Index of microcirculatory resistance, a new index representing microvascular integrity, is a reliable early on-site determinant of myocardial viability and LV recovery after primary stenting for AMI.

Statin therapy is beneficial in reducing cardiovascular events and mortalities in patients with atherosclerotic cardiovascular diseases. Yet, there have been concerns of increased risk of diabetes with statin use. This study was aimed to evaluate the association between statins and new onset diabetes mellitus (NODM) in patients with ischemic heart disease (IHD) utilizing the Korean Health Insurance Review and Assessment Service claims database. Among adult patients with preexisting IHD, new statin users and matched nonstatin users were identified on a 1:1 ratio using proportionate stratified random sampling by sex and age. They were subsequently propensity score matched further with age and comorbidities to reduce the selection bias. Overall incidence rates, cumulative rates and hazard ratios (HRs) between statin use and occurrence of NODM were estimated. The subgroup analyses were performed according to sex, age groups, and the individual agents and intensities of statins. A total of 156,360 patients (94,370 in the statin users and 61,990 in the nonstatin users) were included in the analysis. The incidence rates of NODM were 7.8% and 4.8% in the statin users and nonstatin users, respectively. The risk of NODM was higher among statin users (crude HR 2.01, 95% confidence interval [CI] 1.93–2.10; adjusted HR 1.84, 95% CI 1.63–2.09). Pravastatin had the lowest risk (adjusted HR 1.54, 95% CI 1.32–1.81) while those who were exposed to more than one statin were at the highest risk of NODM (adjusted HR 2.17, 95% CI 1.93–2.37). It has been concluded that all statins are associated with the risk of NODM in patients with IHD, and it is believed that our study would contribute to a better understanding of statin and NODM association by analyzing statin use in the real-world setting. Periodic screening and monitoring for diabetes are warranted during prolonged statin therapy in patients with IHD.

Objectives: With the present therapeutic advances in the era of primary percutaneous coronary intervention (PCI), the role of β-blockers in ST elevation acute myocardial infarction (STEMI) has remained contentious. Methods: We analyzed the data and clinical outcomes of 901 STEMI patients who had undergone primary PCI. We classified the patients into β-blocker (n = 598) and non-β-blocker groups (n = 303). Results: The cumulative incidence of all-cause death was 10.0% in the β-blocker group and 25.4% in the non-β-blocker group (p < 0.001). The incidence of major adverse cardiac events (MACE) was 22.1% in the β-blocker group and 34.3% in the non-β-blocker group (p < 0.001). The relative hazard ratio (HR) of β-blockers for all-cause death and MACE with low left ventricle ejection fraction (LVEF; <50%) was 0.55 [95% confidence interval (CI) 0.35-0.86, p = 0.009] and 0.75 (95% CI 0.51-1.09, p = 0.125), respectively. In patients with normal LVEF (≥50%), the relative HR of β-blockers for death and MACE were 0.50 (95% CI 0.29-0.88, p = 0.016) and 0.75 (95% CI 0.51-1.12, p = 0.162), respectively. After propensity score matching of the difference of the baseline characteristics, the Kaplan-Meier survival curve demonstrated lower mortality in the β-blocker group than in the non-β-blocker group with both low LVEF and normal LVEF (p = 0.02 and p = 0.001, respectively). Conclusions: β-Blockers have beneficial clinical outcomes in the era of primary PCI for STEMI, regardless of the LVEF.

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Coronary artery calcium (CAC), which can be measured in various types of computed tomography (CT) examinations, is a hallmark of coronary artery atherosclerosis. However, despite the clinical value of CAC scores in predicting cardiovascular events, routine measurement of CAC scores is limited due to high cost, radiation exposure, and lack of widespread availability. It would be of great clinical significance if CAC could be predicted by electrocardiograms (ECGs), which are cost-effective and routinely performed during various medical checkups. We aimed to develop binary classification artificial intelligence (AI) models that predict CAC using only ECGs as input. Moreover, we aimed to address the generalizability of our model in different environments by externally validating our model on a dataset from a different institution. Among adult patients, standard 12-lead ECGs were extracted if measured within 60 days before or after the CAC scores, and labeled with the corresponding CAC scores. We constructed deep convolutional neural network models based on residual networks using only the raw waveforms of the ECGs as input, predicting CAC at different levels, namely CAC score ≥100, ≥400 and ≥1,000. Our AI models performed well in predicting CAC in the training and internal validation dataset [area under the receiver operating characteristics curve (AUROC) 0.753 ± 0.009, 0.802 ± 0.027, and 0.835 ± 0.024 for the CAC score ≥100, ≥400, and ≥1,000 model, respectively]. Our models also performed well in the external validation dataset (AUROC 0.718, 0.777 and 0.803 for the CAC score ≥100, ≥400, and ≥1,000 model, respectively), indicating that our model can generalize well to different but plausibly related populations. Model performance in terms of AUROC increased in the order of CAC score ≥100, ≥400, and ≥1,000 model, indicating that higher CAC scores might be associated with more prominent structural changes of the heart detected by the model. With our AI models, a substantial proportion of previously unrecognized CAC can be afforded with a risk stratification of CAC, enabling initiation of prophylactic therapy, and reducing the adverse consequences related to ischemic heart disease.