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).
CONCLUSION: Conclusions: The level of MMP-9 more than 194 ng/ml and <55 points in EQ-5D predicts major adverse cardiovascular events, in¬cluding cardiovascular mortality and progressive heart failure, as well as other elements of composite endpoints, during a 1-year follow-up in patients with STEMI after primary PCI. Future studies are needed to clarify this result.
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.
CONCLUSIONS AND RELEVANCE: High amounts of sitting time were associated with increased risk of all-cause mortality and CVD in economically diverse settings, especially in low-income and lower-middle-income countries. Reducing sedentary time along with increasing physical activity might be an important strategy for easing the global burden of premature deaths and CVD.
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.
FUNDING: National Institute for Health Research Health Technology Assessment Programme.
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.
REGISTRATION: URL: https://www.crd.york.ac.uk/prospero; Unique identifier: CRD42022338075.
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).
CONCLUSION: Colchicine addition to standard treatment of STEMI patients undergo PCI reduce NLRP3 level despite statistically insignificant.
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.
METHODS: Revascularised acute myocardial infarction patients with normal left ventricular (LV) systolic function on TTE were assessed by 1.5T CMR. Acute regional diastolic wall motion abnormalities, global diastolic function measurements, acute segmental damage fraction with LGE and mean segmental pre-contrast T1 values were assessed on matching short axis slices.
RESULTS: Forty-four patients were analysed. Mean LVEF was 62.1±9.4%. No difference between NSTEMI (22/44) and STEMI in mean pre-contrast T1 values of infarcted (1025.0±109.2 vs 1011.0±81.6ms, p=0.70), adjacent (948.3±45.3 vs 941.1±46.6ms, p=0.70) and remote (888.8±52.8 vs 881.2±54.5ms, p=0.66) segments was detected. There was no correlation between pre-contrast T1 of infarcted segments with global diastolic dysfunction (E/A, r(2)=0.216, p=0.06; S/D, r(2)=0.243, p=0.053; E/E', r(2)=0.240, p=0.072), but there was significantly positive, moderate correlation with circumferential diastolic strain rate, (r(2)=0.579, p<0.01) with excellent agreement and reproducibility.
CONCLUSION: Cardiac magnetic resonance evaluation of pre-contrast T1 values revealed no difference between NSTEMI and STEMI patients in terms of tissue characterisation post-myocardial infarction. However, pre-contrast T1 of infarcted tissue is significantly correlated with regional diastolic circumferential strain rate.
CASE SUMMARY: We report a case of late presenting MI, where on initial echocardiogram had what was thought to be an intraventricular clot. However, upon further evaluation, the patient actually had an intramyocardial haematoma, with the supporting echocardiographic features to distinguish it from typical left ventricular (LV) clot. While this prevented the patient from receiving otherwise unnecessary anticoagulation, this diagnosis also put him at a much higher risk of mortality. Despite exhaustive medical and supportive management, death as consequence of pump failure occurred after 2 weeks.
DISCUSSION: This report highlights the features seen on echocardiography which support the diagnosis of an intramyocardial haematoma rather than an LV clot, notably the various acoustic densities, a well visualized myocardial dissecting tear leading into a neocavity filled with blood, and an independent endocardial layer seen above the haematoma. Based on this report, we wish to highlight the importance of differentiating intramyocardial haematomas from intraventricular clots in patients with recent MI.
METHOD: We will search PubMed/MEDLINE, EMBASE, ClinicalTrials.gov, WHO International Clinical Trials Registry, CINAHL Database, and the Cochrane Library using a predefined search strategy. Other sources of literature will include proceedings from the European Society of Cardiology, the American College of Cardiology, the American Heart Association, the EUROPCR, and the ProQuest Dissertations and Theses Database. We will include data from observational studies (case-control and cohort study design) and randomized control trials (that have investigated the relationship of D2B time and clinical outcome(s) in an adult (older than 18) STEMI population). Mortality (cardiac related and all-cause) and incidence heart failure (HF) have been prioritized as the primary outcomes. All eligible studies will be assessed for risk of bias using the Risk Of Bias in Non-randomized Studies - of Interventions tool. The Grading of Recommendations, Assessment, and Evaluation (GRADE) framework will be used to report the quality of evidence and strength of recommendations. We will proceed to analyze the data quantitatively if the pre-specified conditions are satisfied.
DISCUSSION: Recent discussion on the negative findings of improved D2B delay over time being unrelated to better STEMI outcomes at the population level has reminded us of an important knowledge gap we have on this domain. This systematic review will serve to address some of these key questions not previously examined. Answers to these questions could clarify the controversies and offer empirical support for or against the suggested hypotheses.
SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42015026069.