Background Sodium–glucose transporter 2 inhibitors (SGLT2-I) could modulate atherosclerotic plaque progression, via down-regulation of inflammatory burden, and lead to reduction of major adverse cardiovascular events (MACEs) in type 2 diabetes mellitus (T2DM) patients with ischemic heart disease (IHD). T2DM patients with multivessel non-obstructive coronary stenosis (Mv-NOCS) have over-inflammation and over-lipids’ plaque accumulation. This could reduce fibrous cap thickness (FCT), favoring plaque rupture and MACEs. Despite this, there is not conclusive data about the effects of SGLT2-I on atherosclerotic plaque phenotype and MACEs in Mv-NOCS patients with T2DM. Thus, in the current study, we evaluated SGLT2-I effects on Mv-NOCS patients with T2DM in terms of FCT increase, reduction of systemic and coronary plaque inflammation, and MACEs at 1 year of follow-up. Methods In a multi-center study, we evaluated 369 T2DM patients with Mv-NOCS divided in 258 (69.9%) patients that did not receive the SGLT2-I therapy (Non-SGLT2-I users), and 111 (30.1%) patients that were treated with SGLT2-I therapy (SGLT2-I users) after percutaneous coronary intervention (PCI) and optical coherence tomography (OCT) evaluation. As the primary study endpoint, we evaluated the effects of SGLT2-I on FCT changes at 1 year of follow-up. As secondary endpoints, we evaluated at baseline and at 12 months follow-up the inflammatory systemic and plaque burden and rate of MACEs, and predictors of MACE through multivariable analysis. Results At 6 and 12 months of follow-up, SGLT2-I users vs. Non-SGLT2-I users showed lower body mass index (BMI), glycemia, glycated hemoglobin, B-type natriuretic peptide, and inflammatory cells/molecules values (p < 0.05). SGLT2-I users vs. Non-SGLT2-I users, as evaluated by OCT, evidenced the highest values of minimum FCT, and lowest values of lipid arc degree and macrophage grade (p < 0.05). At the follow-up end, SGLT2-I users vs. Non-SGLT2-I users had a lower rate of MACEs [n 12 (10.8%) vs. n 57 (22.1%); p < 0.05]. Finally, Hb1Ac values (1.930, [CI 95%: 1.149–2.176]), macrophage grade (1.188, [CI 95%: 1.073–1.315]), and SGLT2-I therapy (0.342, [CI 95%: 0.180–0.651]) were independent predictors of MACEs at 1 year of follow-up. Conclusions SGLT2-I therapy may reduce about 65% the risk to have MACEs at 1 year of follow-up, via ameliorative effects on glucose homeostasis, and by the reduction of systemic inflammatory burden, and local effects on the atherosclerotic plaque inflammation, lipids’ deposit, and FCT in Mv-NOCS patients with T2DM.

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In patients with ACS and malignancy, OMT reduces the risk of adverse events at 1 year; in particular, ACEIs/ARBs and statins were the most protective drugs. (Clinical trials identifier: NCT02466854).

Background Takotsubo syndrome (TTS) is burdened by a not negligible rate of an impaired short-term prognosis. Current existing models, based on classical statistical methods, showed only moderate accuracy to predict the risk of in-hospital adverse events following admission for TTS. We sought to design a machine-learning (ML) based model to predict the risk of in-hospital death among patients admitted for TTS, and to provide clusters of TTS patients associated with different risks of adverse short-term prognosis. Methods A Penalized Logistic Regression-based ML model for predicting in-hospital death was trained and tested on a cohort of 3482 patients with TTS from the international, multicenter, InterTAK Registry. 33 clinically relevant variables were selected to be included in the prediction model. Model performance was assessed according to area under the receiver operating characteristic curve (AUC). A K-Means clustering algorithm was designed to stratify patients into phenotypic groups based on the most relevant features emerging from the main model. Results The overall incidence of in-hospital death was 5.2%. The InterTAK-ML model showed an AUC of 0.88 (95%CI 0.87-0.90) and 0.87 (95%CI 0.83-0.91) with respect to in-hospital death prediction in the train and test cohorts, respectively. By exploiting the 5 variables showing the highest feature importance (use of catecholamines, type of triggering factor, left ventricular ejection fraction, white blood cell count, heart rate), TTS patients were clustered into five groups associated with different risks of in-hospital death (29.4% vs 3.9% vs 1.6% vs 1.3% vs 0.7%). Conclusion A ML-based approach for the identification of TTS patients at risk of adverse short-term prognosis is feasible and effective. The InterTAK-ML model showed accurate discriminative capability for the prediction of in-hospital death. To support clinical decision-making, TTS patients can be clustered into groups entailing different risks of death based on routinely collected variables.