Simple hematological predictors of AF recurrence in patients undergoing atrial fibrillation ablation

Factors predicting the recurrence of atrial fibrillation after catheter ablation: A review

Atrial fibrillation (AF) is the most common and clinically significant type of cardiac arrhythmia. Although catheter ablation (CA) can restore sinus rhythm in patients with AF, some patients experience recurrence after the procedure. This requires us to find a simple and effective way to identify patients at a high risk of recurrence and to intervene early in the high-risk population to improve patient prognosis. The mechanism of AF recurrence is unclear, but it involves several aspects including patient history, inflammation, myocardial fibrosis, and genes. This article summarizes the current predictors of AF recurrence after CA, including myocardial fibrosis markers, inflammatory markers, MicroRNAs, Circular RNAs, AF recurrence scores, and imaging indicators. Each predictor has its own scope of application, and the predictive capacity and joint application of multiple predictors may improve the predictive power. In addition, we summarize the mechanisms involved in AF recurrence. We hope that this review will assist researchers understand the current predictors of AF recurrence and help them conduct further related studies.

Association of RDW, NLR, and PLR with Atrial Fibrillation in Critical Care Patients: A Retrospective Study Based on Propensity Score Matching

Objective

Previous studies have shown inconsistent results in relation to the red cell distribution width (RDW), neutrophil to lymphocyte ratio (NLR), and platelet to lymphocyte ratio (PLR) of atrial fibrillation (AF). This retrospective study is aimed at detecting the association of RDW, NLR, and PLR with AF.

Methods

A total of 4717 critical care patients were screened from the Medical Information Mart for Intensive Care- (MIMIC-) III database. The patients were separated into the non-AF and AF groups. The imbalances between the groups were reduced using propensity score matching (PSM). ROC curves were generated to detect the diagnostic value of RDW, NLR, and PLR. Logistic regression analysis was used to detect the risk factors for AF.

Results

A total of 991 non-AF patients paired with 991 AF patients were included after PSM in this study. The RDW level in the AF group was significantly higher than that in the non-AF group (15.09 ± 1.93vs. 14.89 ± 1.91, P = 0.017). Neither NLR nor PLR showed any significant difference between the two groups (P > 0.05 for each). According to ROC curve, RDW showed a very low diagnostic value of AF (AUC = 0.5341), and the best cutoff of RDW was 14.1 (ACU = 0.5257, sensitivity = 0.658, specificity = 0.395). Logistic regression analysis showed that an elevated RDW level increased 1.308-fold (95%CI = 1.077-1.588, P = 0.007) risk of AF. Neither elevated NLR nor elevated PLR was a significant risk factor for AF (OR = 0.993, 95%CI = 0.802-1.228, P = 0.945 for NLR; OR = 0.945, 95%CI = 0.763-1.170, P = 0.603 for PLR).

Conclusions

Elevated RDW level but not NLR or PLR levels is associated with AF. RDW > 14.1 is a risk factor for AF, but its diagnostic capacity for AF is not of great value.

Predictive Value of HFA-PEFF Score in Patients With Heart Failure With Preserved Ejection Fraction

Aims: HFA-PEFF score has been proposed for diagnosing heart failure with preserved ejection fraction (HFpEF). Currently, there are only a limited number of tools for predicting the prognosis. In this study, we evaluated whether the HFA-PEFF score can predict mortality in patients with HFpEF.

Methods: This single-center, retrospective observational study enrolled patients diagnosed with HFpEF at the First Affiliated Hospital of Dalian Medical University between January 1, 2015, and April 30, 2018. The subjects were divided according to their HFA-PEFF score into low (0–2 points), intermediate (3–4 points), and high (5–6 points) score groups. The primary outcome was all-cause mortality.

Results: A total of 358 patients (mean age: 70.21 ± 8.64 years, 58.1% female) were included. Of these, 63 (17.6%), 156 (43.6%), and 139 (38.8%) were classified into the low, intermediate, and high score groups, respectively. Over a mean follow-up of 26.9 months, 46 patients (12.8%) died. The percentage of patients who died in the low, intermediate, and high score groups were 1 (1.6%), 18 (11.5%), and 27 (19.4%), respectively. A multivariate Cox regression identified HFA-PEFF score as an independent predictor of all-cause mortality [hazard ratio (HR):1.314, 95% CI: 1.013–1.705, P = 0.039]. A Cox analysis demonstrated a significantly higher rate of mortality in the intermediate (HR: 4.912, 95% CI 1.154–20.907, P = 0.031) and high score groups (HR: 5.291, 95% CI: 1.239–22.593, P = 0.024) than the low score group. A receiver operating characteristic (ROC) analysis indicated that the HFA-PEFF score can effectively predict all-cause mortality after adjusting for age and New York Heart Association (NYHA) class [area under the curve (AUC) 0.726, 95% CI 0.651–0.800, P = 0.000]. With an HFA-PEFF score cut-off value of 3.5, the sensitivity and specificity were 78.3 and 54.8%, respectively. The AUC on ROC analysis for the biomarker component of the score was similar to that of the total score.

Conclusions: The HFA-PEFF score can be used both to diagnose HFpEF and predict the prognosis. The higher scores are associated with higher all-cause mortality.