Citations

Introduction: Unsupervised machine learning (ML) can provide insight into a dataset without significant expert time for annotation and labeling, but was never used to assess the heterogeneous MitraClip population. Hypothesis: We hypothesized that unsupervised k-means ML could identify prognostically-distinct phenogroups in MitraClip patients without a priori knowledge of the dataset. Methods: Patients who underwent MitraClip (June 2014-September 2020) at Mayo Clinic sites were identified from the Mayo institutional NCDR database for baseline and follow-up data. Variables with ≥ 50% missing data were excluded. A k-means algorithm was used for clustering analysis. Input variables were grouped according to 4 distinct k-means determined clusters. Kaplan-Meier survival analysis was used to assess the survival of each cluster. Results: A total of 389 consecutive patients were included in the final analysis, and 95 unique variables were used. The mean age was 80.3±8.7 years; 256 (65.8%) were male. The mean STS MV Replace score was 9.1±5.7. Fifty-five (14.5%) patients died during the mean follow duration (185 days). Kaplan-Meier analysis showed significant survival differences among clusters (Figure 1). The cluster (cluster 2, n=120) with best survival performance (all-cause mortality 3.3%) had features of non-calcified mitral leaflets, nonischemic cardiomyopathy, and less than moderate tricuspid regurgitation. Cluster 3 (n=66) had the worst survival performance (all-cause mortality 27.3%) and opposite features than cluster 2. Conclusions: Unsupervised ML can identify distinct phenotype clusters with prognostic significance in MitraClip patients. Patients with non-calcified mitral leaflets, nonischemic cardiomyopathy, and less than moderate tricuspid regurgitation are intrinsically different from other MitraClip patients and should be considered the potential optimal candidates.

Background/Introduction: More than moderate tricuspid regurgitation (TR) at baseline is associated with worse outcome in MitraClip patients. The presence of iatrogenic ASD (iASD) may cause right-sided volume overload and lead to adverse events. However, the real effects of iASD are not well-investigated in these patients who are vulnerable to right-sided volume overload. Methods: Patients with more than moderate TR who underwent a successful MitraClip procedure in the period from June 2014 to September 2020 were identified from the Mayo institutional NCDR database. Follow-up transthoracic echocardiography (TTE) at about 1 year after the procedure was used to determine the presence of iASD, and speckle-tracking strain analysis was done. Patients were grouped according to the presence of iASD (iASD+ group vs. iASD- group) for statistical analysis. Results: A total of 141 consecutive patients were included for the final analysis (iASD+ group n=44 and iASD- group n=97). The mean age was 79.3 ± 8.8 years, and 72.3% were male. Follow-up echocardiography and speckle-tracking strain data did not show a significant difference in either left or right ventricular function in conventional echocardiographic parameters and strain. LA reservoir strain (13.7 ± 6.9 vs 11.5±7.5, P=0.0418) was the only parameter with significant difference among the two groups. Details are summarized in Table 1 . Conclusion: The presence of iASD can decompress the left atrium, but does not affect left or right ventricular strain function in MitraClip patients with more than moderate baseline TR.