Comparison of health-related quality of life in patients with atrial fibrillation treated with catheter ablation or antiarrhythmic drug therapy: a systematic review and meta-analysis protocol

Correlation between family function and quality of life in patients with atrial fibrillation

OBJECTIVES

Many studies have shown that the quality of life for patients with atrial fibrillation (AF) is significantly impaired, but the impact on family function is still unclear. This study aims to evaluate the family function and quality of life in patients with AF using scales, to analyze the correlation between family function and quality of life, and to predict the influencing factors of quality of life.

METHODS

A total of 223 patients with AF who were admitted to the Department of Cardiology and General Medicine of the Lanzhou University Second Hospital from January 1, 2021 to May 1, 2022, were selected as research subjects, the general information of patients with AF were collected via a questionnaire, the family function and quality of life were assessed by the Family Assessment Device (FAD) and Atrial Fibrillation Effect on Quality-of-Life (AFEQT) scale. The patients were divided into a non-family functional disorder group and a family functional disorder group on the basis of their FAD scores. The above data were analyzed using SPSS 26.0 statistical software.

RESULTS

Among the 223 patients, 64 (28.70%) were in the non-family functional disorder group, and 159 (71.30%) were in the family functional disorder group. The total score of FAD and scores of all dimensions in the family functional disorder group were higher than those in the non-family functional disorder group (all P<0.01). AFEQT total score and symptoms, treatment concerns and daily activities in the non-family functional disorder group were significantly higher than those in the family functional disorder group (all P<0.01). The Pearson linear analysis showed that there was a linear negative correlation between the total score and each dimension of FAD with the total score and each dimension of AFEQT (all P<0.01). The variables with statistical significance in the univariate analysis were included in the multiple linear regression analysis, and the result showed that female, and the problem solving, role, affective involvement, and general functioning dimensions of family function had an impact on the quality of life (all P<0.01).

CONCLUSIONS

Most patients with AF have different degrees of family dysfunction. The quality of life in patients with family functional disorder group is generally low. Female, and the problem solving, role, affective involvement, and general functioning of family function have a significant impact on the quality of life in patients with AF. In clinical treatment of AF, attention should be paid to the family function of patients, and family members can be involved in clinical intervention to improve family function and improve the quality of life.

Improved Procedural Efficiency of Atrial Fibrillation Ablation Using a Dedicated Ablation Protocol and Lean Management

OBJECTIVES

In this study the authors hypothesized that "Lean management" within a dedicated ablation protocol could standardize the pulmonary vein isolation (PVI) procedure and improve quality.

BACKGROUND

There is a large variability in safety, effectiveness, and efficiency of PVI.

METHODS

This was a single-center prospective study with inclusion of all consecutive PVI procedures from 2017 to 2019. A 3-step intervention was introduced based on Lean management: step 1) simplification (CLOSE protocol); step 2) waste elimination (higher power shorter duration); and step 3) improved standardization (Lab Optimization Tool [LOT]). PVI was divided into steps that were tracked (in minutes) using LOT. Parameters were compared in 6-month intervals.

RESULTS

Overall, 295 patients (146 patients with LOT) were analyzed. Step 1 reduced skin-to-skin procedure duration (2017: 119 ± 21 min vs. 2018: 77 ± 15 min; p < 0.001) and variance (from 2018 to 2019 p = 0.024). Step 2 reduced the radiofrequency time (2017: 38 ± 6 min vs. 2018: 20 ± 3 min; p < 0.001) and variance (from 2018 to 2019 p < 0.001). Analysis of step 3 demonstrated that only 53% of the entire procedure length (143 ± 22 min) was used for treatment (skin-to-skin time 77 ± 16 min), the remaining time being devoted for setup (42 ± 12 min, 29%); left atrial access (16 ± 7 min, 12%); respiratory gating, left atrial map, and pseudo-circle annotation (10 ± 6 min, 7%); ablation (39 ± 10 min, 27%); and bilateral block validation (10 ± 8 min, 7%).

CONCLUSIONS

Standardization of PVI using a dedicated ablation protocol and Lean management can help to reduce procedure and radiofrequency ablation duration and variance, and increase procedural efficiency without compromising safety. To improve health care utilization, increased efficiency should become an accepted goal in addition to procedural safety and effectiveness.

Machine Learning Approach to Predict Risk of 90-Day Hospital Readmissions in Patients With Atrial Fibrillation: Implications for Quality Improvement in Healthcare

Background:

Atrial fibrillation (AF) in the elderly population is projected to increase over the next several decades. Catheter ablation shows promise as a treatment option and is becoming increasingly available. We examined 90-day hospital readmission for AF patients undergoing catheter ablation and utilized machine learning methods to explore the risk factors associated with these readmission trends.

Methods:

Data from the 2013 Nationwide Readmissions Database on AF cases were used to predict 90-day readmissions for AF with catheter ablation. Multiple machine learning methods such as k-Nearest Neighbors, Decision Tree, and Support Vector Machine were employed to determine variable importance and build risk prediction models. Accuracy, precision, sensitivity, specificity, and area under the curve were compared for each model.

Results:

The 90-day hospital readmission rate was 17.6%; the average age of the patients was 64.9 years; 62.9% of patients were male. Important variables in predicting 90-day hospital readmissions in patients with AF undergoing catheter ablation included the age of the patient, number of diagnoses on the patient’s record, and the total number of discharges from a hospital. The k-Nearest Neighbor had the best performance with a prediction accuracy of 85%. This was closely followed by Decision Tree, but Support Vector Machine was less ideal.

Conclusions:

Machine learning methods can produce accurate models in predicting hospital readmissions for patients with AF. The likelihood of readmission to the hospital increases as the patient age, total number of hospital discharges, and total number of patient diagnoses increase. Findings from this study can inform quality improvement in healthcare and in achieving patient-centered care.

Using Machine Learning to Predict 30-Day Hospital Readmissions in Patients with Atrial Fibrillation Undergoing Catheter Ablation

Atrial fibrillation (AF) cases are expected to increase over the next several decades, due to the rise in the elderly population. One promising treatment option for AF is catheter ablation, which is increasing in use. We investigated the hospital readmissions data for AF patients undergoing catheter ablation, and used machine learning models to explore the risk factors behind these readmissions. We analyzed data from the 2013 Nationwide Readmissions Database on cases with AF, and determined the relative importance of factors in predicting 30-day readmissions for AF with catheter ablation. Various machine learning methods, such as k-nearest neighbors, decision tree, and support vector machine were utilized to develop predictive models with their accuracy, precision, sensitivity, specificity, and area under the curve computed and compared. We found that the most important variables in predicting 30-day hospital readmissions in patients with AF undergoing catheter ablation were the age of the patient, the total number of discharges from a hospital, and the number of diagnoses on the patient’s record, among others. Out of the methods used, k-nearest neighbor had the highest prediction accuracy of 85%, closely followed by decision tree, while support vector machine was less desirable for these data. Hospital readmissions for AF with catheter ablation can be predicted with relatively high accuracy, utilizing machine learning methods. As patient age, the total number of hospital discharges, and the total number of patient diagnoses increase, the risk of hospital readmissions increases.

Health-Related Quality of Life in Patients With Atrial Fibrillation Treated With Catheter Ablation or Antiarrhythmic Drug Therapy: A Systematic Review and Meta-analysis

Background

Catheter ablation (CA) is performed in patients with atrial fibrillation (AF) to reduce symptoms and improve health-related quality of life (HRQL).

Methods

This systematic review and meta-analysis of randomized controlled trials (RCTs) evaluated CA of any energy modality compared with antiarrhythmic drugs (AADs) using inverse-variance random-effects models. We searched for RCTs reporting HRQL and AF-related symptoms at 3, 6, 12, 24, 48, and 60 months after treatment as well as the number of repeat ablations.

Results

Of 15,878 records, we included 13 RCTs of CA vs AADs for the analyses of HRQL, 7 RCTs for the analyses of AF-related symptoms, and 13 RCTs for the number of repeat ablations. For the HRQL analyses at 3 months, there were significant increases in both the Physical Component Summary score (3 months' standardized mean difference = 0.58 [0.39-0.78]; P < 0.00001, I ² = 6%, 3 trials, n = 443) and the Mental Component Summary score (3 months' standardized mean difference = 0.57 [0.37-0.77]; P < 0.00001, I ² = 0%, 3 trials, n = 443), favouring CA over AADs. These differences were sustained at 12 months but not >24 months after randomization. Similar results were seen for AF-related symptoms. The number of repeat ablations and success rates after procedure varied considerably across trials.

Conclusions

Evidence from few trials suggests that CA improves physical and mental health and AF-related symptoms in the short term, but these benefits decrease with time. More trials, reporting both HRQL and AF-related symptoms, at consistent time points are needed to assess the effectiveness of CA for the treatment of AF.