Evidence map›Paper›PMID 40585149›Full record

ArticlemedRxiv : the preprint server for health sciences2025

Multicenter Evaluation of Myocardial Flow Reserve as a Prognostic Marker for Mortality in

Giselle Ramirez, Valerie Builoff, Robert Jh Miller, Mark Lemley, Isabel Carvajal-Juarez, Erick Alexanderson, Thomas L Rosamond, Na Song, Mark I Travin, Leandro Slipczuk and 5 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 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

5 · Who and what money

Authors and funding

15 authors.

Giselle RamirezDepartments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Valerie BuiloffDepartments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Robert Jh MillerDepartments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Mark LemleyDepartments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Isabel Carvajal-JuarezDepartment of Nuclear Cardiology, Ignacio Chavez National Institute of Cardiology, Mexico City, Mexico.
Erick AlexandersonDepartment of Nuclear Cardiology, Ignacio Chavez National Institute of Cardiology, Mexico City, Mexico.
Thomas L RosamondDepartment of Cardiovascular Medicine, The University of Kansas Medical Center, Kansas City, KS, USA.
Na SongDepartment of Radiology (Nuclear Medicine), Montefiore Medical Center and Albert Einstein College of Medicine, Bronx, NY, USA.
Mark I TravinDepartment of Radiology (Nuclear Medicine), Montefiore Medical Center and Albert Einstein College of Medicine, Bronx, NY, USA.
Leandro SlipczukCardiology Division, Montefiore Health System/Albert Einstein College of Medicine, New York, NY, USA.
Andrew J EinsteinDivision of Cardiology, Department of Medicine, and Department of Radiology, Columbia University Irving Medical Center and New York-Presbyterian Hospital, New York, New York, United States.
Samuel WoppererDepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
Marcelo Di CarliDivision of Nuclear Medicine and Molecular Imaging, Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA.
Panithaya ChareonthaitaweeDepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
Piotr SlomkaDepartments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA.ORCID 0000-0002-6110-938X

Funding

Patient-specific Outcome Prediction from Cardiovascular Multimodality Imaging by Artificial IntelligenceR35HL161195 · NHLBI · CEDARS-SINAI MEDICAL CENTER · PI Piotr J Slomka · 2022 to 2026
$5.0M
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
NHLBI NIH HHS R35 HL161195NIBIB NIH HHS R01 EB034586
6 · The paper itself

Abstract

Background: Myocardial flow reserve (MFR), measured by PET MPI, provides valuable information on epicardial coronary disease, diffuse atherosclerosis, and microvascular function. Despite its routine use, the prognostic efficacy of Methods: We considered patients from five sites in the REFINE PET registry who underwent Results: In total, 6277 patients were included (mean age of 64 years, 56% male). Median follow-up time was 3.8 years. There were 1895 patients with MFR ≤2 and 4382 with MFR >2. Patients with MFR ≤2 had significantly higher mortality than those with MFR >2 (n=701 [37.0%] vs. n=537 [12.3%], respectively; p<0.001). Annualized ACM rates by MFR and SSS ranged from 1.7 to 11.6. In multivariable analysis, MFR ≤2 was independently associated with increased ACM in the overall population (HR 2.70, 95% CI 2.41-3.03, p<0.001), even among patients with no perfusion defects (HR 2.36, 95% CI 1.93-2.89; p<0.001). Mortality risk decreased across increasing MFR deciles ranging from HR 2.73 (95% CI 2.39-3.11) to HR 0.35 (95% CI 0.25-0.49). Conclusion: In this large multicenter cohort, MFR derived from

Indexed as

13N-ammoniaAll-cause mortalityMyocardial flow reserveMyocardial perfusion imaging

Identifiers

PMID40585149
PMCPMC12204227

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.