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Background: Conventional risk score for predicting in-hospital mortality following Acute Coronary Syndrome (ACS) is not catered for Asian patients and requires different types of scoring algorithms for STEMI and NSTEMI patients., 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., Conclusions: ACS patients were better classified using a combination of machine learning and deep learning in a multi-ethnic Asian population when compared to TIMI scoring. Machine learning enables the identification of distinct factors in individual Asian populations to improve mortality prediction. Continuous testing and validation will allow for better risk stratification in the future, potentially altering management and outcomes., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2022 Kasim et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: The objective of this study was to assess regional and ethnic differences in an unselected patient population treated with polymer-free sirolimus-eluting stents (PF-SES) in Asia and Europe., 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., Conclusions: PF-SES angioplasty is associated with low clinical event rates in all regions. Further reductions in clinical event rates seem to be associated with longer DAPT regimens., Competing Interests: All centers received a remuneration for each fully documented study patient. We report the following conflicts of interest: FK (lecturing fees B.Braun, Astra Zeneca), VK (consulting Medtronic, Abbott Vascular, Terumo) and MW/LW (full time employment at Medical Scientific Affairs, B.Braun Melsungen AG). All other authors have no other conflict of interest. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Objective: Young women form a minority but an important group of patients with acute myocardial infarction (MI) as it can potentially cause devastating physical and socioeconomic impact. This study was aimed to investigate the characteristics and outcomes of young women with MI in Malaysia., Design: This is a retrospective analysis of women with ST-elevation MI (STEMI) and non-STEMI (NSTEMI) from 18 hospitals across Malaysia using the Malaysian National Cardiovascular Database registry-acute coronary syndrome (NCVD-ACS)., Participants: Women patients diagnosed with acute MI from year 2006 to 2013 were identified and divided into young (age ≤ 45, n=292) and older women (age >45, n=5580)., Primary Outcome Measure: Comparison of demographics, clinical characteristics and in-hospital management was performed between young and older women. In-hospital and 30-day all-cause mortality were examined., Results: Young women (mean age 39±4.68) made up 5% of women with MI and were predominantly of Malay ethnicities (53.8%). They have a higher tendency to present as STEMI compared with older women. Young women have significantly higher rates of family history of premature coronary artery disease (CAD) (20.5% vs 7.8% p<0.0001). The prevalence of risk factors, such as hypertension, diabetes and dyslipidaemia was high in both groups. The primary reperfusion strategy was thrombolysis with no significant differences observed in the choice of intervention for both groups. Other than aspirin, rates of prescriptions for evidence-based medications were similar with >80% prescribed statins and aspirin. The all-cause mortality rates of young women were lower for both in-hospital and 30 days, especially in those with STEMI with adjusted mortality ratio to the older group, was 1:9.84., Conclusion: Young women with MI were over-represented by Malays and those with a family history of premature CAD. Preventive measures are needed to reduce cardiovascular risks in young women. Although in-hospital management was similar, short-term mortality outcomes favoured young compared with older women., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Purpose: Information is lacking on prescribing of preventative cardiovascular pharmacotherapies for patients with non-ST elevation myocardial infarction (NSTEMI) in the Asian region. This study examined the prescribing rate of these pharmacotherapies, comparing NSTEMI to STEMI, and variations across demographics and clinical factors within the NSTEMI group in the multi-ethnic Malaysian population., Methods: This is a retrospective analysis of the Malaysian National Cardiovascular Disease Database-Acute Coronary Syndrome registry from year 2006 to 2013 (n = 30,873). On-discharge pharmacotherapies examined were aspirin, ADP-antagonists, statins, ACE-inhibitors, angiotensin-II-receptor blockers, and beta-blockers. Multivariate logistic regression was used to calculate adjusted odds ratio of receiving individual pharmacotherapies according to patients' characteristics in NSTEMI patients (n = 11,390)., Results: Prescribing rates for cardiovascular pharmacotherapies had significantly increased especially for ADP-antagonists (76%) in NSTEMI patients. More than 85% were prescribed statins and antiplatelets but rates remained significantly lower compared to STEMI. Women and those over 65 years old were less likely to be prescribed these pharmacotherapies compared to men and younger NSTEMI patients. Chinese and Indians were more likely to receive selected pharmacotherapies compared to Malays (main ethnicity). Geographical variations were observed; East Malaysian (Malaysian Borneo) patients were less likely to receive these compared to Western region of Malaysian Peninsular. Underprescribing in patients with risk factors such as diabetes were observed with other co-morbidities influencing prescribing selectively., Conclusion: This study uncovers demographic and clinical variations in cardiovascular pharmacotherapies prescribing for NSTEMI. Concerted efforts by policy makers, specialty societies, and physicians are required focusing on elderly, women, Malays, East Malaysians, and high-risk patients.

Introduction: Achieving target door-needle times for ST elevation myocardial infarction remains challenging. Data on emergency department (ED) doctor-led thrombolysis in developing countries and factors causing delay are limited., Objectives: To assess the effect on door-needle times by transferring responsibility for thrombolysis to the ED doctors and to identify predictors of prolonged door-needle times., Methodology: Data on medical on-call team-led thrombolysis at a tertiary Asian hospital were prospectively collected from May 2007 to Aug 2008 (1st study period). In September 2008, ED doctors were empowered to perform thrombolysis. The practice change was accompanied by new guidelines, tick chart implementation, and training sessions. Data were then consecutively collected from September 2008 to May 2009 (2nd study period). Door-to-needle times for the 1st and 2nd study periods were compared. All cases were analysed for factors of delay by multiple logistic regression., Results: 297 patients were thrombolysed, 169 by the medical on-call team during the 1st study period and 128 by the ED doctors during the 2nd study period. Median door-needle times were 54 and 48 min, respectively (p=0.76). Significant delays were predicted by 'incorrect initial ECG interpretation' (adjusted OR (aOR) 14.3), 'inappropriate triage' (aOR 10.4) and 'multiple referrals' (aOR 5.9). No cases of inappropriate thrombolysis were recorded., Conclusions: Transfer of responsibility for thrombolysis to the ED doctors did not improve door-needle times despite measures introduced to facilitate this change. Key causative factors for this failure were identified.

Background: Traditional risk assessment tools often lack accuracy when predicting the short- and long-term mortality following a non-ST-segment elevation myocardial infarction (NSTEMI) or Unstable Angina (UA) in specific population. 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. Conclusions: In a broad multi-ethnic population, ML approaches outperformed conventional TIMI scoring in classifying patients with NSTEMI and UA. ML allows for the precise identification of unique characteristics within individual Asian populations, improving the accuracy of mortality predictions. Continuous development, testing, and validation of these ML algorithms holds the promise of enhanced risk stratification, thereby revolutionizing future management strategies and patient outcomes. [ABSTRACT FROM AUTHOR]

Background: A decade ago, the iopromide-paclitaxel coated balloon (iPCB) was added to the cardiologist's toolbox to initially treat in-stent restenosis followed by the treatment of de novo coronary lesions. In the meantime, DES technologies have been substantially improved to address in-stent restenosis and thrombosis, and shortened anti-platelet therapy. Recently, sirolimus-coated balloon catheters (SCB) have emerged to provide an alternative drug to combat restenosis. Methods: The objective of this study is to determine the safety and efficacy of a novel crystalline sirolimus-coated balloon (cSCB) technology in an unselective, international, large-scale patient population. Percutaneous coronary interventions of native stenosis, in-stent stenosis, and chronic total occlusions with the SCB in patients with stable coronary artery disease or acute coronary syndrome were included. The primary outcome variable is the target lesion failure (TLF) rate at 12 months, defined as the composite rate of target vessel myocardial infarction (TV-MI), cardiac death or ischemia-driven target lesion revascularization (TLR). The secondary outcome variables include TLF at 24 months, ischemia driven TLR at 12 and 24 months and all-cause death, cardiac death at 12 and 24 months. Discussion: Since there is a wealth of patient-based all-comers data for iPCB available for this study, a propensity-score matched analysis is planned to compare cSCB and iPCB for the treatment of de novo and different types of ISR. In addition, pre-specified analyses in challenging lesion subsets such as chronic total occlusions will provide evidence whether the two balloon coating technologies differ in their clinical benefit for the patient. Trial registration number: ClinicalTrials.gov Identifier: NCT04470934. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Patients with renal impairment often left out from most major clinical trials assessing the optimal treatment for ST-elevation myocardial infarction (STEMI). Large body of evidence from various cardiovascular registries reflecting more 'real-world' experience might contribute to the knowledge on how best to treat this special cohort. We aim to analyze the outcomes of Malaysian STEMI patients with renal impairment treated with coronary angioplasty.Methods: Utilizing the Malaysian National Cardiovascular Disease Database-Percutaneous Coronary Intervention (NCVD-PCI) registry data from 2007 to 2014, STEMI patients treated with percutaneous coronary intervention (PCI) were stratified into presence (GFR < 60 mls/min/1.73m2) or absence (GFR ≥ 60 mls/min/1.73m2) of chronic kidney disease (CKD). Patient's demographics, extent of coronary artery disease, procedural data, discharge medications, short (in-hospital) and long (1 year) term outcomes were critically assessed.Results: A total of 6563 patients were included in the final analysis. STEMI CKD cohort was predominantly male (80%) with mean age of 61.02 ± 9.95 years. They had higher cardiovascular risk factors namely diabetes mellitus (54.6%), hypertension (79.2%) and dyslipidemia (68.8%) in contrast to those without CKD. There were notably higher percentage of CKD patients presented with Killip class 3 and 4; 24.9 vs 8.7%. Thrombolytic therapy remained the most commonly instituted treatment regardless the status of kidney function. Furthermore, our STEMI CKD cohort also was more likely to receive less of evidence-based treatment upon discharge. In terms of outcomes, patients with CKD were more likely to develop in-hospital death (OR: 4.55, 95% CI 3.11-6.65), MACE (OR: 3.42, 95% CI 2.39-4.90) and vascular complications (OR: 1.88, 95% CI 0.95-3.7) compared to the non-CKD patients. The risk of death at 1-year post PCI in STEMI CKD patients was also reported to be high (HR: 3.79, 95% CI 2.84-5.07).Conclusion: STEMI and CKD is a deadly combination, proven in our cohort, adding on to the current evidence in the literature. We noted that our STEMI CKD patients tend to be younger than the Caucasian with extremely high prevalence of diabetes mellitus. The poor outcome mainly driven by immediate or short term adverse events peri-procedural, therefore suggesting that more efficient treatment in this special group is imperative. [ABSTRACT FROM AUTHOR]

Circulating microRNAs (miRNAs) hold great potential as novel diagnostic markers for acute coronary syndrome (ACS). This study sought to identify plasma miRNAs that are differentially expressed in young ACS patients (mean age of 38.5 ± 4.3 years) and evaluate their diagnostic potentials. Small RNA sequencing (sRNA-seq) was used to profile plasma miRNAs. Discriminatory power of the miRNAs was determined using receiver operating characteristic (ROC) analysis. Thirteen up-regulated and 16 down-regulated miRNAs were identified in young ACS patients. Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) validation showed miR-183-5p was significantly up-regulated (8-fold) in ACS patients with non-ST-segment elevated myocardial infarction (NSTEMI) whereas miR-134-5p, miR-15a-5p, and let-7i-5p were significantly down-regulated (5-fold, 7-fold and 3.5-fold, respectively) in patients with ST-segment elevated myocardial infarction (STEMI), compared to the healthy controls. MiR-183-5p had a high discriminatory power to differentiate NSTEMI patients from healthy controls (area under the curve (AUC) of ROC = 0.917). The discriminatory power for STEMI patients was highest with let-7i-5p (AUC = 0.833) followed by miR-134-5p and miR-15a-5p and this further improved (AUC = 0.935) with the three miRNAs combination. Plasma miR-183-5p, miR-134-5p, miR-15a-5p and let-7i-5p are deregulated in STEMI and NSTEMI and could be potentially used to discriminate the two ACS forms. [ABSTRACT FROM AUTHOR]