105 Differences in myocardial mechanics between ischaemic and non-ischaemic cardiomyopathy assessed by cmr: a sub-group analysis of the vindicate trial
Bibliographic record
Abstract
Background Prognosis and treatment of patients with chronic heart failure (CHF) differs according to whether it is ischaemic (ICM) or non-ischaemic cardiomyopathy (NICM). Multi-parametric cardiovascular magnetic resonance (CMR) can distinguish these aetiologies; strain imaging however may confer incremental diagnostic and prognostic information over left ventricular ejection fraction (LVEF). We hypothesised in a prospectively recruited sample of CHF patients, ICM and NICM have different myocardial strain patterns. Methods The VINDICATE trial investigated efficacy of high dose vitamin D in patients with CHF. A subgroup of the trial underwent CMR, blood and cardiopulmonary exercise tests at baseline. 53 patients (31 ICM, 22 NICM) underwent identical 3.0T CMR protocols (Achieva, Philips Healthcare, Best, The Netherlands). Tissue tagging by spatial modulation of magnetization (SPAMM) (spatial resolution 1.51 × 1.57×10mm3, tag separation 7 mm, 18 phases, typical TR/TE 5.8/3.5 ms, flip angle 10°, typical temporal resolution 55 ms) was acquired in short axis slices acquired at the apex, mid-ventricle, and base. Late gadolinium enhancement (LGE) was performed 15 min following administration of 0.15 mmol/kg gadolinium DTPA. CMR data were analysed quantitatively using commercially available software (CVI42, Circle Cardiovascular Imaging Inc. Calgary, Canada and inTag v1.0, CREATIS lab, Lyon, France). Endocardial and epicardial contours were drawn on SPAMM sequences using a semi-automated process. Peak circumferential LV strain (Ecc) was measured at apex, mid-ventricle, and base. LV twist was calculated by subtracting basal from apical rotation. Torsion was determined by: Torsion = Peak Twist x (Apical Radius+Basal Radius)/2xApex to Base length Results The two groups were comparable for baseline demographics (Table 1). The ICM group had significantly more prior revascularisation (CABG/PCI). There was no significant difference between the 2 groups in both LV dimensions and LVEF, however ICM had significantly more LGE (Table 2). There was no significant difference between the 2 groups in Ecc. NICM patients had significantly lower LV twist and torsion compared to the ICM group 6.0±3.68° vs 8.8±4.32° p=0.020 and 6.3±3.79° vs 8.8±4.69° p=0.048 respectively. Conclusion Despite similar EF and Ecc, patients with NICM had significantly less LV torsion than ICM. Myocyte dysfunction in ICM is more sub-endocardial due to the wave-front of ischaemia and more global in NICM. Relative perseveration of LV torsion of ICM over NICM is likely a result of sparing of sub-epicardial fibres and an increased compensatory recruitment of sub-epicardial fibres that are predominantly responsible for LV torsion. Recognition of different torsion patterns of ICM and NICM gives insight into aetiology of CHF, which may assist patient diagnosis and management, especially in those unable to have contrast agents. Abstract 105 Table 1 Demographics ICM NICM P-value Age, years 65.2±15.9 59.0±16.9 0.182 Sex (female)% 29 36 0.582 BMI kg/m2 26.9±3.9 27.6±5.6 0.654 SBP, mmHg 119±21 115±18 0.399 DBP mmHg 70±11 72±11 0.601 Diabetes Mellitus,% 19 4.5 0.195 CABG,% 32 0
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.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".