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Introduction Right ventricular failure (RVF) is a major cause of morbidity and mortality after left ventricular assist device (LVAD), however predicting RVF remains challenging. Hypothesis We hypothesized that an analysis of late gadolinium enhancement (LGE) patterns on pre-operative cardiac MRI (cMRI) could identify patients at risk for RVF after LVAD. Methods We analyzed reports for cMRIs performed on patients within one year prior to LVAD at our institution and abstracted LGE patterns using the 17-segment model. Patients were then grouped into clusters by similarities in LGE patterns using an unsupervised machine learning (ML) algorithm of hierarchical agglomerative clustering. Statistical comparison of the resulting clusters was then performed. Results Patients (N=31) were grouped into 3 clusters (Figure) with varying patterns of LGE. Cluster 1 patients (n=16) had no LGE or atypical LGE patterns and were significantly younger (age 42 ± 18) than other clusters (p=0.029). Cluster 2 patients (n=11) had extensive transmural LGE patterns and were more likely to have hypertension (p=0.006) and dyslipidemia (p=0.002) than other groups. Cluster 3 patients (n=4) had some degree of subendocardial LGE but no extensive transmural LGE patterns. No patients in cluster 2 developed RVF after LVAD, while 4 patients (25%) in cluster 1 and 2 patients in cluster 3 (50%) had RVF after LVAD, though the difference between groups did not reach statistical significance due to small number of patients in the cohort overall (p=0.058). Importantly, traditional factors associated with RVF including hemodynamics and echocardiographic/MRI parameters of LV and RV function were not significantly different between clusters. Further, LGE enhancement of the RV myocardium or RV insertion points were not associated with RVF after LVAD. Conclusions Unsupervised ML of LGE patterns on cMRI can identify clusters of patients at risk for RVF. LGE patterns on cMRI may identify patients with non-ischemic (cluster 1) and mixed (cluster 3) etiologies of their heart failure who are at higher risk for developing RVF due to global biventricular myocardial involvement than patients with a truly ischemic etiology of their heart failure (cluster 2). Future research in a larger cohort is needed to confirm this hypothesis. Figure: Dendrogram produced from agglomerative hierarchal clustering of LGE analysis of cardiac MRIs using standardized myocardial segmentation

Background Right ventricular failure (RVF) following left ventricular assist device (LVAD) implantation is an important source of morbidity and mortality but is difficult to predict. Right atrial (RA) strain by echocardiography is a novel hemodynamic parameter likely indicative of atrial functional capacity and RV diastolic function. Previous studies have suggested that pre-LVAD RV strain may be a predictor of RVF, but no studies have systematically evaluated the association of RA strain with outcomes after LVAD. We hypothesized that pre-LVAD RA strain may improve the ability to predict RVF after LVAD. Methods We evaluated 31 adults who received continuous flow LVAD between 2008-2018. Peak RA and RV strain values were measured by two-dimensional speckle-tracking echocardiography. RA strain was measured during three phases: reservoir phase (end-diastole to onset of ventricular filling), conduit phase (onset of ventricular filling to onset of atrial contraction), and contractile phase (onset of atrial contraction to end-diastole). RVF was defined as need for inotropic support longer than 14 days, inhaled nitric oxide for longer than 48 hours, or unplanned RVAD. Pearson correlation was used to compare RA and RV strain parameters. Logistic regression was performed for the outcome of RVF. Cox-proportional hazards modelling was performed for the outcome of survival free from transplant or device explant/exchange and hazard ratios were calculated per standard deviation change in regressors. Results Four patients (13%) developed RVF. Median time from echo to LVAD was 31 days. Correlation between RV free wall longitudinal strain (FWLS) and RA reservoir (R=-0.45, p=0.01), conduit (R=-0.30, p=0.10), and contractile strain (R=-0.27, p=0.15) was modest at best. None of the RA strain parameters were associated with RVF. Lower RA reservoir strain (HR 2.26, p=0.003) and RA conduit strain (HR 3.63, p=0.004) were associated with decreased survival free from transplant or device explant/exchange, while RA contractile strain was not. Conclusions In this small cohort, RA strain parameters were not associated with early RVF following LVAD implant. However, RA reservoir strain and RA conduit strain were strongly associated with post LVAD long term survival. RA strain parameters may represent a form of RV diastolic dysfunction that impacts long term outcomes more so than early post operative events. The utility of RA strain parameters for predicting outcomes following LVAD implant deserves further investigation and validation in a larger cohort.