Methods: In this cross-sectional study, data from 147 ACS patients aged less than 45 years were analysed.
Results: The mean age was 39.1 (4.9) years, the male to female ratio was 3:1; 21.2% of patients presented with unstable angina, 58.5% had non-ST elevation myocardial infarction and 20.4% had ST elevation myocardial infarction. The most frequent risk factor of ACS was dyslipidaemia (65.3%), followed by hypertension (43.5%). In total, 49.7% of patients had inpatient complication(s), with the most common being heart failure (35.4%), followed by arrhythmia (20.4%). The significant factors associated with ACS complications were current smoking [adjusted odds ratio (AOR) 4.03; 95% confidence interval (CI): 1.33, 12.23;P-value = 0.014], diabetic mellitus [AOR 3.03; 95% CI: 1.19, 7.71;P-value = 0.020], treatments of fondaparinux [AOR 0.18; 95% CI: 0.08, 0.39;P-value < 0.001] and oral nitrates [AOR 0.18; 95% CI: 0.08, 0.42;P-value < 0.001].
Conclusions: Smoking status and diabetes mellitus were modifiable risk factors while pharmacological treatment was an important protective factor for ACS complications in young patients.
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.
FUNDING: None.
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.
CLINICAL TRIAL REGISTRATION: PROSPERO (CRD42015026069).
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.
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.
LIMITATION: Retrospective registry data with inter-hospital variation.
METHODS: All MI patients admitted to the emergency department of Faisalabad Institute of Cardiology from April, 2016 to March, 2017 were recruited into the study. The clinico-laboratory profile and in-hospital outcomes of patients with and without DM were compared using chi-squared test or student t-test, where appropriate.
RESULTS: A total 4063 patients (Mean age: 55.86 ± 12.37years) with male preponderance were included into the study. STEMI was most prevalent (n = 2723, 67%) type of MI among study participants. DM was present in substantial number of cases (n = 3688, 90.8%). Patients with DM presented with increased BMI, higher blood pressure, elevated levels of cholesterol, serum creatinine, and blood urea nitrogen, when compared to the patients without DM (p<0.05). Out of 560 patients who were followed up, cardiogenic shock was frequent (n = 293, 52.3%) adverse outcome followed by heart failure (n = 114, 20.4%), atrial fibrillation (n = 78, 13.9%) and stroke (n = 75, 13.4 %). Moreover, in-hospital adverse outcomes were more prevalent among MI patients with DM than those without DM.
CONCLUSIONS: MI patients with DM present with varying clinico laboratory characteristics as well as experience higher prevalence of adverse cardiovascular events as compared to patients without DM. These patients require individual management strategy on very first day of admission.
DESIGN: A retrospective analysis of STEMI patients from 18 hospitals across Malaysia contributing to the Malaysian National Cardiovascular Database-acute coronary syndrome) registry (NCVD-ACS) year 2006-2013.
PARTICIPANTS: 16 517 patients diagnosed of STEMI from 18 hospitals in Malaysia from the year 2006 to 2013.
PRIMARY OUTCOME MEASURES: In-hospital and 30 day post-discharge mortality.
RESULTS: CS complicates 10.6% of all STEMIs in this study. They had unfavourable premorbid conditions and poor outcomes. The in-hospital mortality rate was 34.1% which translates into a 7.14 times mortality risk increment compared with STEMI without CS. Intravenous thrombolysis remained as the main urgent reperfusion modality. Percutaneous coronary interventions (PCI) in CS conferred a 40% risk reduction over non-invasive therapy but were only done in 33.6% of cases. Age over 65, diabetes mellitus, hypertension, chronic lung and kidney disease conferred higher risk of mortality.
CONCLUSION: Mortality rates of CS complicating STEMI in Malaysia are high. In-hospital PCI confers a 40% mortality risk reduction but the rate of PCI among our patients with CS complicating STEMI is still low. Efforts are being made to increase access to invasive therapy for these 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.