METHODS: Medline and Embase were searched for articles reporting outcomes of ACS patients stratified by SES using a multidimensional index, comprising at least 2 of the following components: Income, Education and Employment. A comparative meta-analysis was conducted using random-effects models to estimate the risk ratio of all-cause mortality in low SES vs high SES populations, stratified according to geographical region, study year, follow-up duration and SES index.

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.

METHODS: Data collected from patients with ACS between 2008 and 2009 for a study on cardiac rehabilitation at the Sarawak General Hospital were used for this study. QOL data were obtained using a validated version of the EuroQol five-dimensional questionnaire at baseline and at 12 months. Health utility scores were calculated using visual analogue scale scores and utility tariffs from Malaysia and the United Kingdom.

RESULTS: Data from 104 subjects from the earlier study was used. The mean age was 56.1 years, with 88.5% being men. The mean hospitalization duration was 6.3 days. The mean utility score was 0.75 at baseline and 0.82 at 12 months. There was a statistically significant improvement in utility from baseline to 12 months based on the Malaysian tariff (P = 0.014) but not with the UK tariff (P = 0.086). The QOL of patients was associated with sex and diagnosis of ST-segment elevation myocardial infarction.

METHODS: We conducted a systematic review and meta-analysis of prospective observational studies that have investigated the relationship of door-to-balloon delay and clinical outcomes. The main outcomes include mortality and heart failure.

RESULTS: 32 studies involving 299 320 patients contained adequate data for quantitative reporting. Patients with ST-elevation MI who experienced longer (>90 min) door-to-balloon delay had a higher risk of short-term mortality (pooled OR 1.52, 95% CI 1.40 to 1.65) and medium-term to long-term mortality (pooled OR 1.53, 95% CI 1.13 to 2.06). A non-linear time-risk relation was observed (P=0.004 for non-linearity). The association between longer door-to-balloon delay and short-term mortality differed between those presented early and late after symptom onset (Cochran's Q 3.88, P value 0.049) with a stronger relationship among those with shorter prehospital delays.

CONCLUSION: Longer door-to-balloon delay in primary percutaneous coronary intervention for ST-elevation MI is related to higher risk of adverse outcomes. Prehospital delays modified this effect. The non-linearity of the time-risk relation might explain the lack of population effect despite an improved door-to-balloon time in the USA.

METHODS: We did a network meta-analysis based on a systematic review of randomised controlled trials comparing fibrinolytic drugs in patients with STEMI. Several databases were searched from inception up to Feb 28, 2017. We included only randomised controlled trials that compared fibrinolytic agents as a reperfusion therapy in adult patients with STEMI, whether given alone or in combination with adjunctive antithrombotic therapy, against other fibrinolytic agents, a placebo, or no treatment. Only trials investigating agents with an approved indication of reperfusion therapy in STEMI (streptokinase, tenecteplase, alteplase, and reteplase) were included. The primary efficacy outcome was all-cause mortality within 30-35 days and the primary safety outcome was major bleeding. This study is registered with PROSPERO (CRD42016042131).

FINDINGS: A total of 40 eligible studies involving 128 071 patients treated with 12 different fibrinolytic regimens were assessed. Compared with accelerated infusion of alteplase with parenteral anticoagulants as background therapy, streptokinase and non-accelerated infusion of alteplase were significantly associated with an increased risk of all-cause mortality (risk ratio [RR] 1·14 [95% CI 1·05-1·24] for streptokinase plus parenteral anticoagulants; RR 1·26 [1·10-1·45] for non-accelerated alteplase plus parenteral anticoagulants). No significant difference in mortality risk was recorded between accelerated infusion of alteplase, tenecteplase, and reteplase with parenteral anticoagulants as background therapy. For major bleeding, a tenecteplase-based regimen tended to be associated with lower risk of bleeding compared with other regimens (RR 0·79 [95% CI 0·63-1·00]). The addition of glycoprotein IIb or IIIa inhibitors to fibrinolytic therapy increased the risk of major bleeding by 1·27-8·82-times compared with accelerated infusion alteplase plus parenteral anticoagulants (RR 1·47 [95% CI 1·10-1·98] for tenecteplase plus parenteral anticoagulants plus glycoprotein inhibitors; RR 1·88 [1·24-2·86] for reteplase plus parenteral anticoagulants plus glycoprotein inhibitors).

INTERPRETATION: Significant differences exist among various fibrinolytic regimens as reperfusion therapy in STEMI and alteplase (accelerated infusion), tenecteplase, and reteplase should be considered over streptokinase and non-accelerated infusion of alteplase. The addition of glycoprotein IIb or IIIa inhibitors to fibrinolytic therapy should be discouraged.

OBJECTIVES: To identify the risk factors associated with mortality for each gender and compare differences, if any, among ST-elevation myocardial infarction (STEMI) patients.

DESIGN: Retrospective analysis.

SETTINGS: Hospitals across Malaysia.

PATIENTS AND METHODS: We analyzed data on all STEMI patients in the National Cardiovascular Database-Acute coronary syndrome (NCVD-ACS) registry for the years 2006 to 2013 (8 years). We collected demographic and risk factor data (diabetes mellitus, hypertension, smoking status, dyslipidaemia and family history of CAD). Significant variables from the univariate analysis were further analysed by a multivariate logistic analysis to identify risk factors and compare by gender.

MAIN OUTCOME MEASURES: Differential risk factors for each gender.

RESULTS: For the 19484 patients included in the analysis, the mortality rate over the 8 years was significantly higher in females (15.4%) than males (7.5%) (P < .001). The univariate analysis showed that the majority of male patients < 65 years while females were >=65 years. The most prevalent risk factors for male patients were smoking (79.3%), followed by hypertension (54.9%) and diabetes mellitus (40.4%), while the most prevalent risk factors for female patients were hypertension (76.8%), followed by diabetes mellitus (60%) and dyslipidaemia (38.1%). The final model for male STEMI patients had seven significant variables: Killip class, age group, hypertension, renal disease, percutaneous coronary intervention and family history of CVD. For female STEMI patients, the significant variables were renal disease, smoking status, Killip class and age group.

CONCLUSION: Gender differences existed in the baseline characteristics, associated risk factors, clinical presentation and outcomes among STEMI patients. For STEMI females, the rate of mortality was twice that of males. Once they reach menopausal age, when there is less protection from the estrogen hormone and there are other risk factors, menopausal females are at increased risk for STEMI.

Methods: A total of 7180 STEMI male patients from the National Cardiovascular Disease Database-Acute Coronary Syndrome (NCVD-ACS) registry for the years 2006-2013 were enrolled. In the development of univariate and multivariate logistic regression model for the STEMI patients, Bayesian Markov Chain Monte Carlo (MCMC) simulation approach was applied. The performance of the model was assessed through convergence diagnostics, overall model fit, model calibration and discrimination.

Results: A set of six risk factors for cardiovascular death among STEMI male patients were identified from the Bayesian multivariate logistic model namely age, diabetes mellitus, family history of CVD, Killip class, chronic lung disease and renal disease respectively. Overall model fit, model calibration and discrimination were considered good for the proposed model.

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.