ARTICLE Classification of Asthma Severity in Children The Contribution of Pulmonary Function Testing James W Stout, MD, MPH; Cynthia M Visness, MA, MPH; Paul Enright, MD; Carin Lamm, MD; Gail Shapiro, MD; Vanthaya N Gan, MD; G Kenneth Adams III, PhD; Herman E Mitchell, PhD Background: Despite increasing awareness of the Na- tional 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 func- tion measurements to clinical history substantially changes the asthma severity classification, thereby influencing treatment decisions Design: Baseline data were studied from children enrolled in multicenter studies: phase 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 and for cohort 2) major metropolitan inner-city areas in the United States 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 intermit- tent asthma, 22.8% in cohort and 27.7% in cohort would be reclassified as having either moderate or severe persistent asthma Of children with symptoms of mild persistent asthma, 31.2% in cohort and 33.3% in cohort would be similarly reclassified Conclusions: In different studies of inner-city chil- dren 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 Participants: Inner-city children aged through 11 years Arch Pediatr Adolesc Med 2006;160:844-850 with asthma T Author Affiliations: Department of Pediatrics, University of Washington School of Medicine, Seattle (Drs Stout and Shapiro); Rho Federal Systems Division Inc, Chapel Hill, NC (Dr Mitchell and Ms Visness); Respiratory Sciences Center, University of Arizona, Tucson (Dr Enright); Departments of Pediatrics, Mount Sinai School of Medicine, New York, NY (Dr Lamm), and University of Texas Southwestern Medical Center at Dallas (Dr Gan); and National Institute of Allergy and Infectious Diseases, Bethesda, Md (Dr Adams) HE 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 levels at initial diagnosis: mild intermittent, mild persistent, moderate persistent, and severe persistent based on symptom frequency and either spirometric (forced expiratory volume in 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 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 asth- (REPRINTED) ARCH PEDIATR ADOLESC MED/ VOL 160, AUG 2006 844 matic patients in managed care, Wolfenden et al4 found that physicians systematically underestimate asthma severity In addition, Baker et al5 provided 24 boardcertified allergists and pulmonologists with 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 WWW.ARCHPEDIATRICS.COM ©2006 American Medical Association All rights reserved Downloaded From: http://archpedi.jamanetwork.com/ on 05/11/2015 but has several limitations compared with the spirometer Peakexpiratoryflowiseffortdependent,andseveralstudies7-10 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 byelevatedresidualvolume/totallungcapacity.Patientswith 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 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 METHODS To increase the generalizability of the findings, we included children enrolled in separate but related multicenter studies: phase 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 to 80 years Of the available normative data sets, these comparisons are the most relevant Table Asthma Severity Classifications According to the Expert Panel Report National Asthma Guidelines Children ⬎5 y Who Can Use a Spirometer or Peak Flowmeter, % Asthma Classification Days Nights With With Symptoms Symptoms FEV1 or PEF (Predicted Normal) PEF Variability Severe persistent Moderate persistent Mild persistent Mild intermittent Continual Daily ⬎2/wk ⱕ2/wk ⱕ60 ⬎60 to ⬍80 ⱖ80 ⱖ80 ⬎30 ⬎30 20-30 ⬍20 Frequent ⱖ5/mo 3-4/mo ⱕ2/mo Abbreviations: FEV1, forced expiratory volume in second; PEF, peak expiratory flow to our study populations Because of the age range of the reference values, our analyses are restricted to children aged to 11 years NATIONAL COOPERATIVE INNER-CITY ASTHMA STUDY Phase of the NCICAS (cohort 1) enrolled 1528 children aged to years who resided in 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 to years attempted spirometry Acceptable measurements for FEV1 and PEFR were available for 257 participants INNER-CITY ASTHMA STUDY The ICAS (cohort 2) enrolled 937 children aged 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 hospitalization or urgent care visits for asthma during the months before screening and had a positive skin test reaction to at least of 11 common indoor allergens (Table 2).18 Except for 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 nights of every week Of the 455 participants aged 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 weeks, school days missed in the past weeks, and use of health care services in the past 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 test- (REPRINTED) ARCH PEDIATR ADOLESC MED/ VOL 160, AUG 2006 845 WWW.ARCHPEDIATRICS.COM ©2006 American Medical Association All rights reserved Downloaded From: http://archpedi.jamanetwork.com/ on 05/11/2015 Table Eligibility Criteria for Cohorts and Cohort (National Cooperative Inner-City Asthma Study) Cohort (Inner-City Asthma Study) Age, y Residence 4-9 Selected census tracts with ⬎40% of household incomes below the 1990 poverty level Recruitment sites Definition of asthma Emergency departments and community primary care sites Physician diagnosis of asthma and asthma symptoms (cough, wheeze, shortness of breath, or whistling or tightness in the chest) for ⬎3 d in the past 12 mo or Asthma symptoms (cough, wheeze, or shortness of breath) for ⬎6 wk in the past 12 mo and that met of the following conditions: a Cough, wheeze, or shortness of breath was present more than half the days and nights during the 6-wk period b Cough, wheeze, or shortness of breath was aggravated by exercise or cold air c There is a parent or sibling with asthma d Child does not have a history of antibiotic therapy for sinusitis accompanying the cough e Cough, wheeze, or shortness of breath that resulted in disturbance of the child’s sleep None 5-11 Selected census tracts with at least 20%-40% of household incomes below the 1990 poverty level and sleeps overnight at address ⱖ5 times per week Hospitals, emergency departments, and clinics Physician diagnosis of asthma and At least hospitalization related to asthma in the past mo or At least unscheduled visits to an emergency department or clinic related to asthma in the past mo Criterion Other ing 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 national asthma guidelines.1 Using the symptom frequency categories given in Table 1, we placed participants into severity levels: mild intermittent, mild persistent, and moderate and severe persistent asthma Using 14-day reports, children who had to days with symptoms and to night with symptoms were classified as mild intermittent Children who had to 13 days with symptoms or nights with symptoms were classified as mild persistent Children who had 14 days with symptoms or to 14 nights with symptoms were classified as moderate or severe persistent The most severe categories (moderate and severe persistent asthma) were collapsed into 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 RESULTS CHARACTERISTICS OF THE STUDY SAMPLES The NCICAS population is designated cohort 1, and the ICAS population is cohort For this analysis, cohort included 257 children, and cohort included 383 children The study samples for both cohorts were demo- Positive allergen skin test reaction to at least indoor allergen (eg, dust mites, cockroaches, mold, rodents, or pets) graphically similar (Table 3) The mean age for cohort was 8.5 years and for cohort 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 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 currently employed household member (76.1% vs 50.2%) 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 children fell into the mild intermittent category compared with cohort children (47.9% vs 38.6%; P=.02) (Table 4) The proportion of children in the mild persistent category was similar in cohorts and (18.7% vs 18.8%; P=.97) Finally, a smaller proportion of cohort vs cohort children were in the moderate or severe persistent category (33.5% vs 42.6%; P=.02) 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) (REPRINTED) ARCH PEDIATR ADOLESC MED/ VOL 160, AUG 2006 846 WWW.ARCHPEDIATRICS.COM ©2006 American Medical Association All rights reserved Downloaded From: http://archpedi.jamanetwork.com/ on 05/11/2015 ASTHMA SEVERITY ACCORDING TO PULMONARY FUNCTION We then examined the distribution of children into normal (ⱖ80% 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 vs cohort 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 had abnormal lung function compared with 35.8% in cohort (P=.003) 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 and 27.7% in cohort would be reclassified as having moderate or severe persistent asthma Among children with symptoms consistent with mild persistent asthma, 31.2% in cohort and 33.3% in cohort 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 and 44.2% in cohort had abnormal pulmonary function Figure shows the overall distribution of severity classifications when using the differing criteria COMMENT These 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 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 hospitalization or acute care visits for asthma in the past 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 Table Description of Study Samples: National Cooperative Inner-City Study (Cohort 1) and Inner-City Asthma Study (Cohort 2)* Cohort (n = 257) Characteristic Cohort (n = 383) Demographic characteristics Child’s age, mean (SD) [range], y 8.5 (0.5) [8-9] 9.5 (1.1) [8-11] Child’s sex, No (%) M 156 (60.7) 241 (62.9) F 101 (39.3) 142 (37.1) Race/ethnicity, No (%) Hispanic 45 (17.7) 148 (38.6) African American 189 (74.4) 151 (39.4) White 28 (7.3) Asian/Pacific Islander (0.5) American Indian 13 (3.4) Mixed/other 20 (7.9) 41 (10.7) Caregiver is high school graduate, 171 (67.3) 274 (72.1) No (%) Caregiver is married, No (%) 60 (23.4) 134 (35.2) Household income ⬍$15 000, 139 (57.9) 215 (59.4) No (%) Household employment, No (%) 128 (50.2) 290 (76.1) Medication use at baseline, No (%) Using inhaled corticosteroids 37 (14.8) 115 (30.0) Using -agonists 207 (82.8) 328 (85.6) Baseline morbidity, mean (SD) Days with wheeze in past 14 d 3.4 (3.9) 4.4 (4.4) Days with slowed activity in 2.7 (3.8) 4.1 (4.8) past 14 d Nights child woke in past 14 d 2.1 (3.5) 2.7 (3.8) School days missed† 0.10 (0.13) 0.93 (1.83) Hospitalizations in past or mo‡ 0.11 (0.40) 0.17 (0.45) Unscheduled visits in 0.79 (1.0) 0.96 (1.19) past or mo‡ *Numbers not always add up to the total because of sporadic missing data †In cohort 1, school days missed is the proportion of days missed of the number of days school was in session in the previous months In cohort 2, school days missed is the actual number of days missed in the previous weeks, typically 10 school days Thus, the average of of 10 days missed is comparable with the 10% in cohort ‡In cohort 1, hospitalizations and unscheduled visits were measured for months, and in cohort they were measured for months Table Asthma Severity Distribution According to Symptom Frequency: National Cooperative Inner-City Study (Cohort 1) and Inner-City Asthma Study (Cohort 2) Symptoms Classified by Cohort 1, % (n = 257) Daytime Nighttime Daytime or nighttime Cohort 2, % (n = 383) Daytime Nighttime Daytime or nighttime Mild Intermittent Mild Persistent Moderate or Severe Persistent 70.0 57.6 47.9 19.8 12.8 18.7 10.1 29.6 33.5 52.0 51.2 38.6 28.2 12.5 18.8 19.8 36.3 42.6 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 (REPRINTED) ARCH PEDIATR ADOLESC MED/ VOL 160, AUG 2006 847 WWW.ARCHPEDIATRICS.COM ©2006 American Medical Association All rights reserved Downloaded From: http://archpedi.jamanetwork.com/ on 05/11/2015 Table Children With Abnormal Lung Function by Symptom Category: National Cooperative Inner-City Study (Cohort 1) and Inner-City Asthma Study (Cohort 2)* ⬍80% of Predicted Participants, No Severity Classification by Symptoms FEV1 PEF Either FEV1 or PEF 180 51 26 12.8 27.4 23.1 15.6 31.4 23.1 20.6 35.3 30.8 148 33 76 15.5 15.2 19.7 21.0 18.2 17.1 25.7 24.2 22.4 123 48 86 257 14.6 18.8 18.6 16.7 17.9 27.1 17.4 19.5 22.8 31.2 23.3 24.5 199 108 76 23.6 26.8 42.1 21.1 22.2 40.8 30.6 33.3 52.6 196 48 139 22.4 35.4 33.8 20.4 27.1 31.6 28.1 41.7 44.6 148 72 163 383 22.3 27.8 33.7 28.2 19.6 22.2 31.9 25.3 27.7 33.3 44.2 35.8 Cohort Symptoms (n = 257) Severity by daytime symptoms Mild intermittent Mild persistent Moderate or severe persistent Severity by nighttime symptoms Mild intermittent Mild persistent Moderate or severe persistent Severity by daytime ⫹ nighttime symptoms Mild intermittent Mild persistent Moderate or severe persistent Overall Cohort Symptoms (n = 383) Severity by daytime symptoms Mild intermittent Mild persistent Moderate or severe persistent Severity by nighttime symptoms Mild intermittent Mild persistent Moderate or severe persistent Severity by daytime ⫹ nighttime symptoms Mild intermittent Mild persistent Moderate or severe persistent Overall Abbreviations: FEV1, forced expiratory volume in second; PEF, peak expiratory flow *Data are given as percentages Mild Intermittent Mild Persistent Moderate or Severe Persistent Cohort Symptoms Only Symptoms or FEV1 Symptoms or PEF Symptoms or FEV1 or PEF Cohort Symptoms Only Symptoms or FEV1 Symptoms or PEF Symptoms or FEV1 or PEF 20 40 60 80 100 % Figure Asthma severity distribution in each cohort according to symptom frequency and lung function FEV1 indicates forced expiratory volume in second; PEF, peak expiratory flow 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-intime 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 (REPRINTED) ARCH PEDIATR ADOLESC MED/ VOL 160, AUG 2006 848 WWW.ARCHPEDIATRICS.COM ©2006 American Medical Association All rights reserved Downloaded From: http://archpedi.jamanetwork.com/ on 05/11/2015 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 of distinct domains of asthma health status, along with quality of life, daytime symptoms and -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 goodquality 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 subsets of children 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 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 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 Accepted for Publication: March 3, 2006 Correspondence: James W Stout, MD, Department of Pediatrics, University of Washington School of Medicine, Box 354920, Seattle, WA 98195-4920 (jstout@u.washington.edu) 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, (REPRINTED) ARCH PEDIATR ADOLESC MED/ VOL 160, AUG 2006 849 WWW.ARCHPEDIATRICS.COM ©2006 American Medical Association All rights reserved Downloaded From: http://archpedi.jamanetwork.com/ on 05/11/2015 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 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 REFERENCES 26 National Asthma Education and Prevention Program Expert Panel Report: Guidelines for the Diagnosis and Management of Asthma Bethesda, Md: US Dept of Health and Human Services, Public Health Service; April 1997 NIH publication 97-4051 Piecoro LT, Potoski M, Talbert JC, Doherty DE Asthma prevalence, cost, and adherence with expert guidelines on the utilization of health care services and costs in a state Medicaid population Health Serv Res 2001;36:357-371 Nestor A, Calhoun AC, Dickson M, Kalik CA Cross-sectional analysis of the relationship between national 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No Severity Classification by Symptoms FEV1 PEF Either FEV1 or PEF 18 0 51 26 12 .8 27.4 23 .1 15.6 31. 4 23 .1 20.6 35.3 30.8 14 8 33 76 15 .5 15 .2 19 .7 21. 0 18 .2 17 .1 25.7 24.2 22.4 12 3 48 86 257 14 .6... 257 14 .6 18 .8 18 .6 16 .7 17 .9 27 .1 17.4 19 .5 22.8 31. 2 23.3 24.5 19 9 10 8 76 23.6 26.8 42 .1 21. 1 22.2 40.8 30.6 33.3 52.6 19 6 48 13 9 22.4 35.4 33.8 20.4 27 .1 31. 6 28 .1 41. 7 44.6 14 8 72 16 3 383 22.3... (0.5) [8-9] 9.5 (1. 1) [8 -11 ] Child’s sex, No (%) M 15 6 (60.7) 2 41 (62.9) F 10 1 (39.3) 14 2 (37 .1) Race/ethnicity, No (%) Hispanic 45 (17 .7) 14 8 (38.6) African American 18 9 (74.4) 15 1 (39.4) White