Atrial Fibrillation Complicating Acute Myocardial Infarction: Prevalence, Impact, and Management Considerations

PURPOSE OF REVIEW

Atrial fibrillation (AF) and myocardial infarction (MI) often coexist, and this overlapping nature leads to heightened morbidity and increases the need for comprehensive risk management strategies. The precise trajectory and implications of atrial fibrillation complicating myocardial infarction remain subjects of debate, with divergent reports presenting varying accounts. This review seeks to provide an in-depth exploration of the existing literature to cover the predictors, implication, and available management of new onset atrial fibrillation (NOAF) complicating acute myocardial infarction (AMI).

RECENT FINDINGS

Clinical risk factors, laboratory markers, echocardiographic findings, and angiographic data can be used to assess patients at risk of developing NOAF post-AMI. The diagnosis of NOAF post MI has been associated with overall worse short- and long-term prognosis with increased risk for mortality, cardiogenic shock, stroke, and bleeding, along with reduced rates of coronary angiography and percutaneous coronary intervention, and higher risk of future recurrence of AF and ischemic stroke. Despite the paucity of preventative treatment, the optimal management of acute coronary syndrome and the use of guideline directed therapy do decrease the risk of development of atrial fibrillation post myocardial infarction.

Prediction Model of New Onset Atrial Fibrillation in Patients with Acute Coronary Syndrome

OBJECTIVE

Atrial fibrillation (AF) is one of the most common complications of acute coronary syndrome (ACS) patients. Possible risk factors related to new-onset AF (NOAF) in ACS patients have been reported in some studies, and several prediction models have been established. However, the predictive power of these models was modest and lacked independent validation. The aim of this study is to define risk factors of NOAF in patients with ACS during hospitalization and to develop a prediction model and nomogram for individual risk prediction.

METHODS

Retrospective cohort studies were conducted. A total of 1535 eligible ACS patients from one hospital were recruited for model development. External validation was performed using an external cohort of 1635 ACS patients from another hospital. The prediction model was created using multivariable logistic regression and validated in an external cohort. The discrimination, calibration, and clinical utility of the model were evaluated, and a nomogram was constructed. A subgroup analysis was performed for unstable angina (UA) patients.

RESULTS

During hospitalization, the incidence of NOAF was 8.21% and 6.12% in the training and validation cohorts, respectively. Age, admission heart rate, left atrial diameter, right atrial diameter, heart failure, brain natriuretic peptide (BNP) level, less statin use, and no percutaneous coronary intervention (PCI) were independent predictors of NOAF. The AUC was 0.891 (95% CI: 0.863-0.920) and 0.839 (95% CI: 0.796-0.883) for the training and validation cohort, respectively, and the model passed the calibration test (P > 0.05). The clinical utility evaluation shows that the model has a clinical net benefit within a certain range of the threshold probability.

CONCLUSION

A model with strong predictive power was constructed for predicting the risk of NOAF in patients with ACS during hospitalization. It might help with the identification of ACS patients at risk and early intervention of NOAF during hospitalization.

Relationship between serum uric acid levels and different types of atrial fibrillation: An updated meta-analysis

AIM

Increasing evidence supports the hypothesis that high serum uric acid (SUA) levels are related to atrial fibrillation (AF). However, the incidence of AF in patients with hyperuricemia and SUA levels in different types of AF is not entirely clear. This meta-analysis was designed to evaluate the relationship between SUA and incidence of AF, and the variation in SUA levels in different types of AF.

DATA SYNTHESIS

Relevant reports were searched for in Embase, PubMed and the Cochrane Library. A fixed-effects model combining relative risk (RR) and the corresponding 95% confidence interval (95% CI) was used to evaluate the correlation between SUA and AF. The standardized mean differences (SMDs) of SUA values were calculated using a random-effects model to evaluate the differences in SUA levels among different types of AF. A total of 31 studies with 504,958 participants were included in this research. The results from 8 cohort studies showed that high SUA levels significantly increased the incidence of AF [RR (95% CI): 1.92 (1.68-2.20); P < 0.01]. The results from 29 studies revealed that SUA levels elevated in patients with AF [SMD (95% CI): 0.55 (0.43-0.66); P < 0.001]. Meanwhile, SUA levels in new-onset AF [SMD (95%CI): 0.24 (0.10-0.38); P = 0.001], paroxysmal AF [SMD (95%CI): 0.52 (0.33-0.72); P < 0.001] and persistent AF [SMD (95%CI): 1.23 (0.98-1.48); P < 0.001] were significantly higher than that in patients without AF.

CONCLUSIONS

High SUA levels had an obvious correlation with the occurrence rate of AF. In addition, SUA levels were significantly different among patients with new-onset, paroxysmal and persistent AF.