METHODS: We surveyed 16 512 adults from July 2020 to August 2021 in 30 territories. Participants self-reported their medical histories and the perceived impact of COVID-19 on 18 lifestyle factors and 13 health outcomes. For each disease subgroup, we generated lifestyle, health outcome, and bridge networks. Variables with the highest centrality indices in each were identified central or bridge. We validated these networks using nonparametric and case-dropping subset bootstrapping and confirmed central and bridge variables' significantly higher indices through a centrality difference test.

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: 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.

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.

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).

PATIENTS AND METHODS: Materials and methods: The study involved 134 ST-segment elevation myocardial infarction patients. Occurrence of post-percutaneous coronary (PCI) intervention epicardial blood flow of TIMI <3 or myocardial blush grade 0-1 along with ST resolution <70% within 2 hours after PCI was qualified as the no-reflow condition. Left ventricle remodeling was defined after 6-months as an increase in left ventricle end-diastolic volume and/or end-systolic volume by more than 10%.

RESULTS: Results: A logistic regression formula was evaluated. Included biomarkers were macrophage migration inhibitory factor and sST2, left ventricle ejection fraction: Y=exp(-39.06+0.82EF+0.096ST2+0.0028MIF) / (1+exp(-39.06+0.82EF+0.096ST2+0.0028MIF)). The estimated range is from 0 to 1 point. Less than 0.5 determines an adverse outcome, and more than 0.5 is a good prognosis. This equation, with sensitivity of 77 % and specificity of 85%, could predict the development of adverse left ventricle remodeling six months after a coronary event (AUC=0.864, CI 0.673 to 0.966, p<0.05).

OBJECTIVES: This study developed a model that predicted 30-day mortality for acute myocardial infarction (AMI) and compared the SMR among 41 Malaysian public hospitals using statistical process control charts.

METHODS & RESULTS: Data from referral centres and specialist hospitals with cardiology services were analysed. Both referral centres and specialist hospitals had comparable mortality, except for Hospitals A and B, which the study considered outliers. Two-thirds of the remaining hospitals had an SMR of above one (SMR 1.05-1.51), but the indices were still within the expected variations.

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: Hospital admissions for selected diagnoses between 1 February 2021 and 30 September 2021 were linked to the national COVID-19 immunisation register. We conducted self-controlled case-series study by identifying individuals who received COVID-19 vaccine and diagnosis of thrombocytopenia, venous thromboembolism, myocardial infarction, myocarditis/pericarditis, arrhythmia, stroke, Bell's Palsy, and convulsion/seizure. The incidence of events was assessed in risk period of 21 days postvaccination relative to the control period. We used conditional Poisson regression to calculate the incidence rate ratio (IRR) and 95% confidence interval (CI) with adjustment for calendar period.

RESULTS: There was no increase in the risk for myocarditis/pericarditis, Bell's Palsy, stroke, and myocardial infarction in the 21 days following either dose of BNT162b2, CoronaVac, and ChAdOx1 vaccines. A small increased risk of venous thromboembolism (IRR 1.24; 95% CI 1.02, 1.49), arrhythmia (IRR 1.16, 95% CI 1.07, 1.26), and convulsion/seizure (IRR 1.26; 95% CI 1.07, 1.48) was observed among BNT162b2 recipients. No association between CoronaVac vaccine was found with all events except arrhythmia (IRR 1.15; 95% CI 1.01, 1.30). ChAdOx1 vaccine was associated with an increased risk of thrombocytopenia (IRR 2.67; 95% CI 1.21, 5.89) and venous thromboembolism (IRR 2.22; 95% CI 1.17, 4.21).

METHODS: Health-related quality of life was captured using the EuroQol-5 Dimension-3 Level (EQ-5D-3L), with data collected at baseline and throughout the trial. Multilevel mixed-effects linear regression with random effects estimated health-related quality of life over time, capturing variation between hospital sites and individuals, and a fixed-effects linear model estimated the impact of cardiovascular and gastrointestinal events.

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.

METHODS: We undertook an international prospective cohort study involving patients 18 years of age or older who underwent cardiac surgery. High-sensitivity cardiac troponin I measurements (upper reference limit, 26 ng per liter) were obtained 3 to 12 hours after surgery and on days 1, 2, and 3 after surgery. We performed Cox analyses using a regression spline that explored the relationship between peak troponin measurements and 30-day mortality, adjusting for scores on the European System for Cardiac Operative Risk Evaluation II (which estimates the risk of death after cardiac surgery on the basis of 18 variables, including age and sex).

RESULTS: Of 13,862 patients included in the study, 296 (2.1%) died within 30 days after surgery. Among patients who underwent isolated coronary-artery bypass grafting or aortic-valve replacement or repair, the threshold troponin level, measured within 1 day after surgery, that was associated with an adjusted hazard ratio of more than 1.00 for death within 30 days was 5670 ng per liter (95% confidence interval [CI], 1045 to 8260), a level 218 times the upper reference limit. Among patients who underwent other cardiac surgery, the corresponding threshold troponin level was 12,981 ng per liter (95% CI, 2673 to 16,591), a level 499 times the upper reference limit.

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.

METHODS: In this study, a time series analysis was used to determine the variation of variables over time. All series were seasonally adjusted and Poisson regression analysis was performed. In the analysis of meteorological data and emotional distress due to religious mourning events, the best results were obtained by autoregressive moving average (ARMA) (5,5) model.

RESULTS: It was determined that average temperature, sunshine, and rain variables had a significant effect on death. A total of 2375 AMI's were enrolled. Average temperate (°C) and sunshine hours a day (h/day) had a statistically significant relationship with the number of AMI's (β = 0.011, P = 0.014). For every extra degree of temperature increase, the risk of AMI rose [OR = 1.011 (95%CI 1.00, 1.02)]. For every extra hour of sunshine, a day a statistically significant increase [OR = 1.02 (95% CI 1.01, 1.04)] in AMI risk occurred (β = 0.025, P = 0.001). Religious mourning events increase the risk of AMI 1.05 times more. The other independent variables have no significant effects on AMI's (P > 0.05).

CASE PRESENTATION: The present report describes a case of F. philomiragia bacteraemia first reported in Malaysia and Asian in a 60-year-old patient with underlying end-stage renal disease (ESRF) and diabetes mellitus. He presented with Acute Pulmonary Oedema with Non-ST-Elevation Myocardial Infarction (NSTEMI) in our hospital. He was intubated in view of persistent type I respiratory failure and persistent desaturation despite post haemodialysis. Blood investigation indicated the presence of ongoing infection and inflammation. The aerobic blood culture growth of F. philomiragia was identified using the matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) mass spectrometry (Score value: 2.16) and confirmed by 16S Ribosomal DNA (16S rDNA) sequencing. He was discharged well on day 26 of admission, after completing one week of piperacillin/tazobactam and two weeks of doxycycline.