Atrial Fibrillation and End-Stage COPD

Detrimental Impact of Atrial Fibrillation among Patients Hospitalized for Acute Exacerbation of COPD: Results of a Population-Based Study in Spain from 2016 to 2021

Background/Objectives: To analyze changes in the prevalence of atrial fibrillation (AF) in patients hospitalized for acute exacerbation of chronic obstructive pulmonary disease (AE-COPD); to evaluate hospital outcomes according to AF status, assessing sex differences; to identify factors associated with AF presence; and to analyze variables associated with in-hospital mortality (IHM) in AE-COPD patients with AF. Methods: We used data from the Registry of Specialized Care Activity-Basic Minimum Data Set (RAE-CMBD) to select patients aged ≥40 years with COPD in Spain (2016-2021). We stratified the study population according to AF presence and sex. The propensity score matching (PSM) methodology was employed to create comparable groups based on age, admission year, and comorbidities at the time of hospitalization. Results: We identified 399,196 hospitalizations that met the inclusion criteria. Among them, 20.58% had AF. The prevalence of AF rose from 2016 to 2021 (18.26% to 20.95%), though the increase was only significant in men. The median length of hospital stay (LOHS) and IHM were significantly higher in patients with AF than in those without AF. After PSM, IHM remained significantly higher for man and women with AF. Older age, male sex, and several comorbidities were factors associated with AF. Additionally, older age, male sex, different comorbidities including COVID-19, hospitalization in the year 2020, mechanical ventilation, and intensive care unit (ICU) admission were associated with higher IHM in patients with AE-COPD and AF. Conclusions: AF prevalence was high in patients hospitalized for AE-COPD, was higher in men than in women, and increased over time. AF presence was associated with worse outcomes. The variables associated with IHM in hospitalized AE-COPD patients with AF were older age, male sex, different comorbidities including COVID-19 presence, hospitalization in the year 2020, need of mechanical ventilation, and ICU admission.

A Simple Logistic Regression Model for Predicting the Likelihood of Recurrence of Atrial Fibrillation Within 1 Year After Initial Radio-Frequency Catheter Ablation Therapy

Background

The clinical factors associated with the recurrence of atrial fibrillation (Af) in patients undergoing catheter ablation (CA) are still ambiguous to date.

Purpose

1. To recognize preoperative serologic factors and clinical features associated with Af recurrence after the first ablation treatment. 2. To Develop a Logical Regression Model for Predicting the Likelihood of Recurrence Within 1 Year After the Initial Radio-Frequency Catheter Ablation (RFCA) Therapy.

Methods

Atrial fibrillation patients undergoing RFCA at our institution from January 2016 to June 2021 were included in the analysis (n = 246). A combined dataset of relevant parameters was collected from the participants (clinical characteristics, laboratory results, and time to recurrence) (n = 200). We performed the least absolute shrinkage and selection operator (Lasso) regression with 100 cycles, selecting variables present in all 100 cycles to identify factors associated with the first recurrence of atrial fibrillation. A logistic regression model for predicting whether Af would recur within a year was created using 70% of the data as a training set and the remaining data to validate the accuracy. The predictions were assessed using calibration plots, concordance index (C-index), and decision curve analysis.

Results

The left atrial diameter, albumin, type of Af, whether other arrhythmias were combined, and the duration of Af attack time were associated with Af recurrence in this sample. Some clinically meaningful variables were selected and combined with recognized factors associated with recurrence to construct a logistic regression prediction model for 1-year Af recurrence. The receiver operating characteristic (ROC) curve for this model was 0.8695, and the established prediction model had a C-index of 0.83. The performance was superior to the extreme curve in the decision curve analysis.

Conclusion

Our study demonstrates that several clinical features and serological markers can predict the recurrence of Af in patients undergoing RFCA. This simple model can play a crucial role in guiding physicians in preoperative evaluation and clinical decision-making.