Inside CKD: Cost-Effectiveness of Multinational Screening for CKD
Bibliographic record
Abstract
Introduction: Early detection of chronic kidney disease (CKD) could slow its progression; however, most patients in earlier stages remain undiagnosed. Our study objective was to assess the cost-effectiveness of multinational CKD screening strategies from the payer perspective across general and higher-risk populations. Methods: Using the published Inside CKD microsimulation, we projected virtual closed populations to assess CKD screening strategies in 31 countries or regions over a lifetime horizon. We considered people aged ≥ 65 or ≥ 45 years in the general population and in high-risk subgroups (type 2 diabetes [T2D], hypertension, or cardiovascular disease [CVD]). Simulated populations could receive 2 serum creatinine (SCr) tests assessing estimated glomerular filtration rate (eGFR), "2 eGFR only", or an additional urinary albumin-to-creatinine ratio test (UACR), "2 eGFR and 1 UACR", versus current practice. Eligible patients received renin-angiotensin system inhibitors (RASi). Results: Screening the general population aged ≥ 45 years for CKD was cost-effective versus current practice in all countries or regions using the "2 eGFR and 1 UACR" strategy, and cost-effective in all but 1 country using the 2 eGFR only strategy. The 2 eGFR and 1 UACR strategy showed consistently higher cost-effectiveness. Screening general populations aged ≥ 45 years increased projected CKD diagnosis rates per 100,000 persons eligible for screening from 459 by current practice to 7475 patients using 2 eGFR only, or 14,392 using 2 eGFR and 1 UACR. Similar trends in cost-effectiveness and diagnosis rates were observed in persons aged ≥ 65 years. Conclusion: CKD screening may be cost-effective in general populations worldwide, including in populations aged ≥ 45 years. Our analysis corroborates global guideline recommendations for simultaneous eGFR and UACR testing if considered in the context of local factors.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".