Prognostic factors of atrial fibrillation following coronary artery bypass graft surgery

Dietary interventions in the management of atrial fibrillation

Atrial fibrillation (AF) represents the most common cardiac arrhythmia with significant morbidity and mortality implications. It is a common cause of hospital admissions, significantly impacts quality of life, increases morbidity and decreases life expectancy. Despite advancements in treatment options, prevalence of AF remains exceptionally high. AF is a challenging disease to manage, not just clinically but also financially. Evidence suggests lifestyle modification, including dietary changes, plays a significant role in the treatment of AF. This review aims to analyze the existing literature on the effects of dietary modifications on the incidence, progression, and outcomes of atrial fibrillation. It examines various dietary components, including alcohol, caffeine, omega-3 polyunsaturated fatty acids and minerals, and their impact on AF incidence, progression, and outcomes. The evidence surrounding the effects of dietary patterns, such as the Mediterranean and low carbohydrate diets, on AF is also evaluated. Overall, this review underscores the importance of dietary interventions as part of a comprehensive approach to AF management and highlights the need for further research in this emerging field.

Left atrial functional measurements' utility in predicting long-term risk of atrial fibrillation after isolated CABG

BACKGROUND

Atrial fibrillation (AF) is the most common cardiac arrhythmia following coronary artery bypass grafting (CABG). We hypothesized that measures of left atrial (LA) function would be useful in predicting AF in patients undergoing CABG.

METHODS AND RESULTS

In the study, 611 patients were included after CABG. All patients had echocardiograms performed preoperatively and LA functional measurements were assessed. These measurements were LA maximum volume index (LAVmax), LA minimum volume index (LAVmin) and LA emptying fraction (LAEF). The endpoint was AF occurring >14 days after surgery. During the follow-up period of a median of 3.7 years, 52 (9%) developed AF. The mean age was 67 years, 84% were male and the average left ventricle ejection fraction was 50%. Patients who developed AF had a lower CCS class and lower LAEF (40 vs. 45%), otherwise no clinical differences were observed between outcome groups. No functional LA measurements were significant predictors of AF in the whole CABG population. However, in patients with normal-sized LA (n = 532, events: 49), both LAEF and LAVmin were univariable predictors of AF. When the functional measurements were adjusted for the CHADS₂ score, both LAVmin (HR = 1.07 [1.01-1.13], p = .014) and LAEF (HR: 1.02 [1.00-1.03], p = .023), remained significant predictors.

CONCLUSION

No echocardiographic measurements were significant predictors of AF after CABG. In patients with a normal LA size, LAVmin as well as LAEF were significant predictors of AF.

Machine Learning Models of Postoperative Atrial Fibrillation Prediction After Cardiac Surgery

OBJECTIVES

This study aimed to use machine learning algorithms to build an efficient forecasting model of atrial fibrillation after cardiac surgery, and to compare the predictive performance of machine learning to traditional logistic regression.

DESIGN

A retrospective study.

SETTING

Second Affiliated Hospital of Zhejiang University School of Medicine.

PARTICIPANTS

The study comprised 1,400 patients who underwent valve and/or coronary artery bypass grafting surgery with cardiopulmonary bypass from September 1, 2013 to December 31, 2018.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Two machine learning approaches (gradient-boosting decision tree and support-vector machine) and logistic regression were used to build predictive models. The performance was compared by the area under the curve (AUC). The clinical practicability was assessed using decision curve analysis. Postoperative atrial fibrillation occurred in 519 patients (37.1%). The AUCs of the support-vector machine, logistic regression, and gradient boosting decision tree were 0.777 (95% CI: 0.772-0.781), 0.767 (95% CI: 0.762-0.772), and 0.765 (95% CI: 0.761-0.770), respectively. As decision curve analysis manifested, these models had achieved appropriate net benefit.

CONCLUSION

In the authors' study, the support-vector machine model was the best predictor; it may be an effective tool for predicting atrial fibrillation after cardiac surgery.

Predictive Capability of Metabolic Panels for Postoperative Atrial Fibrillation in Cardiac Surgery Patients

INTRODUCTION

Postoperative atrial fibrillation (POAF) occurs in up to 65% of cardiac surgery patients and is associated with an increased risk for stroke and mortality. Electrolyte disturbances in sodium (Na+), potassium (K+), total calcium (Ca2+), chloride (Cl-), and magnesium (Mg2+) are predisposing factors for POAF, but these imbalances are yet to be used to predict POAF. The purpose of this study is to determine whether the development of POAF can be predicted by blood plasma ionic composition.

METHODS

Metabolic panels of patients with no prior history of atrial fibrillation who did (n = 763) and did not develop POAF (n = 2144) after cardiac surgery were obtained from the Carilion Clinic electronic medical record system. We initially evaluated serum Na+, K+, Ca2+, Cl-, and Mg2+ in the two groups using descriptive statistics via scatter and spaghetti plots and then with predictive modeling via logistic regression and random forest models.

RESULTS

Neither scatter nor spaghetti plots of electrolyte data revealed a significant difference between those who did and did not develop POAF. Two logistic regression models and two random forest models with POAF status as the outcome were generated using the first observation for each electrolyte and the coefficient of the linear regression, which was obtained from a linear fit of the scatter plot. The random forest model using the first observation had a sensitivity of only 12.2%, but all four models had specificities more than 97%.

CONCLUSIONS

Neither of the two logistic regression nor two random forest models were able to effectively predict the development of POAF from plasma ionic concentrations, but the random forest models effectively classified patients who would not develop POAF.

Optimising risk factors for atrial fibrillation post-cardiac surgery

Postoperative atrial fibrillation (POAF) is an ongoing complication following cardiac surgery, with an incidence of 15%-60%. It is associated with substantial mortality and morbidity, as well increased hospital stays and healthcare costs. The pathogenesis is not fully understood, but the literature suggests that POAF occurs when transient, postoperative triggers act on vulnerable atrial tissue produced by preoperative, procedure-induced and postoperative processes such as inflammation, oxidative stress, autonomic dysfunction and electrophysiological remodelling of the atrial tissues. This sets the stage for arrhythmogenic mechanisms, such as ectopic firing secondary to triggered activity and re-entry mechanisms generating POAF. Preoperative factors include advanced age, sex, ethnicity, cardiovascular risk factors, preoperative drugs, electrocardiogram and echocardiogram abnormalities. Procedural factors include: the use of cardiopulmonary bypass and aortic cross clamp, type of cardiac surgery, use of hypothermia, left ventricular venting, bicaval cannulation and exclusion of the left atrial appendage. Postoperative factors include postoperative drugs, electrolyte and fluid balance and infection. This review explores the pathogenesis of POAF and the contribution of these perioperative factors in the development of POAF. Patients can be risk stratified for targeted treatment and prophylaxis, and how these factors can be attenuated to improve POAF outcomes following cardiac surgery.

Importance of left atrial mechanical function as a predictor of atrial fibrillation risk following cardiac surgery

Postoperative atrial fibrillation (POAF) after cardiac surgery is a major health problem that is associated with a significant financial burden and increased early morbidity and mortality. We investigated the accuracy of new echocardiographic derived indices to predict patients at higher risk of developing POAF. 84 consecutive patients (age 57.9 ± 6.9, 32% female) hospitalized for isolated CABG underwent comprehensive echocardiographic evaluation before surgery. Left atrial (LA) function was quantified through the assessment of phasic LA volumes to calculate LATEF. Speckle tracking echocardiography STE was used to measure LA reservoir strain, conduit strain and booster strain. Patients who developed POAF had increased LA volumes and impaired LA functions assessed by both the volumetric phasic changes and STE. By univariable analysis, all LA function parameters significantly predicted POAF. Multivariate regression analysis showed that age (P = 0.03, OR 1.134, 95% CI 1.012-1.271) and LATEF (P = 0.001, OR 0.814, 95% CI 0.725-0.914) were strong independent factors for POAF with LATEF showing the highest predictive accuracy. After multivariable adjustment to include LA strain indices to the base model, LA contractile strain LACtS (23.93 ± 4.19 vs 37.0 ± 3.35, p < 0.001) was the best discriminated for the highest predictive accuracy (OR 0.429, 95% CI 0.26-0.708). The ROC Curve was calculated for the greatest performance for prediction of POAF (AUC LACtS: 0.992; LATEF: 0.899). Adding new left atrial mechanics parameters is a more sensitive, independent tool that provides an incremental predictive value to discriminate patients at more risk for POAF.

Landiolol for managing atrial fibrillation in post-cardiac surgery

Landiolol is an intravenous ultra-short acting beta-blocker which has been used in Japan for many years to prevent and/or to treat post-operative atrial fibrillation following cardiac surgery. The drug is now available in Europe. This article is a systematic review of literature regarding the use of landiolol in that specific surgical setting.