Heart Failure Readmission Risk Factors: A Modified Delphi Panel Study
Bibliographic record
Abstract
Background: Heart failure (HF) readmission rates have been a significant concern for healthcare systems globally. Accurate predictive models are essential to identify patients at high readmission risk and implement timely interventions. Current models often lack comprehensive variables that reflect both clinical and patient and/or caregiver perspectives. We aimed to develop a consensus-driven approach to identify essential variables for inclusion in HF hospital readmission risk prediction algorithms. Methods: A Delphi panel comprised of clinicians and patient and/or caregiver partners was assembled. The Delphi panelists were recruited from the province of Alberta, Canada. The panel consisted of 13 individuals, including 9 healthcare providers and 4 patients and/or caregivers. The review panel was provided with a list of variables from a previously completed systematic literature review. Three rounds were conducted. The panel also determined the directionality of the association. Results: A total of 99 variables were identified through literature and physician input. Panelists reached a consensus on 61 variables, which were deemed to be associated with the risk of readmission for any cause within 30 days of discharge after HF hospitalization. Clinician ratings on consensus were consistently higher than those of nonclinicians. Conclusions: This study successfully identified 61 variables associated with HF readmission risk through a modified Delphi process, incorporating both clinician and patient and/or caregiver perspectives. These findings provide a foundation for future research and the development of more comprehensive and accurate risk prediction models. Including diverse stakeholder input highlights the importance of integrating medical expertise and patient experiences in improving HF management and reducing readmission rates.
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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 | 0.163 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| 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".