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Record W4405221894 · doi:10.1093/jsxmed/qdae167.015

(017) COST-EFFECTIVENESS MODELING OF IMPLANTABLE PROSTHESIS AS A FIRST-LINE OPTION FOR ERECTILE DYSFUNCTION MANAGEMENT

2024· article· en· W4405221894 on OpenAlexaff
Daniel L. Ranti, Ruth A. Berman, Dagmawi Misgano, F Kazal, Joseph P. Alukal, Gregory Joice

Bibliographic record

VenueThe Journal of Sexual Medicine · 2024
Typearticle
Languageen
FieldEngineering
TopicSafety Systems Engineering in Autonomy
Canadian institutionsColumbia College
Fundersnot available
KeywordsErectile dysfunctionPenile prosthesisMedicineFirst lineProsthesisSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Abstract Introduction Erectile dysfunction (ED) rates in the United States have increased over the past several decades due to multiple factors, including an aging population and rising rates of diabetes and peripheral vascular disease. Implantable penile prostheses (IPP) are highly effective for refractory ED, however, insurance coverage is variable and often requires demonstration of both failed oral and injectable therapy. Delayed IPP placement in high-risk patients is likely to reduce quality of life metrics as well as increase costs associated with failed pharmacological trials. Objective We aimed to evaluate the quality associated life years (QALY) and the cost per QALY of direct IPP placement for PDE5 inhibitor-resistant ED. Methods A literature review was conducted to gather probability functions of success and complications associated with PDE5 inhibitors, intracavernosal injection (ICI), and IPP, as well as costs and QALYs associated with each therapy. TreeAge was used to generate a Markov model, which evaluated two separate strategies: 1) a standard pathway, in which all patients would be trialed on PDE5 inhibitors and ICI for 1 to 6 months each, and 2) a direct-to-IPP pathway, in which patients would immediately be offered an IPP. Each model was run for 10 000 iterations for 10 years per iteration. The standard pathway was run for 9 scenarios, in which each population would have a varying response rate to PDE5 inhibitors and ICI (10% to 90% response). Cumulative QALY, costs, and the incremental cost-effectiveness ratio (ICER) of each strategy were calculated. Results The standard pathway model considered 10% to 90% response rates to traditional medical management, resulting in average 10-year costs ranging between $22 914 and $44 187. In comparison, the direct-to-IPP group had average 10-year costs of $17 750 (Fig. 1). The ICERs for the standard pathway compared to the direct-to-IPP approach ranged linearly from -$34 221 to -$45 595 (from 10% to 90% medical management success). Average total QALY for a 10-year period ranged from 6.99 (90% medical management success) to 7.61 (direct-to-IPP). Conclusions In this analysis, we sought to quantify the cost-effectiveness of the prevailing management strategy for erectile dysfunction compared to an IPP-first strategy. We found that the ICER of medical management compared to a direct-to-IPP strategy ranged from -$34 K to -$46 K, indicating that medical management was both more costly and less effective in providing patients with ED quality of life. While it is unreasonable to suggest that IPP should be a first-line treatment for all patients with erectile dysfunction, these data are informative for counseling in populations particularly susceptible to high rates of erectile dysfunction, such as those with diabetic neuropathy, a history of radical prostatectomy, or peripheral vascular disease. In these populations, counseling directly on penile prosthesis before PDE5 inhibitors or ICI trials could be both cost-effective and fit within the current American Urological Association guideline statements. Disclosure Any of the authors act as a consultant, employee or shareholder of an industry for: Gregory Joice MD is a consultant for Boston Scientific.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0160.001

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.

Opus teacher head0.034
GPT teacher head0.279
Teacher spread0.245 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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