Dynamic handgrip exercise for the detection of myocardial ischemia using fast Strain-ENCoded cardiovascular magnetic resonance
Bibliographic record
Abstract
Previous data suggests dynamic handgrip exercise (DHE) as a potential physiological, needle-free stressor feasible for cardiovascular magnetic resonance (CMR) conditions. DHE-fast Strain-ENCoded imaging (fSENC) is potentially cost-saving, ultra-fast and avoids pharmacological side effects thereby targeting the drawbacks of conventional pharmacological stress CMR. To assess the diagnostic accuracy of DHE-fSENC for detecting ischemia-related wall motion abnormalities in suspected obstructive coronary artery disease (CAD). Patients with known or suspected obstructive CAD referred for CMR stress testing were prospectively enrolled. Diagnostic accuracy was assessed in comparison to pharmacological stress CMR and in a subgroup, compared to invasive coronary angiography (ICA). The CMR protocol was extended by both-handed DHE with 80 repetitions per minute over 2 minutes followed by fSENC short-axis acquisition before pharmacological stress testing. Stress-induced impairment of regional longitudinal strain was graded suspicious for obstructive CAD. Two-hundred sixty individuals with cardiovascular high-risk profile (64±13 years, 75% male) were enrolled. DHE-fSENC provided a sensitivity of 79% (95% CI: 64-89) and specificity of 87% (95% CI 82-91) compared to pharmacological stress CMR. In a subgroup of 105 patients with recent ICA, high diagnostic accuracy was found for the detection of obstructive CAD (sensitivity 82% (95% CI: 67-92), specificity 89% (95% CI: 78-95)). Exam duration of DHE-fSENC was significantly reduced compared to conventional CMR stress protocols (DHE-fSENC 207±69 sec vs. adenosine-perfusion 287±82 sec vs. dobutamine-cine 1132±294 sec, all p< 0.001). DHE-fSENC allows for a reliable and fast detection of obstructive CAD, thereby expanding the applicability of needle-free CMR stress testing. Dynamic handgrip exercise and fast Strain-ENCoded imaging. Schematic visualization of the both-sided, dynamic handgrip exercise at approx. 50% of MVC and a frequency of 80/min including fSENC acquisition at rest and during DHE. The comparison between fSENC at rest and after DHE of the apical slice in this example shows an impairment of LS after DHE of the inferior apical wall. DHE and fSENC acquisition was integrated into CMR routine protocol and was performed before pharmacological stress. DHE-fSENC: response of longitudinal strain. Comparison of LS at rest and during DHE. In ischemic segments, longitudinal strain was significantly impaired during DHE. In contrast, LS was more pronounced during DHE in non-ischemic segments. DHE-fSENC: test accuracy compared to pharmacological stress CMR. Test accuracy of DHE-fSENC compared to pharmacological stress CMR was excellent with a sensitivity of 79% (64-89) and a specificity of 87% (82-91). CMR = cardiac magnetic resonance imaging; DHE = dynamic handgrip exercise; fSENC = fast Strain-ENCoded imaging; LS = longitudinal strain; MVC = maximum voluntary contraction; NPV = negative predictive value; PPV = positive predictive value; SEM = standard error of the mean.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".