Evidence map›Paper›PMID 41364873›Full record

ArticleRetina (Philadelphia, Pa.)2026

MODELING NATURAL HISTORY AND COST-EFFECTIVENESS OF GENE-AGNOSTIC TREATMENT IN RETINITIS PIGMENTOSA.

Patrick W Sullivan, Albert I Wertheimer, Sandeep Grover, Kanishka T Jayasundera

Abstract read
In one paragraph

Article in Retina (Philadelphia, Pa.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Patrick W SullivanCollege of Pharmacy, Nova Southeastern University, Davie, Florida.ORCID 0009-0003-2783-7177
Albert I WertheimerTouro College of Pharmacy, New York, New York.
Sandeep GroverUniversity of Florida College of Medicine, Jacksonville, Florida; and.
Kanishka T JayasunderaKellogg Eye Center, University of Michigan, Ann Arbor, Michigan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTreatment options for retinitis pigmentosa (RP) have been limited. A new generation of innovative but potentially expensive treatments under development may halt or reverse disease progression.

methodsUsing an existing database, a closed-cohort microsimulation model of all genetic subtypes of RP was conducted from a U.S. societal perspective. A hypothetical gene-agnostic therapy was compared with standard care at age 30. Outcomes included VF and VA progression; incremental cost-effectiveness ratio (ICER: dollars per quality-adjusted life-year (QALY) gained); and lifetime medical and productivity-related costs.

resultsBaseline cohort (age 30, N = 381; mean VA logMAR, 0.26 (20/36); mean VF sum total degrees (III4e), 749). By age 45, 79% of patients with untreated RP reached legal blindness or worse, compared with significantly delayed progression with treatment. ICER of gene-agnostic therapy was $158,554/QALY, yielding better outcomes compared with untreated RP (20.5 vs. 16.7 QALYs) and substantial indirect cost savings ($691,961) if a drug price of $1.5 million was assumed.

conclusionA gene-agnostic treatment for RP with drug price of $1.5 million (both eyes) may be cost-effective for most types of RP, particularly with earlier initiation. These findings can inform clinical, economic, and health policy decisions, potentially supporting early intervention strategies and resource allocation for novel RP treatments.

Indexed as

Cost-Benefit AnalysisGenetic TherapyRetinitis PigmentosaAdultDisease ProgressionFemaleHealth Care CostsHumansMaleMiddle AgedQuality-Adjusted Life YearsVisual Acuitycost-effectivenesscost studiescost-utilitygene therapynatural historyophthalmology treatmentretinitis pigmentosavisual functionvisual impairment

Identifiers

PMID41364873
PMCPMC13015803

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.