Comparing five comorbidity indices to predict mortality in chronic kidney disease
Bibliographic record
Abstract
IntroductionMany reports which use healthcare databases adjust the results for patient comorbidity. Several different indices were developed in the general population to summarize patient comorbidity. How well these indices predict one-year all-cause mortality in individuals with chronic kidney disease (CKD) is not well known.
 Objectives and ApproachWe accrued three groups of patients in Ontario, Canada between the years 2004 and 2014, at the time they first received a kidney transplant, received maintenance dialysis, or were confirmed to have an estimated glomerular filtration rate (eGFR) less than 45 mL/min per 1.73 m2. We compared five comorbidity indices: Charlson comorbidity index, end-stage renal disease-modified Charlson comorbidity index, Johns Hopkins’ Aggregated Diagnosis Groups score, Elixhauser score, and Wright-Khan index. Each group was randomly divided 100 times into derivation and validation samples. Model discrimination was assessed using median c-statistics from logistic regression models, and calibration was evaluated graphically using calibration plots.
 ResultsWe identified 4,111 kidney transplant recipients, 23,897 individuals receiving maintenance dialysis, and 181,425 individuals with moderate CKD. Within one year, 108 (2.6%), 4,179 (17.5%), and 17,898 (9.9%) in each group had died, respectively. In the validation sample, model discrimination was inadequate for all five comorbidity indices, with median c-statistics less than 0.7 for all three groups. Calibration was also poor for all models.
 Conclusion/ImplicationsExisting comorbidity indices do not accurately predict one-year mortality in patients with CKD. Current indices could be modified with additional risk factors to improve their performance in CKD, or a new index could be developed for this population.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".