Canadian Cohort Obstructive Lung Disease Research Articles

Identifying abnormal exertional breathlessness in COPD: comparing mMRC and CAT with CPET

Chronic obstructive pulmonary disease (COPD) management is guided by the respiratory symptom burden, assessed using the modified Medical Research Council (mMRC) scale and/or COPD Assessment Test (CAT). What is the ability of mMRC and CAT to detect abnormally high exertional breathlessness on incremental cardiopulmonary cycle exercise testing (CPET) in people with COPD? Analysis of people aged ≥40 years with post-bronchodilator FEV1/FVC<0.70 and ≥10 smoking pack-years from the Canadian Cohort Obstructive Lung Disease study. Abnormal exertional breathlessness was defined as a breathlessness (Borg 0-10) intensity rating > upper limit of normal (ULN) at the symptom-limited peak of CPET using normative reference equations. We included 318 people with COPD (40% women), age 66.5±9.3 years (mean±SD), FEV1 79.5±19.0%predicted; 26% had abnormally low exercise capacity (V'O2peak <lower limit of normal). Abnormally high exertional breathlessness was present in 24%, including 9% and 11% of people with mMRC=0 and CAT<10, respectively. A mMRC≥2 and CAT≥10 was most specific (95%) to detect abnormal exertional breathlessness, but had low sensitivity of only 12%. Accuracy for all scale cut-offs or combinations was <65%. Compared with 'true negatives', people with abnormal exertional breathlessness but low mMRC and/or CAT scores ('false negatives') had worse self-reported and physiological outcomes during CPET, were more likely to have physician-diagnosed COPD, but were not more likely to have any respiratory medication (37% versus 30%; mean difference 6.1%; 95% confidence interval -7.2 to 19.4; p=0.36). In COPD, mMRC and CAT have low concordance with CPET and fail to identify many people with abnormally high exertional breathlessness.

CT Chest Imaging Using Normalized Join-Count: Predicting Emphysema Progression in the CanCOLD Study.

Background Pre-existing emphysema is recognized as an indicator of future worsening in patients with chronic obstructive pulmonary disease (COPD) when observed through CT imaging. However, it remains uncertain whether additional factors, such as the spatial compactness of CT emphysema, might also serve as predictors of disease progression. Purpose To evaluate the relationship between the compactness of CT emphysema voxels and emphysema progression. Materials and Methods This secondary analysis uses data from the prospective Canadian Cohort Obstructive Lung Disease (CanCOLD) study, examining CT images obtained in participants with and without COPD at baseline and a 3-year follow-up time point (November 2009 to November 2018). Measurements of forced expiratory volume in first second of expiration (FEV1) and diffusing capacity of lung for carbon monoxide (DLco) were collected. The normalized join-count (NJC) measurement from baseline CT images and lung density (LD) changes were analyzed. Emphysema progression was defined as an annualized LD change of less than half an SD below the mean of the participants without COPD with no smoking history. Multivariable linear and logistic regression models were used to assess the association between baseline CT NJC measurements and the annualized change in LD, FEV1, DLco, and emphysema progression versus nonprogression. Results A total of 524 participants (mean age, 66 years ± 10 [SD]; 293 male) (FEV1 percent predicted, 88% ± 19; FEV1/FVC, 67% ± 9; DLco percent predicted, 105% ± 25) were analyzed, 187 (36%) of whom had COPD. CT NJC was associated with the annualized change in LD (P < .001), FEV1 (P = .02), and DLco (P = .01). Additionally, CT NJC predicted emphysema progression versus nonprogression (odds ratio, 2.24; 95% CI: 1.37, 3.50; P < .001). Conclusion The spatial distribution, or "compactness," of CT emphysema voxels predicted emphysema progression in individuals with and without COPD. ClinicalTrials.gov Identifier: NCT00920348 © RSNA, 2024 Supplemental material is available for this article.

Enhancing COPD classification using combined quantitative computed tomography and texture-based radiomics: a CanCOLD cohort study.

Recent advances in texture-based computed tomography (CT) radiomics have demonstrated its potential for classifying COPD. Participants from the Canadian Cohort Obstructive Lung Disease (CanCOLD) study were evaluated. A total of 108 features were included: eight quantitative CT (qCT), 95 texture-based radiomic and five demographic features. Machine-learning models included demographics along with texture-based radiomics and/or qCT. Combinations of five feature selection and five classification methods were evaluated; a training dataset was used for feature selection and to train the models, and a testing dataset was used for model evaluation. Models for classifying COPD status and severity were evaluated using the area under the receiver operating characteristic curve (AUC) with DeLong's test for comparison. SHapely Additive exPlanations (SHAP) analysis was used to investigate the features selected. A total of 1204 participants were evaluated (n=602 no COPD; n=602 COPD). There were no differences between the groups for sex (p=0.77) or body mass index (p=0.21). For classifying COPD status, the combination of demographics, texture-based radiomics and qCT performed better (AUC=0.87) than the combination of demographics and texture-based radiomics (AUC=0.81, p<0.05) or qCT alone (AUC=0.84, p<0.05). Similarly, for classifying COPD severity, the combination of demographics, texture-based radiomics and qCT performed better (AUC=0.81) than demographics and texture-based radiomics (AUC=0.72, p<0.05) or qCT alone (AUC=0.79, p<0.05). Texture-based radiomics and qCT features were among the top five features selected (15th percentile of the CT density histogram, CT total airway count, pack-years, CT grey-level distance zone matrix zone distance entropy, CT low-attenuation clusters) for classifying COPD status. Texture-based radiomics and conventional qCT features in combination improve machine‑learning models for classification of COPD status and severity.

CT trachea surface roughness is associated with chronic obstructive pulmonary disease symptoms

Abstract Background Trachea structural abnormalities occur in patients with chronic obstructive pulmonary disease (COPD), yet there are few methods for quantifying trachea surface topology. Purpose To develop a method to quantify trachea surface roughness on CT imaging and investigate the association with airflow limitation and symptoms in COPD. Materials and Methods Participants from the multicenter prospective Canadian Cohort Obstructive Lung Disease study between 2009 and 2015 underwent CT imaging and analysis. Established CT measurements included: tracheal index (TI), defined as the smallest ratio of coronal-to-sagittal trachea diameter, low attenuation areas below –950 HU, and wall thickness of a theoretical 10-mm airway. Trachea surface roughness shape (SRS) was calculated as the percent fraction of the measurement box filled by the surface mesh. Multivariable regression models were used to determine association for CT measurements with forced expiratory volume in 1 second (FEV1) and forced vital capacity (FVC), and Medical Research Council dyspnea scale (MRC)≥3, adjusting for covariates. Results A total of 1253 participants (mean age, 66 ± 10 years; 727 men) from 9 centers were investigated: n = 267 never smokers, n = 369 ever smokers, n = 352 mild COPD, and n = 265 moderate-to-severe COPD. There were no differences between groups for age or race (P &amp;lt; .05). In models including SRS and TI, a 1-standard deviation (SD) increase in SRS was independently associated with a 0.11-SD decrease in FEV1 (β = –0.11; P &amp;lt; .001) and a 0.16-SD decrease in FEV1/FVC (β = –0.16; P &amp;lt; .001); a 1-point increase in SRS was associated with a 13% increased likelihood of MRC ≥ 3 (odds ratio = 1.13; P = .003). In models including SRS, low attenuation areas below –950 HU and wall thickness of a theoretical 10-mm airway, a 1-SD increase in SRS was associated with a 0.21-SD decrease in FEV1 (β = –0.21; P &amp;lt; .001) and a 0.13-SD decrease in FEV1/FVC (β = –0.13; P &amp;lt; .001); a 1-point increase in SRS was associated with a 12% increased likelihood of MRC ≥ 3 (odds ratio = 1.12; P = .006). Conclusion Increased trachea surface shape roughness is independently associated with worse airflow and increased symptom burden in COPD.

Abnormal Exertional Breathlessness on Cardiopulmonary Cycle Exercise Testing in Relation to Self-Reported and Physiologic Responses in Chronic Airflow Limitation

Exertional breathlessness is a cardinal symptom of cardiorespiratory disease. How does breathlessness abnormality, graded using normative reference equations during cardiopulmonary exercise testing (CPET), relate to self-reported and physiological responses in people with chronic airflow limitation (CAL)? An analysis was done of people aged≥ 40 years with CAL undergoing CPET in the Canadian Cohort Obstructive Lung Disease study. Breathlessness intensity ratings (Borg CR10 scale [0-10 category-ratio scale for breathlessness intensity rating]) were evaluated in relation to power output (W), rate of oxygen uptake (V˙o2), and minute ventilation (V˙e) at peak exercise, using normative reference equations as follows: (1) probability of breathlessness normality (probability of having an equal or greater Borg CR10 rating among healthy; lower probability reflecting more severe breathlessness) and (2) presence of abnormal breathlessness (rating above the upper limit of normal). Associations with relevant participant-reported and physiologic outcomes were evaluated. We included 330 participants (44%women): mean ± SD age, 64 ± 10 years (range, 40-89 years); FEV1/FVC, 57.3%± 8.2%; FEV1, 75.6%± 17.9%predicted. Abnormally low exercise capacity (peak V˙o2< lower limit of normal) was present in 26%. Relative to peak W, V˙o2, and V˙e, abnormally high breathlessness was present in 26%, 25%, and 18%of participants. For all equations, abnormally high exertional breathlessness was associated with worse lung function, exercise capacity, self-reported symptom burden, physical activity, and health-related quality of life; and greater physiological abnormalities during CPET. Abnormal breathlessness graded using CPET normative reference equations was associated with worse clinical, physiological, and functional outcomes in people with CAL, supporting construct validity of abnormal exertional breathlessness.

A fully automated pipeline for the extraction of pectoralis muscle area from chest computed tomography scans.

Computed tomography (CT)-derived pectoralis muscle area (PMA) measurements are prognostic in people with or at-risk of COPD, but fully automated PMA extraction has yet to be developed. Our objective was to develop and validate a PMA extraction pipeline that can automatically: 1) identify the aortic arch slice; and 2) perform pectoralis segmentation at that slice. CT images from the Canadian Cohort of Obstructive Lung Disease (CanCOLD) study were used for pipeline development. Aorta atlases were used to automatically identify the slice containing the aortic arch by group-based registration. A deep learning model was trained to segment the PMA. The pipeline was evaluated in comparison to manual segmentation. An external dataset was used to evaluate generalisability. Model performance was assessed using the Dice-Sorensen coefficient (DSC) and PMA error. In total 90 participants were used for training (age 67.0±9.9 years; forced expiratory volume in 1 s (FEV1) 93±21% predicted; FEV1/forced vital capacity (FVC) 0.69±0.10; 47 men), and 32 for external testing (age 68.6±7.4 years; FEV1 65±17% predicted; FEV1/FVC 0.50±0.09; 16 men). Compared with manual segmentation, the deep learning model achieved a DSC of 0.94±0.02, 0.94±0.01 and 0.90±0.04 on the true aortic arch slice in the train, validation and external test sets, respectively. Automated aortic arch slice detection obtained distance errors of 1.2±1.3 mm and 1.6±1.5 mm on the train and test data, respectively. Fully automated PMA measurements were not different from manual segmentation (p>0.05). PMA measurements were different between people with and without COPD (p=0.01) and correlated with FEV1 % predicted (p<0.05). A fully automated CT PMA extraction pipeline was developed and validated for use in research and clinical practice.

Dysanapsis is not associated with exertional dyspnoea in healthy male and female never-smokers aged 40 years and older.

In healthy adults, airway-to-lung (i.e., dysanapsis) ratio is lower and dyspnoea during exercise at a given minute ventilation (V̇E) is higher in females than in males. We investigated the relationship between dysanapsis and sex on exertional dyspnoea in healthy adults. We hypothesized that females would have a smaller airway-to-lung ratio than males and that exertional dyspnoea would be associated with airway-to-lung ratio in males and females. We analyzed data from n=100 healthy never-smokers aged≥40 years enrolled in the Canadian Cohort Obstructive Lung Disease (CanCOLD) study who underwent pulmonary function testing, a chest computed tomography scan, and cardiopulmonary exercise testing. The luminal area of the trachea, right main bronchus, left main bronchus, right upper lobe, bronchus intermedius, left upper lobe, and left lower lobe were 22%-37% smaller (all p<0.001) and the airway-to-lung ratio (i.e., average large conducting airway diameter relative to total lung capacity) was lower in females than in males (0.609±0.070vs. 0.674±0.082; p<0.001). During exercise, there was a significant effect of V̇E, sex, and their interaction on dyspnoea (all p<0.05), indicating that dyspnoea increased as a function of V̇E to a greater extent in females than in males. However, after adjusting for age and total lung capacity, there were no significant associations between airway-to-lung ratio and measures of exertional dyspnoea, regardless of sex (all r<0.34; all p>0.05). Our findings suggest that sex differences in airway size do not contribute to sex differences in exertional dyspnoea.

Normative Reference Equations for Breathlessness Intensity during Incremental Cardiopulmonary Cycle Exercise Testing.

Rationale: Cardiopulmonary exercise testing (CPET) is the gold standard to evaluate exertional breathlessness, a common and disabling symptom. However, the interpretation of breathlessness responses to CPET is limited by a scarcity of normative data. Objectives: We aimed to develop normative reference equations for breathlessness intensity (Borg 0-10 category ratio) response in men and women aged ⩾40 years during CPET, in relation to power output (watts), oxygen uptake, and minute ventilation. Methods: Analysis of ostensibly healthy people aged ⩾40 years undergoing symptom-limited incremental cycle CPET (10 W/min) in the CanCOLD (Canadian Cohort Obstructive Lung Disease) study. Participants had smoking histories <5 pack-years and normal lung function and exercise capacity. The probability of each Borg 0-10 category ratio breathlessness intensity rating by power output, oxygen uptake, and minute ventilation (as an absolute or a relative value [percentage of predicted maximum]) was predicted using ordinal multinomial logistic regression. Model performance was evaluated by fit, calibration, and discrimination (C statistic) and externally validated in an independent sample (n = 86) of healthy Canadian adults. Results: We included 156 participants (43% women) from CanCOLD; the mean age was 65 (range, 42-91) years, and the mean body mass index was 26.3 (standard deviation, 3.8) kg/m2. Reference equations were developed for women and men separately, accounting for age and/or body mass. Model performance was high across all equations, including in the validation sample (C statistic for men = 0.81-0.92, C statistic for women = 0.81-0.96). Conclusions: Normative reference equations are provided to compare exertional breathlessness intensity ratings among individuals or groups and to identify and quantify abnormal breathlessness responses (scores greater than the upper limit of normal) during CPET.

The modified Borg/6-min walk distance ratio: a method to assess exertional breathlessness and leg discomfort using the 6-min walk test.

The 6-min walk test (6MWT) is widely used to assess exercise capacity across chronic health conditions, but is currently not useful to assess symptoms, as the scores do not account for the 6-min walk distance (6MWD). We aimed to 1) develop normative reference equations for breathlessness and leg discomfort intensity expressed as modified Borg (mBorg)/6MWD ratios; and 2) validate the equations in people with COPD. Analysis of people aged ≥40 years who performed two 6MWTs (on a 20-m course) in the Canadian Cohort Obstructive Lung Disease (CanCOLD) study: a healthy cohort (n=291; mean±sd age 67.5±9.4 years; 54% male) with normal 6MWD and lung function, and a COPD cohort (n=156; age 66.2±9.0 years; 56% male; forced expiratory volume in 1 s (FEV1)/forced vital capacity 56.6±8.2%; FEV1 74.4±18.6% pred). The mBorg score was calculated as the Borg 0-10 category ratio intensity rating of breathlessness or leg discomfort recorded at the end of the 6MWT +1 (range 1-11), to avoid zeros and yield ratios proportional to the symptom score and 6MWD-1. Using data from the healthy cohort, sex-specific normative reference equations for breathlessness and leg discomfort mBorg/6MWD ratios were developed using multivariable linear regression, accounting for age, and body mass or body mass index. In the COPD cohort, abnormal breathlessness and leg discomfort (mBorg/6MWD>upper limit of normal) showed strong concurrent validity with worse airflow limitation, Medical Research Council breathlessness and COPD Assessment Test scores. Normative references for the mBorg/6MWD ratio are presented to assess breathlessness and leg discomfort responses to the 6MWT in COPD.

CT Imaging With Machine Learning for Predicting Progression to COPD in Individuals at Risk

Identifying individuals at risk of progressing to COPD may allow for initiation of treatment to potentially slow the progression of the disease or the selection of subgroups for discovery of novel interventions. Does the addition of CT imaging features, texture-based radiomic features, and established quantitative CT scan to conventional risk factors improve the performance for predicting progression to COPD in individuals who smoke with machine learning? Participants at risk (individuals who currently or formerly smoked, without COPD) from the Canadian Cohort Obstructive Lung Disease (CanCOLD) population-based study underwent CT imaging at baseline and spirometry at baseline and follow-up. Various combinations of CT scan features, texture-based CT scan radiomics (n= 95), and established quantitative CT scan (n= 8), as well as demographic (n= 5) and spirometry (n= 3) measurements, with machine learning algorithms were evaluated to predict progression to COPD. Performance metrics included the area under the receiver operating characteristic curve (AUC) to evaluate the models. DeLong test was used to compare the performance of the models. Among the 294 at-risk participants who were evaluated (mean age, 65.6 ± 9.2 years; 42%female; mean pack-years, 17.9 ± 18.7), 52 participants (23.7%) in the training data set and 17 participants (23.0%) in the testing data set progressed to spirometric COPD at follow-up (2.5 ± 0.9 years from baseline). Compared with machine learning models with demographics alone (AUC, 0.649), the addition of CT imaging features to demographics (AUC, 0.730; P< .05) or CT imaging features and spirometry to demographics (AUC, 0.877; P< .05) significantly improved the performance for predicting progression to COPD. Heterogeneous structural changes occur in the lungs of individuals at risk that can be quantified using CT imaging features, and evaluation of these features together with conventional risk factors improves performance for predicting progression to COPD.

Short-term air pollution exposure and exacerbation events in mild to moderate COPD: a case-crossover study within the CanCOLD cohort

BackgroundInfections are considered as leading causes of acute exacerbations of chronic obstructive pulmonary disease (COPD). Non-infectious risk factors such as short-term air pollution exposure may play a clinically important role....

Reference values for six-minute walk distance and six-minute walk work in Caucasian adults

RationaleThe six-minute walk test (6MWT) is a practical and simple field-based test to assess physical capacity. Several reference equations for six-minute walking distance (6MWD, m) exist, but have a number of limitations that decrease their clinical utility. In addition, no reference equations exist for the 6MWT-derived outcome six-minute walk work (6MWORK, kg.m). ObjectivesTo establish new reference equations for 6MWD and 6MWORK on a 20 m course using data from the population-based Canadian Cohort Obstructive Lung Disease study. Methods and MeasurementsA total of 335 participants without obstructive or restrictive pulmonary function, with normal self-reported health status, normal exercise capacity, and <30 pack years cigarette smoking history were selected to create a representative sample of Canadian adults aged ≥40 years. All participants performed two 6MWTs. Reference equations were derived using multiple regression analyses. Main ResultsOn average, 6MWD and 6MWORK were 541±98 m and 41.3 ± 11.2 kg.m, respectively. All outcomes were significantly greater in males than females. Sex-specific reference equations were derived from the results of 6MWD and 6MWORK with an explained variance of 24 to 35%. ConclusionsThis study established reference equations for 6MWD and 6MWORK on a 20 m course in Caucasian males and females aged ≥40 years with normal pulmonary function, self-reported health status and exercise capacity. These newly derived reference equations add value to the assessment of functional capacity in clinical practice.

Impaired Spirometry and COPD Increase the Risk of Cardiovascular Disease: A Canadian Cohort Study

Individuals with COPD and preserved ratio impaired spirometry (PRISm) findings in clinical settings have an increased risk of cardiovascular disease (CVD). Do individuals with mild to moderate or worse COPD and PRISm findings in community settings have a higher prevalence and incidence of CVD compared with individuals with normal spirometry findings? Can CVD risk scores be improved when impaired spirometry is added? The analysis was embedded in the Canadian Cohort Obstructive Lung Disease (CanCOLD). Prevalence of CVD (ischemic heart disease [IHD] and heart failure [HF]) and their incidence over 6.3 years were compared between groups with impaired and normal spirometry findings using logistic regression and Cox models, respectively, adjusting for covariables. Discrimination of the pooled cohort equations (PCE) and Framingham risk score (FRS) in predicting CVD were assessed with and without impaired spirometry. Participants (n= 1,561) included 726 people with normal spirometry findings and 835 people with impaired spirometry findings (COPD Global Initiative for Chronic Obstructive Lung Disease [GOLD] stage 1 disease, n= 408; GOLD stage≥ 2, n= 331; PRISm findings, n= 96). Rates of undiagnosed COPD were 84%in GOLD stage 1 and 58%in GOLD stage≥ 2 groups. Prevalence of CVD (IHD or HF) was significantly higher among individuals with impaired spirometry findings and COPD compared with those with normal spirometry findings, with ORs of 1.66 (95%CI, 1.13-2.43; P= .01∗) (∗ indicates statistical significane with P < .05) and 1.55 (95%CI, 1.04-2.31; P= .033∗), respectively. Prevalence of CVD was significantly higher in participants having PRISm findings and COPD GOLD stage≥ 2, but not GOLD stage 1. CVD incidence was significantly higher, with hazard ratios of 2.07 (95%CI, 1.10-3.91; P= .024∗) for the impaired spirometry group and 2.09 (95%CI, 1.10-3.98; P= .024∗) for the COPD group compared to individuals with normal spirometry findings. The difference was significantly higher among individuals with COPD GOLD stage≥ 2, but not GOLD stage 1. The discrimination for predicting CVD was low and limited when impaired spirometry findings were added to either risk score. Individuals with impaired spirometry findings, especially those with moderate or worse COPD and PRISm findings, have increased comorbid CVD compared with their peers with normal spirometry findings, and having COPD increases the risk of CVD developing.

Physical Activity and Symptom Burden in COPD: The Canadian Obstructive Lung Disease Study.

The relationship between symptom burden and physical activity (PA) in chronic obstructive pulmonary disease (COPD) remains poorly understood with limited data on undiagnosed individuals and those with mild to moderate disease. The primary objective was to evaluate the relationship between symptom burden and moderate-to-vigorous intensity PA (MVPA) in individuals from a random population-based sampling mirroring the population at large. Baseline participants of the Canadian Cohort Obstructive Lung Disease (n=1558) were selected for this cross-sectional sub-study. Participants with mild COPD (n=406) and moderate COPD (n=331), healthy individuals (n=347), and those at risk of developing COPD (n=474) were included. The Community Healthy Activities Model Program for Seniors (CHAMPS) questionnaire was used to estimate MVPA in terms of energy expenditure. High symptom burden was classified using the COPD Assessment Test ([CAT] ≥10). Significant associations were demonstrated between high symptom burden and lower MVPA levels in the overall COPD sample (β=-717.09; 95% confidence interval [CI]=-1079.78, -354.40; p<0.001) and in the moderate COPD subgroup (β=-694.1; 95% CI=-1206.54, -181.66; p=0.006). A total of 72% of the participants with COPD were previously undiagnosed. The undiagnosed participants had significantly higher MVPA than those with physician diagnosed COPD (β=-592.41 95% CI=-953.11, -231.71; p=0.001). MVPA was found to be inversely related to symptom burden in a large general population sample that included newly diagnosed individuals, most with mild to moderate COPD. Assessment of symptom burden may help identify patients with lower MVPA, especially for moderate COPD and for relatively inactive individuals with mild COPD.

Factors Associated with Attrition in a Longitudinal Cohort of Older Adults in the Community.

Retaining participants in longitudinal studies increases their power. We undertook this study in a population-based longitudinal cohort of adults with COPD to determine the factors associated with increased cohort attrition. In the longitudinal population-based Canadian Cohort of Obstructive Lung Disease (CanCOLD) study, 1561 adults > 40 years old were randomly recruited from 9 urban sites. Participants completed in-person visits at 18-month intervals and also were followed up every 3 months over the phone or by email. The cohort retention for the study and the reasons for attrition were analyzed. Hazard ratios and robust standard errors were calculated using Cox regression methods to explore the associations between participants who remained in the study and those who did not. The median follow-up (years) of the study is 9.0 years. The overall mean retention was 77%. Reasons for attrition (23%) were: dropout by participant (39%), loss of contact (27%), investigator-initiated withdrawal (15%), deaths (9%), serious disease (9%), and relocation (2%). Factors independently associated with attrition were lower educational attainment, higher pack-year tobacco consumption, diagnosed cardiovascular disease, and a higher Hospital Anxiety and Depression Scale score: adjusted hazard ratios (95% confidence interval) were 1.43(1.11, 1.85); 1.01(1.00, 1.01); 1.44(1.13, 1.83); 1.06(1.02, 1.10) respectively. Identification and awareness of risk factors for attrition could direct targeted retention strategies in longitudinal studies. Moreover, the identification of patient characteristics associated with study dropout could address any potential bias introduced by differential dropouts.

Comparison of Feature Selection Methods and Machine Learning Classifiers for Predicting Chronic Obstructive Pulmonary Disease Using Texture-Based CT Lung Radiomic Features

Texture-based radiomics analysis of lung computed tomography (CT) images has been shown to predict chronic obstructive pulmonary disease (COPD) status using machine learning models. However, various approaches are used and it is unclear which provides the best performance. To compare the most commonly used feature selection and classification methods and determine the optimal models for classifying COPD status in a mild, population-based COPD cohort. CT images from the multi-center Canadian Cohort Obstructive Lung Disease (CanCOLD) study were pre-processed by resampling the image to a 1mm isotropic voxel volume, segmenting the lung and removing the airways (VIDA Diagnostics Inc.), and applying a threshold of -1000HU-to-0HU. A total of 95 texture features were then extracted from each CT image. Combinations of 17 feature selection methods and 9 classifiers were tested and evaluated. In addition, the role of data cleaning (outlier removal and highly correlated feature removal) was evaluated. The area under the curve (AUC) from the receiver operating characteristic curve was used to evaluate model performance. A total of 1204 participants were evaluated (n=602 no COPD, n=602 COPD). There were no significant differences between the groups for female sex (no COPD=46.3%; COPD=38.5%; p=0.77), or body mass index (no COPD=27.7 kg/m2; COPD=27.4 kg/m2; p=0.21). The highest AUC value for predicting COPD status (AUC=0.78 [0.73, 0.84]) was obtained following data cleaning and feature selection using Elastic Net with the Linear-SVM classifier. In a population-based cohort, the optimal combination for radiomics-based prediction of COPD status was Elastic Net as the feature selection method and Linear-SVM as the classifier.

External validation of lung function predictions in real-world cohorts of COPD

BackgroundAn individualized FEV1 prediction model has recently been developed based on the Lung Health Study (LHS). An ongoing evaluation of the utility of prediction models in representative settings outside the setup of clinical trials is needed. ObjectiveTo evaluate the external validity of the LHS FEV1 prediction model in a representative sample of Canadian ever-smoker COPD patients. MethodsWe used the sample of ever-smokers with spirometrically-confirmed COPD from the Canadian Cohort of Obstructive Lung Disease (CanCOLD), an ongoing prospective, longitudinal, multicenter population-based cohort study. We compared the mean predicted against the mean observed FEV1 at follow-up visits. We calculated the root mean square error (RMSE) and the actual coverage probability of the 95% individualized prediction intervals. ResultsThe final CanCOLD sample consisted of 360 ever-smoker COPD patients, contributing to 970 FEV1 observations over an average of 3 years of follow-up (standard deviation (SD): 0.6 years). The mean observed vs. predicted FEV1 for the first follow-up visit (1.67 years from baseline) was 2.28L vs. 2.28L, and for the second visit (3.13 years from baseline) was 2.19L vs. 2.18L. The RMSE of the predictions was 0.205L, and the actual coverage probability of the 95% prediction intervals was 93%. The model had a similar performance across multiple subgroups. ConclusionsThe model showed robust performance in predicting FEV1 in the entire sample and in several subgroups of CanCOLD. Our results demonstrate that LHS predictions are valid for at least three years in the general COPD population.

Analysis of GWAS-nominated loci for lung cancer and COPD revealed a new asthma locus

BackgroundAsthma, lung cancer (LC) and chronic obstructive pulmonary disease (COPD) are three respiratory diseases characterized by complex mechanisms underlying and genetic predispositions, with asthma having the highest calculated heritability. Despite efforts deployed in the last decades, only a small part of its heritability has been elucidated. It was hypothesized that shared genetic factors by these three diseases could help identify new asthma loci.MethodsGWAS-nominated LC and COPD loci were selected among studies performed in Caucasian cohorts using the GWAS Catalog. Genetic analyses were carried out for these loci in the Saguenay–Lac-Saint-Jean (SLSJ) asthma familial cohort and then replicated in two independent cohorts (the Canadian Cohort Obstructive Lung Disease [CanCOLD] and the Epidemiological Study of the Genetics and Environment of Asthma [EGEA]).ResultsAnalyses in the SLSJ cohort identified 2851 and 4702 genetic variants to be replicated in the CanCOLD and EGEA cohorts for LC and COPD loci respectively. Replication and meta-analyses allowed the association of one new locus with asthma, 2p24.3, from COPD studies. None was associated from LC studies reported.ConclusionsThe approach used in this study contributed to better understand the heritability of asthma with shared genetic backgrounds of respiratory diseases.

Ambient Air Pollution and Dysanapsis: Associations with Lung Function and Chronic Obstructive Pulmonary Disease in the Canadian Cohort Obstructive Lung Disease Study.

Rationale: Outdoor air pollution is a potential risk factor for lower lung function and chronic obstructive pulmonary disease (COPD). Little is known about how airway abnormalities and lung growth might modify this relationship. Objectives: To evaluate the associations of ambient air pollution exposure with lung function and COPD and examine possible interactions with dysanapsis. Methods: We made use of cross-sectional postbronchodilator spirometry data from 1,452 individuals enrolled in the CanCOLD (Canadian Cohort Obstructive Lung Disease) study with linked ambient fine particulate matter (PM2.5) and nitrogen dioxide (NO2) air pollution estimates. Dysanapsis, or the ratio of the airway-to-lung volume calculated from thoracic computed tomography images, was used to examine possible interactions. Measurements and Main Results: In adjusted models, 101.7 ml (95% confidence interval [CI], -166.2 to -37.2) and 115.0 ml (95% CI, -196.5 to -33.4) lower FEV1 were demonstrated per increase of 2.4 ug/m3 PM2.5 and 9.2 ppb NO2, respectively. Interaction between air pollution and dysanapsis was not statistically significant when modeling the airway-to-lung ratio as a continuous variable. However, a 109.8 ml (95% CI, -209.0 to -10.5] lower FEV1 and an 87% (95% CI, 12% to 213%) higher odds of COPD were observed among individuals in the lowest, relative to highest, airway-to-lung ratio, per 2.4 μg/m3 increment of PM2.5. Conclusions: Ambient air pollution exposure was associated with lower lung function, even at relatively low concentrations. Individuals with dysanaptic lung growth might be particularly susceptible to inhaled ambient air pollutants, especially those at the extremes of dysanapsis.

The Canadian Cohort Obstructive Lung Disease (CanCOLD): New Insights for Primary Care Application

The Canadian Cohort Obstructive Lung Disease (CanCOLD): New Insights for Primary Care Application