Feasibility of fast cardiovascular magnetic resonance strain imaging in patients presenting with acute chest pain
Bibliographic record
Abstract
BACKGROUND: Cardiovascular magnetic resonance (CMR) is the current reference standard for the quantitative assessment of ventricular function. Fast Strain-ENCoded (fSENC)-CMR imaging allows for the assessment of myocardial deformation within a single heartbeat. The aim of this pilot study was to identify obstructive coronary artery disease (oCAD) with fSENC-CMR in patients presenting with new onset of chest pain. METHODS AND RESULTS: In 108 patients presenting with acute chest pain, we performed fSENC-CMR after initial clinical assessment in the emergency department. The final clinical diagnosis, for which cardiology-trained physicians used clinical information, serial high-sensitive Troponin T (hscTnT) values and-if necessary-further diagnostic tests, served as the standard of truth. oCAD was defined as flow-limiting CAD as confirmed by coronary angiography with typical angina or hscTnT dynamics. Diagnoses were divided into three groups: 0: non-cardiac, 1: oCAD, 2: cardiac, non-oCAD. The visual analysis of fSENC bull´s eye maps (blinded to final diagnosis) resulted in a sensitivity of 82% and specificity of 87%, as well as a negative predictive value of 96% for identification of oCAD. Both, global circumferential strain (GCS) and global longitudinal strain (GLS) accurately identified oCAD (area under the curve/AUC: GCS 0.867; GLS 0.874; p<0.0001 for both), outperforming ECG, hscTnT dynamics and EF. Furthermore, the fSENC analysis on a segmental basis revealed that the number of segments with impaired strain was significantly associated with the patient´s final diagnosis (p<0.05 for all comparisons). CONCLUSION: In patients with acute chest pain, myocardial strain imaging with fSENC-CMR may serve as a fast and accurate diagnostic tool for ruling out obstructive coronary artery disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".