Markov Multistate Modeling of Longitudinal Kidney Replacement Therapy Patterns in a Canadian Population with Advanced CKD
Bibliographic record
Abstract
Background: Chronic kidney disease (CKD) is a condition characterized by the gradual loss of kidney function. As computer decision aids and other practice-changing tools become more widespread, estimates of kidney replacement therapy patterns are needed to support cost-effectiveness research and guide tool selection. Methods: Data were acquired from ICES in Ontario, Canada. Separate multi-state models were developed in patients with, 1) a 2-year kidney failure risk equation (KFRE) prediction ≥ 10%, and 2) eGFR < 30 mL/min/1.72m2 with urine albumin-to-creatinine ratio (UACR) ≥ 30 mg/g. Indexing criteria required a second confirmatory test within one year of the first. A model was developed to describe the sequence of possible transitions from CKD to kidney transplantation, peritoneal dialysis, hemodialysis, and death. The state space was further augmented to represent important transition pathways, including patients who initiated dialysis in an unplanned manner (during a hospital admission), and those who underwent pre-emptive kidney transplantation. Individual parametric hazard models were fit to estimate transition-specific hazards. Model fit was compared for both time-homogeneous hazards and semi-Markov hazards using the Akaike Information Criterion (AIC). Results: 33,494 patients with mean (SD) age 69 (15) years, eGFR 23 (7) ml/min per 1.73 m2 were included as part of the KFRE cohort, and 49,457 patients with mean (SD) age 72 (13) years, eGFR 24 (5) ml/min per 1.73 m2 were included as part of the eGFR + UACR cohort. Patient state was not static, with potentially multiple transitions occurring throughout follow-up. The semi-Markov models had improved AIC values compared to the time-homogeneous models. Conclusion: These multi-state models reveal the dynamic nature of kidney replacement therapy and may facilitate economic evaluations towards guiding the effective selection of decision-support tools in CKD. Funding: Government Support – Non-U.S.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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".