Quality Improvement Initiative: Suboptimal Utilization of Loop Diuretics in Peritoneal Dialysis Patients
Bibliographic record
Abstract
Background: The prescription of high dose loop diuretics is safe and beneficial for PD patients to increase urine output, control of volume status, and decrease the need for high PD fluid glucose concentrations. The aim of this study is to assess the current state and develop an algorithm for rational diuretic use in PD pts to optimize dose, frequency, and reduce pill count in patients with urine output while reducing diuretics in anuric patients. Methods: This was a prospective cohort QI initiative in prevalent PD pts. The algorithm considered PD fluid glucose > 1.5 % used, the volume status, current and historical urine volume trend, and clinical assessment. The dosing of loop diuretics was increased in pts with residual urine output > 200 ml/24 hrs when increased ultrafiltration was needed, while diuretics were stopped in anuric pts. The outcomes were the proportion of pts on loop diuretic in those with and without urine, the dose (median total daily, frequency) and the pill count before and 3 months after the intervention. In the algorithm, Furosemide prescriptions of 40 mg tablets were converted to 500 mg tablets divided as needed where possible. Results: The study included 91 pts, mean age 63 yrs, 45% female, 75% Caucasian, 64% with DM, median time on PD of 1.58 yrs. Furosemide was the only loop diuretic used. At base line median total daily dose was 120 mg, BID 27 %, OD 73 %, and mean pill count was 3.6 pills/day. The proportions of patients prescribed diuretics among those with and without urine output were 54/84 (63%) and 8/17 (47%) respectively. Three months after the intervention the median total daily dose was 240 mg, BID 53 % and OD 47%, mean pill count was 2.96 pills/day, and the proportions of pts on Furosemide for those with and without urine output improved to 85% and 27% respectively (all changes p < 0.05). Conclusions: This short-term study suggests that QI intervention using an algorithm aimed at optimizing loop diuretic use in PD patients based on PD fluid glucose concentration used, and urine volume can increase the prevalence of diuretic use, increase the single and total daily dose, improve dosing frequency, and reduce pill burden in patients with urine output while reducing unnecessary use in anuric pts. This study is ongoing to examine outcomes of urine volume, glucose load of PD fluid, and electrolytes with the intervention.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".