Background A wrong traditional belief persists among people that opium consumption beneficially affects cardiovascular disease and its risk factors. However, no evidence exists regarding the effect of opium consumption or cessation on the long-term risk of major adverse cardio-cerebrovascular events after coronary artery bypass grafting. We therefore aimed to evaluate the effect of persistent opium consumption after surgery on the long-term outcomes of coronary artery bypass grafting. Methods The study population consisted of 28,691 patients (20,924 men, mean age 60.9 years), who underwent coronary artery bypass grafting between 2007 and 2016 at our centre. The patients were stratified into three groups according to the status of opium consumption: never opium consumers ( n = 23,619), persistent postoperative opium consumers ( n = 3636) and enduring postoperative opium withdrawal ( n = 1436). Study endpoints were 5-year mortality and 5-year major adverse cardio-cerebrovascular events, comprising all-cause mortality, acute coronary syndrome, cerebrovascular accident and revascularisation. Results After surgery, 3636 patients continued opium consumption, while 1436 patients persistently avoided opium use. The multivariable survival analysis demonstrated that persistent post-coronary artery bypass grafting opium consumption increased 5-year mortality and 5-year major adverse cardio-cerebrovascular events by 28% (hazard ratio (HR) 1.28, 95% confidence interval (CI) 1.06–1.54; P = 0.009) and 25% (HR 1.25, 95% CI 1.13–1.40; P < 0.0001), respectively. It also increased the 5-year risk of acute coronary syndrome by 34% (sub-distribution HR 1.34, 95% CI 1.16–1.55; P < 0.0001). Conclusions The present data suggest that persistent post-coronary artery bypass grafting opium consumption may significantly increase mortality, major adverse cardio-cerebrovascular events and acute coronary syndrome in the long term. Future studies are needed to confirm our findings.

Background Preoperative coronary artery disease risk factors (CADRFs) distribution and pattern may also have an important role in determining major adverse cardiovascular events (MACEs). In this study, we aimed to evaluate the CADRFs distribution and trend over 10 years and also the long-term outcome of CABG in different age-sex categories. Method In this registry-based serial cross-sectional study, we enrolled 24,328 patients who underwent isolated CABG and evaluated the prevalence of CADRFs according to sex and age. We used inverse probability weighting (IPW) to compare survival and MACE between the sexes. We also used Cox regression to determine each CADRFs effect on survival and MACEs. Results In general, DLP (56.00%), HTN (53.10%), DM (38.40%), and positive family history (38.30%) were the most frequent risk factors in all patients. Prevalence of HTN, DLP, DM, obesity, and positive family history were all higher in women, all statistically significant. The median follow-up duration was 78.1 months (76.31–79.87 months). After inverse probability weighting (to balance risk factors and comorbidities), men had lower MACEs during follow-up (HR 0.72; 95% CI 0.57–0.91; P value 0.006) and there was no significant difference in survival between sexes. DM and HTN were associated with higher mortality and MACEs in both sexes. Conclusion Although DLP is still the most frequent CADRF among the CABG population, the level of LDL and TG is decreasing. Women experience higher MACE post CABG. Therefore, health care providers and legislators must pay greater attention to female population CADRFs and ways to prevent them at different levels.

Objective Pathophysiological mechanisms and pathways linking cardiovascular mortality and morbidity with air pollution were recently hypothesized. The present study evaluated association between air pollution and changes in heart rate variability as a marker of cardiac autonomic function in healthy individuals, and also determined the frequency of cardiac arrhythmias and QT interval changes on polluted compared to unpolluted days. Methods Continuous Holter electrocardiography (ECG) monitoring was conducted on 21 young healthy individuals in the two episodes of clean air and elevated air pollution in Tehran. All subjects underwent a medical history review, a physical examination and echocardiography in order to rule out structural heart diseases. Measured pollutants and parameters included NO 2 , CO 2 , O 3 , SO 2 , and PM10, which all showed significantly higher concentrations on polluted days. Holter parameters were measured for 24-h time segments and compared. Results Maximum heart rate was significantly lower in polluted air conditions in comparison with clean air conditions (115.1 ± 32.2 vs. 128.9 ± 17.7), and the square root of the mean of squared differences between adjacent NN intervals (r-MSSD) was higher in polluted air compared to clean air (99.0 ± 58.2 vs. 58.5 ± 26.4). Also, the occurrence of nonsustained supraventricular tachycardia was reported in 42.9% of participants in air pollution episodes, whereas this arrhythmia was not seen in clear air conditions (p = 0.001).Conclusion Changes in air pollution indices may lead to the occurrence of nonsustained supraventricular tachycardia, a slight reduction in maximum heart rate, and an increase in r-MSSD in healthy individuals. Air quality monitoring in cities associated with a high exposure to air pollutants is recommended in order to prevent such events.

BackgroundAs the era of big data analytics unfolds, machine learning (ML) might be a promising tool for predicting clinical outcomes. This study aimed to evaluate the predictive ability of ML models for estimating mortality after coronary artery bypass grafting (CABG).Materials and methodsVarious baseline and follow-up features were obtained from the CABG data registry, established in 2005 at Tehran Heart Center. After selecting key variables using the random forest method, prediction models were developed using: Logistic Regression (LR), Support Vector Machine (SVM), Naïve Bayes (NB), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), and Random Forest (RF) algorithms. Area Under the Curve (AUC) and other indices were used to assess the performance.ResultsA total of 16,850 patients with isolated CABG (mean age: 67.34 ± 9.67 years) were included. Among them, 16,620 had one-year follow-up, from which 468 died. Eleven features were chosen to train the models. Total ventilation hours and left ventricular ejection fraction were by far the most predictive factors of mortality. All the models had AUC &gt; 0.7 (acceptable performance) for 1-year mortality. Nonetheless, LR (AUC = 0.811) and XGBoost (AUC = 0.792) outperformed NB (AUC = 0.783), RF (AUC = 0.783), SVM (AUC = 0.738), and KNN (AUC = 0.715). The trend was similar for two-to-five-year mortality, with LR demonstrating the highest predictive ability.ConclusionVarious ML models showed acceptable performance for estimating CABG mortality, with LR illustrating the highest prediction performance. These models can help clinicians make decisions according to the risk of mortality in patients undergoing CABG.

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