COPD Population in US Primary Care: Data From the Optimum Patient Care DARTNet Research Database and the Advancing the Patient Experience in COPD Registry

Abstract

PURPOSETo describe demographic and clinical characteristics of chronic obstructive pulmonary disease patients managed in US primary care. METHODSThis was an observational registry study using data from the Chronic Obstructive Pulmonary Disease (COPD) Optimum Patient Care DARTNet Research Database from which the Advancing the Patient Experience COPD registry is derived. Registry patients were aged ≥35 years at diagnosis. Electronic health record data were collected from both registries, supplemented with patient-reported information/outcomes from the Advancing the Patient Experience registry from 5 primary care groups in Texas, Ohio, Colorado, New York, and North Carolina (June 2019 through November 2020). RESULTS:Of 17,192 patients included, 1,354 were also in the Advancing the Patient Experience registry. Patients were predominantly female (56%; 9,689/17,192), White (64%; 9,732/15,225), current/ex-smokers (80%; 13,784/17,192), and overweight/obese (69%; 11,628/16,849). The most commonly prescribed maintenance treatments were inhaled corticosteroid with a long-acting β 2 -agonist (30%) and inhaled corticosteroid with a long-acting muscarinic antagonist (27%). Although 3% (565/17,192) of patitents were untreated, 9% (1,587/17,192) were on short-acting bronchodilator monotherapy, and 4% (756/17,192) were on inhaled corticosteroid monotherapy. Despite treatment, 38% (6,579/17,192) of patients experienced 1 or more exacerbations in the last 12 months. These findings were mirrored in the Advancing Patient Experience registry with many patients reporting high or very high impact of disease on their health (43%; 580/1,322), a breathlessness score 2 or more (45%; 588/1,315), and 1 or more exacerbation in the last 12 months (50%; 646/1,294).CONCLUSIONS Our findings highlight the high exacerbation, symptom, and treatment burdens experienced by COPD patients managed in US primary care, and the need for more real-life effectiveness trials to support decision making at the primary care level.

Use of feature importance statistics to accurately predict asthma attacks using machine learning: A cross-sectional cohort study of the US population

“…One benchmark study identified patients at risk of recurrent asthma attacks using the Optimum Patient Care Research Database, identifying various predictors for attacks and evaluated them using logistic regression. The study resulted in the development of an online asthma risk prediction tool for research and clinical purposes [43][44][45]. However, this study only utilized logistic regression, which has been shown to be a poor classifier in cases of class imbalance.…”