Improving the Model for End‐Stage Liver Disease with sodium by incorporating kidney dysfunction types
Bibliographic record
Abstract
Abstract Background and Aims We investigated the impact of the inclusion of kidney dysfunction type on the discrimination and calibration of the Model for End‐Stage Liver Disease with sodium (MELD‐Na‐KT) score. Approach and Results We included all adults listed for ≥90 days without exception points from January 1, 2008, through December 31, 2018. We defined kidney dysfunction types as follows: acute kidney disease (AKD; an increase of ≥0.3 mg/dL or ≥50% in serum creatinine in the last 7 days or fewer than 72 days of hemodialysis), chronic kidney disease (CKD; an estimated glomerular filtration rate <60 ml/min/1.73 m2 for 90 days or ≥72 days of hemodialysis), AKD on CKD (met both definitions), or none (met neither definition). We then developed and validated a multivariable survival model with follow‐up beginning at the first assessment after 90 days from waitlist registration and ending at the time of death, waitlist removal, or 90 days from enrollment in this study. The predictor variables were MELD‐Na and the derived MELD‐Na‐KT model. In the derivation cohort, kidney dysfunction type was significantly associated with waitlist mortality after controlling for MELD‐Na. There was a significant linear interaction between kidney dysfunction type and MELD‐Na score. In the validation cohort, we saw an improvement in the discrimination of the model with an increase in the c‐index from 0.76 with MELD‐Na to 0.78 with MELD‐Na‐KT (p = 0.002) and a net reclassification index of 10.8% (95% CI, 1.9%–11.4%). The newly derived MELD‐Na‐KT model had lower Brier scores (MELD‐Na‐KT 0.042 vs. MELD‐Na 0.053). Conclusions This study demonstrates the feasibility and the potential for objectively defined kidney dysfunction types to enhance the prognostication of waitlist mortality provided by the MELD‐Na score.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".