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.

PATIENTS AND METHODS: Materials and methods: The study included 165 patients admitted with STEMI within 12 hours of the onset of symptoms be¬tween January 2020 and August 2021. All patients underwent primary PCI according to the guidelines, followed by standard examination and treatment at the hospital. Blood samples for biomarker analysis (MMP-9, cTnI) and other routine tests were taken on admission. At six months after the event, all patients underwent clinical follow-up. Patients were contacted either by phone, through family members or their physicians 1 year after the event.

RESULTS: Results: The composite endpoint reached 9% of patients at one-year follow-up. ROC analysis of MMP-9 with the one-year com¬posite endpoint showed an AUC=0.711, with 91.7% sensitivity, and 47.4% specificity, 95% CI - 0.604 to 0.802, p=0.0037. ROC analysis of EQ-5D questionnaire with the one-year composite endpoint showed AUC = 0.73, the 95% CI - 0.624 to 0.820, p< 0.0195, with sensitivity 54.5% and specificity 94.7%. A logistic regression model showed a statistical association with the com¬posite endpoint at one year after STEMI in both EQ-5D (OR=0.89, 95% CI: 0.8313- 0.9725, p=0.0079) and MMP-9 (OR=1.0151, 95% CI:1.0001-1.0304, p=0.0481).

MATERIALS AND METHODS: Randomised controlled trial was conducted on STEMI patients who undergo PCI in two hospitals in Jakarta, 104 patients enrolled to this study, and 77 patients completed the trial. 37 patients were randomly assigned to receive colchicines (2 mg loading dose; 0.5 mg thereafter every 12 hour for 48 hours) while 40 patients received placebo. NLRP3 level was measured from venous blood at baseline (BL), after procedure (AP), dan 24-hour post procedure (24H).

RESULTS: No NLRP3 difference was observed initially between colchicine arm and placebo arm 38,69 and 39,0138, respectively (p >0.05). Measurement conducted at 24H, patients received colchicine demonstrate reduction in NLRP3 level (37.67), while placebo arm results increase in NLRP3 level (42.89) despite not statistically significant (p >0,05).

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: We did a systematic review and meta-analysis of randomised controlled trials including IPD. We searched MEDLINE, MEDLINE In-Process & Other Non-Indexed Citations, MEDLINE Epub Ahead of Print, Embase, Science Citation Index, the Cochrane Controlled Trials Register, Cochrane Database of Systematic Reviews, and Database of Abstracts of Review of Effects for literature from 1992 onwards (date of search, Aug 27, 2018). The following inclusion criteria were applied: (1) men aged 18 years and older with a screening testosterone concentration of 12 nmol/L (350 ng/dL) or less; (2) the intervention of interest was treatment with any testosterone formulation, dose frequency, and route of administration, for a minimum duration of 3 months; (3) a comparator of placebo treatment; and (4) studies assessing the pre-specified primary or secondary outcomes of interest. Details of study design, interventions, participants, and outcome measures were extracted from published articles and anonymised IPD was requested from investigators of all identified trials. Primary outcomes were mortality, cardiovascular, and cerebrovascular events at any time during follow-up. The risk of bias was assessed using the Cochrane Risk of Bias tool. We did a one-stage meta-analysis using IPD, and a two-stage meta-analysis integrating IPD with data from studies not providing IPD. The study is registered with PROSPERO, CRD42018111005.

FINDINGS: 9871 citations were identified through database searches and after exclusion of duplicates and of irrelevant citations, 225 study reports were retrieved for full-text screening. 116 studies were subsequently excluded for not meeting the inclusion criteria in terms of study design and characteristics of intervention, and 35 primary studies (5601 participants, mean age 65 years, [SD 11]) reported in 109 peer-reviewed publications were deemed suitable for inclusion. Of these, 17 studies (49%) provided IPD (3431 participants, mean duration 9·5 months) from nine different countries while 18 did not provide IPD data. Risk of bias was judged to be low in most IPD studies (71%). Fewer deaths occurred with testosterone treatment (six [0·4%] of 1621) than placebo (12 [0·8%] of 1537) without significant differences between groups (odds ratio [OR] 0·46 [95% CI 0·17-1·24]; p=0·13). Cardiovascular risk was similar during testosterone treatment (120 [7·5%] of 1601 events) and placebo treatment (110 [7·2%] of 1519 events; OR 1·07 [95% CI 0·81-1·42]; p=0·62). Frequently occurring cardiovascular events included arrhythmia (52 of 166 vs 47 of 176), coronary heart disease (33 of 166 vs 33 of 176), heart failure (22 of 166 vs 28 of 176), and myocardial infarction (10 of 166 vs 16 of 176). Overall, patient age (interaction 0·97 [99% CI 0·92-1·03]; p=0·17), baseline testosterone (interaction 0·97 [0·82-1·15]; p=0·69), smoking status (interaction 1·68 [0·41-6·88]; p=0.35), or diabetes status (interaction 2·08 [0·89-4·82; p=0·025) were not associated with cardiovascular risk.

INTERPRETATION: We found no evidence that testosterone increased short-term to medium-term cardiovascular risks in men with hypogonadism, but there is a paucity of data evaluating its long-term safety. Long-term data are needed to fully evaluate the safety of testosterone.

OBJECTIVE: To investigate the association of sitting time with mortality and major CVD in countries at different economic levels using data from the Prospective Urban Rural Epidemiology study.

DESIGN, SETTING, AND PARTICIPANTS: This population-based cohort study included participants aged 35 to 70 years recruited from January 1, 2003, and followed up until August 31, 2021, in 21 high-income, middle-income, and low-income countries with a median follow-up of 11.1 years.

EXPOSURES: Daily sitting time measured using the International Physical Activity Questionnaire.

MAIN OUTCOMES AND MEASURES: The composite of all-cause mortality and major CVD (defined as cardiovascular death, myocardial infarction, stroke, or heart failure).

RESULTS: Of 105 677 participants, 61 925 (58.6%) were women, and the mean (SD) age was 50.4 (9.6) years. During a median follow-up of 11.1 (IQR, 8.6-12.2) years, 6233 deaths and 5696 major cardiovascular events (2349 myocardial infarctions, 2966 strokes, 671 heart failure, and 1792 cardiovascular deaths) were documented. Compared with the reference group (<4 hours per day of sitting), higher sitting time (≥8 hours per day) was associated with an increased risk of the composite outcome (hazard ratio [HR], 1.19; 95% CI, 1.11-1.28; Pfor trend < .001), all-cause mortality (HR, 1.20; 95% CI, 1.10-1.31; Pfor trend < .001), and major CVD (HR, 1.21; 95% CI, 1.10-1.34; Pfor trend < .001). When stratified by country income levels, the association of sitting time with the composite outcome was stronger in low-income and lower-middle-income countries (≥8 hours per day: HR, 1.29; 95% CI, 1.16-1.44) compared with high-income and upper-middle-income countries (HR, 1.08; 95% CI, 0.98-1.19; P for interaction = .02). Compared with those who reported sitting time less than 4 hours per day and high physical activity level, participants who sat for 8 or more hours per day experienced a 17% to 50% higher associated risk of the composite outcome across physical activity levels; and the risk was attenuated along with increased physical activity levels.

METHODS: Medline and Embase databases were searched without date restriction on May 2022 for articles that examined EAT and cardiovascular outcomes. The inclusion criteria were (1) studies measuring EAT of adult patients at baseline and (2) reporting follow-up data on study outcomes of interest. The primary study outcome was major adverse cardiovascular events. Secondary study outcomes included cardiac death, myocardial infarction, coronary revascularization, and atrial fibrillation.

RESULTS: Twenty-nine articles published between 2012 and 2022, comprising 19 709 patients, were included in our analysis. Increased EAT thickness and volume were associated with higher risks of cardiac death (odds ratio, 2.53 [95% CI, 1.17-5.44]; P=0.020; n=4), myocardial infarction (odds ratio, 2.63 [95% CI, 1.39-4.96]; P=0.003; n=5), coronary revascularization (odds ratio, 2.99 [95% CI, 1.64-5.44]; P<0.001; n=5), and atrial fibrillation (adjusted odds ratio, 4.04 [95% CI, 3.06-5.32]; P<0.001; n=3). For 1 unit increment in the continuous measure of EAT, computed tomography volumetric quantification (adjusted hazard ratio, 1.74 [95% CI, 1.42-2.13]; P<0.001) and echocardiographic thickness quantification (adjusted hazard ratio, 1.20 [95% CI, 1.09-1.32]; P<0.001) conferred an increased risk of major adverse cardiovascular events.

CONCLUSIONS: The utility of EAT as an imaging biomarker for predicting and prognosticating cardiovascular disease is promising, with increased EAT thickness and volume being identified as independent predictors of major adverse cardiovascular events.