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Record W4397047908 · doi:10.1681/asn.20223311s1471b

Cost-Utility of Real-Time Potassium Monitoring in Hemodialysis Patients

2022· article· en· W4397047908 on OpenAlexaff
Ryan J. Bamforth, Thomas W. Ferguson, Paul Komenda

Bibliographic record

VenueJournal of the American Society of Nephrology · 2022
Typearticle
Languageen
FieldMedicine
TopicPotassium and Related Disorders
Canadian institutionsOrthopaedic Innovation Centre
Fundersnot available
KeywordsHemodialysisMedicinePotassiumIntensive care medicineUrologyInternal medicineMetallurgyMaterials science

Abstract

fetched live from OpenAlex

Background: Patients with kidney failure requiring dialysis are at high risk for hyperkalemia, a result of elevated levels of potassium, which is associated with increased morbidity and mortality. Interventions aimed at early detection of hyperkalemic events may be useful to prevent these outcomes and their associated costs. As such, we performed a cost-utility analysis comparing an intervention where a real-time potassium monitoring device is administered in hemodialysis patients in comparison to usual care. Methods: We performed a cost-utility analysis by developing a decision analytic microsimulation model from the perspective of the United States health care payer. Outcomes included the monthly break-even cost per patient of the proposed intervention and the incremental cost-effectiveness ratio (ICER) comparing use of the real-time potassium monitoring device to usual care. Costs associated with hyperkalemic events (emergency department and hospitalization specific) and dialysis were included. Utility estimates from a systematic review and meta-analysis were used to derive utilities for patients on hemodialysis. A reduction in hyperkalemic events of 25% was applied in the intervention scenario as a baseline effectiveness estimate, with a range between 10-50% considered in sensitivity analyses. Results: Threshold analysis yielded a monthly break-even cost of $689.56 US dollars per patient in the base case scenario. In addition, the microsimulation model found the intervention provided 0.04 additional quality-adjusted life-years (QALYs), and as such at any price point below or equal to the break-even cost the intervention was dominant in comparison to usual care. When altering effectiveness estimates between a reduction of hyperkalemic events between 10% to 50%, the monthly break-even cost ranged from $265.36 to $1387.90 USD respectively. Conclusions: Implementing a real-time potassium monitoring device in hemodialysis patients to prevent hyperkalemic events has the potential for cost savings and increased quality of life from the perspective of the Unites States health care payer. Funding: Commercial Support - Proton Intelligence INC

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.148
Threshold uncertainty score0.288

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.275
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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