A Multi-Specialty Delphi Consensus on Assessing and Managing Cardiopulmonary Risk in Patients with COPD
Bibliographic record
Abstract
Mohit Bhutani,1 Jean Bourbeau,2 Shaun G Goodman,3 Nathaniel Mark Hawkins,4 Alan G Kaplan,5 Peter James Lin,6 Erika Dianne Penz,7 Subodh Verma,8 Shelley Zieroth9 1Department of Medicine, Division of Pulmonary Medicine, University of Alberta, Edmonton, Alberta, Canada; 2Department of Medicine, Division of Pulmonary Medicine, McGill University Health Centre, McGill University, Montreal, Quebec, Canada; 3Department of Medicine, Division of Cardiology, University of Toronto, Toronto, Ontario, Canada; 4Department of Medicine, Division of Cardiology, University of British Columbia, Vancouver, British Columbia; 5Department of Family and Community Medicine, University of Toronto, Toronto, Ontario, Canada; 6The Canadian Heart Research Centre, Primary Care Initiatives, Toronto, Ontario, Canada; 7College of Medicine, Division of Respirology, University of Saskatchewan, Saskatoon, Saskatchewan, Canada; 8Department of Surgery, Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ontario, Canada; 9College of Medicine, Section of Cardiology, University of Manitoba, Winnipeg, Manitoba, CanadaCorrespondence: Mohit Bhutani, Department of Medicine, Division of Pulmonary Medicine, University of Alberta, 3-133 Clinical Sciences Building, 11304-83 Avenue NW, Edmonton, Alberta, T6G 2G3, Canada, Tel +1-780-492-3739, Email mohit.bhutani@ualberta.caBackground: In Canada, COPD represents a significant burden to the patient and health system, as it is often under or misdiagnosed and sub-optimally treated. Cardiovascular disease (CVD) is a common co-morbidity in COPD and there is significant interplay between these two chronic conditions. Across all stages of COPD disease severity, deaths can be attributed not only to respiratory causes but also to cardiovascular-related factors. The established links between COPD and CVD suggest the need for a greater degree of collaboration between respirologists and cardiologists. This modified Delphi consensus was initiated to consider how optimal COPD care can be delivered within Canada, with specific consideration of reducing cardiopulmonary risk and outcomes in COPD patients.Methods: A steering group with interest in the management of COPD and CVD from primary care, cardiology, and respirology identified 40 statements formed from four key themes. A 4-point Likert scale questionnaire was sent to healthcare professionals working in COPD across Canada by an independent third party to assess agreement (consensus) with these statements. Consensus was defined as high if ≥ 75% and very high if ≥ 90% of respondents agreed with a statement.Results: A total of 100 responses were received from respirologists (n=30), cardiologists (n=30), and primary care physicians (n=40). Consensus was very strong (≥ 90%) in 28 (70%) statements, strong (≥ 75 and < 90%) in 7 (17.5%) statements and was not achieved (< 75%) in 5 (12.5%) of statements.Conclusion: Based on the consensus scores, 9 key recommendations were proposed by the steering group. These focus on the need to comprehensively risk stratify and manage COPD patients to help prevent exacerbations. Consensus within this study provides a call to action for the expeditious implementation of the latest COPD guidelines from the Canadian Thoracic Society.Keywords: chronic obstructive pulmonary disease, consensus development, consultation and referral, primary care, health care, Canada
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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 | 0.177 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".