Identifying Patients with Undiagnosed COPD in Primary Care Settings: Insight from Screening Tools and Epidemiologic Studies

AutoCOPD-A novel and practical machine learning model for COPD detection using whole-lung inspiratory quantitative CT measurements: a retrospective, multicenter study

Background

The rate of diagnosis for chronic obstructive pulmonary disease (COPD) is low worldwide. Quantitative computed tomography (QCT) parameters add value to quantify alterations in airway and lung parenchyma for COPD. This study aimed to assess the performance of QCT features in COPD detection using a whole-lung inspiratory CT model.

Methods

This multicenter retrospective study was performed on 4106 participants. The derivation cohort containing 1950 participants who enrolled in Guangzhou communities from August 2017 to December 2019, was separated for training and internal validation cohorts, and three external validation cohorts containing 1703 participants were recruited from the public hospitals (Cohort 1: the First Affiliated Hospital of Guangzhou Medical University; Cohort 2: Xiangyang central hospital; Cohort 3: the Second Affiliated Hospital of Xi'an Jiaotong University) in China between April 2017 and May 2024. Questionnaire information, CT reports, and QCT features derived from inspiratory CT were extracted for model development. A novel multimodal framework using eXtreme gradient boosting and hybrid feature selection was established for COPD detection. National Lung Screening Trial (NLST) cohort (n = 453) was applied to validate the multiracial extrapolation and robustness on low-dose CT scans.

Findings

The QCT model (referred to as AutoCOPD) with ten features achieved the highest AUC of 0·860 (95% CI: 0·823-0·898) in the internal validation cohort, and showed excellent discrimination when externally validated [Cohort 1: AUC = 0·915 (95% CI: 0·898-0·931); Cohort 2: AUC = 0·903 (95% CI: 0·864-0·943); Cohort 3: AUC = 0·914 (95% CI: 0·882-0·947); NLST: AUC = 0·881 (95% CI: 0·846-0·915)]. Decision curve analysis demonstrated that AutoCOPD was valuable across a range of COPD risk thresholds between 0·12 and 0·66 compared with intervention in all patients with COPD or no intervention.

Interpretation

Heterogeneous COPD can be well identified using AutoCOPD (https://lwj-lab.shinyapps.io/autocopd/) constructed by a subset of only ten QCT features. It may be generalizable across clinical settings and serve as a feasible tool for early detecting patients with mild or asymptomatic COPD to reduce delayed diagnosis in routine practice.

Funding

The National Natural Science Foundation of China, Guangzhou Laboratory, Natural Science Foundation of Guangdong Province, Guangzhou Municipal Science and Technology grant, State Key Laboratory of Respiratory Disease.

Using machine learning for early detection of chronic obstructive pulmonary disease: a narrative review

Chronic obstructive pulmonary disease (COPD) is a prevalent respiratory disease and ranks third in global mortality rates, imposing a significant burden on patients and society. This review looks at recent research, both domestically and abroad, on the application of machine learning (ML) for early COPD screening. The review discusses the practical application, key optimization points, and prospects of ML techniques in early COPD screening. The aim is to establish a scientific foundation and reference framework for future research and the development of screening strategies.

Impact of a Computerized Clinical Decision Support System to Improve Chronic Obstructive Pulmonary Disease Diagnosis and Testing for Alpha-1 Antitrypsin Deficiency

Rationale: Chronic obstructive pulmonary disease (COPD) and alpha-1 antitrypsin deficiency (AATD) are underrecognized diseases. This is in part due to the underdiagnosis and lack of confirmation of COPD but also from poor adherence to AATD screening recommendations. Objectives: A clinical decision support system (CDSS) to guide primary care providers improves spirometry testing and confirmation of COPD diagnosis in subjects at risk and improves AATD screening in patients with confirmed COPD. Methods: A CDSS was created to be applied to all Veterans attending single-center Veterans Affairs primary care clinics. The CDSS had an algorithmic dialogue with components executed in phases during different clinic visits: screening for COPD risk using the COPD population screening (COPD-PS) questionnaire, spirometry recommendation, and ordering tool for subjects with a prior diagnosis of COPD or subjects considered high risk by the COPD-PS, dialogue to confirm or discard the diagnosis of COPD, and recommendations for AATD screening in subjects with confirmed COPD. The latter was performed by ordering alpha-1 antitrypsin (AAT) serum levels. Each step of the CDSS algorithm approach was recorded and available to be retrieved at a later date for analysis. Results: Over 6 years, a total of 6,235 Veterans >40 years of age completed the CDSS. According to the COPD-PS questionnaire, 962 (18.5%) subjects were identified as high risk for COPD. An additional 579 subjects with a prior diagnosis of COPD also entered the subsequent steps of the CDSS algorithm. Of the high-risk cohort, the CDSS led to an increase in spirometry testing from 24% to 83% and led to a new diagnosis of COPD in 342 (43%). In the prior COPD diagnosis group, spirometry testing increased from 58% to 84%, leading to COPD reconfirmation in only 326 (67%). A total of 489 (68%) subjects with confirmed COPD completed AAT testing prompted by the CDSS, with 23 subjects identified with AATD and one with severe AATD. Conclusions: In the Veterans Affairs system, the use of a clinical decision support system algorithm that incorporates screening for COPD and AATD improves COPD over- and underdiagnosis and screening rates of AATD in a primary care setting.

Development of a prediction model to identify undiagnosed chronic obstructive pulmonary disease patients in primary care settings in China

BACKGROUND

At present, a large number of chronic obstructive pulmonary disease (COPD) patients are undiagnosed in China. Thus, this study aimed to develop a simple prediction model as a screening tool to identify patients at risk for COPD.

METHODS

The study was based on the data of 22,943 subjects aged 30 to 79 years and enrolled in the second resurvey of China Kadoorie Biobank during 2012 and 2013 in China. We stepwisely selected the predictors using logistic regression model. Then we tested the model validity through P-P graph, area under the receiver operating characteristic curve (AUROC), ten-fold cross validation and an external validation in a sample of 3492 individuals from the Enjoying Breathing Program in China.

RESULTS

The final prediction model involved 14 independent variables, including age, sex, location (urban/rural), region, educational background, smoking status, smoking amount (pack-years), years of exposure to air pollution by cooking fuel, family history of COPD, history of tuberculosis, body mass index, shortness of breath, sputum and wheeze. The model showed an area under curve (AUC) of 0.72 (95% confidence interval [CI]: 0.72-0.73) for detecting undiagnosed COPD patients, with the cutoff of predicted probability of COPD=0.22, presenting a sensitivity of 70.13% and a specificity of 62.25%. The AUROC value for screening undiagnosed patients with clinically significant COPD was 0.68 (95% CI: 0.66-0.69). Moreover, the ten-fold cross validation reported an AUC of 0.72 (95% CI: 0.71-0.73), and the external validation presented an AUC of 0.69 (95% CI: 0.68-0.71).

CONCLUSION

This prediction model can serve as a first-stage screening tool for undiagnosed COPD patients in primary care settings.

Discriminative Accuracy of the CAPTURE Tool for Identifying Chronic Obstructive Pulmonary Disease in US Primary Care Settings

Importance

Chronic obstructive pulmonary disease (COPD) is underdiagnosed in primary care.

Objective

To evaluate the operating characteristics of the CAPTURE (COPD Assessment in Primary Care To Identify Undiagnosed Respiratory Disease and Exacerbation Risk) screening tool for identifying US primary care patients with undiagnosed, clinically significant COPD.

Design, Setting, and Participants

In this cross-sectional study, 4679 primary care patients aged 45 years to 80 years without a prior COPD diagnosis were enrolled by 7 primary care practice-based research networks across the US between October 12, 2018, and April 1, 2022. The CAPTURE questionnaire responses, peak expiratory flow rate, COPD Assessment Test scores, history of acute respiratory illnesses, demographics, and spirometry results were collected.

Exposure

Undiagnosed COPD.

Main Outcomes and Measures

The primary outcome was the CAPTURE tool's sensitivity and specificity for identifying patients with undiagnosed, clinically significant COPD. The secondary outcomes included the analyses of varying thresholds for defining a positive screening result for clinically significant COPD. A positive screening result was defined as (1) a CAPTURE questionnaire score of 5 or 6 or (2) a questionnaire score of 2, 3, or 4 together with a peak expiratory flow rate of less than 250 L/min for females or less than 350 L/min for males. Clinically significant COPD was defined as spirometry-defined COPD (postbronchodilator ratio of forced expiratory volume in the first second of expiration [FEV1] to forced vital capacity [FEV1:FVC] <0.70 or prebronchodilator FEV1:FVC <0.65 if postbronchodilator spirometry was not completed) combined with either an FEV1 less than 60% of the predicted value or a self-reported history of an acute respiratory illness within the past 12 months.

Results

Of the 4325 patients who had adequate data for analysis (63.0% were women; the mean age was 61.6 years [SD, 9.1 years]), 44.6% had ever smoked cigarettes, 18.3% reported a prior asthma diagnosis or use of inhaled respiratory medications, 13.2% currently smoked cigarettes, and 10.0% reported at least 1 cardiovascular comorbidity. Among the 110 patients (2.5% of 4325) with undiagnosed, clinically significant COPD, 53 had a positive screening result with a sensitivity of 48.2% (95% CI, 38.6%-57.9%) and a specificity of 88.6% (95% CI, 87.6%-89.6%). The area under the receiver operating curve for varying positive screening thresholds was 0.81 (95% CI, 0.77-0.85).

Conclusions and Relevance

Within this US primary care population, the CAPTURE screening tool had a low sensitivity but a high specificity for identifying clinically significant COPD defined by presence of airflow obstruction that is of moderate severity or accompanied by a history of acute respiratory illness. Further research is needed to optimize performance of the screening tool and to understand whether its use affects clinical outcomes.

Functional Predictors Discriminating Asthma-COPD Overlap (ACO) from Chronic Obstructive Pulmonary Disease (COPD)

Background

A significant proportion of patients with obstructive lung disease have clinical and functional features of both asthma and chronic obstructive pulmonary disease (COPD), referred to as the asthma-COPD overlap (ACO). The distinction of these phenotypes, however, is not yet well-established due to the lack of defining clinical and/or functional criteria. The aim of our investigations was to assess the discriminating power of various lung function parameters on the assessment of ACO.

Methods

From databases of 4 pulmonary centers, a total of 540 patients (231 males, 309 females), including 372 patients with asthma, 77 patients with ACO and 91 patients with COPD, were retrospectively collected, and gradients among combinations of explanatory variables of spirometric (FEV1, FEV1/FVC, FEF25-75), plethysmographic (sReff, sGeff, the aerodynamic work of breathing at rest; sWOB), static lung volumes, including trapped gases and measurements of the carbon monoxide transfer (DLCO, KCO) were explored using multiple factor analysis (MFA). The discriminating power of lung function parameters with respect to ACO was assessed using linear discriminant analysis (LDA).

Results

LDA revealed that parameters of airway dynamics (sWOB, sReff, sGeff) combined with parameters of static lung volumes such as functional residual capacity (FRCpleth) and trapped gas at FRC (VTG FRC) are valuable and potentially important tools discriminating between asthma, ACO and COPD. Moreover, sWOB significantly contributes to the diagnosis of obstructive airway diseases, independent from the state of pulmonary hyperinflation, whilst the diffusion capacity for carbon monoxide (DLCO) significantly differentiates between the 3 diagnostic classes.

Conclusion

The complexity of COPD with its components of interaction and their heterogeneity, especially in discrimination from ACO, may well be differentiated if patients are explored by a whole set of target parameters evaluating, interactionally, flow limitation, airway dynamics, pulmonary hyperinflation, small airways dysfunction and gas exchange disturbances assessing specific functional deficits.

Reversible Airflow Obstruction Predicts Future Chronic Obstructive Pulmonary Disease Development in the SPIROMICS Cohort: An Observational Cohort Study

Rationale: Chronic obstructive pulmonary disease (COPD) is defined by fixed spirometric ratio, FEV1/FVC < 0.70 after inhaled bronchodilators. However, the implications of variable obstruction (VO), in which the prebronchodilator FEV1/FVC ratio is less than 0.70 but increases to 0.70 or more after inhaled bronchodilators, have not been determined. Objectives: We explored differences in physiology, exacerbations, and health status in participants with VO compared with reference participants without obstruction. Methods: Data from the SPIROMICS (Subpopulations and Intermediate Outcome Measures in COPD Study) cohort were obtained. Participants with VO were compared with reference participants without obstruction. Measurements and Main Results: We assessed differences in baseline radiographic emphysema and small airway disease at study entry, baseline, and change in lung function by spirometry, functional capacity by 6-minute walk, health status using standard questionnaires, exacerbation rates, and progression to COPD between the two groups. All models were adjusted for participant characteristics, asthma history, and tobacco exposure. We assessed 175 participants with VO and 603 reference participants without obstruction. Participants with VO had 6.2 times the hazard of future development of COPD controlling for other factors (95% confidence interval, 4.6-8.3; P < 0.001). Compared with reference participants, the VO group had significantly lower baseline pre- and post-bronchodilator (BD) FEV1, and greater decline over time in post-BD FEV1, and pre- and post-BD FVC. There were no significant differences in exacerbations between groups. Conclusions: Significant risk for future COPD development exists for those with pre- but not post-BD airflow obstruction. These findings support consideration of expanding spirometric criteria defining COPD to include pre-BD obstruction. Clinical trial registered with www.clinicaltrials.gov (NCT01969344).

Machine Learning Prediction of Progression in Forced Expiratory Volume in 1 Second in the COPDGene® Study

BACKGROUND

The heterogeneous nature of chronic obstructive pulmonary disease (COPD) complicates the identification of the predictors of disease progression. We aimed to improve the prediction of disease progression in COPD by using machine learning and incorporating a rich dataset of phenotypic features.

METHODS

We included 4496 smokers with available data from their enrollment and 5-year follow-up visits in the COPD Genetic Epidemiology (COPDGene^®) study. We constructed linear regression (LR) and supervised random forest models to predict 5-year progression in forced expiratory in 1 second (FEV₁) from 46 baseline features. Using cross-validation, we randomly partitioned participants into training and testing samples. We also validated the results in the COPDGene 10-year follow-up visit.

RESULTS

Predicting the change in FEV₁ over time is more challenging than simply predicting the future absolute FEV₁ level. For random forest, R-squared was 0.15 and the area under the receiver operator characteristic (ROC) curves for the prediction of participants in the top quartile of observed progression was 0.71 (testing) and respectively, 0.10 and 0.70 (validation). Random forest provided slightly better performance than LR. The accuracy was best for Global initiative for chronic Obstructive Lung Disease (GOLD) grades 1-2 participants, and it was harder to achieve accurate prediction in advanced stages of the disease. Predictive variables differed in their relative importance as well as for the predictions by GOLD.

CONCLUSION

Random forest, along with deep phenotyping, predicts FEV₁ progression with reasonable accuracy. There is significant room for improvement in future models. This prediction model facilitates the identification of smokers at increased risk for rapid disease progression. Such findings may be useful in the selection of patient populations for targeted clinical trials.

Efficacy of a Self-Designed Questionnaire for Community Screening of COPD

Purpose

To evaluate the screening efficacy of a self-designed questionnaire for chronic obstructive pulmonary disease (COPD) and the potential gender disparity in its efficacy.

Patients and Methods

A screening questionnaire, the COPD Screening Questionnaire-Minhang (COPD-MH), was designed with reference to the self-scored COPD population screener (COPD-PS) and the COPD screening questionnaire (COPD-SQ), incorporating characteristics of the local population in Shanghai, China. The revised questionnaire included only five questions. Each question scored 0–4, with a highest total score of 20. The COPD-PS and COPD-SQ comprised 5 and 7 questions, respectively. Their scoring criteria were not consecutive integers and, thus, not easily counted. The COPD-MH focused on symptoms, and each item was set the same answers for convenience. Screening for COPD was conducted among residents over 40 years old in a community in Shanghai using the three aforementioned questionnaires. Each participant also received spirometry tests. A receiver operator characteristic (ROC) curve was drawn, and the area under the curve (AUC) was calculated to assess the validity of each questionnaire.

Results

A total of 1197 community residents in Minhang District completed the screening. A total of 1023 participants were finally included in analysis with a detected prevalence of 12.4% for COPD. The best cut-off values for the COPD-PS, COPD-SQ, and COPD-MH were 4, 16, and 7 points, respectively. The AUCs for these three questionnaires were >0.5, but the sensitivity of the COPD-MH was higher than those of the COPD-PS and COPD-SQ. The sensitivity of COPD-MH was 80.77% for males and 77.5% for females. The COPD-MH had higher diagnostic efficiency and higher sensitivity with gender-specific cut-off values.

Conclusion

The COPD-MH is comparable to and less time-consuming than the existing screening methods for COPD. Gender-related factors affect the optimal cut-off values of the COPD screening questionnaire, and rectifying this can improve the practical screening efficacy.

Point your SmartPhone at the code above. If you have a QR code reader the video abstract will appear. Or use:

https://youtu.be/ZqWIhZrBdeo

Treatment Trials in Young Patients with Chronic Obstructive Pulmonary Disease and Pre-Chronic Obstructive Pulmonary Disease Patients: Time to Move Forward

Chronic obstructive pulmonary disease (COPD) is the end result of a series of dynamic and cumulative gene-environment interactions over a lifetime. The evolving understanding of COPD biology provides novel opportunities for prevention, early diagnosis, and intervention. To advance these concepts, we propose therapeutic trials in two major groups of subjects: "young" individuals with COPD and those with pre-COPD. Given that lungs grow to about 20 years of age and begin to age at approximately 50 years, we consider "young" patients with COPD those patients in the age range of 20-50 years. Pre-COPD relates to individuals of any age who have respiratory symptoms with or without structural and/or functional abnormalities, in the absence of airflow limitation, and who may develop persistent airflow limitation over time. We exclude from the current discussion infants and adolescents because of their unique physiological context and COPD in older adults given their representation in prior randomized controlled trials (RCTs). We highlight the need of RCTs focused on COPD in young patients or pre-COPD to reduce disease progression, providing innovative approaches to identifying and engaging potential study subjects. We detail approaches to RCT design, including potential outcomes such as lung function, patient-reported outcomes, exacerbations, lung imaging, mortality, and composite endpoints. We critically review study design components such as statistical powering and analysis, duration of study treatment, and formats to trial structure, including platform, basket, and umbrella trials. We provide a call to action for treatment RCTs in 1) young adults with COPD and 2) those with pre-COPD at any age.

Cost-effectiveness analysis of COPD screening programs in primary care for high-risk patients in China

We built a decision-analytic model to compare the cost-effectiveness of using portable spirometer and questionnaire to screen chronic obstructive pulmonary diseases (COPD) with no screening (i.e. usual care) among chronic bronchitis patient in China. A lifetime horizon and a payer perspective were adopted. Cost data of health services including spirometry screening and treatment costs covered both maintenance and exacerbation. The result indicated that portable spirometer screening was cost-saving compared with questionnaire screening and no screening, with an incremental cost-effectiveness ratio (ICER) of −5026 and −1766 per QALY, respectively. Sensitivity analyses confirmed the robustness of the results. In summary, portable spirometer screening is likely the optimal option for COPD screening among chronic bronchitis patients China.

Development and Assessment of Prediction Models for the Development of COPD in a Typical Rural Area in Northwest China

Objective

This study aimed to construct and evaluate a clinical predictive model for the development of COPD in northwest China's rural areas.

Methods

A cross-sectional study of a natural population was performed in rural northwest China. After assessing demographic and disease characteristics, a clinical prediction model was developed. First, we used the least absolute shrinkage and selection operator regression model to screen possible factors influencing COPD. Then construct a logistic regression model and draw a nomogram. The discriminability of the model was further evaluated by the calibration diagram, C-index and ROC curve system. Clinical benefit was analyzed using the decision curve. Finally, the 1000 bootstrap resamples and Harrell's C-index was used for internal verification of the nomogram.

Results

Among 3249 patients in the local rural natural population, 394 (12.13%) were diagnosed with COPD. The LASSO regression model was used to find the optimal combination of parameters, and the screened influencing factors included age, gender, barbeque, smoking, passive smoking, energy type, ventilation system and Post-Bronchodilator FEV1. These predictors are used to construct a nomogram. C index is 0.81 (95% confidence interval:0.79-0.83). The combination of the calibration curve and ROC curve indicates that the model has high discriminability. The decision curve shows benefits in clinical practice when the threshold probability is >6% and <58%, respectively. The internal verification results using Harrell's C-Index were 0.80 (95% confidence interval: 0.78-0.83).

Conclusion

Combining information such as age, sex, barbeque, smoking, passive smoking, type of energy, ventilation systems, and Post-Bronchodilator FEV1 can be easily used to predict the risk of COPD in local rural areas.

Protocol Summary of the COPD Assessment in Primary Care To Identify Undiagnosed Respiratory Disease and Exacerbation Risk (CAPTURE) Validation in Primary Care Study

Chronic obstructive pulmonary disease (COPD) often remains undiagnosed and untreated. To date, COPD screening/case finding has not been designed to identify clinically significant COPD, disease ready for therapies beyond smoking cessation. Herein, we describe the ongoing prospective, pragmatic cluster-randomized controlled trial to assess specificity and sensitivity of the COPD Assessment in Primary Care To Identify Undiagnosed Respiratory Disease and Exacerbation Risk (CAPTURE) tool consisting of 5 questions and peak expiratory flow. The tool is designed to identify clinically significant COPD (forced expiratory volume in 1 second [FEV₁] to forced vital capacity [FVC] ratio <.70 plus FEV₁% predicted <60% or increased risk for exacerbation) and the trial will explore the impact of CAPTURE-based screening on COPD diagnosis and treatment rates in primary care patients. Of a total planned enrollment of 5000 English- or Spanish-speaking patients 45 to 80 years of age without a prior COPD diagnosis from 100 primary care practices, a total of 68 practices and 3064 patients have been enrolled in the study. Practices are centrally randomized to either usual care or clinician receipt of patient-level CAPTURE results. All clinicians receive basic COPD education with those in intervention practices also receiving CAPTURE interpretation education. In a single visit, patient participants complete a CAPTURE screening, pre- and post-bronchodilator spirometry and baseline demographic and health questionnaires to validate CAPTURE sensitivity, specificity, and predictive value of identifying undiagnosed, clinically significant COPD. One-year follow-up chart reviews and participant surveys assess the impact of sharing versus not sharing CAPTURE results with clinicians on clinical outcomes including level of respiratory symptoms and events and clinicians' initiation of recommendation-concordant COPD care. This is one of the first U.S. studies to validate and assess impact of a simple COPD screening tool in primary care.

Prevalence and Characteristics of Undiagnosed COPD in Adults 40 Years and Older - Reports from the Tunisian Population-Based Burden of Obstructive Lung Disease Study

This study aimed to investigate the underdiagnosis of COPD and its determinants based on the Tunisian Burden of Obstructive Lung Disease study. We collected information on respiratory history symptoms and risk factors for COPD. Post-bronchodilator (Post-BD) FEV1/FVC < the lower limit of normal (LLN) was used to define COPD. Undiagnosed COPD was considered when participants had post-BD FEV1/FVC < LLN but were not given a diagnosis of emphysema, chronic bronchitis or COPD. 730 adults aged ⩾40 years selected from the general population were interviewed, 661 completed spirometry, 35 (5.3%) had COPD and 28 (80%) were undiagnosed with the highest prevalence in women (100%). When compared with patients with an established COPD diagnosis, undiagnosed subjects had a lower education level, milder airway obstruction (Post-BD FEV1 z-score -2.2 vs. -3.7, p < 0.001), fewer occurrence of wheezing (42.9% vs. 100%, p = 0.009), less previous lung function test (3.6% vs. 42.8%, p = 0.019) and less visits to the physician (32.1% vs. 85.7%, p = 0.020) in the past year. Multivaried analysis showed that the probability of COPD underdiagnosis was higher in subjects who had mild to moderate COPD and in those who did not visit a clinician and did not perform a spirometry in the last year. Collectively, our results highlight the need to improve the diagnosis of COPD in Tunisia. Wider use of spirometry should reduce the incidence of undiagnosed COPD. Spirometry should also predominately be performed not only in elderly male smokers but also in younger women in whom the prevalence of underdiagnosis is the highest.

Development and validation of a prediction model for airflow obstruction in older Chinese: Guangzhou Biobank Cohort Study

OBJECTIVE

To develop and validate a prediction model for airflow obstruction (AO) in older Chinese.

METHODS

DESIGN

Multivariable logistic regression analysis in large population cohort of Chinese aged ≥50 years.

PARTICIPANTS

Model development: 8762 Chinese aged ≥50 years were selected from the early phase recruits to the Guangzhou Biobank Cohort Study (GBCS) (recruited from September 2003 to May 2006). Internal validation: 100 bootstrap samples drawn with replacement from the development sample. External validation: 8395 Chinese aged ≥50 years from later phase GBCS (recruited from September 2006 to January 2008).

OUTCOMES

AO was defined by a forced expiratory volume in 1 s/forced vital capacity ratio < lower limits of normal.

RESULTS

839 (9.6%) and 764 (9.1%) individuals had AO in the development and temporal validation samples respectively. The predictors in the prediction model included sex, age, body mass index groups, smoking status, presence of respiratory symptoms, and history of asthma. Model development and validation was stratified by sex. Model performance including calibration (calibration-in-the-large -0.017 vs. -0.157; and calibration slope 0.88 vs. 1.02), discrimination (C-statistic 0.72 vs. 0.63 with 95% confidence interval 0.69-0.75 vs. 0.62-0.73) and clinical usefulness (decision curve analysis) in the external temporal validation sample were more satisfactory in men than that in women. Prediction models with risk thresholds (13% in men and 7% in women) and easy-to-use nomograms were developed to assess the probability of AO.

CONCLUSION

The diagnostic models based on readily available epidemiologic and clinical information with satisfactory performance can assist physicians to identify older individuals at high risk of AO and may improve the efficiency of spirometry for active case finding. Further validation beyond the Chinese population is warranted.

An accurate prediction model to identify undiagnosed at-risk patients with COPD: a cross-sectional case-finding study

Underuse or unavailability of spirometry is one of the most important factors causing underdiagnosis of COPD. We reported the development of a COPD prediction model to identify at-risk, undiagnosed COPD patients when spirometry was unavailable. This cross-sectional study enrolled subjects aged ≥40 years with respiratory symptoms and a smoking history (≥20 pack-years) in a medical center in two separate periods (development and validation cohorts). All subjects completed COPD assessment test (CAT), peak expiratory flow rate (PEFR) measurement, and confirmatory spirometry. A binary logistic model with calibration (Hosmer-Lemeshow test) and discrimination (area under receiver operating characteristic curve [AUROC]) was implemented. Three hundred and one subjects (development cohort) completed the study, including non-COPD (154, 51.2%) and COPD cases (147; stage I, 27.2%; II, 55.8%; III–IV, 17%). Compared with non-COPD and GOLD I cases, GOLD II-IV patients exhibited significantly higher CAT scores and lower lung function, and were considered clinically significant for COPD. Four independent variables (age, smoking pack-years, CAT score, and percent predicted PEFR) were incorporated developing the prediction model, which estimated the COPD probability (P_COPD). This model demonstrated favorable discrimination (AUROC: 0.866/0.828; 95% CI 0.825–0.906/0.751–0.904) and calibration (Hosmer-Lemeshow P = 0.332/0.668) for the development and validation cohorts, respectively. Bootstrap validation with 1000 replicates yielded an AUROC of 0.866 (95% CI 0.821–0.905). A P_COPD of ≥0.65 identified COPD patients with high specificity (90%) and a large proportion (91.4%) of patients with clinically significant COPD (development cohort). Our prediction model can help physicians effectively identify at-risk, undiagnosed COPD patients for further diagnostic evaluation and timely treatment when spirometry is unavailable.

Prediction models for the development of COPD: a systematic review

Early identification of people at risk of developing COPD is crucial for implementing preventive strategies. We aimed to systematically review and assess the performance of all published models that predicted development of COPD. A search was conducted to identify studies that developed a prediction model for COPD development. The Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies was followed when extracting data and appraising the selected studies. Of the 4,481 records identified, 30 articles were selected for full-text review, and only four of these were eligible to be included in the review. The only consistent predictor across all four models was a measure of smoking. Sex and age were used in most models; however, other factors varied widely. Two of the models had good ability to discriminate between people who were correctly or incorrectly classified as at risk of developing COPD. Overall none of the models were particularly useful in accurately predicting future risk of COPD, nor were they good at ruling out future risk of COPD. Further studies are needed to develop new prediction models and robustly validate them in external cohorts.

Can CAPTURE be used to identify undiagnosed patients with mild-to-moderate COPD likely to benefit from treatment?

Background

COPD Assessment in Primary Care To Identify Undiagnosed Respiratory Disease and Exacerbation Risk (CAPTURE™) uses five questions and peak expiratory flow (PEF) thresholds (males ≤350 L/min; females ≤250 L/min) to identify patients with a forced expiratory volume in 1 second (FEV₁)/forced vital capacity (FVC) <0.70 and FEV₁ <60% predicted or exacerbation risk requiring further evaluation for COPD. This study tested CAPTURE's ability to identify symptomatic patients with mild-to-moderate COPD (FEV₁ 60%-80% predicted) who may also benefit from diagnosis and treatment.

Methods

Data from the CAPTURE development study were used to test its sensitivity (SN) and specificity (SP) differentiating mild-to-moderate COPD (n=73) from no COPD (n=87). SN and SP for differentiating all COPD cases (mild to severe; n=259) from those without COPD (n=87) were also estimated. The modified Medical Research Council (mMRC) dyspnea scale and COPD Assessment Test (CAT™) were used to evaluate symptoms and health status. Clinical Trial Registration: NCT01880177, https://ClinicalTrials.gov/ct2/show/NCT01880177?term=NCT01880177&rank=1.

Results

Mean age (+SD): 61 (+10.5) years; 41% male. COPD: FEV₁/FVC=0.60 (+0.1), FEV₁% predicted=74% (+12.4). SN and SP for differentiating mild-to-moderate and non-COPD patients (n=160): Questionnaire: 83.6%, 67.8%; PEF (≤450 L/min; ≤350 L/min): 83.6%, 66.7%; CAPTURE (Questionnaire+PEF): 71.2%, 83.9%. COPD patients whose CAPTURE results suggested that diagnostic evaluation was warranted (n=52) were more likely to be symptomatic than patients whose results did not (n=21) (mMRC >2: 37% vs 5%, p<0.01; CAT>10: 86% vs 57%, p<0.01). CAPTURE differentiated COPD from no COPD (n=346): SN: 88.0%, SP: 83.9%.

Conclusion

CAPTURE (450/350) may be useful for identifying symptomatic patients with mild-to-moderate airflow obstruction in need of diagnostic evaluation for COPD.

How Well Does CAPTURE Translate?: An Exploratory Analysis of a COPD Case-Finding Method for Spanish-Speaking Patients

BACKGROUND

This study tested the properties of a Spanish translation of CAPTURE (COPD Assessment in Primary Care To Identify Undiagnosed Respiratory Disease and Exacerbation Risk) with selective use of peak expiratory flow (PEF).

METHODS

This study comprised analyses of data from the Spanish-speaking cohort of the cross-sectional, case-control study used to develop CAPTURE. Translation procedures included forward and backward translation, reconciliation, and cognitive interviewing to assure linguistic and cultural equivalence, yielding CAPTURE-S. Spanish-speaking participants were recruited through one center and designated as case subjects (clinically significant COPD: FEV₁ ≤ 60% predicted and/or at risk of COPD exacerbation) or control subjects (no or mild COPD). Subjects completed a questionnaire booklet that included 44 candidate items, the COPD Assessment Test (CAT), and the modified Medical Research Council (mMRC) dyspnea question. PEF and spirometry were also performed.

RESULTS

The study included 30 participants (17 case subjects and 13 control subjects). Their mean (± SD) age was 62.6 (11.49) years, and 33% were male. CAPTURE-S scores were significantly correlated with PEF (r = -0.78), the FEV₁/FVC ratio (r = -0.74), FEV₁ (r = -0.69), FEV₁ % predicted (r = -0.69), the CAT score (r = 0.70), and the mMRC dyspnea question (r = 0.59) (P < .0001), with significantly higher scores in case subjects than in control subjects (t = 6.16; P < .0001). PEF significantly correlated with FEV₁ (r = 0.89), FEV₁ % predicted (r = 0.79), and the FEV₁/FVC ratio (r = 0.75) (P < .0001), with significantly lower PEF in case subjects than in control subjects (t = 5.08; P < .0001). CAPTURE-S score + PEF differentiated case subjects and control subjects with a sensitivity of 88.2% and a specificity of 92.3%.

CONCLUSIONS

CAPTURE-S with selective use of PEF seems to be useful for identifying Spanish-speaking patients in need of diagnostic evaluation for clinically significant COPD who may benefit from initiation of COPD treatment.

TRIAL REGISTRY

ClinicalTrials.gov; No.: NCT01880177; URL: www.clinicaltrials.gov.

A New Approach for Identifying Patients with Undiagnosed Chronic Obstructive Pulmonary Disease

RATIONALE

Chronic obstructive pulmonary disease (COPD) is often unrecognized and untreated.

OBJECTIVES

To develop a method for identifying undiagnosed COPD requiring treatment with currently available therapies (FEV1 <60% predicted and/or exacerbation risk).

METHODS

We conducted a multisite, cross-sectional, case-control study in U.S. pulmonary and primary care clinics that recruited subjects from primary care settings. Cases were patients with COPD and at least one exacerbation in the past year or FEV1 less than 60% of predicted without exacerbation in the past year. Control subjects were persons with no COPD or with mild COPD (FEV1 ≥60% predicted, no exacerbation in the past year). In random forests analyses, we identified the smallest set of questions plus peak expiratory flow (PEF) with optimal sensitivity (SN) and specificity (SP).

MEASUREMENTS AND MAIN RESULTS

PEF and spirometry were recorded in 186 cases and 160 control subjects. The mean (SD) age of the sample population was 62.7 (10.1) years; 55% were female; 86% were white; and 16% had never smoked. The mean FEV1 percent predicted for cases was 42.5% (14.2%); for control subjects, it was 82.5% (15.7%). A five-item questionnaire, CAPTURE (COPD Assessment in Primary Care to Identify Undiagnosed Respiratory Disease and Exacerbation Risk), was used to assess exposure, breathing problems, tiring easily, and acute respiratory illnesses. CAPTURE exhibited an SN of 95.7% and an SP of 44.4% for differentiating cases from all control subjects, and an SN of 95.7% and an SP of 67.8% for differentiating cases from no-COPD control subjects. The PEF (males, <350 L/min; females, <250 L/min) SN and SP were 88.0% and 77.5%, respectively, for differentiating cases from all control subjects, and they were 88.0% and 90.8%, respectively, for distinguishing cases from no-COPD control subjects. The CAPTURE plus PEF exhibited improved SN and SP for all cases versus all control subjects (89.7% and 78.1%, respectively) and for all cases versus no-COPD control subjects (89.7% and 93.1%, respectively).

CONCLUSIONS

CAPTURE with PEF can identify patients with COPD who would benefit from currently available therapy and require further diagnostic evaluation. Clinical trial registered with clinicaltrials.gov (NCT01880177).

Which is the Best Screening Strategy for COPD among Smokers in Primary Care?

We developed a questionnaire to detect cases of chronic obstructive pulmonary disease (COPD) and compared its reliability with other strategies. In order to develop the new questionnaire (COPD screening questionnaire from Terrassa [EGARPOC]) we used data from an epidemiological study on the prevalence of COPD in smokers and calculated the odds ratio for each variable showing significance for the diagnosis of COPD on regression analysis. For comparison among questionnaires and the portable spirometer COPD-6, a cross-sectional multicenter study was performed. The study included 407 smokers or ex-smokers over the age of 40 years with no known diagnosis of COPD, who completed the different questionnaires (EGARPOC, Respiratory Health Screening Questionnaire, COPD-population screener and 2 questions) and underwent spirometry with the COPD-6. We determined the sensitivity, specificity, positive and negative predictive values (S, Sp, PPV and NPV, respectively) and the area under the receiver operating characteristic ROC curve (AUC ROC) of all the questionnaires and the different COPD-6 cut-offs. The prevalence of COPD was 26.3%. The EGARPOC questionnaire showed an S of 81.8%, an Sp of 70.6%, and an NPV of 91.8%; 73.3% of individuals were correctly classified, and the AUC ROC was 0.841. On comparing the questionnaires by the Chi-square test, the 2-question questionnaire showed the worst discrimination; while with an optimal cut-off of forced expiratory volume in one 1 second (FEV₁)/FEV₆ of 0.78, the COPD-6 was significantly better than the questionnaires in the detection of COPD. Using a cut-off of FEV₁/FEV₆ of 0.78 the COPD-6 was found to be the best screening tool for COPD in primary care compared to the questionnaires tested, which did not show differences among them.

A comparison of five surveys that identify individuals at risk for airflow obstruction and chronic obstructive pulmonary disease

BACKGROUND

The predictive characteristics of different screening surveys for the recognition of individuals at risk for airflow obstruction (AFO) have not been evaluated simultaneously in the same population.

PURPOSE

To compare five AFO/COPD screening questionnaires.

METHODS

383 individuals completed the Veterans Airflow Obstruction Screening Questionnaire, Personal Level Screener for COPD (VAFOSQ), the 11-Q COPD Screening Questionnaire (11-Q), the COPD Population Screener (COPD-PS) and the Lung Function Questionnaire (LFQ) and performed spirometry. AFO was defined as forced expiratory volume in one second divided by the forced vital capacity (FEV₁/FVC) < 0.7, fixed ratio (FR) or FEV₁/FVC < lower limit of normal (LLN). The predictive characteristics of the five questionnaires were calculated and non-parametric receiver operating characteristic (ROC) curves estimated by logistic regression.

RESULTS

376 participants completed at least two of the questionnaires and performed technically acceptable spirometry. AFO was present in 102 (27.1%) and 150 (39.9%) based on LLN and FR, respectively. The number of individuals positively selected by the VAFOSQ was 227, PLS 128, 11-Q 236, COPD-PS 217, and LFQ 328. The area under the ROC curves for the questionnaires was between 0.60 and 0.66 (LLN) and 0.58 and 0.66 (FR).

CONCLUSIONS

Although these screening surveys have acceptable and similar predictive ability for the identification of AFO, their published thresholds lead to substantially different classification rates. The choice of an appropriate threshold for the identification of individuals with possible AFO/COPD should consider the underlying prevalence of AFO/COPD in the target population and the relative costs of misclassifying affected and unaffected cases.

CLINICAL TRIAL REGISTRATION

None.

PRIMARY SOURCE OF FUNDING

Veterans Health Administration.

A retrospective study of two populations to test a simple rule for spirometry

Background

Chronic lung disease is common and often under-diagnosed.

Methods

To test a simple rule for conducting spirometry we reviewed spirograms from two populations, occupational medicine evaluations (OME) conducted by Saint Louis and Wake Forest Universities at 3 sites (n = 3260, mean age 64.14 years, 95 % CI 58.94–69.34, 97 % men) and conducted by Wake Forest University preop clinic (POC) at one site (n = 845, mean age 62.10 years, 95 % CI 50.46–73.74, 57 % men). This retrospective review of database information that the first author collected prospectively identified rates, types, sensitivity, specificity and positive and negative predictive value for lung function abnormalities and associated mortality rate found when conducting spirometry based on the 20/40 rule (≥20 years of smoking in those aged ≥ 40 years) in the OME population. To determine the reproducibility of the 20/40 rule for conducting spirometry, the rule was applied to the POC population.

Results

A lung function abnormality was found in 74 % of the OME population and 67 % of the POC population. Sensitivity of the rule was 85 % for an obstructive pattern and 77 % for any abnormality on spirometry. Positive and negative predictive values of the rule for a spirometric abnormality were 74 and 55 %, respectively. Patients with an obstructive pattern were at greater risk of coronary heart disease (odds ratio (OR) 1.39 [confidence interval (CI) 1.00–1.93] vs. normal) and death (hazard ratio (HR) 1.53, 95 % CI 1.20–1.84) than subjects with normal spirometry. Restricted spirometry patterns were also associated with greater risk of coronary disease (odds ratio (OR) 1.7 [CI 1.23–2.35]) and death (Hazard ratio 1.40, 95 % CI 1.08–1.72).

Conclusions

Smokers (≥ 20 pack years) age ≥ 40 years are at an increased risk for lung function abnormalities and those abnormalities are associated with greater presence of coronary heart disease and increased all-cause mortality. Use of the 20/40 rule could provide a simple method to enhance selection of candidates for spirometry evaluation in the primary care setting.

Meeting the challenge of COPD care delivery in the USA: a multiprovider perspective

The burden of chronic obstructive pulmonary disease (COPD) in the USA continues to grow. Although progress has been made in the the development of diagnostics, therapeutics, and care guidelines, whether patients' quality of life is improved will ultimately depend on the actual implementation of care and an individual patient's access to that care. In this Commission, we summarise expert opinion from key stakeholders-patients, caregivers, and medical professionals, as well as representatives from health systems, insurance companies, and industry-to understand barriers to care delivery and propose potential solutions. Health care in the USA is delivered through a patchwork of provider networks, with a wide variation in access to care depending on a patient's insurance, geographical location, and socioeconomic status. Furthermore, Medicare's complicated coverage and reimbursement structure pose unique challenges for patients with chronic respiratory disease who might need access to several types of services. Throughout this Commission, recurring themes include poor guideline implementation among health-care providers and poor patient access to key treatments such as affordable maintenance drugs and pulmonary rehabilitation. Although much attention has recently been focused on the reduction of hospital readmissions for COPD exacerbations, health systems in the USA struggle to meet these goals, and methods to reduce readmissions have not been proven. There are no easy solutions, but engaging patients and innovative thinkers in the development of solutions is crucial. Financial incentives might be important in raising engagement of providers and health systems. Lowering co-pays for maintenance drugs could result in improved adherence and, ultimately, decreased overall health-care spending. Given the substantial geographical diversity, health systems will need to find their own solutions to improve care coordination and integration, until better data for interventions that are universally effective become available.

Insight into Best Variables for COPD Case Identification: A Random Forests Analysis

RATIONALE

This study is part of a larger, multi-method project to develop a questionnaire for identifying undiagnosed cases of chronic obstructive pulmonary disease (COPD) in primary care settings, with specific interest in the detection of patients with moderate to severe airway obstruction or risk of exacerbation.

OBJECTIVES

To examine 3 existing datasets for insight into key features of COPD that could be useful in the identification of undiagnosed COPD.

METHODS

Random forests analyses were applied to the following databases: COPD Foundation Peak Flow Study Cohort (N=5761), Burden of Obstructive Lung Disease (BOLD) Kentucky site (N=508), and COPDGene® (N=10,214). Four scenarios were examined to find the best, smallest sets of variables that distinguished cases and controls:(1) moderate to severe COPD (forced expiratory volume in 1 second [FEV1] <50% predicted) versus no COPD; (2) undiagnosed versus diagnosed COPD; (3) COPD with and without exacerbation history; and (4) clinically significant COPD (FEV1<60% predicted or history of acute exacerbation) versus all others.

RESULTS

From 4 to 8 variables were able to differentiate cases from controls, with sensitivity ≥73 (range: 73-90) and specificity >68 (range: 68-93). Across scenarios, the best models included age, smoking status or history, symptoms (cough, wheeze, phlegm), general or breathing-related activity limitation, episodes of acute bronchitis, and/or missed work days and non-work activities due to breathing or health.

CONCLUSIONS

Results provide insight into variables that should be considered during the development of candidate items for a new questionnaire to identify undiagnosed cases of clinically significant COPD.

Identifying cases of undiagnosed, clinically significant COPD in primary care: qualitative insight from patients in the target population

Background:

Many cases of chronic obstructive pulmonary disease (COPD) are diagnosed only after significant loss of lung function or during exacerbations.

Aims:

This study is part of a multi-method approach to develop a new screening instrument for identifying undiagnosed, clinically significant COPD in primary care.

Methods:

Subjects with varied histories of COPD diagnosis, risk factors and history of exacerbations were recruited through five US clinics (four pulmonary, one primary care). Phase I: Eight focus groups and six telephone interviews were conducted to elicit descriptions of risk factors for COPD, recent or historical acute respiratory events, and symptoms to inform the development of candidate items for the new questionnaire. Phase II: A new cohort of subjects participated in cognitive interviews to assess and modify candidate items. Two peak expiratory flow (PEF) devices (electronic, manual) were assessed for use in screening.

Results:

Of 77 subjects, 50 participated in Phase I and 27 in Phase II. Six themes informed item development: exposure (smoking, second-hand smoke); health history (family history of lung problems, recurrent chest infections); recent history of respiratory events (clinic visits, hospitalisations); symptoms (respiratory, non-respiratory); impact (activity limitations); and attribution (age, obesity). PEF devices were rated easy to use; electronic values were significantly higher than manual (P<0.0001). Revisions were made to the draft items on the basis of cognitive interviews.

Conclusions:

Forty-eight candidate items are ready for quantitative testing to select the best, smallest set of questions that, together with PEF, can efficiently identify patients in need of diagnostic evaluation for clinically significant COPD.