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Coronary artery disease (CAD) is currently the leading cause of death globally, and the prevalence of this disease is growing more rapidly in the Asia-Pacific region than in Western countries. Although the use of metal coronary stents has rapidly increased thanks to the advancement of safety and efficacy of newer generation drug eluting stent (DES), patients are still negatively affected by some the inherent limitations of this type of treatment, such as stent thrombosis or restenosis, including neoatherosclerosis, and the obligatory use of dual antiplatelet therapy (DAPT) with unknown optimal duration. Drug-coated balloon (DCB) treatment is based on a leave-nothing-behind concept and therefore it is not limited by stent thrombosis and long-term DAPT; it directly delivers an anti-proliferative drug which is coated on a balloon after improving coronary blood flow. At present, DCB treatment is recommended as the first-line treatment option in metal stent-related restenosis linked to DES and bare metal stent. For de novo coronary lesions, the application of DCB treatment is extended further, for conditions such as small vessel disease, bifurcation lesions, and chronic total occlusion lesions, and others. Recently, several reports have suggested that fractional flow reserve guided DCB application was safe for larger coronary artery lesions and showed good long-term outcomes. Therefore, the aim of these recommendations of the consensus group was to provide adequate guidelines for patients with CAD based on objective evidence, and to extend the application of DCB to a wider variety of coronary diseases and guide their most effective and correct use in actual clinical practice.

Background Conventional risk score for predicting short and long-term mortality following an ST-segment elevation myocardial infarction (STEMI) is often not population specific. Objective Apply machine learning for the prediction and identification of factors associated with short and long-term mortality in Asian STEMI patients and compare with a conventional risk score. Methods The National Cardiovascular Disease Database for Malaysia registry, of a multi-ethnic, heterogeneous Asian population was used for in-hospital (6299 patients), 30-days (3130 patients), and 1-year (2939 patients) model development. 50 variables were considered. Mortality prediction was analysed using feature selection methods with machine learning algorithms and compared to Thrombolysis in Myocardial Infarction (TIMI) score. Invasive management of varying degrees was selected as important variables that improved mortality prediction. Results Model performance using a complete and reduced variable produced an area under the receiver operating characteristic curve (AUC) from 0.73 to 0.90. The best machine learning model for in-hospital, 30 days, and 1-year outperformed TIMI risk score (AUC = 0.88, 95% CI: 0.846–0.910; vs AUC = 0.81, 95% CI:0.772–0.845, AUC = 0.90, 95% CI: 0.870–0.935; vs AUC = 0.80, 95% CI: 0.746–0.838, AUC = 0.84, 95% CI: 0.798–0.872; vs AUC = 0.76, 95% CI: 0.715–0.802, p < 0.0001 for all). TIMI score underestimates patients’ risk of mortality. 90% of non-survival patients are classified as high risk (>50%) by machine learning algorithm compared to 10–30% non-survival patients by TIMI. Common predictors identified for short- and long-term mortality were age, heart rate, Killip class, fasting blood glucose, prior primary PCI or pharmaco-invasive therapy and diuretics. The final algorithm was converted into an online tool with a database for continuous data archiving for algorithm validation. Conclusions In a multi-ethnic population, patients with STEMI were better classified using the machine learning method compared to TIMI scoring. Machine learning allows for the identification of distinct factors in individual Asian populations for better mortality prediction. Ongoing continuous testing and validation will allow for better risk stratification and potentially alter management and outcomes in the future.

The clinical and angiographic outcome of the combination of the drug-eluting balloon SeQuent Please with a cobalt chromium stent compared to the drug eluting Taxus stent are similar.

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