Evidence map›Paper›PMID 40490096›Full record

ArticleJournal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology2025

Hidesato Fujito, Mark Lemley, Valerie Builoff, Wenhao Zhang, Keiichiro Kuronuma, Giselle Ramirez, Aditya Killekar, Joanna X Liang, Paul Kavanagh, Mark C Hyun and 8 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology, 2025. 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

18 authors.

Hidesato FujitoDepartment of Medicine, Division of Artificial Intelligence in Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States; Department of Cardiology, Nihon University Itabashi Hospital, Tokyo, Japan.
Mark LemleyDepartment of Medicine, Division of Artificial Intelligence in Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States.
Valerie BuiloffDepartment of Medicine, Division of Artificial Intelligence in Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States.
Wenhao ZhangDepartment of Medicine, Division of Artificial Intelligence in Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States.
Keiichiro KuronumaDepartment of Medicine, Division of Artificial Intelligence in Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States; Department of Cardiology, Nihon University Itabashi Hospital, Tokyo, Japan.
Giselle RamirezDepartment of Medicine, Division of Artificial Intelligence in Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States.
Aditya KillekarDepartment of Medicine, Division of Artificial Intelligence in Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States.
Joanna X LiangDepartment of Medicine, Division of Artificial Intelligence in Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States.
Paul KavanaghDepartment of Medicine, Division of Artificial Intelligence in Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States.
Mark C HyunDepartment of Medicine, Division of Artificial Intelligence in Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States.
Sean W HayesDepartment of Medicine, Division of Artificial Intelligence in Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States.
Louise E J ThomsonDepartment of Medicine, Division of Artificial Intelligence in Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States.
John D FriedmanDepartment of Medicine, Division of Artificial Intelligence in Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States.
Serge D Van KriekingeDepartment of Medicine, Division of Artificial Intelligence in Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States.
Marcelo F Di CarliDepartment of Radiology, Division of Nuclear Medicine, and Molecular Imaging, Brigham and Women's Hospital, Boston, MA, United States.
Damini DeyDepartment of Medicine, Division of Artificial Intelligence in Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States.
Daniel S BermanDepartment of Medicine, Division of Artificial Intelligence in Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States.
Piotr J SlomkaDepartment of Medicine, Division of Artificial Intelligence in Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States. Electronic address: Piotr.Slomka@cshs.org.

Funding

Integrating Artificial Intelligence for Optimal Analysis of CardiacPET/CTR01EB034586 · NIBIB · CEDARS-SINAI MEDICAL CENTER · PI DI CARLI, MARCELO F, SLOMKA, PIOTR J · 2022 to 2025
$2.9M
NIBIB NIH HHS R01 EB034586
6 · The paper itself

Abstract

backgroundWe investigated whether the shape of arterial blood input curves affects the diagnostic performance of myocardial blood flow (MBF) on rubidium-82 ( METHODS AND

resultsWe retrospectively enrolled 386 patients without prior CAD who underwent

resultsAmong the five abnormal categories, the AUC of stress MBF in the low SRPR group (n = 193) was lower than in the normal SRPR group (n = 193) in per-patient (.67 [.59-.74] vs. .78 [.70-.84], P = .0499, respectively) and per-vessel analyses (.68 [.63-0.73] vs. .75 [.71-.79], P = .0352, respectively). The AUC in slow activity rise group (n = 167) for stress MBF was lower than in others in per-vessel analysis (n = 219) (.68 [.62-.72] vs. .75 [.70-.79], P = .0270). Other abnormal profiles showed no significant differences (all P > .05).

conclusionsLow SRPR and slow activity rise were associated with reduced diagnostic performance of stress MBF.

Indexed as

Coronary Artery DiseaseCoronary CirculationMyocardial Perfusion ImagingPositron-Emission TomographyRubidium RadioisotopesAgedCoronary AngiographyFemaleHumansMaleMiddle AgedRetrospective StudiesRubidium-82Rubidium RadioisotopesMyocardial blood flowPositron emission tomographyRubidiumTime activity curve

Identifiers

PMID40490096
PMCPMC12624753

What OpenQuestion 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 OpenQuestion graph.