Atrial fibrillation symptom clusters and associated clinical characteristics and outcomes: A cross-sectional secondary data analysis

[Cluster Analysis and Ablation Success Rate in Atrial Fibrillation Patients Undergoing Catheter Ablation]

Objective

Atrial fibrillation (AF) is a disease of high heterogeneity, and the association between AF phenotypes and the outcome of different catheter ablation strategies remains unclear. Conventional classification of AF (e.g. according to duration, atrial size, and thromboembolism risk) fails to provide reference for the optimal stratification of the prognostic risks or to guide individualized treatment plan. In recent years, research on machine learning has found that cluster analysis, an unsupervised data-driven approach, can uncover the intrinsic structure of data and identify clusters of patients with pathophysiological similarity. It has been demonstrated that cluster analysis helps improve the characterization of AF phenotypes and provide valuable prognostic information. In our cohort of AF inpatients undergoing radiofrequency catheter ablation, we used unsupervised cluster analysis to identify patient subgroups, to compare them with previous studies, and to evaluate their association with different suitable ablation patterns and outcomes.

Methods

The participants were AF patients undergoing radiofrequency catheter ablation at West China Hospital between October 2015 and December 2017. All participants were aged 18 years or older. They underwent radiofrequency catheter ablation during their hospitalization. They completed the follow-up process under explicit informed consent. Patients with AF of a reversible cause, severe mitral stenosis or prosthetic heart valve, congenital heart disease, new-onset acute coronary syndrome within three months prior to the surgery, or a life expectancy less than 12 months were excluded according to the exclusion criteria. The cohort consisted of 1102 participants with paroxysmal or persistent/long-standing persistent AF. Data on 59 variables representing demographics, AF type, comorbidities, therapeutic history, vital signs, electrocardiographic and echocardiographic findings, and laboratory findings were collected. Overall, data for the variables were rarely missing (<5%), and multiple imputation was used for correction of missing data. Follow-up surveys were conducted through outpatient clinic visits or by telephone. Patients were scheduled for follow-up with 12-lead resting electrocardiography and 24-hours Holter monitoring at 3 months and 6 months after the ablation procedure. Early ablation success was defined as the absence of documented AF, atrial flutter, or atrial tachycardia >30 seconds at 6-month follow-up. Hierarchical clustering was performed on the 59 baseline variables. All characteristic variables were standardized to have a mean of zero and a standard deviation of one. Initially, each patient was regarded as a separate cluster, and the distance between these clusters was calculated. Then, the Ward minimum variance method of clustering was used to merge the pair of clusters with the minimum total variance. This process continued until all patients formed one whole cluster. The "NbClust" package in R software, capable of calculating various statistical indices, including pseudo t2 index, cubic clustering criterion, silhouette index etc, was applied to determine the optimal number of clusters. The most frequently chosen number of clusters by these indices was selected. A heatmap was generated to illustrate the clinical features of clusters, while a tree diagram was used to depict the clustering process and the heterogeneity among clusters. Ablation strategies were compared within each cluster regarding ablation efficacy.

Results

Five statistically driven clusters were identified: 1) the younger age cluster (n=404), characterized by the lowest prevalence of cardiovascular and cerebrovascular comorbidities but the highest prevalence of obstructive sleep apnea syndrome (14.4%); 2) a cluster of elderly adults with chronic diseases (n=438), the largest cluster, showing relatively higher rates of hypertension, diabetes, stroke, and chronic obstructive pulmonary disease; 3) a cluster with high prevalence of sinus node dysfunction (n=160), with patients showing the highest prevalence of sick sinus syndrome and pacemaker implantation; 4) the heart failure cluster (n=80), with the highest prevalence of heart failure (58.8%) and persistent/long-standing persistent AF (73.7%); 5) prior coronary artery revascularization cluster (n=20), with patients of the most advanced age (median: 69.0 years old) and predominantly male patients, all of whom had prior myocardial infarction and coronary artery revascularization. Patients in cluster 2 achieved higher early ablation success with pulmonary veins isolation alone compared to extensive ablation strategies (79.6% vs. 66.5%; odds ratio [OR]=1.97, 95% confidence interval [CI]: 1.28-3.03). Although extensive ablation strategies had a slightly higher success rate in the heart failure group, the difference was not statistically significant.

Conclusions

This study provided a unique classification of AF patients undergoing catheter ablation by cluster analysis. Age, chronic disease, sinus node dysfunction, heart failure and history of coronary artery revascularization contributed to the formation of the five clinically relevant subtypes. These subtypes showed differences in ablation success rates, highlighting the potential of cluster analysis in guiding individualized risk stratification and treatment decisions for AF patients.

Kidney Function, Albuminuria, and Risk of Incident Atrial Fibrillation: A Systematic Review and Meta-Analysis

RATIONALE & OBJECTIVE

Atrial fibrillation (AF) and chronic kidney disease (CKD) often coexist. However, it is not known whether CKD is an independent risk factor for incident AF. Therefore, we evaluated the association between markers of CKD-estimated glomerular filtration rate (eGFR) and albuminuria-and incident AF.

STUDY DESIGN

Systematic review and meta-analysis of cohort studies and randomized controlled trials.

SETTING & STUDY POPULATIONS

Participants with measurement of eGFR and/or albuminuria who were not receiving dialysis.

SELECTION CRITERIA FOR STUDIES

Cohort studies and randomized controlled trials were included that reported incident AF risk in adults according to eGFR and/or albuminuria.

ANALYTICAL APPROACH

Age- or multivariate-adjusted risk ratios (RRs) for incident AF were extracted from cohort studies, and RRs for each trial were derived from event data. RRs for incident AF were pooled using random-effects models.

RESULTS

38 studies involving 28,470,249 participants with 530,041 incident AF cases were included. Adjusted risk of incident AF was greater among participants with lower eGFR than those with higher eGFR (eGFR<60 vs≥60mL/min/1.73m2: RR, 1.43; 95% CI, 1.30-1.57; and eGFR<90 vs≥90mL/min/1.73m2: RR, 1.42; 95% CI, 1.26-1.60). Adjusted incident AF risk was greater among participants with albuminuria (any albuminuria vs no albuminuria: RR, 1.43; 95% CI, 1.25-1.63; and moderately to severely increased albuminuria vs normal to mildly increased albuminuria: RR, 1.64; 95% CI, 1.31-2.06). Subgroup analyses showed an exposure-dependent association between CKD and incident AF, with the risk increasing progressively at lower eGFR and higher albuminuria categories.

LIMITATIONS

Lack of patient-level data, interaction between eGFR and albuminuria could not be evaluated, possible ascertainment bias due to variation in the methods of AF detection.

CONCLUSIONS

Lower eGFR and greater albuminuria were independently associated with increased risk of incident AF. CKD should be regarded as an independent risk factor for incident AF.

PLAIN-LANGUAGE SUMMARY

Irregular heartbeat, or atrial fibrillation (AF), is the commonest abnormal heart rhythm. AF occurs commonly in people with chronic kidney disease (CKD), and CKD is also common in people with AF. However, CKD in not widely recognized as a risk factor for new-onset or incident AF. In this research, we combined data on more than 28 million participants in 38 studies to determine whether CKD itself increases the chances of incident AF. We found that both commonly used markers of kidney disease (estimated glomerular filtration rate and albuminuria, ie, protein in the urine) were independently associated with a greater risk of incident AF. This finding suggests that CKD should be recognized as an independent risk factor for incident AF.

Managing With Atrial Fibrillation: An Exploratory Model-Based Cluster Analysis of Clinical and Personal Patient Characteristics

Background

Examining characteristics of patients with atrial fibrillation (AF) has the potential to help in identifying groups of patients who might benefit from different management approaches.

Methods

Secondary analysis of online survey data was combined with clinic referral data abstraction from 196 patients with AF attending an AF specialty clinic. Cluster analyses were performed to identify distinct, homogeneous clusters of AF patients defined by 11 relevant variables: CHA2DS2-VASc score, age, AF symptoms, overall health, mental health, AF knowledge, perceived stress, household and recreation activity, overall AF quality of life, and AF symptom treatment satisfaction. Follow-up analyses examined differences between the cluster groups in additional clinical variables.

Results

Evidence emerged for both 2- and 4-cluster solutions. The 2-cluster solution involved a contrast between patients who were doing well on all variables (n = 129; 66%) vs those doing less well (n = 67; 34%). The 4-cluster solution provided a closer-up view of the data, showing that the group doing less well was split into 3 meaningfully different subgroups of patients who were managing in different ways. The final 4 clusters produced were as follows: (i) doing well; (ii) stressed and discontented; (iii) struggling and dissatisfied; and (iv) satisfied and complacent.

Conclusions

Patients with AF can be accurately classified into distinct, natural groupings that vary in clinically important ways. Among the patients who were not managing well with AF, we found 3 distinct subgroups of patients who may benefit from tailored approaches to AF management and support. The tailoring of treatment approaches to specific personal and/or behavioural patterns, alongside clinical patterns, holds potential to improve patient outcomes (eg, treatment satisfaction).

Characterising symptom clusters in patients with atrial fibrillation undergoing catheter ablation

OBJECTIVE

This study aims to leverage natural language processing (NLP) and machine learning clustering analyses to (1) identify co-occurring symptoms of patients undergoing catheter ablation for atrial fibrillation (AF) and (2) describe clinical and sociodemographic correlates of symptom clusters.

METHODS

We conducted a cross-sectional retrospective analysis using electronic health records data. Adults who underwent AF ablation between 2010 and 2020 were included. Demographic, comorbidity and medication information was extracted using structured queries. Ten AF symptoms were extracted from unstructured clinical notes (n=13 416) using a validated NLP pipeline (F-score=0.81). We used the unsupervised machine learning approach known as Ward's hierarchical agglomerative clustering to characterise and identify subgroups of patients representing different clusters. Fisher's exact tests were used to investigate subgroup differences based on age, gender, race and heart failure (HF) status.

RESULTS

A total of 1293 patients were included in our analysis (mean age 65.5 years, 35.2% female, 58% white). The most frequently documented symptoms were dyspnoea (64%), oedema (62%) and palpitations (57%). We identified six symptom clusters: generally symptomatic, dyspnoea and oedema, chest pain, anxiety, fatigue and palpitations, and asymptomatic (reference). The asymptomatic cluster had a significantly higher prevalence of male, white and comorbid HF patients.

CONCLUSIONS

We applied NLP and machine learning to a large dataset to identify symptom clusters, which may signify latent biological underpinnings of symptom experiences and generate implications for clinical care. AF patients' symptom experiences vary widely. Given prior work showing that AF symptoms predict adverse outcomes, future work should investigate associations between symptom clusters and postablation outcomes.

Symptom Clusters, Psychological Distress, and Quality of Life in Patients with Atrial Fibrillation

Background: Patients with atrial fibrillation (AF) experience diverse symptoms such as palpitations, dizziness, and fainting that lead to depression, anxiety, and poor quality of life. Management of symptoms is fundamental for AF, and with the increasing prevalence of AF, studies on management of symptoms in patients with AF are needed. Objectives: This study aimed to assess symptom clusters according to symptom severity in patients with atrial fibrillation and evaluate the relationships between symptom cluster groups and the psychological distress and quality of life of these patients. Design: A descriptive survey was used in this study. Methods: A total of 175 patients were included in this study. Data regarding symptoms, psychological distress, and quality of life were obtained using structured questionnaires and analyzed using frequency and percentage, mean and standard deviation, cluster analysis, t-testing, Chi-square testing, Pearson's correlation coefficient, and multiple regression analysis. The Euclidean distance square of the hierarchical cluster was used to form symptom cluster groups. Results: Two groups of symptom clusters were formed based on the seven most common symptoms (i.e., chest palpitations, fatigue/tiredness, dizziness, lack of energy, pulse skipping, insomnia, and heavy breathing) of atrial fibrillation patients. Psychological distress and quality of life showed significant correlations with the symptom cluster groups (p < 0.001). Conclusion: Symptoms of atrial fibrillation increased patients' depression and anxiety, and further affected their quality of life. Therefore, management of symptoms is critical to maintaining a high quality of life. Nursing interventions based on the characteristics of symptom cluster groups must be developed and attempted.

The identification of clusters of risk factors and their association with hospitalizations or emergency department visits in home health care

AIMS

To identify clusters of risk factors in home health care and determine if the clusters are associated with hospitalizations or emergency department visits.

DESIGN

A retrospective cohort study.

METHODS

This study included 61,454 patients pertaining to 79,079 episodes receiving home health care between 2015 and 2017 from one of the largest home health care organizations in the United States. Potential risk factors were extracted from structured data and unstructured clinical notes analysed by natural language processing. A K-means cluster analysis was conducted. Kaplan-Meier analysis was conducted to identify the association between clusters and hospitalizations or emergency department visits during home health care.

RESULTS

A total of 11.6% of home health episodes resulted in hospitalizations or emergency department visits. Risk factors formed three clusters. Cluster 1 is characterized by a combination of risk factors related to "impaired physical comfort with pain," defined as situations where patients may experience increased pain. Cluster 2 is characterized by "high comorbidity burden" defined as multiple comorbidities or other risks for hospitalization (e.g., prior falls). Cluster 3 is characterized by "impaired cognitive/psychological and skin integrity" including dementia or skin ulcer. Compared to Cluster 1, the risk of hospitalizations or emergency department visits increased by 1.95 times for Cluster 2 and by 2.12 times for Cluster 3 (all p < .001).

CONCLUSION

Risk factors were clustered into three types describing distinct characteristics for hospitalizations or emergency department visits. Different combinations of risk factors affected the likelihood of these negative outcomes.

IMPACT

Cluster-based risk prediction models could be integrated into early warning systems to identify patients at risk for hospitalizations or emergency department visits leading to more timely, patient-centred care, ultimately preventing these events.

PATIENT OR PUBLIC CONTRIBUTION

There was no involvement of patients in developing the research question, determining the outcome measures, or implementing the study.

Social and psychological risk factors for decreased adherence among patients with atrial fibrillation

Adherence has a direct impact on reducing the effectiveness of atrial fibrillation therapy and increasing the risk of thromboembolic events. Among the factors involved in the decrease of adherence, the social and psychological characteristics of patients remain insufficiently studied. At the same time, the available publications allow us to conclude that there are markers of the risk of reduced adherence in patients with atrial fibrillation, which include age, cognitive impairment, psychoemotional disorders (including depression and anxiety) and specific behavioral patterns.

State of the Science: The Relevance of Symptoms in Cardiovascular Disease and Research: A Scientific Statement From the American Heart Association

Symptoms of cardiovascular disease drive health care use and are a major contributor to quality of life. Symptoms are of fundamental significance not only to the diagnosis of cardiovascular disease and appraisal of response to medical therapy but also directly to patients' daily lives. The primary purpose of this scientific statement is to present the state of the science and relevance of symptoms associated with cardiovascular disease. Symptoms as patient-reported outcomes are reviewed in terms of the genesis, manifestation, and similarities or differences between diagnoses. Specifically, symptoms associated with acute coronary syndrome, heart failure, valvular disorders, stroke, rhythm disorders, and peripheral vascular disease are reviewed. Secondary aims include (1) describing symptom measurement methods in research and application in clinical practice and (2) describing the importance of cardiovascular disease symptoms in terms of clinical events and other patient-reported outcomes as applicable.

Hypertrophic cardiomyopathies requiring more monitoring for less atrial fibrillation-related complications: a clustering analysis based on the French registry on hypertrophic cardiomyopathy (REMY)

AIMS

Defining the risk of atrial fibrillation (AF) in hypertrophic cardiomyopathy (HCM) patients is an important clinical and prognostic challenge. The aim of this study is to determine HCM phenogroups with different risk of AF occurrence at 5 years.

METHODS AND RESULTS

We applied retrospectively the Bayesian method, which can analyze a large number of variables, to differentiate phenogroups of patients with different risks of AF and prognoses across a French prospective on-going hospital-based registry of adult HCM patients (REMY). Clinical and imaging data were prospectively recorded, and patients were followed for 5 years. A total of 1431 HCM patients were recruited, including 1275 analyzed in the present study after exclusion criteria. The population included 412 women, 369 patients with obstructive HCM, and 252 implanted with an ICD. AF occurred in 167 (11.6%) patients during the 5 year follow-up. Three phenogroups were defined according to their common clinical and echocardiographic characteristics. Patients at the highest risk were oldest, more often female, with more frequent comorbidities, anteroposterior diameter of the left atrium was significantly greater, with diastolic dysfunction, outflow-tract obstruction, and mitral valve abnormality, and presented higher pulmonary artery pressure and/or right-ventricular dysfunction. These also had a higher risk of all-cause hospitalizations and death.

CONCLUSION

Based on a clustering analysis, three phenogroups of HCM according to the risk of AF occurrence can be identified. It can indicate which patients should be more monitored and/or treated, particular to prevent the risk of stroke.

Machine learning for subtype definition and risk prediction in heart failure, acute coronary syndromes and atrial fibrillation: systematic review of validity and clinical utility

BACKGROUND

Machine learning (ML) is increasingly used in research for subtype definition and risk prediction, particularly in cardiovascular diseases. No existing ML models are routinely used for cardiovascular disease management, and their phase of clinical utility is unknown, partly due to a lack of clear criteria. We evaluated ML for subtype definition and risk prediction in heart failure (HF), acute coronary syndromes (ACS) and atrial fibrillation (AF).

METHODS

For ML studies of subtype definition and risk prediction, we conducted a systematic review in HF, ACS and AF, using PubMed, MEDLINE and Web of Science from January 2000 until December 2019. By adapting published criteria for diagnostic and prognostic studies, we developed a seven-domain, ML-specific checklist.

RESULTS

Of 5918 studies identified, 97 were included. Across studies for subtype definition (n = 40) and risk prediction (n = 57), there was variation in data source, population size (median 606 and median 6769), clinical setting (outpatient, inpatient, different departments), number of covariates (median 19 and median 48) and ML methods. All studies were single disease, most were North American (n = 61/97) and only 14 studies combined definition and risk prediction. Subtype definition and risk prediction studies respectively had limitations in development (e.g. 15.0% and 78.9% of studies related to patient benefit; 15.0% and 15.8% had low patient selection bias), validation (12.5% and 5.3% externally validated) and impact (32.5% and 91.2% improved outcome prediction; no effectiveness or cost-effectiveness evaluations).

CONCLUSIONS

Studies of ML in HF, ACS and AF are limited by number and type of included covariates, ML methods, population size, country, clinical setting and focus on single diseases, not overlap or multimorbidity. Clinical utility and implementation rely on improvements in development, validation and impact, facilitated by simple checklists. We provide clear steps prior to safe implementation of machine learning in clinical practice for cardiovascular diseases and other disease areas.

Atrial Fibrillation Symptom Perception

Atrial fibrillation (AF), the most common cardiac arrhythmia, is associated with a significantly increased risk of ischemic stroke, heart failure, and death. AF is a heterogenous disease both in terms of the pathophysiologic mechanisms that lead to the disease, and in terms of symptom presentation. Although most patients with AF perceive symptoms, their symptom experience is highly variable. The purpose of this paper is to review the: 1) epidemiology and pathophysiology of AF, 2) symptoms associated with AF, and 3) implications for clinical practice based on disparate symptom perception.