Exploring the Cost-Utility of a Biomarker Predicting Persistent Severe Acute Kidney Injury: The Case of C-C Motif Chemokine Ligand 14 (CCL14)
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Bibliographic record
Abstract
Background: Approximately 24% of hospitalized stage 2-3 acute kidney injury (AKI) patients will develop persistent severe AKI (PS-AKI), defined as KDIGO stage 3 AKI lasting ≥3 days or with death in ≤3 days or stage 2 or 3 AKI with dialysis in ≤3 days, leading to worse outcomes and higher costs. There is currently no consensus on an intervention that effectively reverts the course of AKI and prevents PS-AKI in the population with stage 2-3 AKI. This study explores the cost-utility of biomarkers predicting PS-AKI, under the assumption that such intervention exists by comparing C-C motif chemokine ligand 14 (CCL14) to hospital standard of care (SOC) alone. Methods: The analysis combined a 90-day decision tree using CCL14 operating characteristics to predict PS-AKI and clinical outcomes in 66-year-old patients, and a Markov cohort estimating lifetime costs and quality-adjusted life years (QALYs). Cost and QALYs from admission, 30-day readmission, intensive care, dialysis, and death were compared. Clinical and cost inputs were informed by a large retrospective cohort of US hospitals in the PINC AI Healthcare Database. Inputs and assumptions were challenged in deterministic and probabilistic sensitivity analyses. Two-way analyses were used to explore the efficacy and costs of an intervention preventing PS-AKI. Results: Depending on selected costs and early intervention efficacy, CCL14-directed care led to lower costs and more QALYs (dominating) or was cost-effective at the $50,000/QALY threshold. Assuming the intervention would avoid 10% of PS-AKI complications in AKI stage 2-3 patients identified as true positive resulted in 0.066 additional QALYs and $486 reduced costs. Results were robust to substantial parameter variation. Conclusion: The analysis suggests that in the presence of an efficacious intervention preventing PS-AKI, identifying people at risk using CCL14 in addition to SOC is likely to represent a cost-effective use of resources.
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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 | 0.007 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it