Self-Care Peritoneal Dialysis Patients with Cognitive Impairment Have a Higher Risk of Peritonitis in the Second Year
Bibliographic record
Abstract
Introduction Cognitive impairment is common among patients on peritoneal dialysis (PD). We hypothesize that cognitive impairment has a negative impact on the outcome of patients on PD, especially with regard to peritonitis. Methods This was a single-center 2-year prospective cohort study involving 206 patients at 1 PD unit. Cognitive impairment was defined by the latest Hong Kong Montreal Cognitive Assessment Score (HK-MoCA) multiple cut-offs as determined by age and years of education. Eighty percent of patients had come back for interval HK-MoCA. The HK-MoCA was performed at baseline and after 1 year on PD. Potential risk factors for cognitive impairment and peritonitis were studied separately for the first and second year. Results For cognitive impairment at baseline, multivariate analyses showed that age (odds ratio [OR] 1.003, 95% confidence interval [CI] 1.003 – 1.065, p = 0.03), female sex (OR 3.57, 95% CI 1.60 – 7.97, p = 0.002), peripheral vascular disease (PVD) (OR 3.46, 95% CI 1.33 – 9.01, p = 0.01), and hemoglobin level (OR 0.60, 95% CI 0.43 – 0.84, p = 0.003) were statistically significant factors. For cognitive impairment at 1 year, multivariate analyses showed that age (OR 1.07, 95% CI 1.02 – 1.012, p = 0.007), female sex (OR 5.87, 95% CI 1.86 – 18.5, p = 0.003), and PVD (OR 3.68, 95% CI 1.07 – 12.84, p = 0.04) were statistically significant independent factors for cognitive impairment at 1 year. For self-care PD patients in the second year, patients with cognitive impairment had a higher rate of peritonitis and proportionately more patients suffered from both peritonitis and exit-site infection than non-cognitively impaired patients in the second year (0.50 vs 0.27 episodes per year, p = 0.048; 25% vs 7.2%, p = 0.049). Logistic regression showed that only HK-MoCA-defined cognitive impairment and HK-MoCA scores at 1 year were factors predicting peritonitis (risk ratio [RR] 3.2 [95% CI 1.03 – 9.95], p = 0.04 and RR 0.92 [95% CI 0.86 – 0.995], p = 0.04 respectively). Conclusions In summary, increasing age, female sex, anemia, and presence of PVD are risk factors for cognitive impairment in PD patients. Self-care PD with cognitive impairment at 1 year has a higher risk for PD-related peritonitis in the second year. Interval HK-MoCA assessment is recommended to detect cognitive impairment in our local PD patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".