Evidence map›Paper›PMID 42573926›Full record

ArticleCardiovascular engineering and technology2026

The Reverse Calcification Technique (RCT): A Quantitative Parametric Model of Aortic Stenosis Progression.

Maya Sharon, Omer Tal, Ashraf Hamdan, Adi Morany, Danny Bluestein, Rami Haj-Ali

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Article in Cardiovascular engineering and technology, 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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0cells of the map it votes in
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

6 authors.

Maya SharonSchool of Mechanical Engineering, Tel Aviv University, Tel-Aviv, Israel.
Omer TalSchool of Mechanical Engineering, Tel Aviv University, Tel-Aviv, Israel.
Ashraf HamdanDepartment of Cardiology, Rabin Medical Center, Petah Tikva, Israel.
Adi MoranySchool of Mechanical Engineering, Tel Aviv University, Tel-Aviv, Israel.
Danny BluesteinDepartment of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA.
Rami Haj-AliSchool of Mechanical Engineering, Tel Aviv University, Tel-Aviv, Israel. rami98@tau.ac.il.ORCID http://orcid.org/0000-0002-1761-7344

Funding

Clalit Health Services 0605408881
6 · The paper itself

Abstract

introductionAortic stenosis (AS) develops from calcific aortic valve disease (CAVD), which narrows the aortic valve opening as leaflet stiffness increases due to calcium deposition. This study extends the Reverse Calcification Technique (RCT) by incorporating a time dimension to develop a quantitative parametric model for patient-specific prediction of CAVD progression from sequential CT scans.

methodsSeventeen pre-transcatheter aortic valve replacement (TAVR) patients underwent sequential CT scans (1.2-6.5 years); baseline aortic valve calcification (AVC) volumes:

resultsFor scan intervals under 3 years, forward prediction achieved a mean absolute error of 77 mm

conclusionThe quantitative parametric RCT model demonstrates feasibility for patient-specific estimation of CAVD progression and shows promise for optimizing follow-up intervals. External validation in larger independent cohorts and incorporation of patient-specific risk factors are required before clinical implementation.

Indexed as

Aortic stenosisBiomedical imagingCardiovascular diseasesComputed tomographyHeart valvesMathematical models

Identifiers

PMID42573926

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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.