A Cost-Minimization Analysis of Nurse-Led Virtual Case Management in Late-Stage CKD
Bibliographic record
Abstract
INTRODUCTION: Interventions are needed to improve early detection of indications for dialysis before development of severe symptoms or complications. This may reduce suboptimal dialysis starts, prevent hospitalizations, and decrease costs. Our objectives were to explore assumptions around a nurse-led virtual case management intervention for patients with late-stage chronic kidney disease (CKD) with a 2-year Kidney Failure Risk Equation (KFRE) estimated risk of kidney failure ≥80% and to estimate how these assumptions affect potential cost savings. METHODS: We performed a cost-minimization analysis by developing a decision analytic microsimulation model constructed from the perspective of the health payer. Our primary outcome was the break-even point, defined as the maximum amount a health payer could spend on the intervention without incurring any net financial loss or gain. The intervention group received remote telemonitoring, including daily measurement of several health metrics (blood pressure, oxygen saturation, and weight), and a validated symptom questionnaire accompanied by nurse-led case management, whereas the comparator group received usual care. We assumed patients received the intervention for a maximum of 2 years. RESULTS: The break-even point was $7339 per late-stage CKD patient enrolled in the intervention. Based on the distribution of time receiving the intervention, we determined a maximum monthly intervention cost of $703.37. In probabilistic sensitivity analyses, we found that 75% of simulations produced break-even points between $3929 and $9460. CONCLUSION: Nurse-led virtual home monitoring interventions in patients with CKD at high risk of kidney failure have the potential for significant cost savings from the perspective of the health payer.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".