Diagnostic differentiation between asthma and COPD in primary care using lung function testing.

Asthma and COPD are defined as different disease entities, but in practice patients often show features of both diseases making it challenging for primary care clinicians to establish a correct diagnosis. We aimed to establish the added value of spirometry and more advanced lung function measurements to differentiate between asthma and COPD. A cross-sectional study in 10 Dutch general practices was performed. 532 subjects were extensively screened on respiratory symptoms and lung function. Two chest physicians assessed if asthma or COPD was present. Using multivariable logistic regression analysis we assessed the ability of three scenarios (i.e. only patient history; diagnostics available to primary care; diagnostics available only to secondary care) to differentiate between the two conditions. Receiver operator characteristics (ROC) curves and area under the curve (AUC) were calculated for each scenario, with the chest physicians' assessment as golden standard. Results showed that 84 subjects were diagnosed with asthma, 138 with COPD, and 310 with no chronic respiratory disease. In the scenario including only patient history items, ROC characteristics of the model showed an AUC of 0.84 (95% CI 0.78-0.89) for differentiation between asthma and COPD. When adding diagnostics available to primary care (i.e., pre- and postbronchodilator spirometry) AUC increased to 0.89 (95% CI 0.84-0.93; p = 0.020). When adding more advanced secondary care diagnostic tests AUC remained 0.89 (95% CI 0.85-0.94; p = 0.967). We conclude that primary care clinicians' ability to differentiate between asthma and COPD is enhanced by spirometry testing. More advanced diagnostic tests used in hospital care settings do not seem to provide a better overall diagnostic differentiation between asthma and COPD in primary care patients.

Introduction

Asthma and chronic obstructive pulmonary disease (COPD) are both common chronic respiratory diseases affecting approximately 1 in 12 people worldwide[1,2]. The two conditions are defined as different disease entities with unique pathophysiological mechanisms and characteristic clinical features[1,2]. The underlying pathophysiology in COPD is characterized predominantly by neutrophilic inflammation, whereas in asthma the inflammatory pattern is mostly due to eosinophilic inflammation[3]. Asthma typically presents with intermittent respiratory symptoms caused by airflow obstruction predominantly due to bronchial hyperresponsiveness[4]. Asthma is often presented at younger age as part of an atopic constitution, but can also be diagnosed in adulthood[1]. In contrast, COPD is a slowly progressive lung disease with patients having persistent respiratory symptoms and airflow obstruction[2]. In high-income countries like the Netherlands COPD usually presents in patients older than forty who are generally current or former smokers[2]. Patients with asthma or COPD are mostly diagnosed and managed by primary care clinicians.

Looking at the classic pathophysiological and clinical presentations, the distinction between asthma and COPD seems clear, but in clinical practice patients often show features of both diseases[5,6]. These similarities make it difficult for clinicians to distinguish between asthma and COPD[7], especially in older and more diverse patient populations encountered in primary care[8-10]. However, differentiating between the two respiratory conditions is important as they have different pharmacotherapeutic regimens. In patients with asthma, inhaled corticosteroids (ICS) are highly effective in reducing symptoms and reducing the risk of asthma-related mortality[1]. In contrast, patients with COPD respond poorly to ICS and are mainly treated with (long-acting) bronchodilators to relieve symptoms[2]. In addition to this, misdiagnosing asthma for COPD could lead to serious health risks considering that monotherapy with long-acting bronchodilators is contra-indicated in asthmatics since it increases the risk of severe exacerbation[11-13]. On the other hand, (unnecessary) treatment with ICS may cause pneumonia and increased risk of osteoporosis[14-17].

Thus, establishing a correct diagnosis is essential for optimal treatment of asthma and COPD, but this can be challenging for primary care clinicians. Supporting them in the diagnostic process seems therefore essential, but this also depends on the availability of diagnostic tools. Although quality spirometry has shown to be feasible in primary care settings[18] there is substantial room for improvement of its use to accurately diagnose chronic respiratory diseases[19,20]. Thus, the first aim of our current study was to establish which patient characteristics distinguish between patients diagnosed with asthma or COPD. The second and main aim was to establish the added value of spirometry and more advanced lung function measurements to differentiate between these two chronic airways diseases.

Methods

Study design and population

In this observational multi-centre cross-sectional study, we compared patients diagnosed with asthma, patients diagnosed with COPD, and subjects without underlying chronic obstructive lung conditions using data from a previous study, i.e., the Detection, Intervention and Monitoring of COPD’ (DIMCA) program[21]. This program was originally set up to improve early detection of chronic airways disease in general practices. A random sample of 1,749 adult subjects (20–70 years) from ten general practices in The Netherlands were invited to participate[21]. At the start of the program, patients with pre-existing asthma, COPD or another airway disease were excluded. In 2007, ten years after the start of the initial DIMCA program, all subjects (now aged 30–80 years) received an invitation for a comprehensive respiratory assessment consisting of extensive lung function measurements and a myriad of medical history questions[22]. A total of 532 subjects agreed to participate in this follow-up study. The results of the respiratory assessment of these subjects were submitted to two experienced chest physicians who assessed if a chronic airways disease (i.e., COPD or asthma) was present or absent using a standardized protocol[22] that was based on the international clinical guideline criteria that applied at the time of the study (see below). The results of the chest physicians’ assessments were used as the golden standard in the current study.

The study was approved by the medical ethics review board CMO Regio Arnhem – Nijmegen (https://www.radboudumc.nl/over-het-radboudumc/kwaliteit-en-veiligheid/commissie-mensgebonden-onderzoek; file number: 2002/028). Participants provided written informed consent to take part in the study.

Measurements

Study participants were instructed to interrupt the use of any bronchodilators they might use for a specified number of hours before their visit to the pulmonary function laboratory. Lung function testing involved pre- and postbronchodilator spirometry (both static and dynamic) and measurement of carbon monoxide diffusion capacity (DLCO) and bronchial hyperresponsiveness (BHR)[22]. Aerosolized salbutamol 800 µg and/or ipratropium 160 µg were used as bronchodilators and were administered by volume spacer. Postbronchodilator forced expiratory volume in one second (FEV1) was measured 15 min after salbutamol and 45 min after ipratropium. Bronchodilator reversibility was defined as an increase in FEV1 after bronchodilation by at least 12% and 200 mL. BHR was assessed by histamine challenge test and considered positive in case of a >20% drop in FEV1 at a provocative dose histamine of ≤8 mg/mL (PC20)[1,23]. All lung function tests were conducted by certified lung function technicians in a hospital-based pulmonary function laboratory and were performed in accordance with the 1994 American Thoracic Society standards[24]. Predicted normal lung function values for FEV1 were calculated using European Community for Coal and Steel reference values[25]. Following lung function testing, subjects were interviewed by the lung function technician regarding respiratory symptoms, smoking behaviour, presence of allergies and eczema, respiratory problems triggered by environmental exposures, and family history of COPD or asthma[22].

Diagnostic assessment

Based on the results of the respiratory assessment the chest physicians assessed if a chronic airways disease (i.e., asthma or COPD) was present or absent using guideline criteria, their expert knowledge, and their clinical expertise[22]. Study subjects were randomly assigned to the chest physicians in a 1:1 ratio. If a subject was diagnosed with a chronic airways disease by the assigned chest physician the subject’s data was also presented to the other chest physician and a final joint diagnosis was established. To standardize the diagnostic process, a decision tree (Fig. 1) was created based on international clinical guideline criteria for diagnosing asthma (GINA guideline, 2007 update[26]) and COPD (GOLD guideline, 2006 update[27]) that applied at the time, in co-operation with the two chest physicians. In case of uncertainty about the respiratory diagnosis the chest physicians could request additional diagnostic tests (i.e., allergy skin testing, peak expiratory flow (PEF) monitoring) in order to maximize their diagnostic certainty[22]. Because the concept of asthma-COPD overlap (ACO) was introduced after the current study was conducted, the chest physicians did not consider a diagnosis of ACO as a part of their assessment. They were instructed to, based on their systematic assessment of all diagnostic information available, assign one single preferred diagnosis (i.e., either asthma or COPD) that fitted best according to their expert opinion. Figure 2 illustrates the spectrum of chronic obstructive airways disease diagnoses and the parts of the spectrum on which the current study focuses. Strictly for the purpose of describing the study population and its diagnostic subgroups (see Table 1) the Global Lung function Initiative (GLI) reference equations were applied at the time of the data analysis for the current paper[28].

Fig. 1

Decision tree used by the chest physicians to support their assessment of chronic lung disease diagnoses based on GOLD and GINA guidelines[22].

#Postbronchodilator forced expiratory volume. +Postbronchodilator vital capacity. *12% change in FEV1 (after bronchodilation), with a change of at least 200 mL. ~ Bronchial hyperresponsiveness (positive at a provocative histamine concentration ≤ 8mg/mL). @Skin prick test.

Fig. 2

Schematic illustration of the spectrum of chronic obstructive airways disease diagnoses.

The current study focusses on the parts to the left and right of the vertical dotted lines as indicated by the arrows. ACO asthma-COPD overlap, COPD chronic obstructive pulmonary disease.

Table 1

Clinical features and lung function values of patients diagnosed with asthma and patients diagnosed with COPD.

	Chronic airways disease			No chronic airways disease
	Asthma	COPD	p-value
n (% of total)	84 (16)	138 (26)		310 (58)
Demographic characteristics
Age
Mean (SD)	52.0 (11.4)	57.8 (10.0)	<0.001	54.4 (10.5)
Median (IQR)	49.8 (14.6)	57.0 (15.2)	<0.001	53.5 (14.2)
Range (youngest, oldest)	36.6 78.9	36.9 80.5		36.2, 80.7
Gender (% female, n)	59.5 (50)	44.2 (61)	0.027	56.1 (174)
BMI (mean, SD)	27.4 (4.3)	26.7 (4.0)	0.22	26.8 (4.0)
Smoking behaviour
Ever smoking (%, n)	56.0 (47)	81.0 (111)	<0.001	65.5 (203)
Current smoking (%, n)	17.9 (15)	39.1 (54)	0.001	18.1 (56)
Packyear (mean, SD)	8.9 (14.4)	21.3 (19.5)	<0.001	10.8 (14.2)
Atopy (%, n)
Ever allergya	70.2 (59)	19.6 (27)	<0.001	7.7 (24)
Ever eczema	26.2 (22)	26.1 (36)	0.99	16.1 (50)
Hyperresponsiveness (%, n)
Respiratory symptoms triggered by cold air smoke or (exhaust)fumes	71.4 (60)	59.4 (82)	0.071	22.6 (70)
Family historyb (%, n)
Asthma	19.0 (16)	15.9 (22)	0.32	11.9 (37)
COPD	29.8 (25)	36.2 (50)	0.65	17.1 (53)
Current respiratory medicationc (%, n)
Bronchodilator(s)	20.2 (17)	16.7 (23)	0.502	2 (0.6)
Inhaled corticosteroid	13.1 (11)	9.4 (13)	0.392	0
Respiratory symptoms (%, n)
Coughd	20.2 (17)	26.1 (36)	0.32	4.5 (14)
Wheezee	46.4 (39)	27.5 (39)	0.006	4.9 (17)
Phlegmf	11.9 (10)	19.6 (27)	0.14	3.9 (12)
Breathlessnessg	40.5 (34)	30.4 (42)	0.13	4.8 (15)
Spirometry:
PostBD FEV1/FVC (mean, SD)	74.2 (4.9)	63.3 (6.3)	<0.001	75.1 (8.0)
PostBD FEV1/FVC < 0.70 (%, n)	15.7 (13)	97.8 (135)	<0.001	13.6 (42)
PostBD FEV1 % predicted ECCS (mean, SD)	98.9 (13.9)	88.2 (16.2)	<0.001	107.1 (14.0)
PostBD FEV1 % predicted GLIh (mean, SD)	91.8 (16.2)	81.9 (18.2)	<0.001	98.8 (17.9)
Reversibility (%, n)
ΔFEV1 > 12% and >200 ml after BD	9.5 (8)	10.9 (15)	0.75	1.0 (3)
ΔFEV1 > 15% and >400 ml after BDi	7.2 (6)	2.9 (4)	0.18j	0 (0)
Other lung function test
RV/TLC % (mean, SD)	32.2 (8.8)	35.4 (8.1)	0.005	31.0 (6.6)
Bronchial hyperresponsivenessk (%, n)	45.2 (38)	42.8 (59)	0.68	5.5 (17)
Diffusion capacityl (mean, SD)	8.5 (2.2)	7.6 (3.0)	0.016	8.6 (2.2)

p-values are for the comparison between the two diagnostic subgroups. Data of patients with no chronic airways disease as presented in the table serve as a general reference, but were not part of the current analysis.

ECCS European Community of Coal and Steel, GINA global initiative for asthma, GLI global lung function initiative, LLN lower limit of normal based in GLI prediction equations, RV residual volume, SD standard deviation, TLC total lung capacity.

aAllergic to pollen, animals, dust mites or seasonal symptoms.

bFirst degree relatives.

cAs prescribed by the patient’s general practitioner and/or pulmonologist.

dChronic cough in winter.

eWheeze with or without breathlessness (in previous 12 months).

fPhlegm after getting out of bed (in previous 12 months).

gBreathlessness on exertion (in previous 12 months).

hBased on GLI reference equations (http://gli-calculator.ersnet.org/index.html). The % predicted FEV1 values as considered by the two chest physicians in the study were based on the 1993 ECCS reference equations. The GLI-based % predicted FEV1 values were not used by the two chest physicians.

iGINA (2021) states that confidence regarding presence of bronchodilator reversibility is greater if the increase is >15% and >400mls (1).

jFisher’s exact test because one cell had an expected count <5.

kDecrease in FEV1 by >20% at provocative dose histamine of ≤8 mg/ml (PC20).

lDiffusion capacity in mmol/kPa/mi.

Categorization of variables

In the present study, we categorized all items of the respiratory assessment in three subsections based on their availability in different healthcare settings, i.e., public health, primary care, and secondary care (Table 2). Subsection 1 consists of items that are available in any public health or healthcare setting since they require no measurements or testing equipment but only medical history questions (i.e., respiratory symptoms, smoking behaviour, body mass index (BMI)). Subsection 2 contains lung function test results that are available to primary care clinicians (i.e., spirometry and reversibility testing) in countries with well-developed healthcare systems[29-31]. Finally, Subsection 3 contains results from more advanced diagnostic tests as performed mainly in lung function laboratories in hospital care settings. These tests include measurement of static lung volumes, diffusion capacity, and histamine challenge testing.

Table 2

Categorization of variables in three subsections based on diagnostic availability and multivariable logistic regression analysis for the three scenarios.

aAge, gender and BMI.

bEver and current smoking, packyears.

cEver allergy, ever eczema.

dRespiratory symptoms triggered by cold air, smoke or (exhaust) fumes.

eFirst degree relative with asthma or COPD.

fCough, wheeze, phlegm, breathlessness.

gPostbronchodilator FEV1 and FEV1/FVC.

hResidual volume/total lung capacity.

*Not included in multivariable logistic regression as p was <0.20 in univariate analysis.

Statistical analysis

Demographic characteristics, clinical features and lung function values were univariately compared between the subgroups of patients diagnosed with asthma and COPD using independent t-tests and Chi-square tests. The further analysis focussed on assessing the ability to differentiate between these chronic obstructive lung diseases in different healthcare settings. Since physicians are not limited to asking a single medical history question or to conducting a single diagnostic test, we used multivariable logistic regression analysis to construct predictive models based on the data of the subjects who were diagnosed with asthma or COPD by the chest physicians (i.e., the binary outcome measure for this analysis was to have a diagnosis of asthma or a diagnosis of COPD). As described above, the items from the patient assessment were categorized in three subsections based on diagnostic availability and multivariable logistic regression models were run for three ‘scenarios’ (Table 2). In the first scenario, we only used the medical history items from Subsection 1 in the model. In the second scenario, we added diagnostic items available to primary care clinicians (i.e., Subsections 1 plus 2) to the model. In the third scenario, we added diagnostic items available to secondary care clinicians to the model (i.e., Subsections 1 plus 2 plus 3). Only items with a p-value ≤0.20 in the univariate analysis were considered relevant as predictors and were included in the respective models. In each scenario, the item with the highest p-value was manually removed from the model after which the logistic model was re-run (‘backward selection’). This step was repeated until only variables with p-values < 0.10 remained in the model for each scenario. Odds ratios for diagnosing asthma were calculated with COPD as reference group and vice versa. For each scenario a receiver operator characteristics (ROC) curve was created and the percentage explained variance (Nagelkerke R square) determined. Area under the curve (AUC) values from the ROC curves of the three scenarios were statistically compared using a non-parametric approach for correlated ROC curves[32]. SPSS statistics version 25.0 and SAS version 9.4 were used for the analyses. Missing data were not imputed. Two-sided p-values < 0.05 were considered statistically significant, except for the testing of the AUC values between Scenarios 1 and 2 and Scenarios 2 and 3, respectively, in which multiple testing was taken into account by using p < 0.025 to define statistical significance (i.e., Bonferroni correction: p = 0.05/2 = 0.025).

Table 3

Differentiating abilities of relevant items and overall model performance.

		Scenario 1			Scenario 2			Scenario 3
Subsection		Asthma	COPD	pm	Asthma	COPD	pm	Asthma	COPD	pm
Medical history questions	Age	0.97 (0.94, 1.01)	1.03 (1.00, 1.06)	0.096	0.96 (0.92, 0.99)	1.05 (1.01, 1.09)	0.014	0.93 (0.88, 0.97)	1.08 (1.03. 1.13)	0.003
	Gender (female)	xl			x			x
	Packyearsa	0.97 (0.95, 0.99)	1.03 (1.01, 1.06)	0.015	0.98 (0.96, 1.00)	1.02 (1.00, 1.05)	0.10	x
	Wheezeb	2.76 (1.33, 5.57)	0.36 (0.17, 0.75)	0.007	3.62 (1.52, 8.59)	0.28 (0.12, 0.66)	0.004	2.79 (1.15, 6.75)	0.36 (0.15, 0.87)	0.023
	Phlegmc	0.33 (0.12, 0.90)	2.99 (1.11, 8.08)	0.030	x			x
	Breathlessnessd	x			2.60 (1.05, 6.40)	0.39 (0.16, 0.95)	0.038	2.55 (1.01, 6.46)	0.39 (0.15, 0.99)	0.049
	Ever respiratory allergye	6.97 (3.38, 14.35)	0.14 (0.07, 0.30)	<0.001	4.37 (2.01, 9.50)	0.23 (0.11, 0.50)	<0.001	5.47 (2.49, 11.99)	0.18 (0.08, 0.40)	<0.001
	Respiratory problemsf	x			x			x
Lung function tests available to primary care	FEV1 % predicted ECCSg				1.07 (1.03, 1.10)	0.94 (0.91, 0.97)	<0.001	1.08 (1.04, 1.11)	0.93 (0.90, 0.96)	<0.001
	FEV1/FVCh < 0.70				0.14 (0.04, 0.52)	7.25 (1.92, 27.45)	0.004	0.11 (0.03, 0.44)	8.81 (2.27, 34.18)	0.002
Lung function tests available to secondary care	RV/TLCa							1.06 (0.99, 1.14)	0.94 (0.88, 1.01)	0.096
	Diffusion capacityi							x
Model performance	Explained variancej	0.41			0.54			0.56
	AUCk (95%CI) p-value for difference between AUCs	0.84 (0.78–0.89) –			0.89 (0.84–0.93) 0.020n			0.89 (0.85–0.94) 0.967o

Odds ratios (95% confidence intervals) for diagnosing asthma or COPD together with corresponding p-values are calculated for the three different scenarios based on the items available.

AUC area under the curve, ECCS European community of coal and steel, FEV1 forced expiratory volume in 1 s, FVC forced vital capacity, ROC receiver operator characteristics, RV residual volume, TLC total lung capacity.

aPackyears were missing in 2 subjects, RV/TLC in 3 subjects; there were no further missings.

bWheeze with or without breathlessness (in previous 12 months).

cPhlegm after getting out of bed (in previous 12 months).

dBreathlessness on exertion (in previous 12 months).

eAllergic to pollen, animals, dust mites or seasonal symptoms.

fRespiratory symptoms triggered by cold air, smoke or (exhaust)fumes.

gPostbronchodilator FEV1 as % of predicted value.

hPostbronchodilator FEV1/FVC.

iDiffusion capacity in mmol/kPa/min.

jNagelkerke R square.

kAUC of ROC curve with COPD as reference group.

l‘x’ refers to variables manually removed from the model as p-values were >0.10.

mFor the difference between asthma and COPD diagnoses within each scenario separately.

nFor the difference between Scenarios 2 and 1.

oFor the difference between Scenarios 3 and 2.