DESIGN AND METHODS: This was a qualitative study and data was collected through semi-structured in-depth recorded phone interviews with eight Malay male participants. They were screened using a questionnaire and participants that met the inclusion criteria were interviewed, and were admitted to National Heart Centre, Malaysia between January to June 2019 diagnosed with MI. The data collected were analysed using NVivo 12 software and thematic analysis was applied.
RESULTS: Four preliminary themes emerged from the study: 1) beliefs in physical activity; 2) healthy lifestyle: new normal or same old habit; 3) factors determining participation in pa; and 4) physical activity adherence strategies.
CONCLUSIONS: The results of the studies showed that participants understand the need to maintain physical activity, which helps to maintain a healthy life after MI and prevent recurrent infarction. Strategies for developing self-efficacy for physical activity were also discussed. The need to understand that maintaining physical activity as well as adopting a new normal of healthy habit after MI is crucial in order to maintain the health and prevent recurrence of MI.
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.
BACKGROUND: Drug eluting stent (DES) implantation is the treatment of choice for coronary artery disease (CAD) leaving only marginal indications for the use of bare metal stents (BMS). However, selected treatment populations with DES contraindications such as patients who cannot sustain 6-12 months of dual antiplatelet therapy (DAPT) remain candidates for BMS implantations.
METHODS: Thin strut bare metal stenting in a priori defined subgroups were investigated in a non-randomized, international, multicenter «all comers» observational study. Primary endpoint was the 9-month TLR rate whereas secondary endpoints included the 9-month MACE and procedural success rates.
RESULTS: A total of 783 patients of whom 98 patients had AF underwent BMS implantation. Patient age was 70.4 ± 12.8 years. Cardiovascular risk factors in the overall population were male gender (78.2%, 612/783), diabetes (25.2%, 197/783), hypertension (64.1%, 502/783), cardiogenic shock (4.9%, 38/783) and end stage renal disease (4.9%, 38/783). In-hospital MACE was 4.1% (30/783) in the overall population. The 9-month TLR rate was 4.5% (29/645) in the non-AF group and 3.3% (3/90) in the AF group (P = 0.613). At 9 months, the MACE rate in the AF-group and non-AF group was not significantly different either (10.7%, 69/645 vs. 6.7%, 6/90; P = 0.237). Accumulated stroke rates were 0.3% (2/645) in the non-AF subgroup at baseline and 1.1% (1/90) in the AF subgroup (P = 0.264).
CONCLUSION: Bare metal stenting in AF patients delivered acceptably low TLR and MACE rates while having the benefit of a significantly shorter DAPT duration in a DES dominated clinical practice. © 2015 Wiley Periodicals, Inc.
METHODS: A systematic search with Embase, Cochrane CENTRAL, Google scholar, and PubMed was conducted. Studies conducted in patients with STEMI presented to non PCI-capable settings and compared fibrinolytic injection with no injection before referring patients to PCI-capable settings were included. The primary outcome was the composite outcomes of major adverse cardiac events (MACEs) at 30 days. Meta-analyses were performed using random-effect model.
RESULTS: Of 912 articles, three RCTs and three non-RCTs were included. Based on RCTs, fibrinolytic injection before the referral has failed to decrease MACEs compared to non-fibrinolytic injection [relative risk (RR) 1.18; 95% confidence interval (CI), 0.89-1.57, p = 0.237]. Fibrinolytic injection has also failed to decrease mortality, re-infarction, and ischemic stroke. On the other hand, fibrinolytic injection was associated with a higher risk of major bleeding.
CONCLUSIONS: In non PCI-capable settings, fibrinolytic injection before referring patients with STEMI to PCI-capable settings has no clinical benefit but could increase risk of major bleeding. Clinicians might more carefully consider whether fibrinolytic injection should be used in patients with STEMI before the referral.
METHODS: Streptozotocin-nicotinamide induced diabetic rats received oral VVSME for 28days. MI was induced by intraperitoneal injection of isoproterenol on last two days. Prior to sacrifice, blood was collected and fasting blood glucose (FBG), glycated hemoglobin (HbA1c), lipid profile and insulin levels were measured. Levels of serum cardiac injury marker (troponin-I and CK-MB) were determined and histopathological changes in the heart were observed following harvesting. Levels of oxidative stress (LPO, SOD, CAT, GPx and RAGE), inflammation (NF-κB, TNF-α, IL-1β and IL-6) and cardiac ATPases (Na(+)/K(+)-ATPase and Ca(2+)-ATPase) were determined in heart homogenates. LC-MS was used to identify constituents in the extracts.
RESULTS: Consumption of VVSME by diabetic rats with or without MI improved the metabolic profiles while decreased the cardiac injury marker levels with lesser myocardial damage observed. Additionally, VVSME consumption reduced the levels of LPO, RAGE, TNF-α, Iκκβ, NF-κβ, IL-1β and IL-6 while increased the levels of SOD, CAT, GPx, Na(+)/K(+)-ATPase and Ca(2+)-ATPase in the infarcted and non-infarcted heart of diabetic rats (p<0.05). LC-MS analysis revealed 17 major compounds in VVSME which might be responsible for the observed effects.
CONCLUSIONS: Consumption of VVSME by diabetics helps to ameliorate damage to the infarcted and non-infarcted myocardium by decreasing oxidative stress, inflammation and cardiac ATPases dysfunctions.