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Coronary Optical Coherence Tomography and Cardiac Magnetic Resonance Imaging to Determine Underlying Causes of Myocardial Infarction With Nonobstructive Coronary Arteries in Women

2020· article· en· W3102078311 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueCirculation · 2020
Typearticle
Languageen
FieldMedicine
TopicCoronary Interventions and Diagnostics
Canadian institutionsUniversity of AlbertaUniversity of CalgaryVancouver General Hospital
FundersAbbott VascularNational Center for Advancing Translational SciencesYork University
KeywordsMedicineCoronary arteriesMyocardial infarctionCardiologyOptical coherence tomographyMagnetic resonance imagingInternal medicineCardiac magnetic resonance imagingCardiac magnetic resonanceRadiologyArtery

Abstract

fetched live from OpenAlex

Background: Myocardial infarction with nonobstructive coronary arteries (MINOCA) occurs in 6% to 15% of myocardial infarctions (MIs) and disproportionately affects women. Scientific statements recommend multimodality imaging in MINOCA to define the underlying cause. We performed coronary optical coherence tomography (OCT) and cardiac magnetic resonance (CMR) imaging to assess mechanisms of MINOCA. Methods: In this prospective, multicenter, international, observational study, we enrolled women with a clinical diagnosis of myocardial infarction. If invasive coronary angiography revealed <50% stenosis in all major arteries, multivessel OCT was performed, followed by CMR (cine imaging, late gadolinium enhancement, and T2-weighted imaging and T1 mapping). Angiography, OCT, and CMR were evaluated at blinded, independent core laboratories. Culprit lesions identified by OCT were classified as definite or possible. The CMR core laboratory identified ischemia-related and nonischemic myocardial injury. Imaging results were combined to determine the mechanism of MINOCA, when possible. Results: Among 301 women enrolled at 16 sites, 170 were diagnosed with MINOCA, of whom 145 had adequate OCT image quality for analysis; 116 of these underwent CMR. A definite or possible culprit lesion was identified by OCT in 46.2% (67/145) of participants, most commonly plaque rupture, intraplaque cavity, or layered plaque. CMR was abnormal in 74.1% (86/116) of participants. An ischemic pattern of CMR abnormalities (infarction or myocardial edema in a coronary territory) was present in 53.4% (62/116) of participants undergoing CMR. A nonischemic pattern of CMR abnormalities (myocarditis, takotsubo syndrome, or nonischemic cardiomyopathy) was present in 20.7% (24/116). A cause of MINOCA was identified in 84.5% (98/116) of the women with multimodality imaging, higher than with OCT alone ( P <0.001) or CMR alone ( P =0.001). An ischemic cause was identified in 63.8% of women with MINOCA (74/116), a nonischemic cause was identified in 20.7% (24/116) of the women, and no mechanism was identified in 15.5% (18/116). Conclusions: Multimodality imaging with coronary OCT and CMR identified potential mechanisms in 84.5% of women with a diagnosis of MINOCA, 75.5% of which were ischemic and 24.5% of which were nonischemic, alternate diagnoses to myocardial infarction. Identification of the cause of MINOCA is feasible and has the potential to guide medical therapy for secondary prevention. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT02905357.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.468

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.022
GPT teacher head0.258
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it