Unsupervised machine learning using CMR cine-derived diastolic parameters identified a high-risk patient cluster with an adjusted HR of 2.55 (95% CI 1.42-4.57; p=0.002) for adverse cardiac events.
Observational (n=391)
Does machine learning clustering of CMR cine-derived diastolic parameters predict adverse cardiovascular events in patients with preserved ejection fraction and no late gadolinium enhancement?
CMR cine-derived diastolic parameters combined with machine learning clustering can identify prognostic groups and predict adverse events in patients with preserved ejection fraction and no late gadolinium enhancement.
Effect estimate: HR 2.55 (95% CI 1.42-4.57)
Absolute Event Rate: 17.2% vs 2.6%
p-value: p=0.002
Cardiac magnetic resonance (CMR) accurately measures left ventricular (LV) ejection fraction (EF) and late gadolinium enhancement (LGE), but is not commonly used for diastolic function. This study combined unsupervised machine learning and diastolic function with cine imaging to identify prognostic groups. CMR studies from 391 patients with LVEF≥50%, and no LGE were included. Using LV cine diastolic time-volume curves, early peak filling rate (E-PFR) indexed to stroke volume index (SVI), E/A ratio, and deceleration time (Decel t ) were measured, along with left atrial volume index (LAVI) and LV mass index (LVMI). K-means clustering grouped individuals using only these parameters. Primary outcome was a composite of heart failure or ventricular arrhythmia admission, heart transplant, mechanical support, and cardiac death. Median follow-up was 5 years with 30 events. Three clusters were identified with event rates of 2.6% (group 1), 8.5% (group 2), and 17.2% (group 3). Time-volume curves demonstrated a triphasic pattern with group 2 having lower E-PFR (6.5 vs 9.7 and 8.4SVI/s for group 1 and 3 respectively, p<0.001), lower E/A (1.2 vs 2.4 and 1.9 for group 1 and 3 respectively, p<0.001), and longer Decel t (223 vs 79 and 105ms for group 1 and 3 respectively, p<0.001). Group 3 had increased remodeling based on higher LAVI (51.4 vs 26.6 and 27.5 ml/m 2 for group 1 and 2 respectively, p<0.001) and LVMI (78.7 vs 48.7 and 50.6 g/m 2 for group 1 and 2 respectively, p<0.001). Compared to group 1, group 2 and 3 had hazard ratios (HR) of 3.21 (95% confidence interval CI 1.06-9.68, p=0.038) and 2.52 (95% CI 1.06-4.47, p=0.002) for events respectively. Adjusting for comorbidities, only group 3 had a significant HR of 2.55 95% CI 1.42-4.57, p=0.002) compared to group 1. CMR cine-derived diastolic parameters can add prognostic value despite normal EF and absence of LGE. Diastolic Function Methods and Results from Cine Time-Volume Measurements. CMR, cardiac magnetic resonance; LA, left atrial; LV, left ventricular; MACE, major adverse cardiovascular events.
Pan et al. (Wed,) conducted a observational in Preserved Ejection Fraction and Absence of Late Gadolinium Enhancement (n=391). CMR cine-derived diastolic parameters (unsupervised machine learning clustering) vs. Group 1 (lowest risk cluster) was evaluated on Composite of heart failure or ventricular arrhythmia admission, heart transplant, mechanical support, and cardiac death (HR 2.55, 95% CI 1.42-4.57, p=0.002). Unsupervised machine learning using CMR cine-derived diastolic parameters identified a high-risk patient cluster with an adjusted HR of 2.55 (95% CI 1.42-4.57; p=0.002) for adverse cardiac events.
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