Surviving the year: Predictors of mortality in conservative kidney management
Bibliographic record
Abstract
Introduction: Conservative kidney management (CKM) is a recognised treatment option for selected patients with chronic kidney disease stage 5 (CKD G5), but prognostic indicators for mortality and optimal timing for palliative care transition remain uncertain. Method: This is a single-centre, prospective cohort study of CKD G5 patients who opted for CKM, conducted between April 2021 and September 2024, with longitudinal monitoring of Edmonton Symptom Assessment System Revised: Renal; Palliative Performance Scale (PPS); Resources Utilisation Group–Activities of Daily Living (RUG-ADL) scale; Clinical Frailty Score; Karnofsky Performance Score; and clinical and laboratory data. Primary outcomes included identifying baseline mortality predictors and validating the PPS for survival estimation. Cox proportional hazards models were used to identify independent predictors of mortality. Results: Among 109 patients (mean age 79.8±7.3 years, 64.2% female), 62 (56.9%) died during follow-up. Multivariate analysis identified baseline estimated glomerular filtration rate (eGFR) (hazard ratio [HR] 1.32, 95% confidence interval [CI] 1.08–1.68, P<0.01) and serum albumin (HR 1.24, 95% CI 1.08–1.43, P<0.01) as predictors of 1-year mortality. Median survival varied by eGFR: 3.0 months (95% CI 0–6.2) for eGFR ≤5 mL/min/1.73 m2, 13.0 months (95% CI 9.1–16.9) for eGFR 6–10 mL/min/1.73 m2, and 20.0 months (95% CI 16.5–23.5) for eGFR >10 mL/min/1.73 m2 (P<0.01). Subsequent PPS correlated strongly with survival, with median survival of 1.8 months for PPS <50, 5.3 months for PPS 50–60, and 7.9 months for PPS 70–80 (P=0.03). Conclusion: Baseline eGFR and serum albumin predict 1-year mortality in CKM patients. PPS offers a practical tool for identifying patients requiring palliative care transition, supporting personalised care pathways and timely integration of palliative care.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".