Evidence map›Paper›PMID 30706258›Full record

ArticleEJNMMI research2019

Phase analysis single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) detects dyssynchrony in myocardial scar and increases specificity of MPI.

John P Bois, Chris Scott, Panithaya Chareonthaitawee, Raymond J Gibbons, Martin Rodriguez-Porcel

Open access · goldAbstract read
In one paragraph

Article in EJNMMI research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.7field-weighted citation impact, top 16% 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

6 citing papers in PubMed, 12 citations in OpenAlex.

  1. Article
  2. Article
  3. Performance of cardiac PET/CT with and without phase analysis for detection of scar in cardiac sarcoidosis: Comparison to cardiac magnetic resonance imaging.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2022
    Article
  4. Article
  5. Article
  6. Is myocardial viability related to left ventricular dyssynchrony?Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2020
    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

5 authors at 2 institutions in 1 country.

John P BoisDepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA. Bois.john@mayo.edu.ORCID http://orcid.org/0000-0003-4972-5160
Chris ScottDepartment of Biostatistics, Mayo Clinic, Rochester, MN, USA.
Panithaya ChareonthaitaweeDepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
Raymond J GibbonsDepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
Martin Rodriguez-PorcelDepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
Mayo Clinic in Arizona · USMayo Clinic · US

Funding

National Center for Advancing Translational Sciences UL1 TR000135
6 · The paper itself

Abstract

backgroundMyocardial perfusion imaging (MPI) with single-photon emission computed tomography (SPECT) is commonly used to assess patients with cardiovascular disease. However, in certain scenarios, it may have limited specificity in the identification of hemodynamically significant coronary artery disease (e.g., false positive), potentially resulting in additional unnecessary testing and treatment. Phase analysis (PA) is an emerging, highly reproducible quantitative technology that can differentiate normal myocardial activation (synchrony) from myocardial scar (dyssynchrony). The objective of this study is to determine if PA can improve the specificity SPECT MPI.

methodsAn initial cohort of 340 patients (derivation cohort), referred for SPECT-MPI, was prospectively enrolled. Resting MPI studies were assessed for resting perfusion defects (scar). These were utilized as the reference standard for scar. Subsequently, we collected a second independent validation cohort of 138 patients and tested the potential of PA to reclassify patients for the diagnosis of "scar" or "no scar." Patients were assigned to three categories depending upon their pre-test probability of scar based on multiple clinical and imaging parameters: ≤ 10% (no scar), 11-74% (indeterminate), and ≥ 75% (scar). The ability of PA variables to reclassify patients with scar to a higher group and those without scar to a lower group was then determined using the net reclassification index (NRI).

resultsEntropy (≥ 59%) was independently associated with scar in both patient cohorts with an odds ratio greater than five. Furthermore, when added to multiple clinical/imaging variables, the use of entropy significantly improved the area under the curve for assessment of scar (0.67 vs. 0.59, p = 0.04). The use of entropy correctly reclassified 24% of patients without scar, by clinical model, to a lower risk category (as determined by pre-test probability) with an overall NRI of 18% in this validation cohort. DISCUSSION: The use of PA entropy can improve the specificity of SPECT MPI and may serve as a useful adjunctive tool to the interpreting physician. The current study determined the optimal PA parameters to detect scar (derivation cohort) and applied these parameters to a second, independent, patient group and noted that entropy (≥ 59%) was independently associated with scar in both patient cohorts. Therefore, PA, which requires no additional imaging time or radiation, enhances the diagnostic capabilities of SPECT MPI.

conclusionThe use of PA entropy significantly improved the specificity of SPECT MPI and could influence the labeling of a patient as having or not having myocardial scar and thereby may influence not only diagnostic reporting but also potentially prognostic determination and therapeutic decision-making.

Indexed as

Coronary artery diseaseDiagnostic testingNuclear cardiology and PET

Identifiers

PMID30706258
PMCPMC6355889
OpenAlexW2916019671

What OpenQuestion holds

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LicenceCC BY
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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.