Development of three-step holistic care pathways to detect and manage comorbidities in patients with atrial fibrillation: the Horizon 2020 EHRA-PATHS consortium
Bibliographic record
Abstract
Aims: Older patients with AF (≥65 years) have on average four additional comorbidities. Comorbidity management requires a systematic approach for identification, and interdisciplinary care, often lacking in clinical practice. The EHRA-PATHS project's overall aim is to create an approach to systematically address multimorbidity in older patients with AF. Methods and results: This project involves a consortium of 14 partners from 11 European countries. The comorbidity care pathways were developed using a stepwise approach. (i) A literature study. (ii) Online meetings/discussions to create structured care pathways. (iii) A two-round Delphi study for consensus on the final pathways (agreement ≥80%) and to rank the comorbidities for priority. (iv) Selection of comorbidities for evaluation in the planned randomized controlled trial (RCT). Development of care pathways for 23 comorbidities or special clinical settings was obtained and agreed upon. The Delphi surveys were sent to 37 consortium experts. After round 1 (28 responses), 13 pathways reached an agreement ≥80%. Twelve adjusted pathways were presented in round 2 (27 responses), of which 8 received an agreement ≥80%. The last four pathways were finalized after expert consensus. Hypertension, heart failure, and overweight were ranked as the most important comorbidities. Conclusion: A structured process of expert meetings and two Delphi rounds led to the development and ranking of 23 concise care pathways to identify and manage comorbidities in patients with AF. All pathways will be combined into a software tool, providing clinicians with a systematic approach to comorbidity management, which will be tested in the RCT of EHRA-PATHS.
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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 | 0.059 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".