Key result
Latent ODE cardiac motion score improves incident HF prediction, raising C-index by ~0.08 points.
Why the study?
Cardiac magnetic resonance imaging captures rich spatiotemporal information about ventricular structure and motion, but conventional risk models use only a few image-derived indices from selected cardiac phases.
Does a continuous latent ODE biventricular motion risk score from cine cardiac MRI improve incident heart failure prediction beyond pooled cohort equations and conventional CMR indices in adults without baseline cardiovascular disease?
Cohort (n=72,386)
Yes
Does a continuous latent ODE biventricular motion risk score from cine cardiac MRI improve incident heart failure prediction beyond pooled cohort equations and conventional CMR indices in adults without baseline cardiovascular disease?
Continuous full-cycle 3D+t modeling of biventricular motion via latent neural ODEs captures prognostic cardiac phenotypes that improve incident heart failure risk stratification beyond conventional phase-restricted CMR metrics.
Answers are grounded in this paper and cite their sources.
Latent ODE motion score improved HF C-index beyond standard markers; extends prior signatures but leaves open prospective validation before practice change.
Cardiac magnetic resonance imaging (CMR) captures rich spatiotemporal information about ventricular structure and motion, but conventional risk models use only a few image-derived indices from selected cardiac phases. We present a latent dynamical model that encodes bi-ventricular anatomy and full-cycle cine motion as a continuous latent trajectory, using heart-rate-aware neural ordinary differential equation (ODE) dynamics and a graph-based mesh autoencoder to reconstruct anatomically consistent 3D+t ventricular motion. A covariate-conditioned prior defines the expected end-diastolic latent state, and a Cox proportional hazards model tests whether deviations from this prior predict incident heart failure. We studied 72,386 UK Biobank participants without baseline cardiovascular disease, including 367 incident heart failure events. In a held-out evaluation subset, adding the latent score to refitted pooled cohort equations improved the stratified C-index from 0.704 to 0.785, compared with 0.764 for seven established cardiac markers. Compared with non-graph and non-ODE approaches, the proposed model gave the best trade-off between reconstruction fidelity, generative realism, and downstream prognostic performance. These results suggest that continuous full-cycle modeling of ventricular motion provides informative cardiac phenotypes beyond conventional CMR summaries, while external validation in more representative patient cohorts is required before clinical risk-prediction use.
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Brüggemann et al. (2026) conducted a cohort in Incident heart failure risk in individuals without baseline cardiovascular disease (n=72,386). Latent ODE full-cycle biventricular cardiac motion risk score added to pooled cohort equations vs. Refitted pooled cohort equations alone or with seven established cardiac MRI markers was evaluated on Incident heart failure prediction (stratified C-index). Adding the latent ODE cardiac motion score to refitted pooled cohort equations improved the stratified C-index for incident heart failure from 0.704 to 0.785.
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