 0.805–0.934), respectively (Table 5).

Table 5 Stratified Analysis of Patients with Chest-Tightness: Predictive Values of Combinations of Different Variables with FENO in Predicting Airway Hyperresponsiveness

The error rates between the average AUC of 5 different cross-validation models and the whole-model AUC using the entire data set were lower than 0.05 for all chosen variables, indicating that the data model has stable predictive ability for different data sets (Supplementary Tables S1 and S2).

Diagnostic Accuracy of Small-Airway Function Variables Combined with EOS in Blood

We repeated the ROC analyses for spirometry measurements (FEF_50%, FEF_25%-75%, FEV₁/FVC) combined with EOS (Table 6). The AUCs of the combined models were between 0.734 and 0.786, and all had NPV ≥ 85.08%. The AUCs of FEFs combined with EOS were higher in patients with chest tightness than in the population as a whole (0.815, 0.845, and 0.816, for FEF_50%, FEF_75%, and FEF_25%-75%, respectively; Table 5).

Table 6 Predictive Value of the Combination of Different Variables with EOS in Predicting Airway Hyperresponsiveness

Discussion

Early diagnosis of asthma is very important, not only to relieve the patient’s symptoms but also to prevent the development of chronic inflammation and airway remodeling. However, early diagnosis is difficult because patients need to satisfy the 2 criteria of variable symptoms and air-flow limitation. This is especially true for mild asthma, which is often undiagnosed because FEV₁ is normal and the bronchodilation test has a high false-negative rate. Our data indicating that small-airway dysfunction is present in asthmatic patients with FEV₁ ≥ 80% predicted may help to support an early diagnosis in patients with mild asthma. Measurements of small-airway function, including FEF_50% and FEF_25%-75%, combined with measures of airway inflammation (FENO or EOS) supported the best prediction of positive AHR diagnosis in subjects who had both typical asthma-like symptoms and FEV₁ ≥ 80% predicted.

Type II airway inflammation increases both EOS and FENO, which are used in asthma diagnosis and the therapeutic response’s evaluation of anti-asthma drugs.²³ Our data also showed that the levels of both EOS and FENO were higher in the AHR-positive group. Previous studies in guinea pigs have shown that NO may itself contribute to AHR, by increasing plasma exudation via its vasodilator effect and by its transformation into peroxynitrite, which induces AHR.²⁴ Furthermore, in our study, a weak correlation between FENO and PD₂₀ in patients with positive AHR was observed (r = −0.266, P = 0.0005), suggesting that FENO is valuable in predicting AHR, which was also noted by Jatakanon et al.²⁵

Currently, FENO is particularly helpful in ruling out asthma. Its cut-off value for predicting asthma ranged from 10.5 to 64 ppb in different studies.²⁶ FENO < 30 ppb has a high specificity (87%) and NPV (93%) for excluding asthma from untreated nonsmoking adults with chronic cough.²⁷ Schleich et al showed that FENO > 34 ppb had a low predictive value (AUC = 0.62) for predicting AHR in patients with suspected asthma.²⁸ Schneider et al illustrated that, in their sample as a whole, asthma could be ruled in at FENO > 71 ppb (PPV, 80%) and ruled out at FENO ≤ 9 ppb (NPV, 82%), with an AUC of 0.656.²⁹ Importantly, when patients with neutrophilic inflammation were omitted, the AUC was 0.745 and asthma could be ruled in at FENO > 31 ppb (PPV, 82%) and ruled out at FENO ≤ 12 ppb (NPV, 81%).²⁹ Our present study showed that FENO > 41 ppb has a sensitivity of 65.29%, specificity of 78.16%, PPV of 49.33%, and NPV of 87.37%. The AUC for predicting AHR was 0.748, regardless of the inflammatory type, which is similar to the AUC from Schneider et al when patients with neutrophilic inflammation were omitted.

In our current study, patients positive for AHR, but with FEV₁ in the normal range, had abnormal values of small-airway function variables, obtained with spirometry and IOS. Two-thirds of asthmatic patients with FEV₁ ≥ 80% predicted had small-airway dysfunction, and patients with small-airway dysfunction exhibit a greater likelihood of AHR. FEF_50%, FEF_75%, and FEF_25%-75% were weakly correlated with PD₂₀. This might indicate that small-airway dysfunction could be a forerunner of decreased FEV₁ and could be used to detect early disease.

We found the 2 most valuable spirometric variables for predicting AHR were FEF_25%-75% (AUC = 0.763) and FEF_50% (AUC = 0.762) (Table 3). Those two FEFs were strongly correlated and had equivalent value in predicting AHR. However, because both produced AUC < 0.80, using them singly would be insufficient for predicting AHR in patients with suspected asthma. Thus, we combined the FEFs with FENO or EOS to enhance their predictive value for AHR diagnosis. The AUCs of FEFs combined with FENO were significantly higher than those of the univariate AUCs. This suggests that FEFs combined with FENO (2 noninvasive and convenient measurements) can improve the prediction of AHR diagnosis. The cut-off values had certain difference among different studies possibly because we included patients with mild asthma-like symptoms and normal FEV₁, who had higher FEFs levels than those with more severe symptoms.^5,30–32

One main limitation of FEF is that it depends on FVC and lung capacity.^33,34 In contrast to FEV₁, FEF_25%-75% is not normalized to FVC when assessing air-flow obstruction. Therefore, FEF_25%-75% could be artificially low in individuals with restrictive lung or chest bellows disease (eg, obesity) and could therefore overdiagnose asthma. In our study, mean BMI and FVC were in the normal range (23.37 ± 3.477 kg/m² and 101.5% ± 12%, respectively), and neither variable differed between groups.^34,35 Most importantly, all patients in the study had undergone HRCT, therefore guaranteeing that restrictive lung diseases or obesity were excluded and minimizing the possibility of overdiagnosis of asthma in our patients. Furthermore, IOS is a noninvasive and effort-independent alternate test that is done during normal quiet tidal breathing, avoiding the need for any special breathing manoeuvre or any noticeable interference with respiration, and is considered to be more physiological than spirometry^36,37 We evaluated the ability of IOS measurement to assess small-airway function and to predict AHR. In our study, R5-R20 alone exhibited poor predictive value for AHR diagnosis, and the AUC was still lower than 0.80 when we combined R5-R20 and FENO. This finding suggests that when combined with FENO, FEFs provide better value than R5-R20 for predicting AHR even though they are less physiologically relevant.

Chest tightness is a symptom of asthma that more likely reflects muscle tightness or physical difficulty with moving air that is sensed through proprioception and not through pain pathways.^38–40 In asthmatic patients with normal FEV₁ in our study, the most frequent chief complaints were cough and chest tightness rather than wheeze or dyspnea. Relevant clinical subtypes of asthma, “chest tightness variant asthma”⁴¹ and “chest pain variant asthma”^38,42 have been described in the medical literature. Asthmatic patients who only complain of chest tightness are easily misdiagnosed in clinical practice. We found that decreases of FEV₁, FEV₁/FVC, FEF_25%, FEF_50%, FEF_75%, and FEF_25%-75% were more serious in subjects with than without chest tightness, indicating that small-airway dysfunction may be involved in the mechanism of chest tightness. The joint model of small-airway function variables (FEF_50%, FEF_75%, or FEF_25%-75%) and FENO gave particularly high predictive values for AHR in subjects with chest tightness (all of the AUCs ≥ 0.880). In addition, the joint model of EOS with small-airway function variables (FEF_50%, FEF_75%, or FEF_25%-75%) was highly predictive of AHR in subjects with chest tightness (all of the AUCs ≥ 0.815), which it was not for the population as a whole. Since the cost of peripheral blood cell count is much cheaper than FENO, these tests may provide very economic alternatives for predicting AHR in suspected asthmatics, especially in primary hospitals. The diagnosis of asthma should be strongly considered in patients with lower FEFs, high FENO or high EOS, and the symptom of chest tightness.

The progressive statistical design of this study consisted of several steps. First, the possible influencing variables were found from a Mann–Whitney test. Then, the correlation between the relevant variables and PD₂₀ was determined with Spearman analysis. Through the analysis of AUC, the predictive value of those variables was further verified. The repeatability of our data calculation was shown by an 80/20 split-sample cross-validation. The AUCs in the validation sample were close to those in the whole model.

Some limitations also exist in our current study. Firstly, allergic rhinitis and atopy status were not described in our study, which might influence the diagnostic value of FENO and EOS for BHR. Further comprehensive studies are needed to focus on this issue. Secondly, a larger-scale representative sample of CTVA should be collected to confirm our results. Thirdly, PEF variability measurement needs to be conducted in further prospective study. To overcome the limitations of our current study, larger-scale and multicenter prospective clinical trials should be performed to ensure the integrity of the inspection results.

In conclusion, asthmatic patients suffer from small-airway dysfunction, even though their FEV₁ is within the normal range. Patients with small-airway dysfunction exhibit an increased likelihood of having AHR. In order to improve the diagnosis rate of mild asthma and relieve patients’ symptoms as early as possible, we combined 2 simple and noninvasive methods—small-airway function tests and FENO to improve the diagnosis rate of mild asthma. The likelihood of AHR strongly increased with FEF_25%-75% <84.4%, FEF_50% <76.8%, and FENO >41 ppb. FEV_25%-75% and FEF_50%, derived from spirometry, could be combined with FENO to support asthma diagnosis in patients with normal FEV₁ and symptoms suggestive of asthma, allowing the patient to forego MCT for the diagnosis. FEF_25%-75% or FEF_50% combined with EOS can also be a very economic method to predict AHR in suspected asthma subjects with chest tightness.