Validation of Questionnaire-based Case Definitions for Chronic Obstructive Pulmonary Disease

"We adjusted for race": now what? A systematic review of utilization and reporting of race in American Journal of Epidemiology and Epidemiology, 2020-2021

Race is a social construct, commonly used in epidemiologic research to adjust for confounding. However, adjustment of race may mask racial disparities, thereby perpetuating structural racism. We conducted a systematic review of articles published in Epidemiology and American Journal of Epidemiology between 2020 and 2021 to (1) understand how race, ethnicity, and similar social constructs were operationalized, used, and reported; and (2) characterize good and poor practices of utilization and reporting of race data on the basis of the extent to which they reveal or mask systemic racism. Original research articles were considered for full review and data extraction if race data were used in the study analysis. We extracted how race was categorized, used-as a descriptor, confounder, or for effect measure modification (EMM)-and reported if the authors discussed racial disparities and systemic bias-related mechanisms responsible for perpetuating the disparities. Of the 561 articles, 299 had race data available and 192 (34.2%) used race data in analyses. Among the 160 US-based studies, 81 different racial categorizations were used. Race was most often used as a confounder (52%), followed by effect measure modifier (33%), and descriptive variable (12%). Fewer than 1 in 4 articles (22.9%) exhibited good practices (EMM along with discussing disparities and mechanisms), 63.5% of the articles exhibited poor practices (confounding only or not discussing mechanisms), and 13.5% were considered neither poor nor good practices. We discuss implications and provide 13 recommendations for operationalization, utilization, and reporting of race in epidemiologic and public health research.

Automated detection of airflow obstructive diseases: A systematic review of the last decade (2013-2022)

BACKGROUND AND OBJECTIVE

Obstructive airway diseases, including asthma and Chronic Obstructive Pulmonary Disease (COPD), are two of the most common chronic respiratory health problems. Both of these conditions require health professional expertise in making a diagnosis. Hence, this process is time intensive for healthcare providers and the diagnostic quality is subject to intra- and inter- operator variability. In this study we investigate the role of automated detection of obstructive airway diseases to reduce cost and improve diagnostic quality.

METHODS

We investigated the existing body of evidence and applied Preferred Reporting Items for Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to search records in IEEE, Google scholar, and PubMed databases. We identified 65 papers that were published from 2013 to 2022 and these papers cover 67 different studies. The review process was structured according to the medical data that was used for disease detection. We identified six main categories, namely air flow, genetic, imaging, signals, and miscellaneous. For each of these categories, we report both disease detection methods and their performance.

RESULTS

We found that medical imaging was used in 14 of the reviewed studies as data for automated obstructive airway disease detection. Genetics and physiological signals were used in 13 studies. Medical records and air flow were used in 9 and 7 studies, respectively. Most papers were published in 2020 and we found three times more work on Machine Learning (ML) when compared to Deep Learning (DL). Statistical analysis shows that DL techniques achieve higher Accuracy (ACC) when compared to ML. Convolutional Neural Network (CNN) is the most common DL classifier and Support Vector Machine (SVM) is the most widely used ML classifier. During our review, we discovered only two publicly available asthma and COPD datasets. Most studies used private clinical datasets, so data size and data composition are inconsistent.

CONCLUSIONS

Our review results indicate that Artificial Intelligence (AI) can improve both decision quality and efficiency of health professionals during COPD and asthma diagnosis. However, we found several limitations in this review, such as a lack of dataset consistency, a limited dataset and remote monitoring was not sufficiently explored. We appeal to society to accept and trust computer aided airflow obstructive diseases diagnosis and we encourage health professionals to work closely with AI scientists to promote automated detection in clinical practice and hospital settings.

Association of World Trade Center (WTC) Occupational Exposure Intensity with Chronic Obstructive Pulmonary Disease (COPD) and Asthma COPD Overlap (ACO)

INTRODUCTION

Reported associations between World Trade Center (WTC) occupational exposure and chronic obstructive pulmonary disease (COPD) or asthma COPD overlap (ACO) have been inconsistent. Using spirometric case definitions, we examined that association in the largest WTC occupational surveillance cohort.

METHODS

We examined the relation between early arrival at the 2001 WTC disaster site (when dust and fumes exposures were most intense) and COPD and ACO in workers with at least one good quality spirometry with bronchodilator response testing between 2002 and 2019, and no physician-diagnosed COPD before 9/11/2001. COPD was defined spirometrically as fixed airflow obstruction and ACO as airflow obstruction plus an increase of ≥ 400 ml in FEV1 after bronchodilator administration. We used a nested 1:4 case-control design matching on age, sex and height using incidence density sampling.

RESULTS

Of the 17,928 study participants, most were male (85.3%) and overweight or obese (84.9%). Further, 504 (2.8%) and 244 (1.4%) study participants met the COPD and ACO spirometric case definitions, respectively. In multivariable analyses adjusted for smoking, occupation, cohort entry period, high peripheral blood eosinophil count and other covariates, early arrival at the WTC site was associated with both COPD (adjusted odds ratio [ORadj] = 1.34, 95% confidence interval [CI] 1.01-1.78) and ACO (ORadj = 1.55, 95%CI 1.04-2.32).

CONCLUSION

In this cohort of WTC workers, WTC exposure intensity was associated with spirometrically defined COPD and ACO. Our findings suggest that early arrival to the WTC site is a risk factor for the development of COPD or of fixed airway obstruction in workers with pre-existing asthma.

Machine learning-enabled risk prediction of chronic obstructive pulmonary disease with unbalanced data

BACKGROUND AND OBJECTIVE

Since the early symptoms of chronic obstructive pulmonary disease (COPD) are not obvious, patients are not easily identified, causing improper time for prevention and treatment. In present study, machine learning (ML) methods were employed to construct a risk prediction model for COPD to improve its prediction efficiency.

METHODS

We collected data from a sample of 5807 cases with a complete COPD diagnosis from the 2019 COPD Surveillance Program in Shanxi Province and extracted 34 potentially relevant variables from the dataset. Firstly, we used feature selection methods (i.e., Generalized elastic net, Lasso and Adaptive lasso) to select ten variables. Afterwards, we employed supervised classifiers for class imbalanced data by combining the cost-sensitive learning and SMOTE resampling methods with the ML methods (Logistic Regression, SVM, Random Forest, XGBoost, LightGBM, NGBoost and Stacking), respectively. Last, we assessed their performance.

RESULTS

The cough frequently at age 14 and before and other 9 variables are significant parameters for COPD. The Stacking heterogeneous ensemble model showed relatively good performance in the unbalanced datasets. The Logistic Regression with class weighting enjoyed the best classification performance in the balancing data when these composite indicators (AUC, F1-Score and G-mean) were used as criteria for model comparison. The values of F1-Score and G-mean for the top three ML models were 0.290/0.660 for Logistic Regression with class weighting, 0.288/0.649 for Stacking with synthetic minority oversampling technique (SMOTE), and 0.285/0.648 for LightGBM with SMOTE.

CONCLUSIONS

This paper combining feature selection methods, unbalanced data processing methods and machine learning methods with data from disease surveillance questionnaires and physical measurements to identify people at risk of COPD, concluded that machine learning models based on survey questionnaires could provide an automated identification for patients at risk of COPD, and provide a simple and scientific aid for early identification of COPD.

Lung deposition and systemic bioavailability of dose delivered to smoker compared with non-smoker COPD subjects

INTRODUCTION

Inhaled drugs are the most commonly used class of medications for COPD subjects. No studies have been performed to assess the influence of smoking on lung deposition of aerosolized medication, especially for the exacerbated COPD subject. The present study aimed to assess the influence of smoking on the lung deposition of the aerosol delivered to exacerbated COPD subjects.

METHODS

Twenty-four exacerbated COPD subjects using automatic continuous positive airway pressure (Auto-CPAP), 12 smokers (six females) and 12 non-smokers (six females) were recruited in the study. The subjects participated in the study received four salbutamol study doses; 1200 µg (12 puffs 100 µg/puff) of salbutamol emitted from pMDI canister connected to AeroChamber MV spacer; 1200 µg of salbutamol emitted from pMDI canister connected to Combihaler spacer; 1 mL of salbutamol respirable solution (5000 µg/mL) nebulized by Aerogen Solo connected to its T-piece; and 1 mL of salbutamol respirable solution nebulized by Aerogen Solo connected to Combihaler spacer with 2 puffs salbutamol MDI (200 µg salbutamol) before nebulisation. The subjects were randomised to receive the four selected dose-adaptor combination in a sealed envelope design on days 1, 3, 5 and 7. A washout period of 24 hours was provided between each salbutamol dosing. Auto-CPAP was adjusted at non-invasive ventilation mode with the integrated heated humidifier, as a source of humidity. Urine samples were provided by subjects, 30 minutes and cumulatively 24 hours post inhalation, as an index of the relative and systemic bioavailability, respectively, and aliquots were retained for salbutamol analysis using solid-phase extraction and high-performance liquid chromatography (HPLC). On day 2 of the study, a collecting filter was placed between the aerosol generator and the subject's mask so that the subjects would not inhale the salbutamol delivered. The same study doses and/or adapters were delivered to each subject, with filters changed with each dose-adapter combination. Salbutamol entrained on the filter was desorbed to be analysed by the HPLC.

RESULTS

Significantly higher lung deposition (30 minutes urinary salbutamol) was detected with the non-smoker compared with smokers (P < .05). Significantly higher systemic bioavailability (pooled 24-hour urinary salbutamol) for smokers compared with non-smokers was found with Aerogen Solo connected to its T-piece and CombiHaler spacer with pMDI (P < .05) only. Significantly higher amount desorbed from the ex-vivo filter were found from pMDI with both spacers in non-smokers (P < .05) compared with the smokers.

CONCLUSION

The study demonstrated that smoking reduced the lung deposition of inhaled salbutamol delivered by nebulizer or pMDI. However, the smoking effect on cytochrome p450 was observed to increase systemic absorption of the ingested portion of the inhaled dose. The lower lung deposition and possible higher systemic absorption should be taken into consideration while prescribing inhaled medication to COPD smokers especially exacerbated patients that need ventilation. Further studies are needed.