Cost-Utility of Real-Time Potassium Monitoring in United States Patients Receiving Hemodialysis
Bibliographic record
Abstract
Introduction: Patients with kidney failure requiring hemodialysis are at high risk for hyperkalemia between treatments, which is associated with increased cardiovascular morbidity and mortality. Early detection of hyperkalemic events may be useful to prevent adverse outcomes and their associated costs. We performed a cost-utility analysis comparing an intervention where a real-time potassium monitoring device is administered in patients on hemodialysis in comparison to usual care. Methods: We developed a cost-utility model with microsimulation from the perspective of the United States health care payer. Primary outcomes included the monthly cost-effectiveness threshold cost and break-even cost per patient attributable to the intervention and the incremental cost-effectiveness ratio comparing the intervention to usual care. A 25% reduction in hyperkalemic events was applied as a baseline device effectiveness estimate. Concurrent first and second order microsimulations were performed using 10%, 25%, and 50% effectiveness estimates as sensitivity analyses. Results are presented over a 10-year time horizon in 2022 United States dollars and a willingness-to-pay threshold of $100,000 per quality-adjusted life year (QALY) was considered. Results: Over 10 years, threshold and break-even analysis yielded maximum monthly costs of $201.10 and $144.15 per patient, respectively. The intervention was associated with reduced mean costs ($6381.21) and increased mean QALYs (0.03) per patient; therefore, was considered dominant. In sensitivity analysis, the intervention was dominant in 99% of simulations performed at all effectiveness rates. Conclusion: Implementing a real-time potassium monitoring device in patients on hemodialysis has the potential for cost savings and improved outcomes from the perspective of the United States health care payer.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".