Back

Overdiagnosis and treatment of COPD in nonagenarians

Tole, M.; Ascoli, C.; Joo, M.; Rubinstein, I.

2022-01-21 respiratory medicine
10.1101/2022.01.21.22269644 medRxiv
Show abstract

BackgroundThe prevalence of COPD is increasing with age. However, the effects of age-dependent decline in lung function on diagnosis and treatment of COPD in nonagenarians are uncertain. ObjectivesTo determine performance of spirometry, prescription of COPD medications, and COPD-related acute care visits and hospitalizations in patients 90 years and older with physician-diagnosed COPD. MethodsHealth records of 166 consecutive patients 90 years and older with physician-diagnosed COPD at a university-affiliated medical center in Chicago were reviewed. Pertinent demographic, clinical, and physiological data were extracted. ResultsPatients were predominantly ex-smoker (96%), African American (52%) males (96%). Sixty patients (36%) had no spirometry testing on record. Of the remaining 106 patients, 11 (10%) had baseline FEV1/FVC[&ge;]0.70, 24 (23%) had post-bronchodilator FEV1/FVC [&ge;]0.70, 28 (26%) had FEV1/FVC <0.70 and [&ge;]LLN, and 43 (41%) had FEV1/FVC <0.70 and <LLN. Thus, only 71 of 166 patients 90 years and older (43%) fulfilled the Global Initiative for Chronic Obstructive Lung Disease (GOLD) recommendations. Nonetheless, COPD medications, predominantly short-acting {beta}2 agonists and long-acting muscarinic antagonists, were prescribed to 95 of the 166 patients (57%). No significant differences in prevalence of co-morbidities and prescribed COPD medications, including systemic corticosteroids and anti-infectives prescribed during unscheduled healthcare visits and hospitalizations, were found between the four groups. ConclusionsThese data suggest that a large proportion of nonagenarians at our medical center are overdiagnosed with and treated for COPD. A larger, multi-center, prospective study is warranted to support or refute these retrospective observations.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.