Determining Factors Influencing RAS Inhibitors Re-Initiation in ICU: A Modified Delphi Method
Bibliographic record
Abstract
Background:Renin-angiotensin system inhibitors (RASi) are not re-initiated for almost a quarter of patients who suffered acute kidney injury 6 months after discharge. This discontinuation might be partly explained by the nephrotoxicity of these medications, yet they remain of benefit, especially for patients with heart failure.Objective:To determine the factors deemed by clinicians to influence RASi re-initiation and set threshold values for important safety parameters.Design:Three-round modified online Delphi survey.Setting:The study was conducted in Quebec, Canada.Participants:Twenty clinicians from nephrology, intensive care medicine, and internal medicine.Measurements:The factors’ importance was rated on 4-point Likert-type scale, ranging from “not important” to “very important” by the panelists.Methods:We conducted a brief literature review to uncover possible influencing factors followed by a 3-round modified Delphi survey to establish a consensus on the importance of these factors.Results:We recruited 20 clinicians (7 nephrologists, 3 internists, and 10 intensive care physicians). We created a list of 25 factors, 15 of which met consensus. Eleven of these factors, including serum creatinine, glomerular filtration rate, and acute kidney injury (AKI) stage, were deemed as important while 4, such as responsibility ambiguity and absence of feedback, were deemed as not important. The majority of the 10 factors which did not meet consensus were related to the clinical setting, such as a pharmacist follow-up and the required time to ensure optimal RASi re-initiation.Limitations:Quebec clinicians’ agreement might not reflect the opinion of the rest of Canada. The survey measures clinicians’ belief rather than their actual practice.Conclusion:Renin-angiotensin system inhibitors re-initiation is a rather complex concept which encompasses several factors. Our research uncovered some of these factors which may be used to develop guidelines on optimal RASi re-initiation.
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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 | 0.085 | 0.086 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".