Evidence map›Paper›PMID 36258169›Full record

ArticleBMC nephrology2022

A disease progression model estimating the benefit of tolvaptan on time to end-stage renal disease for patients with rapidly progressing autosomal dominant polycystic kidney disease.

Gregory Mader, Deirdre Mladsi, Myrlene Sanon, Molly Purser, Christine L Barnett, Dorothee Oberdhan, Terry Watnick, Stephen Seliger

Open access · goldAbstract read
In one paragraph

Article in BMC nephrology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.3field-weighted citation impact, top 18% of its field
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

7 citing papers in PubMed, 8 citations in OpenAlex.

  1. Combination siRNA delivery as a therapeutic strategy for ADPKD.bioRxiv : the preprint server for biology · 2026
    Article
  2. Article
  3. Inhibition of the inflammasome ameliorates orthologous polycystic kidney disease.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  4. Article
  5. Article
  6. Biomolecules · 2023
    Article
  7. Article
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

8 authors at 3 institutions in 1 country.

Gregory MaderRTI Health Solutions, Research Triangle Park, NC, USA.
Deirdre MladsiRTI Health Solutions, Research Triangle Park, NC, USA. dmladsi@rti.org.
Myrlene SanonOtsuka Pharmaceutical Development & Commercialization, Inc., Princeton, NJ, USA.
Molly PurserRTI Health Solutions, Research Triangle Park, NC, USA.
Christine L BarnettRTI Health Solutions, Research Triangle Park, NC, USA.
Dorothee OberdhanOtsuka Pharmaceutical Development & Commercialization, Inc., Princeton, NJ, USA.
Terry WatnickUniversity of Maryland School of Medicine, Baltimore, MD, USA.
Stephen SeligerUniversity of Maryland School of Medicine, Baltimore, MD, USA.
RTI Health Solutions · USOtsuka (United States) · USUniversity of Maryland, Baltimore · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTolvaptan was approved in the United States in 2018 for patients with autosomal dominant polycystic kidney disease (ADPKD) at risk of rapid progression as assessed in a 3-year phase 3 clinical trial (TEMPO 3:4). An extension study (TEMPO 4:4) showed continued delay in progression at 2 years, and a trial in patients with later-stage disease (REPRISE) provided confirmatory evidence of efficacy. Given the relatively shorter-term duration of the clinical trials, estimating the longer-term benefit associated with tolvaptan via extrapolation of the treatment effect is an important undertaking.

methodsA model was developed to simulate a cohort of patients with ADPKD at risk of rapid progression and predict their long-term outcomes using an algorithm organized around the Mayo Risk Classification system, which has five subclasses (1A through 1E) based on estimated kidney growth rates. The model base-case population represents 1280 patients enrolled in TEMPO 3:4 beginning in chronic kidney disease (CKD) stages G1, G2, and G3 across Mayo subclasses 1C, 1D, and 1E. The algorithm was used to predict longer-term natural history health outcomes. The estimated treatment effect of tolvaptan from TEMPO 3:4 was applied to the natural history to predict the longer-term treatment benefit of tolvaptan. For the cohort, analyzed once reflecting natural history and once assuming treatment with tolvaptan, the model estimated lifetime progression through CKD stages, end-stage renal disease (ESRD), and death.

resultsWhen treated with tolvaptan, the model cohort was predicted to experience a 3.1-year delay of ESRD (95% confidence interval: 1.8 to 4.4), approximately a 23% improvement over the estimated 13.7 years for patients not receiving tolvaptan. Patients beginning tolvaptan treatment in CKD stages G1, G2, and G3 were predicted to experience estimated delays of ESRD, compared with patients not receiving tolvaptan, of 3.8 years (21% improvement), 3.0 years (24% improvement), and 2.1 years (28% improvement), respectively.

conclusionsThe model estimated that patients treated with tolvaptan versus no treatment spent more time in earlier CKD stages and had later onset of ESRD. Findings highlight the potential long-term value of early intervention with tolvaptan in patients at risk of rapid ADPKD progression.

Indexed as

Kidney Failure, ChronicPolycystic Kidney, Autosomal DominantTolvaptanAntidiuretic Hormone Receptor AntagonistsClinical Trials, Phase III as TopicDisease ProgressionHumansTime FactorsAntidiuretic Hormone Receptor AntagonistsTolvaptanAutosomal dominant polycystic kidney diseaseDisease modelingEnd-stage renal diseaseRenal function declineTolvaptan

Identifiers

PMID36258169
PMCPMC9578187
OpenAlexW4306679950

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.