Diagnosing COPD in general practice

COPD prevalence is likely to be underestimated in the UK. Anne Rodman explores the current evidence base for diagnosing COPD Chronic obstructive pulmonary disease (COPD) is an umbrella term for several different pathological processes in the lungs of susceptible individuals. COPD should be considered in any patient who has symptoms and a history of exposure to risk factors for the disease. The cornerstone of COPD diagnosis is to identify risk factors for this preventable condition, recognise and investigate any symptoms that are not commonly found in COPD, and confirm that obstruction is present with correctly performed and interpreted spirometry. This article explores the current evidence base for diagnosing COPD, how to differentiate it from asthma and other conditions with similar symptoms, and the rationale for specialist referral.

COPD Diagnosis: Time for Disruption

Articulating a satisfactory definition of a disease is surprisingly difficult. Despite the alarming individual, societal and economic burden of chronic obstructive pulmonary disease (COPD), diagnosis is still largely based on a physiologically dominated disease conception, with spirometrically determined airflow limitation as a cardinal feature of the disease. The diagnostic inaccuracy and insensitivity of this physiological disease definition is reviewed considering scientific developments of imaging of the respiratory system in particular. Disease must be approached as a fluid concept in response to new scientific and medical discoveries, but labelling as well as mislabelling someone as diseased, will have enormous individual, social and financial implications. Nosology of COPD urgently needs to dynamically integrate more sensitive diagnostic procedures to detect the breadth of abnormalities early in the disease process. Integration of broader information for the identification of abnormalities in the respiratory system is a cornerstone for research models of underlying pathomechanisms to create a breakthrough in research.

Fast decliner phenotype of chronic obstructive pulmonary disease (COPD): applying machine learning for predicting lung function loss

BackgroundChronic obstructive pulmonary disease (COPD) is a heterogeneous group of lung conditions challenging to diagnose and treat. Identification of phenotypes of patients with lung function loss may allow early intervention and improve disease management. We characterised patients with the ‘fast decliner’ phenotype, determined its reproducibility and predicted lung function decline after COPD diagnosis.MethodsA prospective 4 years observational study that applies machine learning tools to identify COPD phenotypes among 13 260 patients from the UK Royal College of General Practitioners and Surveillance Centre database. The phenotypes were identified prior to diagnosis (training data set), and their reproducibility was assessed after COPD diagnosis (validation data set).ResultsThree COPD phenotypes were identified, the most common of which was the ‘fast decliner’—characterised by patients of younger age with the lowest number of COPD exacerbations and better lung function—yet a fast decline in lung function with increasing number of exacerbations. The other two phenotypes were characterised by (a) patients with the highest prevalence of COPD severity and (b) patients of older age, mostly men and the highest prevalence of diabetes, cardiovascular comorbidities and hypertension. These phenotypes were reproduced in the validation data set with 80% accuracy. Gender, COPD severity and exacerbations were the most important risk factors for lung function decline in the most common phenotype.ConclusionsIn this study, three COPD phenotypes were identified prior to patients being diagnosed with COPD. The reproducibility of those phenotypes in a blind data set following COPD diagnosis suggests their generalisability among different populations.

Detection and Staging of Chronic Obstructive Pulmonary Disease Using a Computed Tomography-based Weakly Supervised Deep Learning Approach

BackgroundChronic obstructive pulmonary disease (COPD) remains underdiagnosed globally. The coronavirus disease 2019 pandemic has also severely restricted spirometry, the primary tool used for COPD diagnosis and severity evaluation, due to concerns of virus transmission. Computed tomography (CT)-based deep learning (DL) approaches have been suggested as a cost-effective alternative for COPD identification within smokers. The present study aims to develop weakly supervised DL models that utilize CT image data for the automated detection and staging of spirometry-defined COPD among natural population.MethodsA large, highly heterogenous dataset was established comprising 1393 participants recruited from outpatient, inpatient and physical examination center settings of 4 large public hospitals in China. CT scans, spirometry data, demographic data, and clinical information of each participant were collected for the purpose of model development and evaluation. An attention-based multi-instance learning (MIL) model for COPD detection was trained using CT scans from 837 participants and evaluated using a test set comprised of data from 278 non-overlapping participants. External validation of the COPD detection was performed with 620 low-dose CT (LDCT) scans acquired from the National Lung Screening Trial (NLST) cohort. A multi-channel 3D residual network was further developed to categorize GOLD stages among confirmed COPD patients and evaluated using 5-fold cross validation. Spirometry tests were used to diagnose COPD, with stages defined according to the GOLD criteria.ResultsThe attention-based MIL model used for COPD detection achieved an area under the receiver operating characteristic curve (AUC) of 0.934 on the test set and 0.866 on the LDCT subset acquired from NLST. The model exhibited high generalizability across distinct scanning devices and slice thicknesses, with an AUC above 0.90. The multi-channel 3D residual network was able to correctly grade 76.4% of COPD patients in the test set (423/553) using the GOLD scale, with a Cohen’s weighted Kappa of 0.619 for the assessment of GOLD categorization .ConclusionThe proposed chest CT-DL approach can automatically identify spirometry-defined COPD and categorize patients according to the GOLD scale, with clinically acceptable performance. As such, this approach may be a powerful novel tool for COPD diagnosis and staging at the population level.

Helping Older People Breathe More Easily

One important type of clinically significant lung disease that is frequently encountered by pharmacists working in consultant roles is Chronic Obstructive Pulmonary Disease (COPD). Data recently published in the Journal of the American Medical Association suggests that about 6% of people from the United States aged 40 years or older report a COPD diagnosis, and a major illness affecting more than 1 in 20 of the nation's population is unequivocally an important public health priority. Moreover, in the same report, high rates of comorbidities such as dyslipidaemia, hypertension, heart disease, and cancer are noted, along with increasing emergency department presentations and increasing expenditure on drug therapy used for COPD management.