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Original Research |

Questionnaires and Pocket Spirometers Provide an Alternative Approach for COPD Screening in the General PopulationPocket Spirometers for COPD Screening FREE TO VIEW

Steven B. Nelson, MS; Lisa M. LaVange, PhD; Yonghong Nie, PhD; John W. Walsh; Paul L. Enright, MD; Fernando J. Martinez, MD, FCCP; David M. Mannino, MD, FCCP; Byron M. Thomashow, MD, FCCP
Author and Funding Information

From the American Association for Respiratory Care (Mr Nelson), Irving, TX; University of North Carolina (Drs LaVange and Nie), Chapel Hill, NC; COPD Foundation (Mr Walsh), Miami, FL; University of Arizona (Dr Enright), Tucson, AZ; University of Michigan (Dr Martinez), Ann Arbor, MI; University of Kentucky (Dr Mannino), Lexington, KY; Columbia University (Dr Thomashow), New York, NY.

Correspondence to: Steven Nelson, MS, American Association for Respiratory Care, 9425 N MacArthur Blvd, Irving, TX 75063; e-mail: nelson@aarc.org


Funding/Support: The COPD Foundation provided funding for the performance of the study.

Reproduction of this article is prohibited without written permission from the American College of Chest Physicians. See online for more details.


Chest. 2012;142(2):358-366. doi:10.1378/chest.11-1474
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Background:  In response to the Agency for Healthcare Research and Quality statement questioning the usefulness of “screening spirometry,” the National Heart, Lung, and Blood Institute and the COPD Foundation held a consensus conference in June 2008 to establish a procedure to detect cases of COPD in the general population. Conference participants developed a three-stage approach, using a brief questionnaire, peak flow measurement with a pocket spirometer, and diagnostic quality spirometry. The overall objective of this study was to examine the usefulness of a simple questionnaire and peak flow measurement in screening for COPD in a self-selected population. We hypothesized that this combination would efficiently screen for clinically relevant COPD.

Methods:  We queried individuals attending public events regarding the presence of wheeze and/or asthma, mucus production, dyspnea, exposure to irritants, and tobacco use. Peak expiratory flow (PEF) was then measured with a pocket spirometer. If PEF was < 70% predicted, spirometry was performed. In order to estimate the false-negative rate, a random sample of every 10th participant was also selected for spirometry.

Results:  Between June 2008 and December 2009, 5,761 adults completed the risk assessment questionnaire. The mean age of the respondents was 54 years, 58% were women, and 88% were white. Of these, 5,638 participants completed pocket spirometry, and 315 (5.6%) had PEF < 70% predicted. Of 5,323 with normal PEF, 651 underwent spirometry. The performance of PEF was assessed via positive and negative predictive values relative to a diagnosis of clinically significant airflow obstruction, defined as FEV1/FEV6 < the lower limit of normal and FEV1 < 60% predicted. Of 4,238 subjects with at least two risk factors, 267 (6.3%) had PEF < 70%, compared with 48 of the 1,400 subjects (3.4%) with fewer than two risk factors (P < .001). Based on 729 participants with acceptable spirometry, 63.1% (113 of 179) of those with abnormal PEF tested positive for clinically significant airflow obstruction, compared with 5.5% (30 of 550) with normal PEF (P < .001). The estimated prevalence of significant COPD among the 5,638 screened was 8.7%, and sensitivity and specificity were 40.7% and 97.7%, respectively.

Conclusions:  A staged approach to COPD screening in adults is useful for detecting clinically significant airflow obstruction in our study population.

Figures in this Article

The adverse health consequences of COPD continue to rise in both developed and developing countries.1 Campaigns designed to encourage primary care practitioners (PCPs) to use spirometers in their offices for COPD case finding among patients who smoke cigarettes2 have had only modest success. Most PCPs still do not own a spirometer, and for those who do, a minority of their smoking patients undergo spirometry.3,4 In addition, as little as one-half of the spirometry completed by PCPs meets quality guidelines.5

A series of task force reports published by the Agency for Healthcare Research and Quality advised primary care physicians not to routinely perform spirometry.68 The recommendation states, “do not screen for chronic obstructive pulmonary disease using spirometry,”6 although this applies only to “healthy adults who do not recognize or report symptoms to a clinician and it does not apply to individuals with a family history of α1-antitrypsin deficiency.”

A 2-day workshop (jointly sponsored by the National Heart, Lung, and Blood Institute and the COPD Foundation) was held in June 2008 to develop strategies to detect COPD that did not include performing spirometry on all adults at risk. The participants (e-Appendix 1) performed literature reviews and discussed the results of studies using short questionnaires designed to detect adult smokers with a high pretest probability of COPD.913 One study described the value of peak flow measurements to reduce the number of smokers who required spirometry to rule out COPD.14 The participants suggested a three-staged COPD screening study focusing on initial peak expiratory flow (PEF) measurements to detect significant, previously undiagnosed COPD. We hypothesized that such an approach would efficiently screen for clinically relevant COPD.

Population

We selected events in large cities, including health fairs, health expositions, and national conventions of older adults (such as the American Association of Retired Persons), and we also focused on selected pharmacies. Brochures about lung health were offered, but no specific information about pharmacotherapy was provided. To maintain confidentiality, no contact information from participants was collected, and the names provided by participants on the informed consent forms were not entered into a database. A consecutive identification number was assigned to link the questionnaire, the PEF results, and the spirometry results (stored digitally by the spirometer software). Results and consent forms were securely stored in different locations to prevent the possibility of connecting results to a name. No compensation was given to participants for participation. The study was approved by the Columbia University Human Subjects Committee (IRB-AAAD 7624).

Quality Control

Three to eight respiratory therapists with pulmonary function testing certification performed or supervised on-site testing. Before each event, all technologists reviewed an online presentation of the study protocol. The quality of the first 10 spirometry tests done by each technologist was reviewed on-site to ensure that at least eight of the 10 tests met American Thoracic Society (ATS)/European Respiratory Society goals15 for good quality.

The pocket spirometer (asma-1; Vitalograph), chosen because it met ATS accuracy standards for the measurement of PEF, was designed to minimize cross-contamination between patients by using disposable one-way mouthpieces. Testing of a healthy control subject was used to verify that the pocket spirometers remained accurate within 5%. The diagnostic quality office spirometer (EasyOne Frontline; NDD) was chosen because it met ATS accuracy standards15 and National Lung Health Education Program guidelines for office spirometer features and automated quality checks.16 The spirometer automatically graded the quality of each test session from A to F. Cross-contamination was minimized by using disposable plastic mouthpieces. Spirometer accuracy was checked before each testing day using a 3.00-L calibration syringe. Spirometers found not accurate within 3% of 3.00 L were not used for testing.

Inclusion and Exclusion Criteria

Potential study participants were asked several questions to determine eligibility and spirometry contraindications. Exclusion criteria are listed in Table 1.

Table Graphic Jump Location
Table 1 —Exclusion
Questionnaire and Peak Flow Screening

After signing the informed consent, participants were asked questions about COPD risk factors, including age, wheezing during the previous 12 months, chronic cough, any physician diagnosis of asthma, exposure to secondhand smoke, chemicals, fumes, dust, or air pollution, current or former smoking status, and dyspnea. Height was measured using a Seca 213 stadiometer (Seca). PEF was measured using the pocket spirometer. At least three acceptable PEF measurements were written on the report form. Acceptability of each PEF maneuver was determined automatically by the pocket spirometer as a time to PEF of < 120 milliseconds. The highest PEF value among all maneuvers performed was displayed by the pocket spirometer and transcribed to the data collection form by the technologist. If the highest PEF was < 70% predicted (using National Health and Nutrition Examination Survey III sex- and race-specific reference equations17), diagnostic-quality spirometry was performed.

Spirometry

Diagnostic-quality spirometry was performed following ATS/European Respiratory Society guidelines.15 Participants were sitting, and nose clips were available if the technicians suspected a leak. After a demonstration of the correct breathing maneuvers, the participant was coached to perform at least three acceptable maneuvers. Postbronchodilator (post-BD) spirometry was not performed. Spirometric maneuvers were graded on quality, with grades A, B, and C considered acceptable. Spirometry with a D or F grade was excluded from further analysis. Sex- and race-specific National Health and Nutrition Examination Survey III reference equations were used to compute predicted values of lung function parameters.17 Control group members were chosen by asking every 10th participant to undergo spirometry regardless of questionnaire and PEF results.

Advice and Referrals

All participants were given a copy of their results, and those with abnormal spirometry tests were encouraged to discuss the results with their physician. All self-reported current smokers, regardless of their PEF or spirometry results, were advised to quit smoking and referred to the national quit line for help (1-800-QUITNOW). Smokers with normal PEF were told that they likely did not currently have worrisome COPD, but that they could still have milder COPD and that their risk of heart attack or stroke remained higher than for nonsmokers.

Statistical Analyses

As stated earlier, the overall objective of this study was to examine the usefulness of a simple questionnaire and PEF measurement in screening for clinically relevant COPD in a self-selected population. The primary analysis objective was to evaluate the predictive value of the PEF screening test relative to clinically significant airflow obstruction, and the secondary objective was to determine whether risk factors assessed by the questionnaire significantly predicted airflow obstruction, independent of the PEF screening test. The primary analysis strategy was to determine the positive predictive values (PPVs) and negative predictive values (NPVs) of the proposed PEF screening test. Sample-size determination was based on the NPV, because failing to detect COPD cases was thought to be more harmful than identifying subjects who screened positive but were not true cases. A sample size of 6,000 was chosen to provide a half-width of a 95% CI about the estimated false-negative rate (1 − NPV) of 1.8%, assuming a false-negative rate of 5%, a COPD case prevalence of 8%, and a 10% random sample of control subjects. PPV and NPV were calculated conditional on the outcome of the PEF test relative to a diagnosis of clinically significant airflow obstruction, defined as a ratio of FEV1 to FEV6 (FEV1/FEV6) below the lower limit of normal (LLN) and FEV1 < 60% predicted.8 The LLN was used because of the expected prevalence of an older population.18

Secondary analyses consisted of first examining the relationship between risk factors assessed via the questionnaire and PEF screening results and between risk factors and airflow obstruction using χ2 tests of association. In order to determine if the questionnaire-assessed risk factors provided additional predictive value after controlling for the results of the PEF screening test, logistic regression models were fit to the severity of airflow obstruction that included risk factors and PEF test results as independent variables.

In order to determine the sensitivity and specificity of the PEF screening test, an estimate of the prevalence of clinically significant airflow obstruction in the study population was required. Because the subjects with normal PEF who underwent spirometry were selected randomly, it was possible to compute sampling weights for purposes of estimating population prevalences. Initial sampling weights were computed for each subject, with spirometry outcomes as the inverse of the probability that the subject was selected for spirometry. Final sampling weights incorporated poststratification adjustments to account for missing and/or poor quality spirometry maneuvers. A detailed description of the sampling weight computations is provided in e-Appendix 2.

To assess the robustness of our findings, the PEF screening test was evaluated relative to two other clinically relevant outcomes: (1) a modified version of the GOLD (Global Initiative for Chronic Obstructive Lung Disease) criteria19 for stage III COPD and (2) any lung function abnormality (ie, restricted, mild, moderate, or severe). Both outcomes were based on diagnostic quality spirometry and were defined as follows:

  • Normal: FEV1/FEV6 > 70% and FEV1 ≥ 80% predicted

  • Restricted: FEV1/FEV6 > 70% and FEV1 < 80% predicted

  • Stage I (mild COPD): FEV1/FEV6 < 70% and FEV1 ≥ 80% predicted

  • Stage II (moderate COPD): FEV1/FEV6 < 70% and 50% ≤ FEV1 < 80% predicted

  • Stage III (severe COPD): FEV1/FEV6 < 70% and FEV1 < 50%

The performance of the peak flow screening test relative to additional case definitions is summarized in e-Appendix 2.

Between June 2008 and December 2009, 5,761 people visited a testing venue (Fig 1), provided demographic data, and completed the risk assessment questionnaire. Of these, 5,638 underwent PEF screening and comprise the analysis population for the study. Participants’ mean age was 54.4 years (SD = 14.3 years), and ages ranged from 18 to 93 years (84.2% were ≥ 40 years of age). A majority of participants were women (57.9%). Participants were primarily white or Hispanic/Latino (87.5%); 12.5% were black (Table 2).

Figure Jump LinkFigure 1. Disposition of study participants. PEF = peak expiratory flow; Spiro = spirometry.Grahic Jump Location
Table Graphic Jump Location
Table 2 —Demographic Characteristics and COPD Risk Factors for All Subjects Screened

As step 1 of the screening process, participants responded to seven questionnaire items corresponding to risk factors of COPD (Table 2). The most frequently reported risk factor was exposure to smoke, chemicals, or dust (61.2%), and nearly 40% reported ever smoking.

Of the 5,638 participants completing pocket spirometry, 315 (5.6%) had PEF < 70% predicted and, therefore, qualified for diagnostic spirometry. In addition, 651 of the 5,323 underwent spirometry as control subjects, including the 10% random sample and approximately 110 persons requesting spirometry. COPD risk factors assessed via the questionnaire, including age > 40 years, were positively associated with abnormal PEF. Among the 4,238 subjects with two or more risk factors, 267 (6.3%) had PEF < 70%, compared with 48 (3.4%) of the 1,400 subjects with fewer than two risk factors (P < .001) (Table 3).

Table Graphic Jump Location
Table 3 —Peak Flow Screening Results by Number of Risk Factors for All Subjects Screened

Data are presented as No. (%). Possible risk factors include seven questionnaire items and age ≥ 40 y. P value for χ2 test of association (1 df < .001). df = degrees of freedom; PEF = peak expiratory flow.

Of the 315 participants with abnormal PEF during screening, 251 (61.3%) completed spirometry, and of these, 179 (71.3%) produced results of adequate quality (grades A-C). Importantly, 28.7% (n = 72) did not perform adequate-quality spirometric maneuvers. Of the 651 subjects with normal PEF who underwent spirometry as control subjects, 550 (84.5%) were of acceptable quality for interpretation and classification, whereas 101 (15.5%) were of poor quality and were therefore unacceptable.

Based on 729 participants with high-quality spirometry, 113 of 179 (63.1%) with abnormal PEF at screening tested positive for clinically significant airflow obstruction (defined as FEV1/FEV6 < LLN and FEV1 < 60% predicted), compared with 30 of 550 (5.5%) with normal PEF (P < .001) (Table 4). The estimated PPV and NPV of PEF were 63.1% (95% CI, 56.1%-70.2%) and 94.5% (95% CI, 92.7%-96.4%), respectively.

Table Graphic Jump Location
Table 4 —Positive and Negative Predictive Values of Peak Flow Screening for Clinically Significant Airflow Obstruction

Data are presented as No. (%). P value for χ2 test of association (1 df) < .001. Positive predicted value = 63.1% (95% CI, 56.1%-70.2%); negative predicted value = 94.5% (95% CI, 92.7%-96.4%). See Table 3 legend for expansion of abbreviations.

a 

FEV1/FEV6 ≥ lower limit of normal or FEV1 ≥ 60% predicted.

b 

FEV1/FEV6 < lower limit of normal and FEV1 < 60% predicted.

Incorporating sampling weights into the analysis enabled an evaluation of the PEF screening test based on the estimated COPD status of all participants screened (ie, as if spirometry were performed on the entire study population). The weighted estimate of the prevalence of clinically significant airflow obstruction among the 5,638 screened was 8.7% (95% CI, 6.8%-10.6%). Based on this prevalence estimate, weighted estimates of sensitivity and specificity for the entire screened population were 40.7% (95% CI, 30.9%-50.4%) and 97.7% (95% CI, 97.2%-98.3%), respectively (Table 5).

Table Graphic Jump Location
Table 5 —Weighted Estimates of Screened Population Distribution for Clinically Significant Airflow Obstruction

Data are presented as No. (%). P value for χ2 test of association (1 df) < .001. Estimated prevalence of significant obstruction = 8.7% (95% CI, 6.8%-10.6%). Sensitivity = 40.7% (95% CI, 30.9%-50.4%). Specificity = 97.7% (95% CI, 97.2%-98.3%). See Table 3 legend for expansion of abbreviations.

a 

FEV1/FEV6 ≥ lower limit of normal or FEV1 ≥ 60% predicted.

b 

FEV1/FEV6 < lower limit of normal and FEV1 < 60% predicted.

Having two or more risk factors was associated with both abnormal PEF (P < .001) and clinically significant airflow obstruction (P < .001). Based on a logistic regression analysis of all risk factors considered simultaneously, wheezing, asthma, ever smoked, and age ≥ 40 years were significant predictors of clinically significant airflow obstruction. However, after adjusting for PEF, only asthma and ever smoked remained significant predictors (OR for asthma, 1.9; 95% CI, 1.0-3.3; P = .04; and OR for ever smoked, 2.2; 95% CI, 1.3-3.9; P = .005).

Using the modified GOLD stage III criteria, 86 of the 179 participants with abnormal PEF were classified as severe COPD cases, compared with 16 of the 550 control subjects (P < .001) (Table 6). Thus, the PPV of PEF for GOLD stage III was 48.0% (95% CI, 40.7%-55.4%), whereas the NPV was 97.1% (95% CI, 95.7%-98.5%). In the weighted analysis, the estimated prevalence of GOLD stage III in the study population was 5.4% (95% CI, 3.99%-6.88%). Based on this prevalence estimate, the sensitivity and specificity were 49.4% (95% CI, 36.1%-62.8%) and 96.9% (95% CI, 96.3%-97.6%), respectively.

Table Graphic Jump Location
Table 6 —Positive and Negative Predictive Values of Peak Flow Screening for GOLD Stage III

Data are presented as No. (%). P value for χ2 test of association (1 df) < .001. Positive predictive value = 48.0% (95% CI, 40.7%-55.4%). Negative predictive value = 97.1% (95% CI, 95.7%-98.5%). GOLD = Global Initiative for Chronic Obstructive Lung Disease. See Table 3 legend for expansion of other abbreviations.

a 

FEV1/FEV6 ≥ 0.70 or FEV1 ≥ 50% predicted.

b 

FEV1/FEV6 < 0.70 and FEV1 < 50% predicted.

Evaluating the performance of PEF screening relative to any degree of abnormal lung function (moderate or severe COPD or presence of restricted spirometry) yielded a higher PPV (93.3%; 95% CI, 89.6%-97.0%) and lower NPV (68.2%; 95% CI, 64.3%-72.1%) (Table 7). Weighted estimates of population sensitivity and specificity followed a similar pattern: 14.8% (95% CI, 12.1%-17.5%) and 99.4% (95% CI, 99.1%-99.8%), respectively. A detailed assessment of the operating characteristics of the PEF screening test relative to a wide range of clinical outcomes is presented in e-Table 1.

Table Graphic Jump Location
Table 7 —Positive and Negative Predictive Values of Peak Flow Screening for Abnormal Lung Function

Data are presented as No. (%). P value for χ2 test of association (1 df) < .001. Positive predictive value = 93.3% (95% CI, 89.6%-97.0%). Negative predictive value = 68.2% (95% CI, 64.3%-72.1%). See Table 3 and 6 legends for expansion of abbreviations.

The results reported here demonstrate that a three-stage approach to COPD screening in the general population is both feasible and useful and avoids the need to conduct diagnostic-quality spirometry on all individuals at risk. The staged approach we used consisted of a questionnaire-based screening test, followed by a pocket spirometry assessment, followed by diagnostic spirometry. The cutoff used for our questionnaire screen (two or more risk factors) identified 84.8% of those who had PEF < 70% (267 out of 315). Our second-stage screener, PEF < 70%, which sent 5.6% of the population (315 out of 5,323) on to spirometry, significantly predicted abnormal pulmonary function. In this study, PEF ≥ 70% makes clinically significant airflow obstruction highly unlikely and essentially rules out severe COPD. PEF < 70% strongly suggests that lung function is abnormal and that further testing, including formal spirometry, is indicated.

In 2008, the US Preventive Services Task Force concluded that “screening for COPD using spirometry is likely to identify a predominance of patients with mild to moderate airflow obstruction who would not experience additional health benefits if labeled as having COPD.”7 Although that conclusion remains controversial, we elected to follow those recommendations by searching for individuals with more significant COPD, those with FEV1 < 60% predicted, as suggested by the clinical practice guidelines of the American College of Physicians,8 as well as those with severe COPD by GOLD criteria. Evidence suggests that even those with significant airflow obstruction often go undiagnosed, increasing the importance of an effective screening program.20 The relatively low false-negative rate (or high NPV) observed for the PEF screening represents a successful outcome for the study, even in the presence of modest sensitivity.

Our results extend those from the Burden of Lung Disease (BOLD) and Proyecto Latinoamericano de Investigación en Obstrucción Pulmonar (PLATINO) studies,14 which used an inexpensive pocket spirometer (electronic peak flowmeter). A staged approach, as implemented in our study, may improve efficiency because only the proportion of patients with low PEF need referral to specialty services for good-quality spirometry to confirm airflow obstruction.21 Pharmacy-based screening programs have also been instituted in Barcelona, Spain, although that study did not use a staged approach and only screened 161 patients.22

All smokers, regardless of spirometry results, should be helped by PCPs to quit smoking.23 The relationship between knowledge of lung function and smoking cessation is unclear, with some studies suggesting smokers are not more likely to successfully quit smoking when faced with abnormal spirometry results.24,25 Several new studies refute this conclusion.9, 2628 In the study by Parkes et al,27 all patients (smokers aged 35 years and older) had spirometry and equal exposure to cessation resources, but the intervention group, told their lung age, had validated cessation rates more than double that of the control group (6.4% vs 13.6%).

For many PCPs, the best solution may be to use a pocket spirometer to rule out more significant COPD, and to refer the fraction of patients with low PEF to a third-party expert to perform the necessary spirometry tests to confirm airflow obstruction or restriction.2931 In the United States, about one-half of the spirometry tests done at the time of the initial diagnosis of COPD are done in a traditional pulmonary function testing laboratory,32 but this approach is greatly underused, perhaps because of long delays, inconvenience, or excessive cost. Regularly scheduled “free clinics” in convenient locations (such as neighborhood pharmacies or community centers) have been conducted successfully in Poland.33 Another approach, which has proven successful in some settings, is for an itinerant nurse or certified technologist to schedule monthly visits to the general practitioner’s office to test the patients who have an indication for spirometry.34

Study Limitations

Only 5.6% of participants had PEF < 70%. Only 6.3% of 3,791 with at least two risk factors on screening questionnaire had abnormal PEF. These low percentages likely reflect the screened population. Many of these subjects were attending health fairs, suggesting greater awareness of health-related issues and potentially a healthier population. Although 40% gave a smoking history, only 11% reported smoking over the previous 6 months. In addition, 16% were under age 40, again decreasing the likelihood of finding significant chronic airflow obstruction. The study needs to be repeated in other settings such as medical clinics and doctors’ offices, where the yield may well be higher. However, these results also suggest the potential weakness of the screening questionnaire instrument used, and stress the need for improved screeners who could identify a higher percentage of people with abnormal lung function, including those with frequent exacerbations.

The results of our study may not fully apply to primary care settings in which mechanical PEF meters may be used by untrained office staff. It is likely that these devices would perform similarly to the pocket spirometer we chose, but additional validation work is required. The rate of falsely low PEF values, due to poor patient efforts and inaccurate meters, is likely to reduce the efficiency of a case-finding program when compared with our study, in which skilled technologists performed the PEF tests using electronic pocket spirometers. When patients with low PEF are identified in a primary care setting, they may not complete a referral for pre- and post-BD testing. Untoward results of the use of PEF in office settings might include diagnosis of COPD in all those with low PEF, without confirmation by good-quality post-BD spirometry.

We initially planned a “healthy” control group made up of a 10% random subsample of participants with normal peak flow who were selected via systematic sampling to perform spirometry. The inclusion of the control group allowed direct estimation of PPVs and NPVs, the primary analysis planned to evaluate the screening procedure. During the study, additional subjects with normal peak flow volunteered for spirometry, presumably out of curiosity about their results. Although we considered excluding these volunteers in the analysis, it was felt that the potential bias induced by their inclusion was offset by the increase in sample size they provided.

COPD is defined by most clinical practice guidelines as airflow obstruction that persists after the patient inhales a short-acting β-agonist bronchodilator.19 We did not perform post-BD spirometry for our participants to confirm post-BD airflow obstruction. However, very few adult outpatients start with an FEV1 below 50% predicted and have a bronchodilator response so large that post-BD FEV1 increases into the normal range.35 It is also important to note that 19% of our subjects (173 of 902) did not meet the quality standards for spirometric maneuvers despite highly trained personnel. This limitation has been noted by others,3641 and has been suggested as a limitation to the widespread use of spirometry as a screening tool. On the other hand, the more efficient application of spirometry in the clinical setting via a staged approach, including peak flow monitoring, as we used, could improve COPD screening efforts.

It is important to highlight that we used a “gold standard” for COPD diagnosis that was based on spirometric criteria. It should be noted that international guidelines have strongly supported that COPD diagnosis reflect a synthesis of clinical and physiologic information.19 This approach was impractical for the current study design but should be incorporated into future studies. The optimal threshold for referral for diagnostic spirometry and additional clinical testing depends on many factors, including the pretest probability of lung disease, the accuracy of the pocket spirometer or peak flowmeter, the match of the reference equations to the population being tested, and a thoughtful comparison of the consequences of false-negative vs false-positive results. We have shown that a PEF screener with a threshold of < 70% of predicted value is useful in identifying persons for whom diagnostic spirometry is warranted while avoiding needless screening of a large number of non-COPD cases. Other screeners are of interest and may have greater sensitivity in identifying a larger proportion of the cases occurring in the general population. As a result of incorporating the random sample of negative screens in the study design, comparisons between the PEF screener used in this study and other possible screening tests (ie, use of cutoffs other than 70% predicted PEF and use of other parameters recorded from the pocket spirometer, such as FEV1) are possible and are the focus of further research.

A pocket spirometer costs < $100. Diagnostic-quality office spirometers typically cost > $1,000. Measuring PEF requires much less training than do FVC maneuvers and can be performed rapidly by physician office staff. Using a pocket spirometer in a screening program can reduce the number of diagnostic spirometry tests required. A step-wise approach to detect undiagnosed people with clinically significant airflow obstruction can reduce costs and increase accuracy. We believe that the increased efficiency of this approach has the potential to increase disease awareness and improve the overall care of the COPD population in clinical practice.

Author contributions: Mr Nelson had full access to the data and takes responsibility for the integrity and accuracy of the data.

Mr Nelson: contributed to the study concept and design, acquisition of data, preparation and critical revision of the manuscript, and final approval of the version to be published.

Dr LaVange: contributed to the study concept and design, statistical analysis, preparation and critical revision of the manuscript, and final approval of the version to be published.

Dr Nie: contributed to the statistical analysis, preparation and critical revision of the manuscript, and final approval of the version to be published.

Mr Walsh: contributed to the study concept and design and revision of the manuscript.

Dr Enright: contributed to the study concept and design, acquisition of data, drafting of the article, preparation and critical revision of the manuscript, and final approval of the version to be published.

Dr Martinez: contributed to the study concept and design, preparation and critical revision of the manuscript, and final approval of the version to be published.

Dr Mannino: contributed to the study concept and design, preparation and critical revision of the manuscript, and final approval of the version to be published.

Dr Thomashow: contributed to the study concept and design as the principal investigator, analysis and interpretation of data, preparation and critical revision of the manuscript, and final approval of the version to be published.

Financial/nonfinancial disclosures: The authors have reported to CHEST the following conflicts of interest: Mr Nelson is an employee of the American Association for Respiratory Care and a member of the Medical and Scientific Advisory Board of the COPD Foundation. Dr LaVange previously owned stock options in Inspire Pharmaceuticals. She served on data and safety monitoring boards for several pharmaceutical companies in the past 3 years, namely MAP Pharmaceuticals; Merck & Co, Inc; and GlaxoSmithKline. Her role was to review data from ongoing clinical trials and evaluate whether the patients’ safety was at risk. Mr Walsh is president and a member of the board of directors of the COPD Foundation. Dr Enright is a member of the Medical and Scientific Advisory Board of the COPD Foundation. Dr Martinez has participated in advisory boards in COPD development for Actelion Pharmaceuticals Ltd; Almirall; American Institutes for Research; AstraZeneca; Bayer; BoomComm; Center for Health Care Education; Forest Laboratories, Inc; GlaxoSmithKline; Ikaria; MedImmune, LLC; Merck & Co, Inc; Novartis AG; Nycomed; Pearl; Pfizer, Inc; and Schering. He has been a member of steering committee for COPD studies sponsored by Actelion Pharmaceuticals Ltd; GlaxoSmithKline; Forest Laboratories, Inc; MPex; and Nycomed. He has participated in Food and Drug Administration mock panels for Boehringer Ingelheim GmbH and Forest Laboratories, Inc. The University of Michigan received funds from Boehringer Ingelheim GmbH for a COPD study. Dr Martinez has served on speaker’s bureaus or in continuing medical education activities sponsored by American College of Chest Physicians; American Lung Association; Almirall; AstraZeneca; Beaumont; Boehringer Ingelheim GmbH; CME Incite; ePocrates; Forest Laboratories, Inc; France Foundation; GlaxoSmithKline; Lovelace; MedEd; NACE; Nycomed; Potomac; Prescott; Sanofi-Aventis; St. Luke’s; and UpToDate. He has received royalties from Associates in Medical Marketing, Castle Connolly. Dr Mannino is a member of the medical and scientific advisory board and the board of directors of the COPD Foundation. He has served on advisory boards for Boehringer Ingelheim GmbH; Pfizer, Inc; GlaxoSmithKline; Sepracor (now Sunovion Pharmaceuticals, Inc); AstraZeneca; Novartis AG; and Ortho Biotech and has received research grants from AstraZeneca, GlaxoSmithKline, Novartis AG, and Pfizer, Inc. Dr Thomashow is a member of the medical and scientific advisory board and chairman of the board of directors of the COPD Foundation. He has served on advisory boards for GlaxoSmithKline, Boehringer Ingelheim GmbH, Talecris, Intermune, and Forest Laboratories, Inc. Dr Nie has reported that no potential conflicts of interest exist with any companies/organizations whose products or services may be discussed in this article.

Role of sponsors: The COPD Foundation provided the entire funding for this study, including payment of all expenses, both direct and indirect, for the COPD Foundation Mobile Spirometry Unit at venues selected by the study steering committee. The American Association for Respiratory Care (AARC) provided in kind support of senior staff participation, and study project management.

Additional information: The e-Appendixes and e-Table can be found in the “Supplemental Materials” area of the online article.

ATS

American Thoracic Society

GOLD

Global Initiative for Chronic Obstructive Lung Disease

LLN

lower limit of normal

NPV

negative predictive value

PCP

primary care practitioner

PEF

peak expiratory flow

post-BD

postbronchodilator

PPV

positive predictive value

Buist AS, McBurnie MA, Vollmer WM, et al;; BOLD Collaborative Research Group BOLD Collaborative Research Group. International variation in the prevalence of COPD (the BOLD Study): a population-based prevalence study. Lancet. 2007;370(9589):741-750.
 
The National Lung Health Education Program (NLHEP)The National Lung Health Education Program (NLHEP). Strategies in preserving lung health and preventing COPD and associated diseases. Chest. 1998;113(suppl 2):123S-163S.
 
Lusuardi M, De Benedetto F, Paggiaro P, et al. A randomized controlled trial on office spirometry in asthma and COPD in standard general practice: data from spirometry in Asthma and COPD: a comparative evaluation Italian study. Chest. 2006;129(4):844-852.
 
Bednarek M, Maciejewski J, Wozniak M, Kuca P, Zielinski J. Prevalence, severity and underdiagnosis of COPD in the primary care setting. Thorax. 2008;63(5):402-407.
 
Leuppi JD, Miedinger D, Chhajed PN, et al. Quality of spirometry in primary care for case finding of airway obstruction in smokers. Respiration. 2010;79(6):469-474.
 
U.S. Preventive Services Task ForceU.S. Preventive Services Task Force. Screening for chronic obstructive pulmonary disease using spirometry: US Preventive Services Task Force recommendation statement. Ann Intern Med. 2008;148(7):529-534.
 
Lin K, Watkins B, Johnson T, Rodriguez JA, Barton MB; U.S. Preventive Services Task Force U.S. Preventive Services Task Force. Screening for chronic obstructive pulmonary disease using spirometry: summary of the evidence for the U.S. Preventive Services Task Force. Ann Intern Med. 2008;148(7):535-543.
 
Qaseem A, Snow V, Shekelle P, et al;; Clinical Efficacy Assessment Subcommittee of the American College of Physicians Clinical Efficacy Assessment Subcommittee of the American College of Physicians. Diagnosis and management of stable chronic obstructive pulmonary disease: a clinical practice guideline from the American College of Physicians. Ann Intern Med. 2007;147(9):633-638.
 
Van Schayck CP, Loozen JMC, Wagena E, Akkermans RP, Wesseling GJ. Detecting patients at a high risk of developing chronic obstructive pulmonary disease in general practice: cross sectional case finding study. BMJ. 2002;324(7350):1370.
 
Martinez FJ, Raczek AE, Seifer FD, et al;; COPD-PS Clinician Working Group COPD-PS Clinician Working Group. Development and initial validation of a self-scored COPD Population Screener Questionnaire (COPD-PS). COPD. 2008;5(2):85-95.
 
Yawn BP, Mapel DW, Mannino DM, et al;; Lung Function Questionnaire Working Group Lung Function Questionnaire Working Group. Development of the Lung Function Questionnaire (LFQ) to identify airflow obstruction. Int J Chron Obstruct Pulmon Dis. 2010;5:1-10.
 
Price DB, Tinkelman DG, Nordyke RJ, Isonaka S, Halbert RJ; COPD Questionnaire Study Group COPD Questionnaire Study Group. Scoring system and clinical application of COPD diagnostic questionnaires. Chest. 2006;129(6):1531-1539.
 
Kotz D, Nelemans P, van Schayck CP, Wesseling GJ. External validation of a COPD diagnostic questionnaire. Eur Respir J. 2008;31(2):298-303.
 
Perez-Padilla R, Vollmer WM, Vázquez-García JC, Enright PL, Menezes AM, Buist AS; BOLD and PLATINO Study Groups BOLD and PLATINO Study Groups. Can a normal peak expiratory flow exclude severe chronic obstructive pulmonary disease?. Int J Tuberc Lung Dis. 2009;13(3):387-393.
 
Miller MR, Hankinson J, Brusasco V, et al;; ATS/ERS Task Force ATS/ERS Task Force. Standardisation of spirometry. Eur Respir J. 2005;26(2):319-338.
 
National Lung Health Education Program. Spirometer Review Process (SRP)-revised May 2008. Irving, TX: National Lung Health Education Program website.www.nlhep.org/spirometer-review-process.html. 2009. Accessed March 3, 2011.
 
Hankinson JL, Odencrantz JR, Fedan KB. Spirometric reference values from a sample of the general US population. Am J Respir Crit Care Med. 1999;159(1):179-187.
 
Vandevoorde J, Verbanck S, Schuermans D, Kartounian J, Vincken W. FEV1/FEV6and FEV6as an alternative for FEV1/FVC and FVC in the spirometric detection of airway obstruction and restriction. Chest. 2005;127(5):1560-1564.
 
Global Initiative for Chronic Obstructive Lung Disease (GOLD)Global Initiative for Chronic Obstructive Lung Disease (GOLD). Global strategy for diagnosis, management, and prevention of COPD. Global Initiative for Chronic Obstructive Lung Disease website.http://goldcopd.com/. Updated December 2010. Accessed March 3, 2011.
 
Peña VS, Miravitlles M, Gabriel R, et al. Geographic variations in prevalence and underdiagnosis of COPD: results of the IBERPOC multicentre epidemiological study. Chest. 2000;118(4):981-989.
 
Celli BR, MacNee W; ATS/ERS Task Force ATS/ERS Task Force. Standards for the diagnosis and treatment of patients with COPD: a summary of the ATS/ERS position paper. Eur Respir J. 2004;23(6):932-946.
 
Castillo D, Guayta R, Giner J, et al;; FARMAEPOC group FARMAEPOC group. COPD case finding by spirometry in high-risk customers of urban community pharmacies: a pilot study. Respir Med. 2009;103(6):839-845.
 
Tønnesen P, Carrozzi L, Fagerström KO, et al. Smoking cessation in patients with respiratory diseases: a high priority, integral component of therapy. Eur Respir J. 2007;29(2):390-417.
 
Buffels J, Degryse J, Decramer M, Heyrman J. Spirometry and smoking cessation advice in general practice: a randomised clinical trial. Respir Med. 2006;100(11):2012-2017.
 
Wilt TJ, Niewoehner D, Kane RL, MacDonald R, Joseph AM. Spirometry as a motivational tool to improve smoking cessation rates: a systematic review of the literature. Nicotine Tob Res. 2007;9(1):21-32.
 
Bednarek M, Gorecka D, Wielgomas J, et al. Smokers with airway obstruction are more likely to quit smoking. Thorax. 2006;61(10):869-873.
 
Parkes G, Greenhalgh T, Griffin M, Dent R. Effect on smoking quit rate of telling patients their lung age: the Step2quit randomised controlled trial. BMJ. 2008;336(7644):598-600.
 
Stratelis G, Mölstad S, Jakobsson P, Zetterström O. The impact of repeated spirometry and smoking cessation advice on smokers with mild COPD. Scand J Prim Health Care. 2006;24(3):133-139.
 
Cooper BG. Limitations to spirometry being performed in ‘the office’. Chron Respir Dis. 2005;2(2):113-115.
 
Enright P. Provide GPs with spirometry, not spirometers. Thorax. 2008;63(5):387-388.
 
Poels PJP, Schermer TRJ, Jacobs A, et al. Variation in spirometry utilization between trained general practitioners in practices equipped with a spirometer. Scand J Prim Health Care. 2006;24(2):81-87.
 
Han MK, Kim MG, Mardon R, et al. Spirometry utilization for COPD: how do we measure up?. Chest. 2007;132(2):403-409.
 
Zieliñski J, Bednarek M; Know the Age of Your Lung Study Group Know the Age of Your Lung Study Group. Early detection of COPD in a high-risk population using spirometric screening. Chest. 2001;119(3):731-736.
 
Walters JA, Hansen EC, Johns DP, Blizzard EL, Walters EH, Wood-Baker R. A mixed methods study to compare models of spirometry delivery in primary care for patients at risk of COPD. Thorax. 2008;63(5):408-414.
 
Tashkin DP, Celli B, Decramer M, et al. Bronchodilator responsiveness in patients with COPD. Eur Respir J. 2008;31(4):742-750.
 
Carvalhaes-Neto N, Lorino H, Gallinari C, et al. Cognitive function and assessment of lung function in the elderly. Am J Respir Crit Care Med. 1995;152(5 pt 1):1611-1615.
 
Bellia V, Pistelli R, Catalano F, et al. Quality control of spirometry in the elderly. The S.A.R.A. study. Am J Respir Crit Care Med. 2000;161(4):1094-1100.
 
Pezzoli L, Giardini G, Consonni S, et al. Quality of spirometric performance in older people. Age Ageing. 2003;32(1):43-46.
 
Allen S, Yeung P, Janczewski M, Siddique N. Predicting inadequate spirometry technique and the use of FEV1/FEV3as an alternative to FEV1/FVC for patients with mild cognitive impairment. Clin Respir J. 2008;2(4):208-213.
 
Bellia V, Pistelli F, Giannini D, et al. Questionnaires, spirometry and PEF monitoring in epidemiological studies on elderly respiratory patients. Eur Respir J Suppl. 2003;40:21s-27s.
 
Hegewald MJ, Lefor MJ, Jensen RL, et al. Peak expiratory flow is not a quality indicator for spirometry: peak expiratory flow variability and FEV1are poorly correlated in an elderly population. Chest. 2007;131(5):1494-1499.
 

Figures

Figure Jump LinkFigure 1. Disposition of study participants. PEF = peak expiratory flow; Spiro = spirometry.Grahic Jump Location

Tables

Table Graphic Jump Location
Table 1 —Exclusion
Table Graphic Jump Location
Table 2 —Demographic Characteristics and COPD Risk Factors for All Subjects Screened
Table Graphic Jump Location
Table 3 —Peak Flow Screening Results by Number of Risk Factors for All Subjects Screened

Data are presented as No. (%). Possible risk factors include seven questionnaire items and age ≥ 40 y. P value for χ2 test of association (1 df < .001). df = degrees of freedom; PEF = peak expiratory flow.

Table Graphic Jump Location
Table 4 —Positive and Negative Predictive Values of Peak Flow Screening for Clinically Significant Airflow Obstruction

Data are presented as No. (%). P value for χ2 test of association (1 df) < .001. Positive predicted value = 63.1% (95% CI, 56.1%-70.2%); negative predicted value = 94.5% (95% CI, 92.7%-96.4%). See Table 3 legend for expansion of abbreviations.

a 

FEV1/FEV6 ≥ lower limit of normal or FEV1 ≥ 60% predicted.

b 

FEV1/FEV6 < lower limit of normal and FEV1 < 60% predicted.

Table Graphic Jump Location
Table 5 —Weighted Estimates of Screened Population Distribution for Clinically Significant Airflow Obstruction

Data are presented as No. (%). P value for χ2 test of association (1 df) < .001. Estimated prevalence of significant obstruction = 8.7% (95% CI, 6.8%-10.6%). Sensitivity = 40.7% (95% CI, 30.9%-50.4%). Specificity = 97.7% (95% CI, 97.2%-98.3%). See Table 3 legend for expansion of abbreviations.

a 

FEV1/FEV6 ≥ lower limit of normal or FEV1 ≥ 60% predicted.

b 

FEV1/FEV6 < lower limit of normal and FEV1 < 60% predicted.

Table Graphic Jump Location
Table 6 —Positive and Negative Predictive Values of Peak Flow Screening for GOLD Stage III

Data are presented as No. (%). P value for χ2 test of association (1 df) < .001. Positive predictive value = 48.0% (95% CI, 40.7%-55.4%). Negative predictive value = 97.1% (95% CI, 95.7%-98.5%). GOLD = Global Initiative for Chronic Obstructive Lung Disease. See Table 3 legend for expansion of other abbreviations.

a 

FEV1/FEV6 ≥ 0.70 or FEV1 ≥ 50% predicted.

b 

FEV1/FEV6 < 0.70 and FEV1 < 50% predicted.

Table Graphic Jump Location
Table 7 —Positive and Negative Predictive Values of Peak Flow Screening for Abnormal Lung Function

Data are presented as No. (%). P value for χ2 test of association (1 df) < .001. Positive predictive value = 93.3% (95% CI, 89.6%-97.0%). Negative predictive value = 68.2% (95% CI, 64.3%-72.1%). See Table 3 and 6 legends for expansion of abbreviations.

References

Buist AS, McBurnie MA, Vollmer WM, et al;; BOLD Collaborative Research Group BOLD Collaborative Research Group. International variation in the prevalence of COPD (the BOLD Study): a population-based prevalence study. Lancet. 2007;370(9589):741-750.
 
The National Lung Health Education Program (NLHEP)The National Lung Health Education Program (NLHEP). Strategies in preserving lung health and preventing COPD and associated diseases. Chest. 1998;113(suppl 2):123S-163S.
 
Lusuardi M, De Benedetto F, Paggiaro P, et al. A randomized controlled trial on office spirometry in asthma and COPD in standard general practice: data from spirometry in Asthma and COPD: a comparative evaluation Italian study. Chest. 2006;129(4):844-852.
 
Bednarek M, Maciejewski J, Wozniak M, Kuca P, Zielinski J. Prevalence, severity and underdiagnosis of COPD in the primary care setting. Thorax. 2008;63(5):402-407.
 
Leuppi JD, Miedinger D, Chhajed PN, et al. Quality of spirometry in primary care for case finding of airway obstruction in smokers. Respiration. 2010;79(6):469-474.
 
U.S. Preventive Services Task ForceU.S. Preventive Services Task Force. Screening for chronic obstructive pulmonary disease using spirometry: US Preventive Services Task Force recommendation statement. Ann Intern Med. 2008;148(7):529-534.
 
Lin K, Watkins B, Johnson T, Rodriguez JA, Barton MB; U.S. Preventive Services Task Force U.S. Preventive Services Task Force. Screening for chronic obstructive pulmonary disease using spirometry: summary of the evidence for the U.S. Preventive Services Task Force. Ann Intern Med. 2008;148(7):535-543.
 
Qaseem A, Snow V, Shekelle P, et al;; Clinical Efficacy Assessment Subcommittee of the American College of Physicians Clinical Efficacy Assessment Subcommittee of the American College of Physicians. Diagnosis and management of stable chronic obstructive pulmonary disease: a clinical practice guideline from the American College of Physicians. Ann Intern Med. 2007;147(9):633-638.
 
Van Schayck CP, Loozen JMC, Wagena E, Akkermans RP, Wesseling GJ. Detecting patients at a high risk of developing chronic obstructive pulmonary disease in general practice: cross sectional case finding study. BMJ. 2002;324(7350):1370.
 
Martinez FJ, Raczek AE, Seifer FD, et al;; COPD-PS Clinician Working Group COPD-PS Clinician Working Group. Development and initial validation of a self-scored COPD Population Screener Questionnaire (COPD-PS). COPD. 2008;5(2):85-95.
 
Yawn BP, Mapel DW, Mannino DM, et al;; Lung Function Questionnaire Working Group Lung Function Questionnaire Working Group. Development of the Lung Function Questionnaire (LFQ) to identify airflow obstruction. Int J Chron Obstruct Pulmon Dis. 2010;5:1-10.
 
Price DB, Tinkelman DG, Nordyke RJ, Isonaka S, Halbert RJ; COPD Questionnaire Study Group COPD Questionnaire Study Group. Scoring system and clinical application of COPD diagnostic questionnaires. Chest. 2006;129(6):1531-1539.
 
Kotz D, Nelemans P, van Schayck CP, Wesseling GJ. External validation of a COPD diagnostic questionnaire. Eur Respir J. 2008;31(2):298-303.
 
Perez-Padilla R, Vollmer WM, Vázquez-García JC, Enright PL, Menezes AM, Buist AS; BOLD and PLATINO Study Groups BOLD and PLATINO Study Groups. Can a normal peak expiratory flow exclude severe chronic obstructive pulmonary disease?. Int J Tuberc Lung Dis. 2009;13(3):387-393.
 
Miller MR, Hankinson J, Brusasco V, et al;; ATS/ERS Task Force ATS/ERS Task Force. Standardisation of spirometry. Eur Respir J. 2005;26(2):319-338.
 
National Lung Health Education Program. Spirometer Review Process (SRP)-revised May 2008. Irving, TX: National Lung Health Education Program website.www.nlhep.org/spirometer-review-process.html. 2009. Accessed March 3, 2011.
 
Hankinson JL, Odencrantz JR, Fedan KB. Spirometric reference values from a sample of the general US population. Am J Respir Crit Care Med. 1999;159(1):179-187.
 
Vandevoorde J, Verbanck S, Schuermans D, Kartounian J, Vincken W. FEV1/FEV6and FEV6as an alternative for FEV1/FVC and FVC in the spirometric detection of airway obstruction and restriction. Chest. 2005;127(5):1560-1564.
 
Global Initiative for Chronic Obstructive Lung Disease (GOLD)Global Initiative for Chronic Obstructive Lung Disease (GOLD). Global strategy for diagnosis, management, and prevention of COPD. Global Initiative for Chronic Obstructive Lung Disease website.http://goldcopd.com/. Updated December 2010. Accessed March 3, 2011.
 
Peña VS, Miravitlles M, Gabriel R, et al. Geographic variations in prevalence and underdiagnosis of COPD: results of the IBERPOC multicentre epidemiological study. Chest. 2000;118(4):981-989.
 
Celli BR, MacNee W; ATS/ERS Task Force ATS/ERS Task Force. Standards for the diagnosis and treatment of patients with COPD: a summary of the ATS/ERS position paper. Eur Respir J. 2004;23(6):932-946.
 
Castillo D, Guayta R, Giner J, et al;; FARMAEPOC group FARMAEPOC group. COPD case finding by spirometry in high-risk customers of urban community pharmacies: a pilot study. Respir Med. 2009;103(6):839-845.
 
Tønnesen P, Carrozzi L, Fagerström KO, et al. Smoking cessation in patients with respiratory diseases: a high priority, integral component of therapy. Eur Respir J. 2007;29(2):390-417.
 
Buffels J, Degryse J, Decramer M, Heyrman J. Spirometry and smoking cessation advice in general practice: a randomised clinical trial. Respir Med. 2006;100(11):2012-2017.
 
Wilt TJ, Niewoehner D, Kane RL, MacDonald R, Joseph AM. Spirometry as a motivational tool to improve smoking cessation rates: a systematic review of the literature. Nicotine Tob Res. 2007;9(1):21-32.
 
Bednarek M, Gorecka D, Wielgomas J, et al. Smokers with airway obstruction are more likely to quit smoking. Thorax. 2006;61(10):869-873.
 
Parkes G, Greenhalgh T, Griffin M, Dent R. Effect on smoking quit rate of telling patients their lung age: the Step2quit randomised controlled trial. BMJ. 2008;336(7644):598-600.
 
Stratelis G, Mölstad S, Jakobsson P, Zetterström O. The impact of repeated spirometry and smoking cessation advice on smokers with mild COPD. Scand J Prim Health Care. 2006;24(3):133-139.
 
Cooper BG. Limitations to spirometry being performed in ‘the office’. Chron Respir Dis. 2005;2(2):113-115.
 
Enright P. Provide GPs with spirometry, not spirometers. Thorax. 2008;63(5):387-388.
 
Poels PJP, Schermer TRJ, Jacobs A, et al. Variation in spirometry utilization between trained general practitioners in practices equipped with a spirometer. Scand J Prim Health Care. 2006;24(2):81-87.
 
Han MK, Kim MG, Mardon R, et al. Spirometry utilization for COPD: how do we measure up?. Chest. 2007;132(2):403-409.
 
Zieliñski J, Bednarek M; Know the Age of Your Lung Study Group Know the Age of Your Lung Study Group. Early detection of COPD in a high-risk population using spirometric screening. Chest. 2001;119(3):731-736.
 
Walters JA, Hansen EC, Johns DP, Blizzard EL, Walters EH, Wood-Baker R. A mixed methods study to compare models of spirometry delivery in primary care for patients at risk of COPD. Thorax. 2008;63(5):408-414.
 
Tashkin DP, Celli B, Decramer M, et al. Bronchodilator responsiveness in patients with COPD. Eur Respir J. 2008;31(4):742-750.
 
Carvalhaes-Neto N, Lorino H, Gallinari C, et al. Cognitive function and assessment of lung function in the elderly. Am J Respir Crit Care Med. 1995;152(5 pt 1):1611-1615.
 
Bellia V, Pistelli R, Catalano F, et al. Quality control of spirometry in the elderly. The S.A.R.A. study. Am J Respir Crit Care Med. 2000;161(4):1094-1100.
 
Pezzoli L, Giardini G, Consonni S, et al. Quality of spirometric performance in older people. Age Ageing. 2003;32(1):43-46.
 
Allen S, Yeung P, Janczewski M, Siddique N. Predicting inadequate spirometry technique and the use of FEV1/FEV3as an alternative to FEV1/FVC for patients with mild cognitive impairment. Clin Respir J. 2008;2(4):208-213.
 
Bellia V, Pistelli F, Giannini D, et al. Questionnaires, spirometry and PEF monitoring in epidemiological studies on elderly respiratory patients. Eur Respir J Suppl. 2003;40:21s-27s.
 
Hegewald MJ, Lefor MJ, Jensen RL, et al. Peak expiratory flow is not a quality indicator for spirometry: peak expiratory flow variability and FEV1are poorly correlated in an elderly population. Chest. 2007;131(5):1494-1499.
 
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