Evidence map›Paper›PMID 31202740›Full record

ArticleJACC. Cardiovascular imaging2020

5-Year Prognostic Value of Quantitative Versus Visual MPI in Subtle Perfusion Defects: Results From REFINE SPECT.

Yuka Otaki, Julian Betancur, Tali Sharir, Lien-Hsin Hu, Heidi Gransar, Joanna X Liang, Peyman N Azadani, Andrew J Einstein, Mathews B Fish, Terrence D Ruddy and 11 more

Open access · greenAbstract readComparative StudyMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in JACC. Cardiovascular imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
64citing papers in PubMed, 1 pooled it
9.2field-weighted citation impact, top 2% of its field
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

64 citing papers in PubMed, 1 synthesis or guideline pooled it, 93 citations in OpenAlex.

  1. Pooled it
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  13. The Updated Registry of Fast Myocardial Perfusion Imaging with Next-Generation SPECT (REFINE SPECT 2.0).Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2024
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  17. Holistic AI analysis of hybrid cardiac perfusion images for mortality prediction.medRxiv : the preprint server for health sciences · 2024
    Article
  18. Article
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  20. CT attenuation correction improves quantitative risk prediction by cardiac SPECT in obese patients.European journal of nuclear medicine and molecular imaging · 2024
    Article

4 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

21 authors at 9 institutions in 5 countries.

Yuka OtakiDivision of Nuclear Medicine, Departments of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
Julian BetancurDivision of Nuclear Medicine, Departments of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
Tali SharirDepartment of Nuclear Cardiology, Assuta Medical Centers, Tel Aviv, and Ben Gurion University of the Negev, Beer Sheba, Israel.
Lien-Hsin HuDivision of Nuclear Medicine, Departments of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California; Department of Nuclear Medicine, Taipei Veterans General Hospital, Taipei, Taiwan.
Heidi GransarDivision of Nuclear Medicine, Departments of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
Joanna X LiangDivision of Nuclear Medicine, Departments of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
Peyman N AzadaniDivision of Nuclear Medicine, Departments of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
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.
Mathews B FishOregon Heart and Vascular Institute, Sacred Heart Medical Center, Springfield, Oregon.
Terrence D RuddyDivision of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada.
Philipp A KaufmannDepartment of Nuclear Medicine, Cardiac Imaging, University Hospital Zurich, Zurich, Switzerland.
Albert J SinusasSection of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut.
Edward J MillerSection of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut.
Timothy M BatemanCardiovascular Imaging Technologies LLC, Kansas City, Missouri.
Sharmila DorbalaDepartment of Radiology, Division of Nuclear Medicine and Molecular Imaging, Brigham and Women's Hospital, Boston, Massachusetts.
Marcelo Di CarliDepartment of Radiology, Division of Nuclear Medicine and Molecular Imaging, Brigham and Women's Hospital, Boston, Massachusetts.
Balaji K TamarappooDivision of Nuclear Medicine, Departments of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
Guido GermanoDivision of Nuclear Medicine, Departments of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
Damini DeyDivision of Nuclear Medicine, Departments of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
Daniel S BermanDivision of Nuclear Medicine, Departments of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
Piotr J SlomkaDivision of Nuclear Medicine, Departments of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California. Electronic address: Piotr.Slomka@cshs.org.
Cedars-Sinai Medical Center · USBrigham and Women's Hospital · USYale University · USBen-Gurion University of the Negev · ILHeart Imaging Technologies (United States) · USNewYork–Presbyterian Hospital · USProvidence Sacred Heart Medical Center · USUniversity Hospital of Zurich · CHUniversity of Ottawa · CA

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Quantitative Prediction of Disease and Outcomes from Next Generation SPECT and CTR01HL089765 · NHLBI · CEDARS-SINAI MEDICAL CENTER · PI SLOMKA, PIOTR J · 2007 to 2021
$6.1M
NCATS NIH HHS UL1 TR001863NHLBI NIH HHS R01 HL089765
6 · The paper itself

Abstract

objectivesThis study compared the ability of automated myocardial perfusion imaging analysis to predict major adverse cardiac events (MACE) to that of visual analysis.

backgroundQuantitative analysis has not been compared with clinical visual analysis in prognostic studies.

methodsA total of 19,495 patients from the multicenter REFINE SPECT (REgistry of Fast Myocardial Perfusion Imaging with NExt generation SPECT) study (64 ± 12 years of age, 56% males) undergoing stress Tc-99m-labeled single-photon emission computed tomography (SPECT) myocardial perfusion imaging were followed for 4.5 ± 1.7 years for MACE. Perfusion abnormalities were assessed visually and categorized as normal, probably normal, equivocal, or abnormal. Stress total perfusion deficit (TPD), quantified automatically, was categorized as TPD = 0%, TPD >0% to <1%, ≤1% to <3%, ≤3% to <5%, ≤5% to ≤10%, or TPD >10%. MACE consisted of death, nonfatal myocardial infarction, unstable angina, or late revascularization (>90 days). Kaplan-Meier and Cox proportional hazards analyses were performed to test the performance of visual and quantitative assessments in predicting MACE.

resultsDuring follow-up examinations, 2,760 (14.2%) MACE occurred. MACE rates increased with worsening of visual assessments, that is, the rate for normal MACE was 2.0%, 3.2% for probably normal, 4.2% for equivocal, and 7.4% for abnormal (all p < 0.001). MACE rates increased with increasing stress TPD from 1.3% for the TPD category of 0% to 7.8% for the TPD category of >10% (p < 0.0001). The adjusted hazard ratio (HR) for MACE increased even in equivocal assessment (HR: 1.56; 95% confidence interval [CI]: 1.37 to 1.78) and in the TPD category of ≤3% to <5% (HR: 1.74; 95% CI: 1.41 to 2.14; all p < 0.001). The rate of MACE in patients visually assessed as normal still increased from 1.3% (TPD = 0%) to 3.4% (TPD ≥5%) (p < 0.0001).

conclusionsQuantitative analysis allows precise granular risk stratification in comparison to visual reading, even for cases with normal clinical reading.

Indexed as

Coronary CirculationTomography, Emission-Computed, Single-PhotonAgedFemaleHeart DiseasesHumansMaleMiddle AgedMyocardial Perfusion ImagingPredictive Value of TestsPrognosisRegistriesRisk AssessmentRisk FactorsTime Factorsmajor adverse cardiac eventmyocardial perfusion imagingprognosisquantification

Identifiers

PMID31202740
PMCPMC6899217
OpenAlexW2952434489

What OpenQuestion holds

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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.