The sex difference in 6-month MACEs and its explaining variables in acute myocardial infarction survivors: Data from CPACS-3 study

Sex Disparity in Characteristics, Management, and In-Hospital Outcomes of Patients with ST-Segment Elevated Myocardial Infarction: Insights from Henan STEMI Registry

Background. Women hospitalized with ST-elevation myocardial infarction (STEMI) experience higher risk of early mortality than men. We aimed to investigate the potential impact of risk factors, clinical characteristics, and management among gender-related risk differences. Method. We analyzed 5063 STEMI patients prospectively enrolled from 66 hospitals during 2016–2018 and compared sex differences in mortality, death, or treatment withdrawal and main adverse cardiovascular and cerebrovascular events (MACCE) using the generalized linear mixed model, following sequential adjustment for covariates. Results. Women were older and had a higher prevalence of hypertension (53.3% vs. 41.1%, $P<0.001$ ) and diabetes (24.5% vs. 15.2%, $P<0.001$ ). Eligible women were less likely to receive reperfusion therapy (56.1% vs. 62.4%, $P<0.001$ ); the onset to first medical contact (FMC) (255 vs. 190 minutes, $P<0.001$ ), onset to fibrinolysis (218 vs. 185 minutes, $P<0.001$ ), and onset to percutaneous coronary intervention (PCI) (307 vs. 243 minutes, $P<0.001$ ) were significantly delayed in women. The incidence of in-hospital death (6.8% vs. 3.0%, $P<0.001$ ), death or treatment withdrawal (14.5% vs. 5.6%, $P<0.001$ ), and MACCE (18.5% vs. 9.4%, $P<0.001$ ) were notably higher. The gender disparities persist in death (OR: 1.61, 95% CI: 1.12–2.33), death or treatment withdrawal (OR: 1.68, 95% CI: 1.26–2.24), and MACCE (OR: 1.37, 95% CI: 1.08–1.74) after adjustment for covariates. Among possible explanatory factors, age (−58.46%, −59.04%, −62.20%) and cardiovascular risk factors (−40.77%, −39.36%, −41.73%) accounted for most of the gender-associated risk differences. Conclusions. Women experienced worse in-hospital outcomes, and age and cardiovascular risk factors were major factors influencing sex-related differences. The sex disparity stressed the awareness and importance of quality improvement efforts against female patients in clinical practice.

Clinical Nomogram to Predict Major Adverse Cardiac Events in Acute Myocardial Infarction Patients within 1 Year of Percutaneous Coronary Intervention

The purpose of this study was to summarize the clinical characteristics and risk factors of major adverse cardiovascular events (MACEs) in patients who had had acute myocardial infarction (AMI) within 1 year of percutaneous coronary intervention (PCI). A total of 421 AMI patients who were treated with PCI and experienced MACEs within 1 year of their admission were included in this retrospective study. In addition, patients were matched for age, sex, and presentation with 561 patients after AMI who had not had MACEs. The clinical characteristics and risk factors for MACEs within 1 year in AMI patients were investigated, to develop a nomogram for MACEs based on univariate and multivariate analyses. The C statistic was used to assess the discriminative performance of the nomogram. In addition, calibration curve and decision curve analyses were conducted to validate the calibration performance and utility, respectively, of the nomogram. After univariate and multivariate analyses, a nomogram was constructed based on age (odds ratio (OR): 1.030; 95% confidence interval (CI): 1.014–1.047), diabetes mellitus (OR: 1.667; 95% CI: 1.151–2.415), low-density lipoprotein cholesterol (OR: 1.332; 95% CI: 1.134–1.565), uric acid (OR: 1.003; 95% CI: 1.001–1.005), lipoprotein (a) (OR: 1.003; 95% CI: 1.002–1.003), left ventricular ejection fraction (OR: 0.929; 95% CI: 0.905–0.954), Syntax score (OR: 1.075; 95% CI: 1.053–1.097), and hypersensitive troponin T (OR: 1.002; 95% CI: 1.002–1.003). The C statistic was 0.814. The calibration curve showed good concordance of the nomogram, while decision curve analysis demonstrated satisfactory positive net benefits. We developed a convenient, practical, and effective prediction model for predicting MACEs in AMI patients within 1 year of PCI. To ensure generalizability, this model requires external validation.

Impact of Three-Dimensional Strain on Major Adverse Cardiovascular Events after Acute Myocardial Infarction Managed by Primary Percutaneous Coronary Intervention-A Pilot Study

Background: Three-dimensional speckle-tracking echocardiography (3D-STE) allows simultaneous assessment of multidirectional components of strain. However, there are few data on its usefulness to predict prognosis in patients with acute myocardial infarction (AMI). The objective of our pilot study was to evaluate the prognostic value of four different 3D-STE parameters (global longitudinal strain (GLS-3D), global circumferential strain (GCS-3D), global radial strain (GRS-3D), and global area strain (GAS)) in AMI, after successful revascularization by primary PCI. Methods: We enrolled 94 AMI patients (66 ± 13 years, 56% men) who underwent coronary angiography. All patients had been 3D-STE assessed and followed-up for 1 year for the occurrence of MACE. Results: A total of 25 MACE were recorded over follow-up. Cut-off values of −17% for GAS (HR = 3.1, 95% CI: 1.39–6.92, p = 0.005), −12% for GCS-3D (HR = 3.06, 95% CI: 1.36–6.8, p = 0.006), −10% for GLS-3D (HR = 3.04, 95% CI: 1.36–6.78, p = 0.006), and 25% for GRS-3D (HR = 2.89, 95% CI: 1.29–6.46, p = 0.009) showed moderate accuracy in MACE prediction. Multivariate regression showed that GAS (HR = 1.1, 95% CI: 1.03–1.16), GLS-3D (HR = 1.13, 95% CI: 1.03–1.26), and GCS-3D (HR = 1.13, 95% CI: 1.03–1.23) remained independent predictors of MACE (HR = 1.07, 95% CI: 1.01–1.14 for GAS, and HR = 1.1, 95% CI: 1.01–1.2 for GCS-3D). However, post hoc power analysis indicated adequate sample size (power of 80%) only for GAS and GCS-3D for the ROC curve analysis and for GAS, GCS-3D, and GRS-3D for the log-rank test. Conclusion: Patients with AMI might benefit from early risk stratification with the aid of 3D-STE measurements, particularly GAS and GCS-3D, but larger studies are necessary to determine the optimal cut-off values to predict MACE.