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August 2006

Classification of Asthma Severity in Children: The Contribution of Pulmonary Function Testing

Author Affiliations

Copyright 2006 American Medical Association. All Rights Reserved. Applicable FARS/DFARS Restrictions Apply to Government Use.2006

Arch Pediatr Adolesc Med. 2006;160(8):844-850. doi:10.1001/archpedi.160.8.844

Background  Despite increasing awareness of the National Asthma Education and Prevention Program guidelines, the relative contribution of symptom frequency or pulmonary function to the recommended asthma severity levels remains poorly understood.

Objective  To determine whether adding lung function measurements to clinical history substantially changes the asthma severity classification, thereby influencing treatment decisions.

Design  Baseline data were studied from children enrolled in 2 multicenter studies: phase 1 of the National Cooperative Inner-City Asthma Study (1992-1994) (cohort 1) and the Inner-City Asthma Study (1998-2001) (cohort 2).

Setting  Fifteen (8 for cohort 1 and 7 for cohort 2) major metropolitan inner-city areas in the United States.

Participants  Inner-city children aged 8 through 11 years with asthma.

Main Outcome Measures  Proportion of children reclassified from less severe asthma categories based on symptom frequency into more severe categories because of lung function.

Results  Of children with symptoms of mild intermittent asthma, 22.8% in cohort 1 and 27.7% in cohort 2 would be reclassified as having either moderate or severe persistent asthma. Of children with symptoms of mild persistent asthma, 31.2% in cohort 1 and 33.3% in cohort 2 would be similarly reclassified.

Conclusions  In 2 different studies of inner-city children with asthma, approximately one third of the participants were reclassified into higher National Asthma Education and Prevention Program asthma severity categories when pulmonary function was considered in addition to symptom frequency. This may have direct implications for the undertreatment of asthma.

The National Asthma Education and Prevention Program (NAEPP) guidelines recommend that medical professionals use a combination of clinical findings and objective measurement of lung function for the diagnosis of asthma.1 According to these guidelines, asthma is classified into 4 levels at initial diagnosis: mild intermittent, mild persistent, moderate persistent, and severe persistent based on symptom frequency and either spirometric (forced expiratory volume in 1 second [FEV1]) or peak expiratory flow (PEF) measurements (Table 1). Despite increasing awareness of these guidelines, the relative contribution of symptom frequency and pulmonary function to the recommended asthma severity levels remains poorly understood.

Table 1. 
Asthma Severity Classifications According to the Expert Panel Report 2 National Asthma Guidelines
Asthma Severity Classifications According to the Expert Panel Report 2 National Asthma Guidelines

Choice of therapy depends on an accurate determination of asthma severity because underestimation will result in suboptimal treatment and increased morbidity.2,3 In a study of more than 4000 asthmatic patients in managed care, Wolfenden et al4 found that physicians systematically underestimate asthma severity. In addition, Baker et al5 provided 24 board-certified allergists and pulmonologists with 8 asthma case summaries and found low levels of agreement for NAEPP guideline asthma severity classification.

In a sample of more than 200 hospitalized asthmatic patients, Warman et al6 showed that 83% should be classified as having persistent asthma but that only 35% were receiving daily anti-inflammatory agents. They reported ascertainment of severity using symptom frequency. The potential contribution of pulmonary function was not addressed.6

Peak expiratory flow and FEV1 are commonly used measures of lung function, and they are used particularly for assessment of the airway obstruction typically seen with asthma. Both are recommended in the NAEPP guidelines as measures of severity assessment. The PEF meter is a simple and inexpensive device that is widely available but has several limitations compared with the spirometer. Peak expiratory flow is effort dependent, and several studies7-10 have shown that it underestimates the degree of airway obstruction. Eid et al11 found that PEF had poor negative predictive value for patients with air trapping as determined by elevated residual volume/total lung capacity. Patients with air trapping can generate a peak burst of airflow, yielding a normal PEF measurement, but as exhalation continues, abnormalities in measurements such as FEV1 and forced expiratory flow between 25% and 75% are detected. The spirometric measurement, FEV1, is reliable and has good correlation with degree of airway obstruction.12

Despite the NAEPP recommendations to assess lung function by means of FEV1 or peak flow, these measurements, particularly FEV1, which requires a spirometer, are not routinely determined.13 Picken et al14 reported that when primary care physicians were given the opportunity to adapt the NAEPP guidelines for their own local use, they included clinical criteria and PEF measurement. Spirometry was recommended only for patients with an incomplete response to inhaled corticosteroids as determined by clinical examination, for “unusual” patients, or for patients in whom an alternate diagnosis was suspected. In a survey13 of primary care physicians, only 21% reported using FEV1 to establish the diagnosis of asthma, and only 8% used FEV1 measurements in routine follow-up. In contrast, 75% reported using PEF in the initial diagnosis or follow-up.13 In a study by Diette et al,15 only half of the patients reported ever undergoing pulmonary function testing.

The NAEPP guidelines organize asthma severity into 4 categories, using “or” criteria for daytime and nighttime symptom frequency, office-based pulmonary functions (FEV1 or PEF rate [PEFR]), and home-based diurnal variability in PEFR (Table 1). The purpose of this study is to determine whether asthma severity classification based on clinical history alone is changed by the addition of spirometric measures of lung function. If more patients needing treatment are identified in this manner, it could reduce the problem of undertreatment of asthma.


To increase the generalizability of the findings, we included children enrolled in 2 separate but related multicenter studies: phase 1 of the National Cooperative Inner-City Asthma Study (NCICAS), conducted between January 2, 1993, and November 12, 1994, and the Inner-City Asthma Study (ICAS), conducted between July 24, 1998, and August 9, 2001. For both studies, each participating site received approval from the human subjects review committee at their institution. Informed consent was obtained from all the parents or legal guardians, and the children provided age-appropriate assent.

Pulmonary function findings are based on predicted values for height, race, and for certain measures, the sex of the child, and the findings are expressed as a percentage of the predicted value. We chose the third National Health and Nutrition Examination Survey normative data set for the study population's pulmonary function values.16 These spirometric reference values include data from 7429 asymptomatic nonsmoking participants and compare white, African American, and Mexican American individuals aged 8 to 80 years. Of the available normative data sets, these comparisons are the most relevant to our study populations. Because of the age range of the reference values, our analyses are restricted to children aged 8 to 11 years.

National cooperative inner-city asthma study

Phase 1 of the NCICAS (cohort 1) enrolled 1528 children aged 4 to 9 years who resided in 8 major metropolitan inner-city areas in the United States.17 The NCICAS asthma inclusion criteria were a combination of physician diagnosis and symptom history (Table 2). Participants in the NCICAS sample resided in census tracts in which approximately 20% to 40% of the households had incomes below the 1990 federal poverty level. Of the 1376 previously diagnosed asthmatic children, 327 children aged 8 to 9 years attempted spirometry. Acceptable measurements for FEV1 and PEFR were available for 257 participants.

Table 2. 
Eligibility Criteria for Cohorts 1 and 2
Eligibility Criteria for Cohorts 1 and 2
Inner-city asthma study

The ICAS (cohort 2) enrolled 937 children aged 5 to 11 years with moderate to severe asthma using inclusion criteria intended to result in participants with more severe asthma than those in the NCICAS sample. Children and their families were eligible for the ICAS if the child had at least 1 hospitalization or 2 urgent care visits for asthma during the 6 months before screening and had a positive skin test reaction to at least 1 of 11 common indoor allergens (Table 2).18 Except for 1 site, where alternate criteria for poverty were used, children enrolled in the ICAS had to live in a census tract in which at least 20% of households reported a household income below the federal poverty level, and they had to sleep in the intervention home at least 5 nights of every week. Of the 455 participants aged 8 to 11 years, complete data were available for 383.

For both study cohorts, a comprehensive baseline evaluation was conducted at enrollment. A variety of questions were asked of the caregiver, including the child's medication use and adherence, home environment and smoking history, perceived stress and stressful life events, mental health measures for the caregiver and the child participant, and recent morbidity. Morbidity measures included asthma symptoms in the past 2 weeks, school days missed in the past 2 weeks, and use of health care services in the past 2 months. In addition, each child answered questions regarding stress and quality of life and underwent allergy skin testing. In the NCICAS, pulmonary function testing was performed using a Pulmonary Screen IIE/VRS system (S&H Instrument, Doylestown, Pa), and in the ICAS, a Renaissance II spirometer (Nellcor Puritan Bennett, Pleasanton, Calif) was used. The pulmonary function measures reported in this article, including PEFRs, were obtained from these respective instruments. All testing was performed by trained technicians and followed American Thoracic Society guidelines.19

We classified the asthma severity of study participants according to the Expert Panel Report 2 national asthma guidelines.1 Using the symptom frequency categories given in Table 1, we placed participants into 3 severity levels: mild intermittent, mild persistent, and moderate and severe persistent asthma. Using 14-day reports, children who had 0 to 4 days with symptoms and 0 to 1 night with symptoms were classified as mild intermittent. Children who had 5 to 13 days with symptoms or 2 nights with symptoms were classified as mild persistent. Children who had 14 days with symptoms or 3 to 14 nights with symptoms were classified as moderate or severe persistent. The 2 most severe categories (moderate and severe persistent asthma) were collapsed into 1 because with this data set we could not reliably differentiate moderate from severe persistent asthma based on patient symptom frequency self-report alone (ie, differentiating between “daily” and “continual” daytime symptoms or defining “frequent” nights with symptoms).1,20

We examined the pulmonary function test results at the baseline evaluation for each study to determine the proportion of children in each asthma severity category who would be reclassified from symptom-only severity categories based on the addition of these lung function results. Consistent with the NAEPP guidelines, we examined FEV1 and PEFR separately and together as “or” criteria along with symptom frequency so that the addition of lung function could result either in no change in asthma severity categories if pulmonary function was normal or in an increase in asthma severity categories if pulmonary function findings were abnormal.

Characteristics of the study samples

The NCICAS population is designated cohort 1, and the ICAS population is cohort 2. For this analysis, cohort 1 included 257 children, and cohort 2 included 383 children. The study samples for both cohorts were demographically similar (Table 3). The mean age for cohort 1 was 8.5 years and for cohort 2 was 9.5 years, reflecting their different age inclusion criteria. Approximately 60% of the participants in both cohorts were boys. In cohort 1, most study participants (74.4%) were African American. In cohort 2, Hispanic and African American children were enrolled in similar proportions (almost 40% for each). In both cohorts, approximately 70% of the caregivers reported graduating from high school, and almost 60% of respondents in each study reported household incomes of less than $15 000. Caregivers in cohort 2 were more likely to report being married (35.2% vs 23.4%) and, consistent with recent reforms in welfare legislation, were also more likely to report at least 1 currently employed household member (76.1% vs 50.2%).

Table 3. 
Description of Study Samples: National Cooperative Inner-City Study (Cohort 1) and Inner-City Asthma Study (Cohort 2)*
Description of Study Samples: National Cooperative Inner-City Study (Cohort 1) and Inner-City Asthma Study (Cohort 2)*
Asthma severity according to symptom frequency

We first examined the distribution of participants into asthma severity categories based on symptom frequency categories alone and on daytime symptoms and nighttime symptoms separately and together as “or” criteria, consistent with the NAEPP guidelines (Table 4). As expected, given the eligibility criteria, a greater proportion of cohort 1 children fell into the mild intermittent category compared with cohort 2 children (47.9% vs 38.6%; P = .02) (Table 4). The proportion of children in the mild persistent category was similar in cohorts 1 and 2 (18.7% vs 18.8%; P = .97). Finally, a smaller proportion of cohort 1 vs cohort 2 children were in the moderate or severe persistent category (33.5% vs 42.6%; P = .02).

Table 4. 
Asthma Severity Distribution According to Symptom Frequency: National Cooperative Inner-City Study (Cohort 1) and Inner-City Asthma Study (Cohort 2)
Asthma Severity Distribution According to Symptom Frequency: National Cooperative Inner-City Study (Cohort 1) and Inner-City Asthma Study (Cohort 2)

For both cohorts, consideration of nighttime symptoms alone places more participants in the moderate or severe persistent categories than use of daytime symptoms alone (cohort 1: 29.6% vs 10.1%; cohort 2: 36.3% vs 19.8%; P<.001 for both).

Asthma severity according to pulmonary function

We then examined the distribution of children into normal (≥80% of predicted) or abnormal (<80% of predicted) categories of FEV1 and PEFR according to the NAEPP guidelines. For FEV1, a smaller proportion of children in cohort 1 vs cohort 2 had abnormal lung function (16.7% vs 28.2%; P<.001) (Table 5). The PEFR was less sensitive to differences in cohort severity, with the difference between cohorts only approaching significance (19.5% vs 25.3%; P = .08). When either FEV1 or PEFR was considered (the recommended NAEPP guidelines approach), 24.5% of children in cohort 1 had abnormal lung function compared with 35.8% in cohort 2 (P = .003).

Table 5. 
Children With Abnormal Lung Function by Symptom Category: National Cooperative Inner-City Study (Cohort 1) and Inner-City Asthma Study (Cohort 2)*
Children With Abnormal Lung Function by Symptom Category: National Cooperative Inner-City Study (Cohort 1) and Inner-City Asthma Study (Cohort 2)*
Influence of pulmonary function in changing the severity classification

To address the main study question, we examined how the severity distribution would change when abnormal pulmonary function—either FEV1 or PEFR—is also considered (Table 5). Among children with symptoms consistent with mild intermittent asthma, 22.8% in cohort 1 and 27.7% in cohort 2 would be reclassified as having moderate or severe persistent asthma. Among children with symptoms consistent with mild persistent asthma, 31.2% in cohort 1 and 33.3% in cohort 2 would be reclassified as having moderate or severe persistent asthma. Among children who were already classified as having moderate or severe persistent asthma by symptoms alone, 23.3% in cohort 1 and 44.2% in cohort 2 had abnormal pulmonary function. Figure 1 shows the overall distribution of severity classifications when using the differing criteria.

Figure 1.
Asthma severity distribution in each cohort according to symptom frequency and lung function. FEV1 indicates forced expiratory volume in 1 second; PEF, peak expiratory flow.

Asthma severity distribution in each cohort according to symptom frequency and lung function. FEV1 indicates forced expiratory volume in 1 second; PEF, peak expiratory flow.


These 2 cohorts of inner-city children with asthma differed somewhat in severity, reflecting the differences in eligibility criteria. Regardless, approximately one third of the children in each cohort were reclassified to higher NAEPP asthma severity categories when pulmonary function was considered in addition to symptom frequency. These results demonstrate that the NAEPP severity assessment algorithm is highly dependent on the availability of symptom frequency and pulmonary function data.

The findings are from 2 populations of relatively severe asthmatic children living in an inner-city environment and may not be generalizable to all asthmatic children. The participants from the ICAS, for example, were included only if they had 1 hospitalization or 2 acute care visits for asthma in the past 6 months (Table 2). Most of the children in this analysis were in the moderate and severe asthma categories, which is consistent with our strategy to enroll children with significant and active asthma morbidity for this study.

It may be that, at least for some patients, PEF and FEV1 are not the most sensitive indicators of small-airway obstruction. Klein et al21 found that some children with symptoms suggestive of moderately severe asthma had normal PEF and FEV1 measurements but decreased forced expiratory flow between 25% and 75%. Moy and colleagues22 found that intensity of shortness of breath was a predictor of quality of life at all severity levels in contrast to lung function, which did not independently predict quality of life at any asthma severity level. A recent environmental intervention was demonstrated to reduce symptoms but had no effect on lung function.23

Other studies have shown the weakness of symptoms alone as a predictor of asthma severity. For example, Osborne et al24 found that a 2-year review of medical records, including exacerbations, urgent care visits, hospitalizations, and medications, correlated well with pulmonary function and glucocorticoid use but not with asthma symptoms.

There are inherent limitations with the assessment of asthma, a dynamic chronic illness, at any given point. Calhoun et al25 found that in repeated assessments of asthma severity based on symptoms and PEF, a single point-in-time classification of asthma was highly unreliable. Unfortunately, initial therapeutic decisions must be based on such limited information.

Within the symptom frequency categories, many more participants in this study were classified as severe according to nighttime vs daytime symptoms, a finding corroborated by Colice et al.26 This raises the importance of a careful nocturnal symptom history, realizing that this history may be an underestimate of what is really occurring. For example, variables such as the location of the historian's (ie, parent’s) bed to the patient's bed and whether the historian is a deep sleeper can affect the accuracy of recall and thus the accuracy of asthma severity classification and the resulting treatment.

Bacharier et al27 described a weak relationship among symptom frequency, medication use, and FEV1. However, they also demonstrated a decrease in the FEV1/forced vital capacity ratio as asthma severity increased. Their findings underscore the imperfect overlap between symptom frequency and pulmonary function at a moment in time, and the value of using multiple domains when diagnosing and assessing asthma.

Nair et al28 recently corroborated our finding, demonstrating that the use of spirometry identified a large proportion of asthmatic children with abnormal lung function who otherwise had mild asthma based on history or physical examination findings alone. Fuhlbrigge et al29 demonstrated a strong association between decreased FEV1 and risk of an asthma attack in the subsequent year. Juniper et al30 demonstrated via factor analysis that airway caliber (ie, pulmonary function) was 1 of 4 distinct domains of asthma health status, along with quality of life, daytime symptoms and β-agonist use, and nighttime symptoms.

These findings support the use of spirometry when assessing patients with asthma. Administering the forced expiratory maneuver requires good patient coaching, which in turn requires careful and adequate support staff and provider training. If well-trained personnel are in place and quality criteria are diligently used, the increased availability of reliable and user-friendly spirometers may make possible the incorporation of pulmonary function testing in primary care settings. Spirometry is often problematic for children, and 16% to 21% of our curves were unacceptable, underscoring the importance of ensuring that a good-quality flow volume curve is being interpreted.

Approximately the same number of children with “abnormal” lung function were identified using PEFR as using FEV1. However, these 2 subsets of children do not overlap completely. The FEV1 and FEV1/forced vital capacity, when performed and interpreted properly, provide an objective view of the expiratory phase that may help identify patients with airway obstruction otherwise missed by history and physical examination. A single measure of PEF, however, is considered to be of limited value when using predicted equations.31 In addition, PEFR determined using a mechanical peak flowmeter is likely to differ from peak flow determined using an automated spirometer, as reported in our study.32 Hankinson et al33,34 found that differences in the frequency response and accuracy of mechanical peak flowmeters and spirometers often produce values that may be 15% higher or lower than the comparison instrument. In cohort 1, we had same-day measurements of PEFR using both instruments and found that the correlation between them was only 0.60. Because of the inexact nature of asthma severity assessment and the lack of a universal gold standard, we may also need additional discriminators. Miller et al35 recently highlighted the potential contribution of asthma-related health care and medication use as additional discriminators of asthma severity. Exhaled nitric oxide concentration, as another example, is a biomarker of inflammation that may eventually prove to be a better predictor of current or future asthma severity.36

This study has several limitations. The PEF values were obtained using an automated spirometer and may differ from values obtained using a handheld mechanical peak flowmeter. In the lowest ranges (<200 L/min), our data show a significant discrepancy between values obtained using these 2 methods. The findings from an inner-city population may or may not be generalizable to other populations. It is also possible that findings from children in a limited age range (8-11 years) may not be generalizable to other age groups. The inclusion criterion of a recent hospitalization or 2 acute care visits was meant to identify asthmatic children with significant morbidity. This recruitment strategy partially explains the high proportion of these participants who were in the more severe asthma categories.

We show that using symptom frequency alone to classify asthma severity underestimates the number of children with moderate to severe persistent asthma. This finding suggests that the often described phenomenon of undertreatment of more severe asthma with controller medications, most notably inhaled corticosteroids, may be due, in part, to an underestimate of asthma severity. Increased use of spirometry may lead to better identification of asthma severity and thereby improve treatment with daily anti-inflammatory medication.

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Article Information

Correspondence: James W. Stout, MD, Department of Pediatrics, University of Washington School of Medicine, Box 354920, Seattle, WA 98195-4920 (jstout@u.washington.edu).

Accepted for Publication: March 3, 2006.

Author Contributions: Dr Mitchell, principal investigator, Data Coordinating Center, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: Stout, Enright, Shapiro, and Mitchell. Acquisition of data: Stout, Enright, Gan, and Lamm. Analysis and interpretation of data: Stout, Visness, Enright, Gan, Adams, and Mitchell. Drafting of the manuscript: Stout, Visness, Lamm, Shapiro, Gan, and Mitchell. Critical revision of the manuscript for important intellectual content: Stout, Visness, Enright, Gan, Adams, and Mitchell. Statistical analysis: Visness and Mitchell. Obtained funding: Stout and Mitchell. Administrative, technical, and material support: Stout, Visness, Enright, Adams, and Mitchell. Study supervision: Stout, Lamm, and Mitchell.

Group Members: The Inner-City Asthma Study was a collaboration of the following institutions and investigators. Boston University School of Medicine, Boston, Mass: George O’Connor, MD (principal investigator [PI], Suzanne Steinbach, MD, Amy Lang Zapata, MPH, Jodie Cline Casagrande, MSW, MPH, and Linda Schneider, MD (Children's Hospital, Boston); Albert Einstein College of Medicine/Jacobi Medical Center, Bronx, NY: Ellen Crain, MD, PhD (PI), Laurie Bauman, PhD, Yvonne Senturia, MD, and David Rosenstreich, MD; Children's Memorial Hospital, Chicago, Ill: Richard Evans III, MD (PI), Jacqueline Pongracic, MD, Anne Sawyer, and Kristin Koridek; University of Texas Southwestern Medical Center at Dallas: Rebecca S. Gruchalla, MD, PhD (PI), Vanthaya Gan, MD Yvonne Coyle, MD, and Nina F. Gorham; Mount Sinai School of Medicine, New York, NY: Meyer Kattan, MD, CM (PI), Carin Lamm, MD, Morton Lippmann, PhD, Elisabeth Luder, PhD, Mark Chassin, MD, and Gloria Xanthos; University of Washington School of Medicine and Public Health, Seattle: James Stout, MD (PI), Gail Shapiro, MD, Lenna Liu, MD, Jane Koenig, PhD, Mary Lasley, MD, Sandra Randels, and Helen Powell, MS; The University of Arizona College of Medicine, Tucson: Wayne Morgan, MD, CM (PI), Paul Enright, MD, Jamie Goodwin, PhD, and Terri Garcia (El Rio Health Clinic, Tucson); Data Coordinating Center, Rho Inc, Chapel Hill: Herman Mitchell, PhD (PI), Michelle Walter, MS, Henry Lynn, MS, Sheri Hart, William Tolbert, and Elizabeth Nuebler; Allergen Assay Laboratories, Harvard School of Public Health, Boston: Harriet Burge, PhD, Michael Muilenberg, MS, and Diane Gold, MD; The Johns Hopkins Dermatology, Allergy and Clinical Immunology Reference Laboratory, Johns Hopkins University School of Medicine, Baltimore, Md: Robert Hamilton, PhD; National Institute of Allergy and Infectious Diseases, Bethesda, Md: Marshall Plaut, MD, Ernestine Smartt, RN, and G. Kenneth Adams, PhD; and National Institute of Environmental Health Sciences, Research Triangle Park, NC: George Malindzak, PhD, and Patrick Mastin, PhD.

Financial Disclosure: Dr Stout received funding from the Centers for Disease Control for production of a CD-ROM entitled “Spirometry Fundamentals: A Basic Guide to Lung Function Testing.” He plans to license this tool through the University of Washington's Office of Tech Transfer.

Funding/Support: This research was supported by grants AI-39769, AI-39900, AI-39902, AI-39789, AI-39901, AI-39761, AI-39785, and AI-39776 from the National Institute of Allergy and Infectious Diseases and by the National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services; and by grant M01 RR00533 from the National Center for Research Resources.

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