Predictive Effect of Renal Function on Clinical Outcomes in Older Adults With Acute Myocardial Infarction: Results From an Observational Cohort Study in China

Prediction models for major adverse cardiovascular events following ST-segment elevation myocardial infarction and subgroup-specific performance

Background

ST-segment elevation myocardial infarction (STEMI) patients are at a high residual risk of major adverse cardiovascular events (MACEs) after revascularization. Risk factors modify prognostic risk in distinct ways in different STEMI subpopulations. We developed a MACEs prediction model in patients with STEMI and examined its performance across subgroups.

Methods

Machine-learning models based on 63 clinical features were trained in patients with STEMI who underwent PCI. The best-performing model (the iPROMPT score) was further validated in an external cohort. Its predictive value and variable contribution were studied in the entire population and subgroups.

Results

Over 2.56 and 2.84 years, 5.0% and 8.33% of patients experienced MACEs in the derivation and external validation cohorts, respectively. The iPROMPT score predictors were ST-segment deviation, brain natriuretic peptide (BNP), low-density lipoprotein cholesterol (LDL-C), estimated glomerular filtration rate (eGFR), age, hemoglobin, and white blood cell (WBC) count. The iPROMPT score improved the predictive value of the existing risk score, with an increase in the area under the curve to 0.837 [95% confidence interval (CI): 0.784-0.889] in the derivation cohort and 0.730 (95% CI: 0.293-1.162) in the external validation cohort. Comparable performance was observed between subgroups. The ST-segment deviation was the most important predictor, followed by LDL-C in hypertensive patients, BNP in males, WBC count in females with diabetes mellitus, and eGFR in patients without diabetes mellitus. Hemoglobin was the top predictor in non-hypertensive patients.

Conclusion

The iPROMPT score predicts long-term MACEs following STEMI and provides insights into the pathophysiological mechanisms for subgroup differences.

Association between baseline smoking status and clinical outcomes following myocardial infarction

Background

Whether the effect of smoking on clinical outcomes following an acute myocardial infarction (AMI) is beneficial or detrimental remains inconclusive. We invesetigated the effect of smoking on the clinical outcomes in patients following an AMI.

Methods

Among 13,104 patients between November 2011 and June 2015 from a nationwide Korean AMI registry, a total of 10,193 participants were extracted then classified into two groups according to their smoking habit: (1) smoking group (n = 6,261) and (2) non-smoking group (n = 3,932). The participants who smoked were further subclassified according to their smoking intensity quantified by pack years (PYs): (1) <20 PYs (n = 1,695); (2) 20–40 PYs (n = 3,018); and (3) ≥40 PYs (n = 2,048). Each group was compared to each other according to treatment outcomes. The primary outcome was the incidence of major adverse cardiac and cerebrovascular events (MACCEs), which is a composite of all-cause mortality, non-fatal MI (NFMI), any revascularization, cerebrovascular accident, rehospitalization, and stent thrombosis. Secondary outcomes included the individual components of MACCEs. The Cox proportional hazard regression method was used to evaluate associations between baseline smoking and clinical outcomes following an AMI. Two propensity score weighting methods were performed to adjust for confounders, including propensity score matching and inverse probability of treatment weighting.

Results

While the incidence of all clinical outcomes, except for stent thrombosis, was lower in the smoking group than in the non-smoking group in the unadjusted data, the covariates-adjusted data showed statistical attenuation of these differences but a higher all-cause mortality in the smoking group. For smokers, the incidence of MACCEs, all-cause mortality, cardiac and non-cardiac death, and rehospitalization was significantly different between the groups, with the highest rates of MACCE, all-cause mortality, non-cardiac death, and rehospitalization in the group with the highest smoking intensity. These differences were statistically attenuated in the covariates-adjusted data, except for MACCEs, all-cause mortality, and non-cardiac death, which had the highest incidence in the group with ≥40 PYs.

Conclusion

Smoking had no beneficial effect on the clinical outcomes following an AMI. Moreover, for those who smoked, clinical outcomes tended to deteriorate as smoking intensity increased.