Bi-ventricular cardiovascular magnetic resonance strain phenotypes are predictive of cardiovascular outcomes in patients with asymptomatic hypertrophic cardiomyopathy
Bibliographic record
Abstract
Abstract Background Cardiac myosin inhibition therapy reduces symptoms in patients with obstructive hypertrophic cardiomyopathy (HCM) [1] and is being explored for non-obstructive phenotypes. The identification of asymptomatic (NYHA-I) patients at risk of future major adverse cardiovascular events (MACE) represents a future sub-cohort of interest for targeted HCM therapeutics. Cardiac magnetic resonance (CMR) is routinely used in the assessment of HCM to quantify left ventricular (LV) wall thickness and myocardial fibrosis, both shown to be of value for the prediction of MACE [2]. Expanding interest in additional tools, particularly deformation (strain) analysis, has emerged. LV strain has demonstrated capacity to identify higher risk HCM populations [3], however, little is known regarding the prognostic value of LV and right ventricular (RV) strain phenotyping in asymptomatic HCM patients. Purpose This study explored the prognostic value of CMR-based LV and RV strain phenotyping for the prediction of MACE in NYHA-I HCM patients. Methods 293 NYHA-I HCM patients undergoing CMR were identified from the CIROC registry. LV global longitudinal (GLS), circumferential (GCS), and radial strain (GRS) amplitude, as well as RV GLS amplitude, were quantified from cine images using feature tracking (cvi42). Patients were followed for a composite outcome of all-cause mortality, heart failure hospitalization, new onset of atrial fibrillation/flutter, ventricular tachycardia, or survival of sudden cardiac arrest. Cox models were used to study associations of strain with future outcomes. Results A total of 293 NYHA-I HCM patients were studied (mean age 55.6±14.4 years; 70% males; 26% obstructive). Baseline characteristics are shown in Figure 1. Over a median follow-up of 3.1 years, 29 patients (9.9%) experienced the primary composite outcome. On univariable analysis, LV GCS (HR 1.25 per 1%, 95% CI 1.10–1.42, p<0.001), LV GRS (HR 0.91 per 1%, 95% CI 0.86–0.97, p=0.002), and RV GLS amplitude (HR 1.09 per 1%, 95% CI 1.01–1.17, p = 0.03) were significantly associated with the outcome, but not LV GLS. Using a survival-based approach, optimal cut points for LV GCS, LV GRS, and RV GLS were calculated as -15.5%, 24.3%, and -18.9%, respectively. Kaplan-Meier curves are shown in Figure 2. Adjusting for age, LV mass, and LVOT obstruction, LV GCS and RV GLS worse than the optimal cut points remained independently associated with the primary outcome (respective adjusted HR 3.52, 95% CI 1.55-8.01, p = 0.003 and 2.59, 95% CI 1.04-6.47, p= 0.04; Figure 2). LV GRS was not included in the model due to collinearity with LV GCS. Conclusion To our knowledge, this is the first study exploring the combined prognostic value of LV and RV strain in patients with NYHA-I HCM. LV and RV strain profiles identify NYHA-I HCM patients at elevated risk of incident cardiovascular outcomes, offering a unique population for the future consideration of targeted therapeutics.Figure 1 Figure 2
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".