Evidence map›Paper›PMID 39915136›Full record

ReviewJournal of cardiovascular computed tomography

Novel CT-derived markers for enhanced long-term risk stratification in the planning of TAVR for aortic stenosis.

Daniel Lorenzatti, Annalisa Filtz, Jolien Geers, Kajetan Grodecki, Vita Jaspan, Colin Pierce, Matthew J Miller, Christine Park, Alexandrina Danilov, Ron Blankstein and 7 more

Abstract readReview
In one paragraph

Review in Journal of cardiovascular computed tomography. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. 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

17 authors.

Daniel LorenzattiMontefiore Health System/Albert Einstein College of Medicine, Bronx, NY, USA.
Annalisa FiltzMontefiore Health System/Albert Einstein College of Medicine, Bronx, NY, USA; RCCS Ospedale Ca' Granda Maggiore Policlinico, Universita' degli Studi di Milano, Milan, Italy.
Jolien GeersCedars-Sinai Medical Center, Los Angeles, CA, USA.
Kajetan GrodeckiCedars-Sinai Medical Center, Los Angeles, CA, USA.
Vita JaspanMontefiore Health System/Albert Einstein College of Medicine, Bronx, NY, USA.
Colin PierceMontefiore Health System/Albert Einstein College of Medicine, Bronx, NY, USA.
Matthew J MillerUniversity of Iowa, Iowa City, IA, USA.
Christine ParkMontefiore Health System/Albert Einstein College of Medicine, Bronx, NY, USA.
Alexandrina DanilovMontefiore Health System/Albert Einstein College of Medicine, Bronx, NY, USA.
Ron BlanksteinBrigham and Women's Hospital, Boston, MA, USA.
Thomas A TreibelInstitute of Cardiovascular Science, University College London, London, United Kingdom; Barts Heart Centre, St Bartholomew's Hospital, West Smithfield, London, EC1A 7BE, United Kingdom.
João L CavalcanteAllina Health Minneapolis Heart Institute, Minneapolis, MN, USA.
Leslee J ShawIcahn School of Medicine at Mount Sinai, Cardiovascular Research Foundation, New York, NY, USA.
Marc R DweckBritish Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, United Kingdom.
Piotr J SlomkaCedars-Sinai Medical Center, Los Angeles, CA, USA.
Damini DeyCedars-Sinai Medical Center, Los Angeles, CA, USA.
Leandro SlipczukMontefiore Health System/Albert Einstein College of Medicine, Bronx, NY, USA. Electronic address: lslipczukb@montefiore.org.

Funding

Integrated prediction of cardiovascular events by automated coronary plaque and pericoronary adipose tissue quantification from CT AngiographyR01HL148787 · NHLBI · CEDARS-SINAI MEDICAL CENTER · PI DEY, DAMINI · 2020 to 2023
$3.0M
Integrated analysis of coronary anatomy and biology with 18F-fluoride PET and CT angiographyR01HL135557 · NHLBI · CEDARS-SINAI MEDICAL CENTER · PI SLOMKA, PIOTR J · 2017 to 2020
$3.0M
AI-enabled characterization of fibrocalcific aortic valve disease and inflammation from CT Angiography: Patient-specific outcome predictionR01HL175875 · NHLBI · CEDARS-SINAI MEDICAL CENTER · PI Damini Dey · 2024 to 2026
$2.4M
NHLBI NIH HHS R01 HL135557NHLBI NIH HHS R01 HL148787NHLBI NIH HHS R01 HL175875
6 · The paper itself

Abstract

In an era of rapidly expanding use of transcatheter aortic valve replacement (TAVR), cardiovascular computed tomography (CCT) has become an essential component in the evaluation process for the growing number of patients. Because of the nature of the guideline-recommended protocol -involving several different CCT acquisitions-it represents a unique dataset for comprehensive phenotyping of the patient with significant aortic stenosis. A substantial body of data has established CCT as a central tool in pre-procedural implantation planning. However, emerging evidence suggests a potential new role for CCT in phenotyping patient risk beyond the index procedure. This new role could represent a unique opportunity in patient selection, medication optimization and follow up post TAVR aiming to improve long-term prognosis. This review highlights emerging data on CCT imaging features for risk stratification in patients during long-term follow-up after TAVR. We summarize the existing literature on this topic and explore whether comprehensive CCT-derived information could be integrated into clinical practice, potentially enhancing TAVR patient selection and post-procedural care.

Indexed as

Aortic ValveAortic Valve StenosisTomography, X-Ray ComputedTranscatheter Aortic Valve ReplacementClinical Decision-MakingHumansPatient SelectionPredictive Value of TestsRisk AssessmentRisk FactorsSeverity of Illness IndexTime FactorsTreatment OutcomeAortic stenosisLong-termPhenotypingRisk stratificationTAVR

Identifiers

PMID39915136
PMCPMC12322149

What Socratic holds

Textmetadata
LicenceTDM
Read underepoch 390

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 Socratic graph.