The Impact of Acute Worsening Events on Daily Lives and Healthcare Seeking Behaviour in Patients With COPD: An International Qualitative Study
Bibliographic record
Abstract
Abstract Background: In patients with COPD, acute worsening events (AWEs), i.e., clinically relevant deteriorations of peak expiratory flow, reliever use and/or symptoms, are often underreported in both clinical trials as in the real-world. Yet, little is known about the patients’ perception of, and healthcare seeking behaviour during or after, such events. Objective: (1) to create more insights into the impact of an AWE on daily lives of patients with COPD and (2) to identify and explain AWE associated behaviour. Methods: A qualitative international sub-study was performed in the Netherlands, Spain, the United States, Canada, and the United Kingdom during 2023-2024. Interviews were audio-recorded, transcribed and analysed using the grounded-theory approach. Nine-teen patients, with moderate-to-severe COPD, were recruited while they participated in a randomised controlled trial. The first interview was triggered by the occurrence of an AWE, and the second interview took place 6 weeks after the first. Results: Patients identification of bad days showed large variability (e.g., attributed to inability to perform physical activity, worsening of symptoms, add-on treatment). About half of the AWEs were recognized based on patients’ evaluation of recent days. Most of the 2-days AWEs were not noticed by patients, whereas longer (>2 days) AWE events were often recognized. The majority of patients were seeking help in case they felt the need to. Difficulties in getting an appointment with a clinician on a short notice was mentioned as an important external barrier, whereas a reactive stance to worsenings seemed to be a key internal barrier – both influencing the decision to consult a healthcare professional. Conclusions: AWEs impact the daily life of patients with COPD. Patients differ widely in terms of how they perceive and recognise (previous) worsenings and their threshold for seeking healthcare. A lack of recognition of day-to-day variations in disease stability may lead to underreporting of the frequency and impact of worsening events to healthcare professionals. The patient's experience of identifying and managing (previous) worsenings should not be overlooked when integrating this into COPD action plans. The variation in duration and frequency of AWEs should be considered when implementing this as endpoint in clinical trials for COPD
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".