Skip to main content
Download PDF

Anticipating undiagnosed asthma in symptomatic adults with normal pre- and post-bronchodilator spirometry: a decision tool for bronchial challenge testing

BMC Pulmonary Medicine volume 23, Article number: 496 (2023) Cite this article

Abstract

Background

Some patients with asthma demonstrate normal spirometry and remain undiagnosed without further testing.

Objective

To determine clinical predictors of asthma in symptomatic adults with normal spirometry, and to generate a tool to help clinicians decide who should undergo bronchial challenge testing (BCT).

Methods

Using random-digit dialling and population-based case-finding, we recruited adults from the community with respiratory symptoms and no previous history of diagnosed lung disease. Participants with normal pre- and post-bronchodilator spirometry subsequently underwent BCT. Asthma was diagnosed in those with symptoms and a methacholine provocative concentration (PC20) of < 8 mg/ml. Sputum and blood eosinophils, and exhaled nitric oxide were measured. Univariate analyses identified potentially predictive variables, which were then used to construct a multivariable logistic regression model to predict asthma. Model sensitivity, specificity, and area under the receiver operating curve (AUC) were calculated.

Results

Of 132 symptomatic individuals with normal spirometry, 34 (26%) had asthma. Of those ultimately diagnosed with asthma, 33 (97%) answered ‘yes’ to a question asking whether they experienced cough, chest tightness or wheezing provoked by exercise or cold air. Other univariate predictors of asthma included female sex, pre-bronchodilator FEV1 percentage predicted, and percent positive change in FEV1 post bronchodilator. A multivariable model containing these predictive variables yielded an AUC of 0.82 (95% CI: 0.72–0.91), a sensitivity of 82%, and a specificity of 66%. The model was used to construct a nomogram to advise clinicians which patients should be prioritized for BCT.

Conclusions

Four readily available patient characteristics demonstrated a high sensitivity and AUC for predicting undiagnosed asthma in symptomatic adults with normal pre- and post-bronchodilator spirometry. These characteristics can potentially help clinicians to decide which individuals with normal spirometry should be investigated with bronchial challenge testing. However, further prospective validation of our decision tool is required.

Peer Review reports

Background

Asthma is a chronic respiratory disease estimated to affect 262 million people globally [1]. Asthma is characterized by symptoms of shortness of breath, wheezing, and coughing, often leading to frequent healthcare visits and absenteeism. A missed diagnosis may lead to non-treatment or under-treatment, with potential negative impacts on quality of life (QOL) [2].

Asthma is subject to both under- and over-diagnosis. Socioeconomic status, under-reporting of symptoms, and diagnostic insensitivity of spirometry were factors noted to affect underdiagnosis [3, 4]. While spirometry is the most accessible and frequently used test to diagnose asthma, normal values are not exclusive [5]. Schneider and colleagues examined the diagnostic ability of office spirometry and found up to 80% of symptomatic patients had no abnormalities on testing [6]. Currently, guidelines suggest alternative diagnoses or further testing if reversible airflow limitation is not demonstrated with spirometry [7]. Bronchial challenge testing (BCT) is usually the preferred method to diagnose asthma in this situation, but it is unclear how many symptomatic adults are appropriately referred.

Guidelines suggest BCT be used when there is clinical suspicion, but no airflow obstruction found with spirometry [8, 9]. An optimal diagnostic value of this test is reported to be achieved when pretest probability of asthma is 30–70% [10]. However, when Dales and colleagues asked chest physicians ordering methacholine BCTs to predict airway hyperresponsiveness based on clinical gestalt, they found no association between predictions and actual test results [11]. A study assessing the pattern of BCT practices in Canada found large variations across the provinces [12], suggesting that BCT referral is often subject to the clinician’s judgement.

Recent studies have assessed potential variables associated with BCT results consistent with asthma [13, 14]. These studies were limited to retrospective chart analyses and assessment of demographic differences via univariate analyses. There have been no prospective studies that help to predict BCT results.

The objective of this study was to find predictors of asthma in symptomatic adults with normal pre- and post-bronchodilator spirometry, and to generate a tool to help clinicians prioritize who should undergo BCT.

Materials and methods

Study design

This was a prospective, multicenter sub-study of subjects enrolled in the larger Undiagnosed COPD and Asthma Population (UCAP) case-finding study, performed in five Canadian respiratory centers in accordance with Strengthening the Reporting of Observational Studies in Epidemiology guidelines [15]. Full details of the larger case-finding study [16, 17] and this sub-study cohort [18] have been published previously. Approval from each local ethics committee was obtained. All subjects signed written, informed consent.

Study participants

Participants to this sub-study were recruited from a large case-finding study aiming to identify asthma or COPD in symptomatic Canadian adults without prior history of diagnosed airway disease [17]. Adults ≥18-years-old, who had no history of previously diagnosed lung or airway disease, were recruited in a two-step process. In the first step, landlines and cellphones within a 90-minute radius of each study site from June 2017 to March 2020 were random-digit dialled. A scripted message questioned if anyone in the household was 18 years or older and had respiratory symptoms (shortness of breath, wheezing, increased mucus or sputum, and/or prolonged cough). If the response was affirmative, they received a call back from the local study coordinator, who then consented and screened the symptomatic individual for study entry. Individuals were screened using the Asthma Screening Questionnaire (ASQ) [19] and the COPD Diagnostic Questionnaire (COPD-DQ) [20]. Symptomatic participants who scored ≥6 points on the ASQ or > 19.5 points on the COPD-DQ were invited to the study site.

At the study site, participants provided written consent and underwent pre- and post-bronchodilator spirometry to determine whether they had airflow obstruction or a significant bronchodilator response. We defined airflow obstruction on spirometry as any of the following criteria: 1) pre-bronchodilator forced expiratory volume in 1 second (FEV1) ≤80%, or 2) pre-bronchodilator FEV1/forced vital capacity (FVC) ≤0.70 or ≤ LLN. We defined a significant bronchodilator response as FEV1 response to 400 mcg inhaled salbutamol ≥12% and ≥ 200 mL. Subjects who did not show evidence of airflow obstruction or a significant bronchodilator response were offered participation in the sub-study. Pregnant or lactating women, participants who had experienced a respiratory tract infection in the 4 weeks preceding the study visit, or those with medical contraindications to methacholine were excluded [21]. Participants who had used inhaled corticosteroids, or systemic immune-suppressive or immune-modulatory drug therapy within 3 months prior to study visit, or antibiotics in the 4 weeks preceding study visit were also excluded. Those subjects who met eligibility criteria for the sub-study returned for a second visit where they underwent measurement of FeNO, methacholine BCT, blood testing, and induced sputum collection.

Outcomes

The primary study outcome was a positive finding of asthma. Based on ERS 2022 guidelines for diagnosis of asthma in adults who are steroid-naïve [5], those subjects with respiratory symptoms who demonstrated a 20% fall in FEV1 produced by a methacholine provocation concentration (PC20) < 8 mg/mL were considered in this study to have asthma [21]. The secondary outcome was airway hyperresponsiveness (AHR), defined as a PC20 of < 16 mg/mL [22].

Data collection

Sociodemographic data, occupational history, comorbid medical history, smoking history, data on healthcare utilization, work/school absenteeism, medication use, family history of asthma, and atopy were collected from all participants. Validated questionnaires were administered to assess symptoms and QOL scores, including: the COPD Assessment Test (CAT) [23], and the St. George’s Respiratory Questionnaire (SGRQ) [24]. For this sub-study, all participants also completed the Asthma Control Questionnaire (ACQ) [25], the Leicester Cough Questionnaire (LCQ) [26], and a questionnaire adapted from the European Community Respiratory Health Survey (ECRHS III) [18, 27]. Blood tests were performed to assess white blood cell count and differential, particularly for eosinophils, as well as total IgE. Methods based on the work of Pizzichini and colleagues were used to perform induced sputum analysis [28]. Slides were sent to one of the participating sites and sputum differential cell counts were performed by an experienced lab technician blinded to the clinical characteristics of participants.

Allergen testing was performed with skin prick tests utilizing a battery of common aeroallergens, with normal saline and histamine as negative and positive controls, respectively. Atopy was defined as a skin wheal diameter > 3 mm to any allergen. In accordance with American Thoracic Society (ATS) guidelines [29], Fractional exhaled Nitric Oxide (FeNO) measurements were made using a NiOX Mino handheld analyzer (Circassia, Morrisville, NC, USA) before any airflow measurements were performed.

Pre- and post-bronchodilator spirometry was performed according to ATS standards [30] by certified study personnel. The FEV1 predicted values and percentage predicted values were calculated using the Global Initiative (GLI-2012) lung function calculator [31]. Methacholine BCT was performed using the tidal breathing method [9, 22].

Statistical analysis

Baseline demographic characteristics of participants were summarized using frequency distributions for categorical variables and arithmetic means with standard deviations for quantitative variables. Univariate statistical comparisons were made between subjects with asthma and those without. Chi-square tests were utilized to evaluate the association of the asthma outcome with categorical variables. Logistic regression was used to evaluate the association with quantitative variables. The same analyses were performed for comparisons of AHR. Differences were considered significant and retained for further analysis if univariate tests produced P-values < 0.05. Univariate statistical comparisons as well as judgments about clinical relevance were used to select a candidate pool of potentially important decision variables. Goodness of fit for logistic regression models was tested using the Hosmer-Lemeshow test. Receiver Operating Characteristic (ROC) curve analyses, including Area Under the Curve (AUC), were conducted to evaluate, compare, and select a final decision model. Several risk cutoffs for the final decision model were selected to compare model performance in terms of sensitivities and specificities. A simple nomogram was constructed to aid a clinician contemplating referral of a patient for BCT. STATA version 17.0 (StataCorp, College Station, TX, USA), was used to complete all statistical analyses.

The strategy for risk factor selection

We chose a strategy for selecting risk factors for the decision tool that relies heavily on clinical judgment and real-world clinical tests available to practicing clinicians. We decided a priori to limit factor selection to variables readily available in a physician’s practice including: patients’ sociodemographic variables, medical history, medications, respiratory symptom and QOL questionnaires, pre- and post-BD spirometry, with consideration of blood eosinophils and FeNO tests if they proved to be highly important. Clinical considerations primarily guided variable selection, with supplementation by statistical techniques to take variable correlations into account.

Results

Study population

The flow of study participants is exhibited in Fig. 1. A total of 275 potentially eligible subjects from the UCAP Study were invited to participate in this sub-study, and 136 provided informed consent for enrolment. Of the 136 consented participants, 4 were excluded because they could not complete BCT. Thus, 132 subjects were included in the analyses.

Fig. 1
figure 1

Flow of study participants

Full size image

Factors associated with asthma

Among the 132 study subjects, 34 (26%; 95% CI 19–34%) had asthma, defined by PC20 of < 8 mg/mL. Baseline subject characteristics are exhibited in Table 1, classified by the BCT outcome. A higher proportion of subjects with asthma were female compared to those who tested negative for asthma: 74% (95% CI 56–87%) versus 44% (95% CI 34–54%), P = 0.003. There were no significant differences between the two groups when characteristics of age, BMI, smoking history, eczema, atopy, and mean number of allergens were compared. A higher proportion of subjects with asthma had previously used salbutamol for symptoms: 29% (95% CI 15–47%) versus 8% (95% CI 4–15%), P = 0.002.

Table 1 Subject characteristics
Full size table

Average pre-bronchodilator FEV1% predicted was notably lower in subjects with asthma 97% (95% CI 94–100%) compared to those without 104% (95% CI 101–106%), P = 0.007. Participants with asthma demonstrated a greater mean FEV1 response to bronchodilator 5% (95% CI 3–7%) than the no-asthma group 2% (95% CI 1–3%), P = 0.004. The European Respiratory Society (ERS) recently recommended assessment of percentage bronchodilator response using post-bronchodilator FEV1 minus pre-bronchodilator FEV1, divided by the FEV1 predicted value determined using the appropriate Global Lung Function Initiative (GLI) spirometry equation [30, 32]. There was a significantly higher bronchodilator response using the ERS calculation in the asthma group compared with the no-asthma group, with means of 4% (95% CI 3–5%) and 2% (95% CI 1–3%), respectively, P = 0.018.

While all 132 patients were able to complete blood tests, 122 (92%) completed FeNO testing, and only 86 (65%) were able to provide induced sputum for analysis. FeNO, blood and sputum eosinophils were not significantly higher among individuals with asthma. Although no significant differences were noted for total scores on CAT, SGRQ, ACQ5, and LCQ questionnaires, a significantly higher proportion of asthma subjects answered ‘yes’ to a single Global Asthma Symptom (GAS) question reading “When you exercise, work hard physically, or when you inhale cold and dry air during the winter, do you ever cough, feel tightness in your chest, have wheezing or whistling or start to be out of breath?” In the asthma group, 97% (95% CI 85–100%) answered ‘yes’ to this question, versus 74% (95% CI 65–83%) in the no-asthma group, P = 0.004.

Decision model for bronchial challenge testing

Eight clinically important variables were selected to analyze in a multivariable model. These variables were: female sex, age, pre-bronchodilator FEV1% predicted, FEV1% response post-bronchodilator (defined by both traditional and ERS calculations), prior salbutamol use, absolute blood eosinophil count, and GAS question response.

Only 26 of 132 subjects answered ‘no’ to the GAS question. Only 1 subject of the 26 (3.8%) was found to have asthma. Given this, we reasoned that a ‘no’ answer to the GAS question was a reliable screen to exclude asthma. We thus confined our multivariable analysis to the 106 subjects who answered ‘yes’ to the GAS question. Estimation of a multivariate logistic model containing the remaining 7 variables revealed that 4 of the variables (FEV1% response post-bronchodilator based on the ERS calculation, prior salbutamol use, age, and absolute blood eosinophil count) could be eliminated because they did not contribute appreciably to model performance (likelihood ratio test P = 0.634). There were no missing data for the remaining variables. The ultimate model (Table 2) for subjects answering ‘yes’ to the GAS question contained the following 3 variables; sex, pre-bronchodilator FEV1% predicted, and the percent change in FEV1 post-bronchodilator. All three variables made strong individual contributions to model performance, as confirmed by their small P-values. A Hosmer-Lemeshow test of this model using 10 quantile groups confirmed an acceptable fit (P = 0.37).

Table 2 Multivariable analysis of risk factors associated with Asthma for subjects responding “Yes” to the GAS question a
Full size table

The final analysis was based on results in the two subgroups, the group who answered ‘no’ to the GAS question with 26 subjects and the group who answered ‘yes’ with 106 subjects. When the decision rules for the two subgroups are merged, the model’s ROC curve reflects the composite decision results of the two subgroups and is presented in Fig. 2, where its AUC is shown as 0.82 (95% CI: 0.72–0.91).

Fig. 2
figure 2

ROC curve for the asthma prediction model

Full size image

To select a risk cutoff for this decision model we considered that a methacholine BCT is costly to the patient and the healthcare system both in direct test costs and indirect lost-time costs of patients undergoing further testing [1,2,3,4]. However, given the lifelong morbidity costs associated with undiagnosed/untreated asthma, the cost of a missed diagnosis was deemed to outweigh that of a test used to rule out this diagnosis. We calculated sensitivities and specificities of several risk cutoffs, ranging from 10 to 40% (Table 3). A 20% risk of asthma was selected as having the best balance of sensitivity and specificity. Table 4 presents a 2 × 2 cross-classification of predictions and true disease states for this decision model, incorporating results from both subgroups of subjects. The table shows the 20% risk cutoff has 82% sensitivity and 66% specificity. The table also shows that 46% of subjects are predicted by the model to have asthma, implying that slightly less than half of patients with normal spirometry would be referred for BCT if this model and the 20% risk cutoff were used in clinical practice.

Table 3 Sensitivity and specificity of the asthma prediction model at varying risk cut-offs for asthma
Full size table
Table 4 Classification table of predicted and true disease at a risk cut-off of 20% for asthma
Full size table

Use in clinical practice

Figure 3 depicts a nomogram constructed to help determine which symptomatic patients with normal spirometry should be prioritized for BCT. Those patients who answer ‘yes’ to the GAS question and have lung function coordinates that lie above the diagonal line matching their sex would be predicted by the model to have a 20% or higher probability of asthma, and BCT referral should be considered. An example of a study participant is included to illustrate how to use the nomogram.

Fig. 3
figure 3

The bronchial challenge test referral nomogram

Full size image

Secondary analysis for airway hyperresponsiveness

Among the 132 study subjects, 47 (36%; 95% CI 27–44%) had AHR, defined by PC20 of < 16 mg/mL. Baseline characteristics of subjects with AHR and those without AHR are shown in S1 Table. Similar to the subgroups distinguishing asthma diagnosis, female sex, absolute pre-bronchodilator FEV1 values, pre-bronchodilator FEV1% predicted, and FEV1% reversibility post-bronchodilator were predictive of AHR. It was noted that the mean blood absolute eosinophil count was marginally higher in the AHR subgroup 211 cells/mm3 (95% CI 160–263) compared to the no AHR subgroup 160 cells/mm3 (95% CI 137–183), P = 0.049. However, sputum eosinophils and FeNO were not predictive of AHR.

A multivariable logistic model (S2 Table) was constructed in a similar fashion as was done previously for asthma. Unlike the asthma decision model, the GAS question was not dominant in predicting AHR, although its impact is statistically important. Specifically, the AHR multivariable model includes a statistically significant interaction variable for the FEV1 response to bronchodilator and the response to the GAS question. The interaction variable captures how the impact of the FEV1 response variable on AHR risk depends on the response to the GAS question. The strong positive effect is shown by the odds ratio for the interaction variable (OR = 1.367, P = 0.003). S3 Table shows the model’s sensitivities and specificities at several risk cutoffs. The 2 × 2 table cross-classifying predictions and true disease states for this model is shown in S4 Table. The table shows the 20% risk cutoff has 89% sensitivity and 42% specificity. The AUC is 0.79 (95% CI: 0.70–0.88), (S1 Figure).

Discussion

This study explored various clinical and laboratory characteristics of subjects in a population with unexplained respiratory symptoms to help recognize those who may have asthma even when their spirometry results are normal. In this population, 26% had a BCT result in keeping with asthma. We found that a single question exploring respiratory symptoms associated with exertion and cold weather was highly predictive in ruling out potential asthma. When response to this one question was combined with three potential risk factors (sex, pre-bronchodilator FEV1% predicted, and FEV1% response to bronchodilator), a resultant multivariable decision model had high sensitivity for predicting asthma. Use of this simple decision tool can identify patients with normal spirometry whose probability of asthma is ≥20%. Our suggestion is to prioritize these patients for further investigations, including subsequent BCT. Because we are considering adults with significant respiratory symptoms, all patients merit further investigation even if they are not chosen for BCT or their test outcome is negative.

Previous studies have shown that spirometry alone is relatively insensitive to confirm a diagnosis of asthma [3,4,5,6]. Louis and colleagues determined in their retrospective study comparing bronchodilator reversibility with methacholine bronchoprovocation testing that while baseline airway obstruction may be predictive of reversibility, it was not predictive of airway hyperresponsiveness [33]. As Busse highlights in his discussion regarding diagnostic approach to asthma, normal baseline lung function does not rule out the diagnosis of asthma, and further testing such as with bronchoprovocation may provide useful clinical information [34]. Despite recommendations for further testing in symptomatic individuals with normal spirometry [5, 35], there is a lack of data regarding which individuals should undergo BCT when spirometry is normal. Our study helps clarify which symptomatic individuals are most likely to test positive for asthma using BCT.

Several recent studies have assessed potential predictors of asthma in symptomatic individuals with normal spirometry. Nickels performed a retrospective chart review of 1322 adults with non-obstructive spirometry results to assess if FeNO testing is predictive of a positive methacholine BCT, which did not reveal any statistically significant findings [36]. Chevrier conducted a retrospective chart review of 1126 subjects with normal spirometry results who underwent methacholine BCT [14]. The authors identified younger age, female sex, body mass index (BMI) > 40, and symptoms of wheezing as factors associated with an abnormal BCT of PC20 < 16 mg/mL. Peled and colleagues identified in their retrospective analysis that all baseline spirometry values were significantly lower in the group with airway hyperresponsiveness and highlighted the potential role of the forced mid-expiratory flow rate (FEF50%) of predicting AHR [37]. Selvanathan and colleagues, in a large cohort study, assessed the proportion of adults with a positive BCT among those with self-reported physician-diagnosed asthma and a negative bronchodilator response. They found that 43% had a positive BCT. They describe baseline airflow limitation as being predictive of a positive BCT and that the negative predictive value of spirometry with bronchodilator response for ruling out asthma is low [38].

This is the first prospective study to determine useful clinical predictors of asthma in symptomatic subjects with non-obstructive spirometry results. These subjects were a representative sample of individuals selected randomly from the population who reported no prior asthma or airway disease history. Our aim was to provide a decision-making aid for the clinician regarding BCT referral to avoid possible detrimental delays in diagnoses and treatment, as there is evidence that individuals may incur loss in pulmonary function with untreated mild asthma over time [39, 40]. The variability of BCT referral across regions [12] suggests that clinicians may benefit from a standard tool. This tool recognizes that a BCT may be costly [12, 41], but a misdiagnosis of asthma may produce higher costs for the patient and healthcare system [42, 43]. A standard tool may reduce inappropriate referrals, encourage more timely and accurate diagnoses, and in turn potentially diminish costs.

The four factors in our model (sex, FEV1% predicted, FEV1% response to bronchodilator, and self-reported respiratory symptoms associated with exertion and/or cold weather) are readily accessible in a primary care practice that has access to spirometry testing. These factors are universal and are well known to be associated with asthma widely. Asthma is more prevalent in female adults than male adults [44, 45], and FEV1% predicted, FEV1% response to bronchodilator, and symptoms associated with exertion and/or cold weather are universally utilized for asthma diagnosis and monitoring [32, 46,47,48]. Our decision tool is easy to utilize in various clinical settings, which could reduce delays in testing and diagnosis. Although FeNO, blood and sputum eosinophils were assessed, these laboratory-based variables were not significant discriminators of asthma in our analyses.

This study identified female sex as a strong predictor for asthma. It is well known that male children are more likely to have asthma than female children, however the opposite is true for adults [41, 42]. Although the pathophysiology is not completely understood, there are many studies characterizing genomic differences and the physiologic hormonal milieu that may impact bronchial response [49, 50]. Of note, we found in our study that FVC and height were comparatively lower in subjects who had asthma compared to those who did not. Physiologically, FVC, height, and female sex are strongly correlated as females have smaller lung volumes than men on average. Given that the BCT measures the methacholine concentration required to cause a 20% fall in FEV1 compared to baseline, the question arose as to whether women are more likely to test positive for asthma because of a smaller volume change needed to achieve a 20% decline. This question has been posed and evaluated in multiple studies previously [51]. Leynaert and colleagues tested this hypothesis with multiple analyses adjusting for lung function parameters and concluded that the higher proportion of AHR in women could not be accounted for by their lung volumes alone [52].

Our study has the following limitations. Although sputum variables may be clinically relevant, only 86 of the 132 participants were able to produce induced sputum samples. This variable was not significant in the univariate analysis and was not included in the multivariable model. Our decision model is developed for methacholine challenge testing and therefore may not be reliable for other methods of bronchial provocation. Because of constraints imposed on recruitment and bronchial provocation testing by the COVID19 pandemic our study was halted in March 2020, and our sample size of 132 individuals was not large enough for both model derivation and validation. A further independent study is required to validate the model we have developed. Finally, our recruitment strategy relied on patient-reported symptoms. Individuals with asthma who under-reported or minimized their respiratory symptoms would not have been diagnosed using our methodology.

Conclusion

This study demonstrated that in a population of subjects with respiratory symptoms suggestive of asthma but with normal pre- and post-bronchodilator spirometry, 26% had asthma when they proceeded to BCT. Predictors of asthma included having respiratory symptoms associated with exertion and/or cold weather, female sex, relatively lower pre-bronchodilator FEV1% predicted, and greater FEV1 response to bronchodilator. A decision model generated from these variables had a high sensitivity and AUC. Our study may help clinicians recognize which subjects with respiratory symptoms who have normal spirometry should be referred for further investigation with BCT.

Availability of data and materials

All data generated or analysed during this study are included in this published article and its supplementary information files.

References

  1. Vos T, Lim SS, Abbafati C, et al. Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the global burden of disease study 2019. Lancet. 2020;396:1204–22.

    Article  Google Scholar 

  2. Nurmagambetov T, Kuwahara R, Garbe P. The economic burden of asthma in the United States, 2008-2013. Ann Am Thorac Soc. 2018;15(3):348–56.

    Article  PubMed  Google Scholar 

  3. Aaron SD, Vandemheen KL, FitzGerald JM, et al. Canadian respiratory research network. Reevaluation of diagnosis in adults with physician-diagnosed asthma. JAMA. 2017;317:269–79.

    Article  PubMed  Google Scholar 

  4. Aaron SD, Boulet LP, Reddel HK, Gershon AS. Underdiagnosis and Overdiagnosis of asthma. Am J Respir Crit Care Med. 2018;198(8):1012–20.

    Article  PubMed  Google Scholar 

  5. Louis R, Satia I, Ojanguren I, et al. European Respiratory Society guidelines for the diagnosis of asthma in adults [published online ahead of print, 2022 Feb 15]. Eur Respir J. 2022:2101585. https://doi.org/10.1183/13993003.01585-2021.

  6. Schneider A, Gindner L, Tilemann L, et al. Diagnostic accuracy of spirometry in primary care. BMC Pulm Med. 2009;9:31.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Reddel HK, Bacharier LB, Bateman ED, et al. Global initiative for asthma strategy 2021: executive summary and rationale for key changes. Eur Respir J. 2021;59(1):2102730. https://doi.org/10.1183/13993003.02730-2021.

    Article  CAS  PubMed  Google Scholar 

  8. Pepys J, Hutchcroft BJ. Bronchial provocation tests in etiologic diagnosis and analysis of asthma. Am Rev Respir Dis. 1975;112(6):829–59.

    CAS  PubMed  Google Scholar 

  9. Crapo R, Casaburi R, Coates A, et al. Guidelines for methacholine and exercise challenge testing-1999. This official statement of the American Thoracic Society was adopted by the ATS Board of directors, July 1999. Am J Respir Crit Care Med. 2000;161(1):309–29.

    Article  CAS  PubMed  Google Scholar 

  10. Perpiñá M, Pellicer C, de Diego A, Compte L, Macián V. Diagnostic value of the bronchial provocation test with methacholine in asthma. A Bayesian analysis approach. Chest. 1993;104(1):149–54.

    Article  PubMed  Google Scholar 

  11. Dales RE, Nunes F, Partyka D, Ernst P. Clinical prediction of airways hyperresponsiveness. Chest. 1988;93(5):984–6.

    Article  CAS  PubMed  Google Scholar 

  12. Thériault R, Raz A. Patterns of bronchial challenge testing in Canada. CJRT. 2018;54(2):41–7. https://doi.org/10.29390/cjrt-2018-006.

    Article  Google Scholar 

  13. Chen A, D’Urzo KA, D’Urzo AD. Airway hyperresponsiveness in patients with normal spirometry results and symptoms compatible with asthma: primary care retrospective chart review. Can Fam Physician. 2021;67(3):e84–9. https://doi.org/10.46747/cfp.6703e84.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Chevrier S, Abdulnour J, Saint-Pierre MD. Predictors of methacholine challenge testing results in subjects without airflow obstruction. J Asthma. 2022;59(10):2060–8. https://doi.org/10.1080/02770903.2021.1986838.

    Article  CAS  PubMed  Google Scholar 

  15. von Elm E, Altman DG, Egger M, et al. The strengthening of the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet. 2007;370(9596):1453–7. https://doi.org/10.1016/S0140-6736(07)61602-X.

    Article  Google Scholar 

  16. Cherian M, Magner KMA, Whitmore GA, et al. Patient and physician factors associated with symptomatic undiagnosed asthma or COPD. Eur Respir J. 2022:2201721. https://doi.org/10.1183/13993003.01721-2022.

  17. Preteroti M, Whitmore GA, Vandemheen KL, et al. Population-based case-finding to identify subjects with undiagnosed asthma or COPD. Eur Respir J. 2020;55(6):2000024.

    Article  PubMed  Google Scholar 

  18. Boulet LP, Boulay M, Cote A, et al. Airway inflammation and hyperresponsiveness in subjects with respiratory symptoms and normal spirometry. European Resp J. https://doi.org/10.1183/13993003.01194-2022. Online ahead of print

  19. Shin B, Cole SL, Park SJ, Ledford DK, Lockey RF. A new symptom-based questionnaire for predicting the presence of asthma. J Investig Allergol Clin Immunol. 2010;20(1):27–34.

    CAS  PubMed  Google Scholar 

  20. Price DB, Tinkelman DG, Halbert RJ, et al. Symptom-based questionnaire for identifying chronic obstructive pulmonary disease in smokers. Respiration. 2006;73:285–95.

    Article  PubMed  Google Scholar 

  21. Hallstrand TS, Leuppi JD, Joos G, et al. American Thoracic Society (ATS)/European Respiratory Society (ERS) Bronchoprovocation testing task force. ERS technical standard on bronchial challenge testing: pathophysiology and methodology of indirect airway challenge testing. Eur Respir J. 2018;52(5)

  22. Coates AL, Wanger J, Cockcroft DW, et al. ERS technical standard on bronchial challenge testing: general considerations and performance of methacholine challenge tests. Eur Respir J. 2017;49(5):1601526.

    Article  PubMed  Google Scholar 

  23. Jones PW, Harding G, Berry P, Wiklund I, Chen WH, Kline LN. Development and first validation of the COPD assessment test. Eur Respir J. 2009;34(3):648–54.

    Article  CAS  PubMed  Google Scholar 

  24. Jones PW, Quirk FH, Baveystock CM. The St George's respiratory questionnaire. Respir Med. 1991;85(Suppl B):25–37. https://doi.org/10.1016/s0954-6111(06)80166-6.

    Article  PubMed  Google Scholar 

  25. Juniper EF, O'Byrne PM, Guyatt GH, Ferrie PJ, King DR. Development and validation of a questionnaire to measure asthma control. Eur Respir J. 1999;14(4):902–7.

    Article  CAS  PubMed  Google Scholar 

  26. Birring SS, Prudon B, Carr AJ, Singh SJ, Morgan MD, Pavord ID. Development of a symptom specific health status measure for patients with chronic cough: Leicester cough questionnaire (LCQ). Thorax. 2003;58:339–43.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Burney PG, Luczynska C, Chinn S, Jarvis D. The European Community respiratory health survey. Eur Res J. 1994;7(5):954–60.

    Article  CAS  Google Scholar 

  28. Pizzichini E, Pizzichini MM, Efthimiadis A, Hargreave FE, Dolovich J. Measurement of inflammatory indices in induced sputum: effects of selection of sputum to minimize salivary contamination. Eur Respir J. 1996;9:1174–80.

    Article  CAS  PubMed  Google Scholar 

  29. Dweik RA, Boggs PB, Erzurum SC, et al. An official ATS clinical practice guideline: interpretation of exhaled nitric oxide levels (FENO) for clinical applications. Am J Respir Crit Care Med. 2011;184:602–15.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Miller MR, Hankinson J, Brusasco V, et al. Standardisation of spirometry. Eur Respir J. 2005;26:319–38.

    Article  CAS  PubMed  Google Scholar 

  31. Quanjer PH, Stanojevic S, Cole TJ, et al. Multi-ethnic reference values for spirometry for the 3-95-yr age range: the global lung function 2012 equations. Eur Respir J. 2012;40:1324–43.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Stanojevic S, Kaminsky DA, Miller MR, et al. ERS/ATS technical standard on interpretive strategies for routine lung function tests. Eur Respir J. 2022;60(1):2101499. https://doi.org/10.1183/13993003.01499-2021.

    Article  PubMed  Google Scholar 

  33. Louis R, Bougard N, Guissard F, Paulus V, Henket M, Schleich F. Bronchodilation test with inhaled salbutamol versus bronchial Methacholine challenge to make an asthma diagnosis: do they provide the same information? J Allergy Clin Immunol Pract. 2020;8(2):618–625.e8. https://doi.org/10.1016/j.jaip.2019.09.007.

    Article  PubMed  Google Scholar 

  34. Busse WW. What is the best pulmonary diagnostic approach for wheezing patients with normal spirometry? Respir Care. 2012;57(1):39–49. https://doi.org/10.4187/respcare.01449.

    Article  PubMed  Google Scholar 

  35. Chung KF, Wenzel SE, Brozek JL, et al. International ERS/ATS guidelines on definition, evaluation and treatment of severe asthma. Eur Respir J. 2014;43(2):343–73.

    Article  CAS  PubMed  Google Scholar 

  36. Nickels AS, Lim KG. Evaluation of exhaled nitric oxide's ability to predict methacholine challenge in adults with nonobstructive spirometry. Ann Allergy Asthma Immunol. 2016;117(4):365–369.e1. https://doi.org/10.1016/j.anai.2016.07.032.

    Article  CAS  PubMed  Google Scholar 

  37. Peled M, Ovadya D, Cohn J, et al. Baseline spirometry parameters as predictors of airway hyperreactivity in adults with suspected asthma. BMC Pulm Med. 2021;21(1):–153. https://doi.org/10.1186/s12890-021-01506-6.

  38. Selvanathan J, Aaron SD, Sykes JR, et al. Performance characteristics of spirometry with negative bronchodilator response and Methacholine challenge testing and implications for asthma diagnosis. Chest. 2020;158(2):479–90. https://doi.org/10.1016/j.chest.2020.03.052.

    Article  CAS  PubMed  Google Scholar 

  39. Reddel HK, Busse WW, Pedersen S, et al. Should recommendations about starting inhaled corticosteroid treatment for mild asthma be based on symptom frequency: a post-hoc efficacy analysis of the START study. Lancet. 2017;389:157–66.

    Article  PubMed  Google Scholar 

  40. Haahtela T, Jarvinen M, Kava T, et al. Comparison of a beta 2- agonist, terbutaline, with an inhaled corticosteroid, budesonide, in newly detected asthma. N Engl J Med. 1991;325:388–92.

    Article  CAS  PubMed  Google Scholar 

  41. Kennedy WA, Girard F, Chaboillez S, et al. Cost-effectiveness of various diagnostic approaches for occupational asthma. Can Respir J. 2007;14(5):276–80. https://doi.org/10.1155/2007/206519.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Ng B, Sadatsafavi M, Safari A, FitzGerald JM, Johnson KM. Direct costs of overdiagnosed asthma: a longitudinal, population-based cohort study in British Columbia, Canada. BMJ Open. 2019;9(11):e031306. Published 2019 Nov 7. https://doi.org/10.1136/bmjopen-2019-031306.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Pakhale S, Sumner A, Coyle D, Vandemheen K, Aaron S. (correcting) misdiagnoses of asthma: a cost effectiveness analysis. BMC Pulm Med. 2011;11:27. Published 2011 May 23. https://doi.org/10.1186/1471-2466-11-27.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Chowdhury NU, Guntur VP, Newcomb DC, et al. Sex and gender in asthma. Eur Respir Rev. 2021;30(162):210067. https://doi.org/10.1183/16000617.0067-2021.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Postma DS. Gender differences in asthma development and progression. Gend Med. 2007;4(Suppl B):S133–46. https://doi.org/10.1016/s1550-8579(07)80054-4.

    Article  PubMed  Google Scholar 

  46. Kitch BT, Paltiel AD, Kuntz KM, et al. A single measure of FEV1 is associated with risk of asthma attacks in long-term follow-up. Chest. 2004;126(6):1875–82. https://doi.org/10.1378/chest.126.6.1875.

    Article  PubMed  Google Scholar 

  47. Tweeddale PM, Alexander F, McHardy GJ. Short term variability in FEV1 and bronchodilator responsiveness in patients with obstructive ventilatory defects. Thorax. 1987;42(7):487–90. https://doi.org/10.1136/thx.42.7.487.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Brand PL, Quanjer PH, Postma DS, Kerstjens HA, Koëter GH, Dekhuijzen PN, Sluiter HJ. Interpretation of bronchodilator response in patients with obstructive airways disease. The Dutch Chronic Non-Specific Lung Disease (CNSLD) Study Group. Thorax. 1992;47(6):429–36. https://doi.org/10.1136/thx.47.6.429.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Fuseini H, Newcomb DC. Mechanisms driving gender differences in asthma. Curr Allergy Asthma Rep. 2017;17(3):19. https://doi.org/10.1007/s11882-017-0686-1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Leynaert B, Sunyer J, Garcia-Esteban R, et al. Gender differences in prevalence, diagnosis and incidence of allergic and non-allergic asthma: a population-based cohort. Thorax. 2012;67(7):625–31. https://doi.org/10.1136/thoraxjnl-2011-201249.

    Article  PubMed  Google Scholar 

  51. Kanner RE, Connett JE, Altose MD, et al. Gender difference in airway hyperresponsiveness in smokers with mild COPD. The lung health study. Am J Respir Crit Care Med. 1994;150(4):956–61.

    Article  CAS  PubMed  Google Scholar 

  52. Leynaert B, Bousquet J, Henry C, Liard R, Neukirch F. Is bronchial hyperresponsiveness more frequent in women than in men? A population-based study. Am J Respir Crit Care Med. 1997;156(5):1413–20.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The authors gratefully acknowledge the invaluable assistance of the following individuals from the study sites: Ottawa Hospital Research Institute: Taylor Poulin, Vicky Panteleakos RRT, Joanne Cassidy RRT, Susan Deveau RRT, Alicia Storey MLAT; Vancouver General Hospital: Shelley Abercromby BSc, Mary Justine Angeles, BA Health Sciences, Ravneet Mahal BSC; Hôpital du Sacré Coeur de Montréal: Simon CHaboillez MT; Institut Universitaire de Cardiologie et de Pneumologie de Québec-Université Laval: Johane Lepage RN, Joanne Milot BScN, Mylène Bertrand RT; Kingston General Hospital: Ann Taite BSc, Alison Morra BScN, Emma Bullock HBSc, Taylar Wall RRT. We also thank the study participants who gave their time and came in for the study visits.

Funding

This study was funded by The Ottawa Hospital Foundation through an anonymous donor and via a Canadian Institute of Health Research Foundation Grant (FDN#154322).

Author information

Authors and Affiliations

  1. The Ottawa Hospital Research Institute, University of Ottawa, Ottawa, Canada

    Sheojung Shin, Katherine L. Vandemheen, Gonzalo G. Alvarez, Sunita Mulpuru & Shawn D. Aaron

  2. Desautels Faculty of Management, McGill University, Montreal, Canada

    George Alex Whitmore

  3. Institut universitaire de cardiologie et de pneumologie de Québec-Université Laval, Québec, QC, Canada

    Louis-Philippe Boulet, Marie-Ève Boulay & Andréanne Côté

  4. The Lung Center, Vancouver General Hospital, Vancouver, BC, Canada

    Céline Bergeron

  5. Hôpital Sacré-Coeur, Montréal, QC, Canada

    Catherine Lemière

  6. Queen’s University, Kingston, ON, Canada

    M. Diane Lougheed

Authors
  1. Sheojung Shin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. George Alex Whitmore
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Louis-Philippe Boulet
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Marie-Ève Boulay
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Andréanne Côté
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Céline Bergeron
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Catherine Lemière
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. M. Diane Lougheed
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Katherine L. Vandemheen
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Gonzalo G. Alvarez
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Sunita Mulpuru
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Shawn D. Aaron
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors made substantial contributions to the conception and design of the work and interpretation of data, revised the work critically for important intellectual content, made final approval of the version submitted for publication and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. SS, GAW and SDA drafted the manuscript.

Corresponding author

Correspondence to Shawn D. Aaron.

Ethics declarations

Ethics approval and consent to participate

Before study initiation, this study was approved by the Ottawa Health Science Network Research Ethics Board (Reference No. 2017–0182-01H), Clinical Trials Ontario (Reference No. 1357), Vancouver Coastal Health Research Authority (Reference No. H17–03087) and Centre intégré universitaire de santé et de services sociaux du Nord-de-I’lle-de -Montreal (Reference No. MP-32-2018-1548) for the protocol, consent, study materials, study process. All participants provided informed written consent to participate and for publication.

Competing interests

SS has nothing to disclose.

GAW has nothing to disclose.

LPB has grants from Amgen, AstraZeneca, Glaxo SmithKline, Merck, Novartis, and Sanofi-Regeneron for participation to multicenter studies and research projects proposed by the investigator; royalties from UptoDate and Taylor and Francis; lecture fees from AstraZeneca, Covis, Glaxo SmithKline, Novartis, Merck and Sanofi; is chair of the Global initiative for Asthma (GINA) board of directors, president of the Global Asthma Organisation (Interasma), holder of the Laval University Chair on Knowledge Transferm prevention and Education in respiratory and Cardiovascular Health membership on the Canadian Thoracic Society Respiratory Guidelines Committee;

MEB has nothing to disclose.

AC has research grants from Glaxo SmithKline; speakers fees from AstraZeneca, Glaxo SmithKline, Valeo and Sanofi; participation to advisory boards for Glaxo SmithKline, AstraZeneca, Sanofi, Valeo.

CB reports consulting fees from Sanofi, AstraZeneca and Takeda; payments for presentations from Grifols, AstraZeneca, Sanofi and Valeo.

CL has royalties from UptoDate, consulting fees from Glaxo SmithKline, AstraZeneca and Sanofi; payments for presentations from Glaxo SmithKline, AstraZeneca and Sanofi.

MDL received grants from the Manitoba Workers Compensation Board, the Ontario Lung Association, the Ontario Thoracic Society, the Government of Ontario’s Innovation Fund, Queen’s University, European Respiratory Journal AstraZeneca and Glaxo SmithKline; payments for co-development and co-presentation of a severe asthma preparation course from the Canadian Thoracic Society and for co-development of an accredited CME module on severe asthma from MDBriefcase; participation on advisory board from AstraZeneca; membership on the Canadian Thoracic Society Asthma Clinical Assembly and Canadian Thoracic Society Asthma Clinical Assembly Steering Committee, Health Quality Ontario’s Asthma in Adults and Asthma in Children Quality Standard Advisory Committee; is past chair of the Canadian Thoracic Society Asthma Clinical Assembly, is a Canadian Thoracic Society representative on the Lung Association’s Board of Directors and a Canadian Thoracic Society representative to the European Respiratory Society.

KLV has nothing to disclose.

GGA has nothing to disclose.

SM has nothing to disclose.

SDA reports payments for lectures from AstraZeneca, Glaxo SmithKline and Sanofi; participation on advisory boards for AstraZeneca, Glaxo SmithKline, Sanofi and Covis.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: S1 Table.

Subject Characteristics. S2 Table. Multivariate analysis of risk factors associated with airway hyperresponsiveness. S3 Table. Associated sensitivity and specificity at varying risk cutoffs for airway hyperresponsiveness. S4 Table. Classification table of predicted and true disease at risk cutoff of 20% for airway hyperresponsiveness. S1 Figure. ROC curve for the prediction model for airway hyperresponsiveness.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shin, S., Whitmore, G.A., Boulet, LP. et al. Anticipating undiagnosed asthma in symptomatic adults with normal pre- and post-bronchodilator spirometry: a decision tool for bronchial challenge testing. BMC Pulm Med 23, 496 (2023). https://doi.org/10.1186/s12890-023-02806-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12890-023-02806-9

Keywords

BMC Pulmonary Medicine

ISSN: 1471-2466

Contact us