Predicting Mortality in Older Adults with Kidney Disease: A Pragmatic Prediction Model
Bibliographic record
Abstract
OBJECTIVES: To develop mortality risk prediction models for older adults with chronic kidney disease (CKD) that include comorbidities and measures of health status and use not associated with particular comorbid conditions (nondisease-specific measures). DESIGN: Retrospective cohort study. SETTING: Kaiser Permanente Northwest (KPNW) Health Maintenance Organization. PARTICIPANTS: Individuals with severe CKD (estimated glomerular filtration rate<30 mL/min per 1.73 m2; N=4,054; n=1,915 aged 65-79, n=2,139 aged ≥80) who received care at KPNW between 2000 and 2008. MEASUREMENTS: Cox proportional hazards analysis was used to examine the association between selected participant characteristics and all-cause mortality and to generate age group-specific risk prediction models. Predicted and observed risks were evaluated according to quintile. Predictors from the Cox models were translated into a points-based system. Internal validation was used to provide best estimates of how these models might perform in an external population. RESULTS: The risk prediction models used 16 characteristics to identify participants with the highest risk of mortality at 2 years for adults aged 65 to 79 and 80 and older. Predicted and observed risks agreed within 5% for each quintile; a 4 to 5 times difference in 2-year predicted mortality risk was observed between the highest and lowest quintiles. The c-statistics for each model (0.68-0.69) indicated effective discrimination without evidence of significant overfit (slope shrinkage 0.06-0.09). Models for each age group performed similarly for mortality prediction at 6 months and 2 years in terms of discrimination and calibration. CONCLUSION: When validated, these risk prediction models may be helpful in supporting discussions about prognosis and treatment decisions sensitive to prognosis in older adults with CKD in real-world clinical settings.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".