Prospective Impact of Panic Disorder and Panic-Anxiety on Asthma Control, Health Service Use, and Quality of Life in Adult Patients With Asthma Over a 4-Year Follow-Up
Bibliographic record
Abstract
Background Panic disorder (PD) is a common anxiety disorder among asthmatic patients with overlapping symptoms (e.g., hyperventilation). However, the longitudinal impact of PD on asthma control remains poorly understood. This study assessed the impact of PD and panic-anxiety on asthma control over a 4.3-year follow-up in 643 adult asthmatic patients. Methods Consecutive patients presenting to a tertiary asthma clinic underwent a sociodemographic, medical history, and psychiatric (Primary Care Evaluation of Mental Disorders) interview and completed questionnaires including the Anxiety Sensitivity Index (ASI) to assess panic-anxiety. At follow-up, patients completed the Asthma Control (ACQ) and Asthma Quality of Life (AQLQ) questionnaires and reported emergency department visits and hospitalizations during the follow-up. Results Baseline frequency of PD was 10% (n = 65). In fully adjusted models, analyses revealed that PD and ASI scores predicted worse follow-up ACQ total scores (β = 0.292, p = .037; β = 0.012, p = .003) but not AQLQ total scores. ASI scores also predicted greater nocturnal and waking symptoms, activity limitations, and bronchodilator use on the ACQ (β = 0.012-0.018, p < .05) as well as lower symptom (β = -0.012, p = .006) and emotional distress (β = -0.014, p = .002) subscale scores on the AQLQ. Neither PD nor ASI scores were associated with hospitalizations, although ASI scores (but not PD) were associated with an increased risk of emergency department visits (relative risk = 1.023, 95% confidence interval = 1.001-1.044). Conclusions PD and anxiety sensitivity are prospectively associated with poorer asthma control and may be important targets for treatment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".