Rate of Decline of Residual Renal Function in Patients on Continuous Peritoneal Dialysis and Factors Affecting It
Bibliographic record
Abstract
OBJECTIVE: We analyzed residual renal function (RRF) in a large number of new peritoneal dialysis (PD) patients to prospectively define the time course of decline of RRF and to evaluate the risk factors assumed to be associated with faster decline. STUDY DESIGN: Single-center, prospective cohort study. SETTING: Home PD unit of a tertiary care University Hospital. PATIENTS: The study included 242 patients starting continuous PD between January 1994 and December 1997, with a minimum follow-up of 6 months and at least three measurements of RRF. MEASUREMENT: All patients had data on demographic and laboratory variables, episodes of peritonitis and the use of aminoglycoside (AG) antibiotics, temporary hemodialysis, and number of radiocontrast studies. Adequacy of PD was measured from 24-hour urine and dialysate collection and peritoneal equilibration test using standard methodology. Further data on RRF was collected every 3 to 4 months until the patient became anuric (urine volume < 100 mL/day or creatinine clearance < 1.0 mL/min) or until the end of study in December 1998. OUTCOME MEASURE: The slope of the decline of residual glomerular filtration rate (GFR) (an average of renal urea and creatinine clearance) was the main outcome measure. Risk factors associated with faster decline were evaluated by a comparative analysis between patients in the highest and the lowest quartiles of the slopes of GFR, and a multivariate analysis using a stepwise option within linear regression and general linear models. RESULTS: There was a gradual deterioration of residual GFR with time on PD, with 40% of patients developing anuria at a mean of 20 months after the initiation of PD. On multivariate analysis, use of a larger volume of dialysate (p = 0.0001), higher rate of peritonitis (p = 0.0005), higher use of AG (p = 0.0006), presence of diabetes mellitus (p = 0.005), larger body mass index (BMI) (p = 0.01), and no use of antihypertensive medications (p = 0.04) independently predicted the steep slope of residual GFR. Male gender, higher grades of left ventricular dysfunction, and higher 24-hour proteinuria were associated with faster decline on univariate analysis only. CONCLUSION: Faster decline of residual GFR corresponds with male gender, large BMI, presence of diabetes mellitus, higher grades of congestive heart failure, and higher 24-hour proteinuria. Higher rate of peritonitis and use of AG for the treatment of peritonitis is also associated independently with faster decline of residual GFR. Whether the type of PD (CAPD vs CCPD/NIPD) is associated with faster decline of residual GFR remains speculative.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".