Lester, S J

Abstract Introduction Cancer therapy-related cardiac dysfunction (CTRCD) after anthracycline-based therapy is a dreaded complication due to its significant morbidity and mortality. Sodium-glucose cotransporters-2 inhibitors (SGLT2i) have been shown to improve cardiovascular outcomes in multiple scenarios. However, its potential benefits in patients who develop CTRCD due to anthracycline-based chemotherapy are not well established. Purpose To investigate the benefits of SGLT2i in cancer patients who develop CTRCD secondary to anthracycline-based chemotherapy. Methods All patients who underwent anthracycline therapy between 2002 and 2022 across three tertiary centers and had a diagnosis of moderate or severe CTRCD according to 2022 European Society of Cardiology (ESC) guidelines were evaluated. Baseline characteristics, echocardiograms pre and post chemotherapy, and mortality were reviewed. Kaplan Meier analysis was conducted to compare all-cause and cardiovascular mortality between patients treated with SGLT2i (both patients on chronic SGLT2i who continued their therapy following CTRCD and those who commenced the treatment after CTRCD diagnosis) and those not receiving this therapy. Results In total, 398 patients diagnosed with moderate or severe CTRCD were included; median age was 66.82 (58.07, 74.46) years and 59.29% were males. Among them, 39 (9.80%) received SGLT2i. Mortality rates at 1- and 5-years were significantly lower among patients who were on SGLT2i treatment compared to patients who were not (8.40% vs 46.30%, p= 0.0001 and 17.30% vs 65%, p= 0.0001, respectively) (Figure1). Furthermore, cardiovascular mortality rate at 5 years was significantly lower in patients receiving SGLT2i compared to patients not receiving this therapy (0% vs 11%, p= 0.046). Conclusions SGLT2i may have survival benefit in cancer patients who develop CTRCD secondary to anthracycline-based chemotherapy and might be considered as an adjunctive treatment to improve survival outcomes in this population.Figure 1.Kaplan-Meier Curves

Abstract Background Topological data analysis (TDA) is the state-of-the-art unsupervised machine learning framework that can provide insight into the dataset and visualize condensed information via the topological network graph. This robust approach was never used to assess the heterogeneous MitraClip population. Purpose We aim to develop a TDA model that will identify prognostically-distinct phenogroups in MitraClip patients without a priori knowledge of the population and their outcomes. Method Patients who underwent MitraClip (June 2014-September 2020) at Mayo Clinic sites were identified from the institutional database for baseline and follow-up data. Thirteen variables were used for TDA. The topological network graph was created using the Python Scikit-TDA Kepler-Mapper package (v. 2.0.1), and clustering was performed at the graph level with Louvain's modularity method. Kaplan-Meier survival analysis was used to assess the all-cause mortality endpoint of each cluster identified in an unsupervised manner. The dataset with cluster labels was also used to train a Light Gradient Boosted Machine model, and SHapley Additive exPlanations (SHAP) analysis was applied to determine the feature importance. Results A total of 389 consecutive patients were included in the final analysis and two major clusters consisting of 384 patients were identified. The mean age was 80.3±8.7 years; 256 (65.8%) were male. The mean Society of Thoracic Surgeons Mitral Valve Replacement risk score was 9.6±6.9%. Fifty-five (14.5%) patients died during the mean follow duration (185 days). Kaplan-Meier analysis showed significant survival differences among the two clusters (HR: 2.38; 95% CI: 1.39–4.06, p=0.001; Figure 1). Clusters 1 (n=195) was associated with > mild residual mitral regurgitation and worse survival performance and was characterized with worse tricuspid regurgitation severity, a higher proportion of patients with atrial fibrillation/flutter, anterior leaflet prolapse, and mitral annular/leaflet calcification, as summarized in Table 1. Conclusion TDA can identify distinct phenotype clusters with prognostic significance in MitraClip patients based on mitral valve morphology and clinical risk factors. This simple model can facilitate clinical risk stratification for MitraClip patients regarding procedural success and survival performance. Funding Acknowledgement Type of funding sources: None.