Optimizing Renin Angiotensin-Aldosterone System Inhibition (RAASi) for Kidney Function Preservation in Peritoneal Dialysis (PD): A Quality Improvement (QI) Project
Bibliographic record
Abstract
Background: Preserving residual kidney function is crucial for improving outcomes in PD. RAASi helps preserve kidney function, protect cardiovascular health, and regulate blood pressure (BP). However, it is often discontinued due to concerns about hyperkalemia, and hypotension. Using QI methodology, we explored reasons for discontinuation and developed solutions to improve RAASi in PD patients. We aimed to develop and implement solutions using QI methods to increase the proportion of patients on RAASi. Methods: We reviewed charts of 54 PD patients at Sunnybrook Health Sciences Centre between July 2022-Sept 2023. Data on RAASi usage, baseline potassium (K), and residual urine output were collected. Root-cause analyses, using surveys and Pareto charts, identified gaps in RAASi usage. Results: Our analysis revealed only 55% of patients were prescribed a RAASi (Figure 1). The median residual urine volume was 861.8 ± 72.3 mL, and the average serum K was 4.4 ± 0.1 mmol/L. The root-cause analysis identified two reasons for the low prescription: cessation of RAASi before PD initiation and failure to restart therapy. We implemented the PD Passport in electronic medical records to identify patients not on RAASi to ensure monitoring of urine output, serum K, and BP every six months, with initiation of RAASi if appropriate. There was a slight increase in patients on RAASi after the project was announced between July-Sept 2023, suggestive of a possible Hawthorne effect prior to initiation of formal interventions. Conclusion: The PD Passport, launched in May, aims to increase RAASi use by 20%. We will evaluate its impact on prescription rates, residual urine volume, hyperkalemia, and BP. We demonstrate the feasibility of using QI principles in PD to enhance care. With the PD Passport, we aim to preserve residual kidney function with RAASi, while monitoring patients to maximize benefits and minimize side effects. This initiative promises improved patient outcomes and PD management.
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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 | 0.030 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".