131 Left ventricular indexed mass associated with ventricular arrhythmias in patients with hypertrophic cardiomyopathy – a tertiary centre mri registry
Bibliographic record
Abstract
Background Hypertrophic cardiomyopathy (HCM) is a common inherited cardiac condition. Multiple factors have been identified towards high risk of sudden cardiac death (SCD) as outlined in the ESC guidelines. LV wall thickness (LVwt) is an identified risk. Left ventricular mass indexed to body surface area (LVIBSA) increases with wall thickness and may predict risk not currently utilised in the risk score. Objectives 1. Compare differences in LVwt using transthoracic echocardiogram(TTE) and MRI and the effect on ESC risk score. 2. Observe for association of LVIBSA and incidence of non-sustained ventricular tachycardia(NSVT) either on 24 hour holter monitor or following ICD insertion. Methods We retrospectively reviewed patients between January 2010 to July 2015 who were confirmed to have HCM on MRI. ESC Risk was calculated using LV TTE and MRI LVwt. LVIBSA was calculated from MRI images and compared with incidence of VA on holter monitors. Patients who received ICD had follow up and VA incidence recorded. Results 103 patients with confirmed HCM were identified with median age of 60 (range 15–87). Non sustained VT (NSVT) was recorded in 20 (19.4%) patients while 16 patients had missing or no record of holter. Primary prevention ICDs (ICD 1*) were inserted in 20 (19.4%) patients. MRI identified a higher absolute LVwtcompared to TTE in 68.9% of patients. This lead to an increase in ESC risk score from low risk to high risk in 5% of the patients (ESC score>4). LVIBSA was higher in the patients with holter positive for VA (mean 109.7g/m2, 95% CI [92.8, 126.6] vs 89.8g/m2, 95% CI [83.2, 96.4], p=<0.05). Patients with ICD (n=20) were followed up for 52.6 months±5.8 months. One patient with ICD 1* had VA detected after 7 months of ICD insertion and treated successfully with 1 anti-tachycardia pacing algorithm. Conclusion The detection of HCM through use of MRI allows for earlier diagnosis of patients and provides for accurate and reproducible measurements of LVwt and LVIBSA x as opposed to TTE. In our retrospective study it is suggested that higher LVIBSA is related to increase incidence of VA. If LVIBSA derived from MRI were applied to ESC guidelines, this could result in more patients ICD 1*. We feel this should be explored with larger studies to see if LVIBSA is an independent risk factor for SCD.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".