BACKGROUND: Treatment of coronary in-stent restenosis (ISR) remains challenging. PCBs are an established treatment option outside the United States with a Class I, Level of Evidence: A recommendation in the European guidelines. However, their efficacy is better in bare-metal stent (BMS) ISR compared with drug-eluting stent (DES) ISR.

METHODS: Fifty patients with DES ISR were enrolled in a randomized, multicenter trial to compare a novel SCB (SeQuent SCB, 4 μg/mm2) with a clinically proven PCB (SeQuent Please Neo, 3 μg/mm2) in coronary DES ISR. The primary endpoint was angiographic late lumen loss at 6 months. Secondary endpoints included procedural success, major adverse cardiovascular events, and individual clinical endpoints such as stent thrombosis, cardiac death, target lesion myocardial infarction, clinically driven target lesion revascularization, and binary restenosis.

RESULTS: Quantitative coronary angiography revealed no differences in baseline parameters. After 6 months, in-segment late lumen loss was 0.21 ± 0.54 mm in the PCB group versus 0.17 ± 0.55 mm in the SCB group (p = NS; per-protocol analysis). Clinical events up to 12 months also did not differ between the groups.

SETTING: Fifteen participating cardiology centres contributed to the Malaysian National Cardiovascular Disease Database-Percutaneous Coronary Intervention (NCVD-PCI) registry.

PARTICIPANTS: 28 742 patients from the NCVD-PCI registry who had their first PCI between January 2007 and December 2014 were included. Those without their BMI recorded or BMI <11 kg/m2 or >70 kg/m2 were excluded.

MAIN OUTCOME MEASURES: In-hospital death, major adverse cardiovascular events (MACEs), vascular complications between different BMI groups were examined. Multivariable-adjusted HRs for 1-year mortality after PCI among the BMI groups were also calculated.

RESULTS: The patients were divided into four groups; underweight (BMI <18.5 kg/m2), normal BMI (BMI 18.5 to <23 kg/m2), overweight (BMI 23 to <27.5 kg/m2) and obese (BMI ≥27.5 kg/m2). Comparison of their baseline characteristics showed that the obese group was younger, had lower prevalence of smoking but higher prevalence of diabetes, hypertension and dyslipidemia. There was no difference found in terms of in-hospital death, MACE and vascular complications after PCI. Multivariable Cox proportional hazard regression analysis showed that compared with normal BMI group the underweight group had a non-significant difference (HR 1.02, p=0.952), while the overweight group had significantly lower risk of 1-year mortality (HR 0.71, p=0.005). The obese group also showed lower HR but this was non-significant (HR 0.78, p=0.056).

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.

MATERIALS AND METHODS: In a cross-sectional study using self-administered questionnaires, a total of 24 nurses and 43 doctors were assessed for patient-centredness, psychological distress, and job satisfaction using the Patient-Practitioner Orientation Scale, Hospital Anxiety and Depression Scale, and Job Satisfaction Scale. Data were analysed using descriptive statistics, independent samples t-test and MANCOVA, with p<0.05 considered significant.

RESULTS: Overall response rate was 95.6% (43/45) for physicians and 85.7% (24/28) for nurses. Even after adjusting for known covariates, our principal finding was that doctors reported greater psychological distress compared to nurses (p=0.009). Doctors also reported lower job satisfaction compared to nurses (p = 0.017), despite higher levels of patient-centredness found in nurses (p=0.001). Findings may be explained in part by differences in job characteristics and demands.

METHODS: Thirty healthy Muslim men participated in the study. Their electrocardiograms and EEGs were continuously recorded before, during, and after salat practice with a computer-based data acquisition system (MP150, BIOPAC Systems Inc., Camino Goleta, California). Power spectral analysis was conducted to extract the RPα and HRV components.

Aims: This study aimed to investigate their vasorelaxation potential and the possible involvement of autonomic receptors and nitric oxide in mediating their effect.

Settings and Design: Both extracts will be tested on isolated thoracic aorta rings of WKY and SHR. The involvement of autonomic receptors and nitric oxide will be elucidated using respective blockers.

Materials and Methods: Isolated thoracic aorta rings from WKY and SHR were mounted onto myograph chambers to measure changes in the aorta tension. Increasing concentrations of AESP and MESP, from 1 μg/ml to 10 mg/ml were added onto the myograph chambers. Blockers such as atropine (1 μM), phentolamine (1 μM), propranolol (1 μM), and Nω-nitro-l-arginine methyl ester (100 μM) were preincubated before addition of extracts to check for involvement of muscarinic, α- and β-adrenergic receptors (AR) as well as nitric oxide, respectively.

Statistical Analysis Used: Two-way ANOVA, followed by post hoc Bonferroni test was used, where P < 0.05 (two-tailed) was considered statistically significant.

Results: AESP and MESP caused significant vasorelaxations through nitric oxide pathway. The former was mediated through α-AR while the latter was mediated by β-adrenergic and muscarinic receptors.

Conclusion: Vasorelaxation effect by AESP and MESP involved nitric oxide pathway which is possibly mediated by the autonomic receptors.

OBJECTIVE: To derive a single algorithm using deep learning and machine learning for the prediction and identification of factors associated with in-hospital mortality in Asian patients with ACS and to compare performance to a conventional risk score.

METHODS: The Malaysian National Cardiovascular Disease Database (NCVD) registry, is a multi-ethnic, heterogeneous database spanning from 2006-2017. It was used for in-hospital mortality model development with 54 variables considered for patients with STEMI and Non-STEMI (NSTEMI). Mortality prediction was analyzed using feature selection methods with machine learning algorithms. Deep learning algorithm using features selected from machine learning was compared to Thrombolysis in Myocardial Infarction (TIMI) score.

RESULTS: A total of 68528 patients were included in the analysis. Deep learning models constructed using all features and selected features from machine learning resulted in higher performance than machine learning and TIMI risk score (p < 0.0001 for all). The best model in this study is the combination of features selected from the SVM algorithm with a deep learning classifier. The DL (SVM selected var) algorithm demonstrated the highest predictive performance with the least number of predictors (14 predictors) for in-hospital prediction of STEMI patients (AUC = 0.96, 95% CI: 0.95-0.96). In NSTEMI in-hospital prediction, DL (RF selected var) (AUC = 0.96, 95% CI: 0.95-0.96, reported slightly higher AUC compared to DL (SVM selected var) (AUC = 0.95, 95% CI: 0.94-0.95). There was no significant difference between DL (SVM selected var) algorithm and DL (RF selected var) algorithm (p = 0.5). When compared to the DL (SVM selected var) model, the TIMI score underestimates patients' risk of mortality. TIMI risk score correctly identified 13.08% of the high-risk patient's non-survival vs 24.7% for the DL model and 4.65% vs 19.7% of the high-risk patient's non-survival for NSTEMI. Age, heart rate, Killip class, cardiac catheterization, oral hypoglycemia use and antiarrhythmic agent were found to be common predictors of in-hospital mortality across all ML feature selection models in this study. The final algorithm was converted into an online tool with a database for continuous data archiving for prospective validation.

OBJECTIVE: To employ machine learning (ML) and stacked ensemble learning (EL) methods in predicting short- and long-term mortality in Asian patients diagnosed with NSTEMI/UA and to identify the associated features, subsequently evaluating these findings against established risk scores.

METHODS: We analyzed data from the National Cardiovascular Disease Database for Malaysia (2006-2019), representing a diverse NSTEMI/UA Asian cohort. Algorithm development utilized in-hospital records of 9,518 patients, 30-day data from 7,133 patients, and 1-year data from 7,031 patients. This study utilized 39 features, including demographic, cardiovascular risk, medication, and clinical features. In the development of the stacked EL model, four base learner algorithms were employed: eXtreme Gradient Boosting (XGB), Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest (RF), with the Generalized Linear Model (GLM) serving as the meta learner. Significant features were chosen and ranked using ML feature importance with backward elimination. The predictive performance of the algorithms was assessed using the area under the curve (AUC) as a metric. Validation of the algorithms was conducted against the TIMI for NSTEMI/UA using a separate validation dataset, and the net reclassification index (NRI) was subsequently determined.

RESULTS: Using both complete and reduced features, the algorithm performance achieved an AUC ranging from 0.73 to 0.89. The top-performing ML algorithm consistently surpassed the TIMI risk score for in-hospital, 30-day, and 1-year predictions (with AUC values of 0.88, 0.88, and 0.81, respectively, all p < 0.001), while the TIMI scores registered significantly lower at 0.55, 0.54, and 0.61. This suggests the TIMI score tends to underestimate patient mortality risk. The net reclassification index (NRI) of the best ML algorithm for NSTEMI/UA patients across these periods yielded an NRI between 40-60% (p < 0.001) relative to the TIMI NSTEMI/UA risk score. Key features identified for both short- and long-term mortality included age, Killip class, heart rate, and Low-Molecular-Weight Heparin (LMWH) administration.

METHODS: Patients who received PF-SES were investigated in an unselected large-scale international, single-armed, multicenter, 'all comers' observational study. The primary endpoint was the 9-month target lesion revascularisation (TLR) rate, whereas secondary endpoints included the 9-month major adverse cardiac events (MACE) and procedural success rates. A priori defined subgroups such as patients with ACS, diabetes, lesion subsets and procedural characteristics relative to DAPT were investigated.

RESULTS: A total of 2877 patients of whom 1084 had ACS were treated with PF-SES (1.31±0.75 stents per patient). At 9 months, the accumulated overall TLR rate was 2.3% (58/2513). There was no significant difference between ACS and stable CAD (2.6% vs 2.1%, p=0.389). However, the overall MACE rate was 4.3% (108/2513) with a higher rate in patients with ACS when compared with the stable CAD subgroup (6.1%, 58/947 vs 3.2%, 50/1566, p<0.001).

CONCLUSIONS: PF-SES angioplasty is safe and effective in the daily clinical routine with low rates of TLR and MACE in an unselected patient population. Our data are in agreement with prior clinical findings that extended DAPT duration beyond 6 months do not improve clinical outcomes in patients with stable CAD (ClinicalTrials.gov Identifier NCT02629575).

METHODS: Two all-comers observational studies based on the same protocol (ClinicalTrials.gov Identifiers: NCT02629575 and NCT02905214) were combined for data analysis to assure sufficient statistical power. The primary endpoint was the accumulated target lesion revascularization (TLR) rate at 9-12 months.

RESULTS: Of the total population of 7243 patients, 44.0% (3186) were recruited in the Mediterranean region and 32.0% (2317) in central Europe. The most prominent Asian region was South Korea (17.6%, 1274) followed by Malaysia (5.7%, 413). Major cardiovascular risk factors varied significantly across regions. The overall rates for accumulated TLR and MACE were low with 2.2% (140/6374) and 4.4% (279/6374), respectively. In ACS patients, there were no differences in terms of MACE, TLR, MI and accumulated mortality between the investigated regions. Moreover, dual antiplatelet therapy (DAPT) regimens were substantially longer in Asian countries even in patients with stable coronary artery disease as compared to those in Europe.