A Contemporary Economic Model of CKD in the United States
Bibliographic record
Abstract
Background: Chronic kidney disease (CKD) impacts an estimated 14% of US adults, and is associated with reduced quality of life, progression to kidney failure, and cardiovascular disease (CVD), resulting in high healthcare costs. The recently updated Kidney Disease: Improving Global Outcomes (KDIGO) guidelines have highlighted the importance of CVD management to improve CKD outcomes and including a role for sodium-glucose cotransporter-2 (SGLT2) inhibitors in delaying disease progression and improving CVD outcomes. Methods: A state transition model was developed (see figure) to follow a hypothetical cohort of US adults with CKD over their lifetime. Progression of CKD was tracked through KDIGO health states defined by estimated glomerular filtration rate (eGFR) and urine albumin creatinine ratio (uACR). Individuals with CKD could transition to kidney failure, major adverse cardiovascular event (myocardial infarction or stroke), heart failure, or death from other causes. Probability of a first event was determined by eGFR, uACR, and diabetes status, with age and sex determining the background mortality risks. Individuals who had a non-fatal first event were followed until death. Resource utilization, costs, transition probabilities, and utilities were derived from peer-reviewed studies. Results: The model predicted clinical events associated with current management as well as healthcare costs and quality adjusted life years (QALY). Under usual care, the model estimated lifetime outcomes of 6.8 QALYs and $150,386, with time prior to a clinical event contributing most to the QALYs, and dialysis contributing most to the healthcare costs. KDIGO guidelines for optimizing CVD treatment and SGLT2 inhibitor treatment were shown to be cost-effective (cost-per-QALY<$100,000). Conclusion: The current model can predict clinical events and consequent impacts on healthcare costs and QALYs. This enables estimation of the value of various guideline-recommended strategies designed to treat patients at all stages of CKD. Funding: Commercial Support - Boehringer Ingelheim
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".