Breathing New Life Into Acute Respiratory Care: Proactively Improving Long-Term Outcomes
Bibliographic record
Abstract
Admissions due to asthma contribute substantially to the burden faced by emergency departments (ED) worldwide, with a considerable impact arising from the high number of readmissions among patients with severe asthma. Repeated ED readmittance not only places considerable demand on healthcare resources, but also increases the humanistic burden on patients through reduced lung function, decreased quality of life, and increased exposure to systemic corticosteroids (SCS) and oral corticosteroids (OCS). In addition, patients are subject to the increased morbidity and mortality risk, and quality of life deficit associated with repeated asthma exacerbations. Admission to the ED should be seen as an opportunity to break this readmission cycle and prevent further admissions, while offering patient-centric benefits, such as investigation into the underlying causes of disease, and optimisation of care to prevent further exacerbations. Actions that require no additional resource may be taken directly in the ED, including biomarker tests among routine blood tests, or teaching inhaler technique as part of patient education and safety-netting. In addition, patient discharge may be considered as an opportunity for improving guidance implementation and breaking the cycle of readmission. Unlike emergency cardiac care, where >90% of patients are discharged on secondary prevention drugs and 85% of patients are referred to follow-up rehabilitation, guidelines for care following an ED visit for asthma are not always followed. Furthermore, current tools designed to accelerate specialist referral are not always rigorously implemented following an ED visit, meaning that follow-up may be delayed. Finally, further efforts should be made to identify high-risk patients in the community earlier in the disease pathway, allowing timely intervention before further lung function impairment, or the onset of adverse events due to OCS over-exposure. This article summarises an AstraZeneca-sponsored symposium delivered on 12th September 2023, as part of the European Respiratory Society (ERS) International Congress in Milan, Italy. The faculty, consisting of David Price, Head of the Observational and Pragmatic Research Institute, Singapore; Mona Al-Ahmad, Consultant Allergist and Clinical Immunologist at the Ministry of Health in Kuwait; and Mohit Bhutani, Professor of Medicine at the University of Alberta, Edmonton, Canada, each gave a brief presentation on proactive strategies to improve long-term outcomes in acute respiratory care. During panel discussions following each presentation, Anne Marie Marley, Respiratory Nurse Consultant from Belfast Health and Social Care Trust, UK, provided examples of implementing transition of care by bridging hospital and community care settings.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".