Evidence map›Paper›PMID 42118201›Full record

ArticleLa Radiologia medica2026

Real-world insights into coronary CTA prognostication: value of semiquantitative scores.

Anna Palmisano, Alberto Colombo, Elisa Bruno, Davide Vignale, Axel Bartoli, Francesco Pisu, Vittorio Morrone, Arianna Esposito, Davide Serra, Gioele Gambato and 9 more

Abstract read
In one paragraph

Article in La Radiologia medica, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

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

19 authors.

Anna PalmisanoAdvanced Imaging for Personalized Medicine Unit, Experimental Imaging Center, IRCCS San Raffaele Hospital, Via Olgettina 58-60, 20132, Milan, Italy.
Alberto ColomboAdvanced Imaging for Personalized Medicine Unit, Experimental Imaging Center, IRCCS San Raffaele Hospital, Via Olgettina 58-60, 20132, Milan, Italy.
Elisa BrunoAdvanced Imaging for Personalized Medicine Unit, Experimental Imaging Center, IRCCS San Raffaele Hospital, Via Olgettina 58-60, 20132, Milan, Italy.
Davide VignaleAdvanced Imaging for Personalized Medicine Unit, Experimental Imaging Center, IRCCS San Raffaele Hospital, Via Olgettina 58-60, 20132, Milan, Italy.
Axel BartoliAdvanced Imaging for Personalized Medicine Unit, Experimental Imaging Center, IRCCS San Raffaele Hospital, Via Olgettina 58-60, 20132, Milan, Italy.
Francesco PisuSchool of Medicine, Vita-Salute San Raffaele University, Milan, Italy.
Vittorio MorroneAdvanced Imaging for Personalized Medicine Unit, Experimental Imaging Center, IRCCS San Raffaele Hospital, Via Olgettina 58-60, 20132, Milan, Italy.
Arianna EspositoAdvanced Imaging for Personalized Medicine Unit, Experimental Imaging Center, IRCCS San Raffaele Hospital, Via Olgettina 58-60, 20132, Milan, Italy.
Davide SerraAdvanced Imaging for Personalized Medicine Unit, Experimental Imaging Center, IRCCS San Raffaele Hospital, Via Olgettina 58-60, 20132, Milan, Italy.
Gioele GambatoAdvanced Imaging for Personalized Medicine Unit, Experimental Imaging Center, IRCCS San Raffaele Hospital, Via Olgettina 58-60, 20132, Milan, Italy.
Gloria MarrasAdvanced Imaging for Personalized Medicine Unit, Experimental Imaging Center, IRCCS San Raffaele Hospital, Via Olgettina 58-60, 20132, Milan, Italy.
Andrea BettinelliAdvanced Imaging for Personalized Medicine Unit, Experimental Imaging Center, IRCCS San Raffaele Hospital, Via Olgettina 58-60, 20132, Milan, Italy.
Martina FilipponeAdvanced Imaging for Personalized Medicine Unit, Experimental Imaging Center, IRCCS San Raffaele Hospital, Via Olgettina 58-60, 20132, Milan, Italy.
Raffaella VitaleAdvanced Imaging for Personalized Medicine Unit, Experimental Imaging Center, IRCCS San Raffaele Hospital, Via Olgettina 58-60, 20132, Milan, Italy.
Chiara GnassoAdvanced Imaging for Personalized Medicine Unit, Experimental Imaging Center, IRCCS San Raffaele Hospital, Via Olgettina 58-60, 20132, Milan, Italy.
Beatrice Maria CivelliPorini Srl, Milan, Italy.
Gabriele ChiodiniPorini Srl, Milan, Italy.
Carlo TacchettiSchool of Medicine, Vita-Salute San Raffaele University, Milan, Italy.
Antonio EspositoAdvanced Imaging for Personalized Medicine Unit, Experimental Imaging Center, IRCCS San Raffaele Hospital, Via Olgettina 58-60, 20132, Milan, Italy. esposito.antonio@hsr.it.ORCID http://orcid.org/0000-0002-1170-6266

Funding

Unione Europea B49J23000290005
6 · The paper itself

Abstract

purposeSeveral semiquantitative coronary computed tomography angiography (CCTA) scores including different parameters describing stenosis degree, plaque burden and plaque features have been developed for diagnostic and prognostic purposes. However, their clinical application is still limited. The aim of the study is to assess the prognostic implication of semiquantitative coronary CCTA scores in clinical practice. MATERIAL AND

methodsIn this retrospective, single-center study, 6818 adults who underwent elective CCTA between 2016 and 2020 were screened. A total of 1878 patients were enrolled based on low-to-intermediate pretest risk, good image quality (Likert score ≥ 4), and ≥ 3 years of follow-up. Clinical data were collected, and the following CCTA scores were calculated: CACS, CAD-RADS, Leiden, CT Leaman, SSS and SIS. Prognostic performance for five-point and two-point MACE composite outcomes was evaluated using survival analysis and multivariable models.

resultsOver the follow-up period, 10% (187/1878) experienced a five-point MACE and 5.4% (102/1878) a two-point MACE. All CCTA scores stratify the risk of MACE, and all CCTA scores were predictors of outcome with HR increasing as the score increases, with the highest HR in case of severe CAD-RADS (five-point MACE composite outcome: HR 16.35, 95%CI [7.5-35.61]; p < .001 and two-point MACE composite outcome: HR 19.49, 95%CI [6.01-63.2]; p < .001). CAD-RADS outperformed other scores in multivariable models including age, sex and cardiovascular risk factors with a C-index of 0.75 for five-point MACE and of 0.78 for two-point MACE, always p < 0.001. High-risk features were not predictors of outcome.

conclusionsCAD-RADS is the most effective CCTA-derived score for MACE prediction in real-world application.

Indexed as

Computed Tomography AngiographyCoronary AngiographyCoronary Artery DiseaseAgedFemaleHumansMaleMiddle AgedPrognosisRetrospective StudiesRisk AssessmentSeverity of Illness IndexArtificial intelligenceCAD-RADSComputed tomographyCoronary artery diseaseDiagnosisPrognosis

Identifiers

PMID42118201
PMCPMC13368836

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Registered trials

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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.