Clinical Phenotype Classification of Atrial Fibrillation Patients Using Cluster Analysis and Associations with Trial-Adjudicated Outcomes

Comorbidity patterns and mortality in atrial fibrillation: a latent class analysis of the EURopean study of Older Subjects with Atrial Fibrillation (EUROSAF)

BACKGROUND

Most older patients with atrial fibrillation (AF) have comorbidities. However, it is unclear whether specific comorbidity patterns are associated with adverse outcomes. We identified comorbidity patterns and their association with mortality in multimorbid older AF patients with different multidimensional frailty.

METHODS

Hospitalised adults aged ≥65 years with non-valvular AF were followed for 12 months in the multicentre EURopean study of Older Subjects with Atrial Fibrillation (EUROSAF). Demographic characteristics, coexisting medical conditions, use of medications including anticoagulants, and the Multidimensional Prognostic Index (MPI) were captured on discharge. We used latent class analysis (LCA) to identify comorbidity phenotypes and Cox regression to determine associations between identified phenotypes and 12-month mortality.

RESULTS

Amongst n = 2,019 AF patients (mean ± SD age 82.9 ± 7.5 years), a 3-class LCA solution was considered optimal for phenotyping. The model identified phenotype 1 (hypertensive, other circulatory conditions, metabolic diseases; 33%), phenotype 2 (digestive diseases, infection, injury, non-specific clinical and laboratory abnormalities; 26%), and phenotype 3 (heart failure, respiratory diseases; 41%). Overall, 512 patients (25%) died within 12 months. Compared to phenotype 1, after adjusting for age, sex, use of anticoagulants, cardiovascular medications, and proton pump inhibitors, and individual MPI domains, phenotype 3 had a significantly higher risk of mortality (adjusted hazard ratio = 1.27, 95% CI = 1.01 to 1.60). In contrast, the risk of mortality in phenotype 2 was not different to phenotype 1.

CONCLUSION

We observed an association between comorbidity phenotypes identified using LCA and mortality in older AF patients. Further research is warranted to identify the mechanisms underpinning such associations.

Clinical phenotypes of severe atrial cardiomyopathy and their outcome: A cluster analysis

Background

Atrial cardiomyopathy (AtCM) encompasses patients with diverse demographics and comorbidities. This study aimed to identify phenotype groups with similar clinical characteristics, compare their mortality and atrial fibrillation (AF) event rates, and assess predictors of mortality.

Methods and Results

We performed a hierarchical cluster analysis using Ward's Method, based on 11 clinical variables. Among 724 consecutive patients with a dilated left atrium (LA), only 196 met the criterion for severe AtCM- defined as a dilated LA with a volume index ≥ 50 ml/m2. We identified 4 clusters: Cluster 1 -younger overweight patients with paroxysmal AF; Cluster 2 -older patients with heart failure (HF) and low BMI; Cluster 3 - diabetic patients with HF; and Cluster 4 - older patients with tachycardia-bradycardia syndrome and implanted pacemakers. Over a median follow-up of 20.6 months, Cluster 2 had the highest mortality rate (29.1 %), followed by Cluster 3 (20.6 %), compared to Clusters 1 and 4 (11.4 % and 10.8 %, respectively, p = 0.045). For AF events, Cluster 1 had the highest incidence (37 %), followed by Cluster 3 (35 %), Cluster 2 (24 %), and Cluster 4 (19 %, p = 0.309). Heart failure (HR 4.4, CI 1.5-12.7, p = 0.006), cancer (HR 3.3, CI 1.6-6.9, p = 0.002), and severe tricuspid regurgitation (HR 5.4, CI 2.6-11.3, p < 0.001) were predictors of poor outcomes.

Conclusion

In severe AtCM patients, four clusters were identified, each with unique comorbidities and mortality rates but similar AF event rates. Clinical and echocardiographic factors were linked to higher mortality risk.

Efficacy of the ABC Pathway for Integrated Care Across Phenotypes of Patients with Atrial Fibrillation: A Latent-Class Analysis Report from the mAFA-II Clinical Trial

BACKGROUND

The mAFA-II cluster randomised trial demonstrated the efficacy of a mobile health-technology implemented 'Atrial fibrillation Better Care' (ABC) pathway (mAFA intervention) for integrated care management of patients with AF.

OBJECTIVE

To evaluate the effect of mAFA intervention across phenotypes of patients with AF.

DESIGN

We conducted a latent-class analysis (LCA) according to eight variables, including age and comorbidities.

PARTICIPANTS

The mAFA-II trial enrolled AF patients between June 2018 and August 2019 across 40 centres in China.

MAIN MEASURES

We evaluated the interaction between the groups identified through LCA, and the effect of mAFA intervention on the risk of the primary composite outcome of all-cause death, stroke/thromboembolism, and rehospitalisations. Results were expressed as adjusted hazard ratio (aHR) and 95% confidence intervals (95% CI).

KEY RESULTS

Across the 3324 patients included in the trial (mean age 68.5 ± 13.9 years, 38.0% females), we identified three phenotypes: (i) low morbidity phenotype (n = 1234, 37.1%), (ii) hypertensive/coronary artery disease (CAD) phenotype (n = 1534, 46.2%), and (iii) mixed morbidity phenotype (n = 556, 16.7%). The effect of mAFA intervention on the primary outcome appeared greater in the low morbidity phenotype (aHR, 0.08; 95% CI 0.02-0.33) compared to the hypertensive/CAD (aHR, 0.30; 95% CI 0.16-0.58) and the mixed morbidity phenotype (aHR, 0.68; 95% CI 0.37-1.24), with a statistically significant interaction (pint = 0.004).

CONCLUSIONS

In patients with AF, the ABC pathway improved prognosis across different comorbidity phenotypes, although with some differences in the magnitude of risk reduction. Patients with more complex phenotypes require further efforts to improve their outcomes, considering their high baseline risk of adverse events.

TRIAL REGISTRATION

WHO International Clinical Trials Registry Platform (ICTRP) Registration number: ChiCTR-OOC-17014138.

Latent Class Analysis for the Identification of Phenotypes Associated with Increased Risk in Atrial Fibrillation Patients: The COOL-AF Registry

Patients with atrial fibrillation (AF) often have clinical complexity phenotypes. Latent class analysis (LCA) is based on the concept of modeling of both observed and unobserved (latent) variables. We hypothesized that LCA can help in identification of AF patient groups with different risk profiles and identify patients who benefit most from the Atrial fibrillation Better Care (ABC) pathway.We studied non-valvular AF patients in the prospective multicenter COOL-AF registry. The outcomes were all-cause death, ischemic stroke/systemic embolism (SSE), major bleeding, and heart failure. Components of CHA2DS2-VASc score, HAS-BLED score, and ABC pathway were recorded.A total of 3,405 patients were studied. We identified 3 LCA groups from 42 variables: LCA class 1 (n = 1,238), LCA class 2 (n = 1,790), and LCA class 3 (n = 377). Overall, the incidence rates of composite outcomes, death, SSE, major bleeding, and heart failure were 8.69, 4.21, 1.51, 2.27, and 2.84 per 100 person-years, respectively. When compared to LCA class 1, hazard ratios (HR) of composite outcome of LCA classes 3 and 2 were 3.86 (3.06-4.86) and 2.31 (1.91-2.79), respectively. ABC pathway compliance was associated with better outcomes in LCA classes 2 and 3 with the HR of 0.63 (0.51-0.76) and 0.57 (0.39-0.84), but not in LCA class 1.LCA can identify patients who are at risk of developing adverse clinical outcomes. The implementation of holistic management based on the ABC pathway was associated with a reduction in the composite outcomes as well as the individual outcomes.

Evaluation of the ABC pathway in patients with atrial fibrillation: A machine learning cluster analysis

Background

Atrial fibrillation Better Care (ABC) pathway is recommended by guidelines on atrial fibrillation (AF) and exerts a protective role against adverse outcomes of AF patients. But the possible differences in its effectiveness across the diverse range of patients in China have not been systematically evaluated. We aim to comprehensively evaluate multiple clinical characteristics of patients, and probe clusters of ABC criteria efficacy in patients with AF.

Methods

We used data from an observational cohort that included 2,016 patients with AF. We utilized 45 baseline variables for cluster analysis. We evaluated the management patterns and adverse outcomes of identified phenotypes. We assessed the effectiveness of adherence to the ABC criteria at reducing adverse outcomes of phenotypes.

Results

Cluster analysis identified AF patients into three distinct groups. The clusters include Cluster 1: old patients with the highest prevalence rates of atherosclerotic and/or other comorbidities (n = 964), Cluster 2: valve-comorbidities AF in young females (n = 407), and Cluster 3: low comorbidity patients with paroxysmal AF (n = 644). The clusters showed significant differences in MACNE, all-cause death, stroke, and cardiovascular death. All clusters showed that full adherence to the ABC pathway was associated with a significant reduction in the risk of MACNE (all P < 0.05). For three clusters, adherence to the different 'A'/'B'/'C' criterion alone showed differential clinic impact.

Conclusion

Our study suggested specific optimization strategies of risk stratification and integrated management for different groups of AF patients considering multiple clinical, genetic and socioeconomic factors.

Outcomes in Atrial Fibrillation Patients with Different Clinical Phenotypes: Insights from the French Population

Background: Atrial fibrillation (AF) patients represent a clinically complex, heterogeneous population comprising multiple homogeneous cohorts. Purpose: We aimed to identify the common clinical phenotypes of AF patients and compare clinical outcomes between these subgroups. Methods: A 1% representative sample of all AF patients hospitalized between 2010 and 2019 was identified from the French national database. Agglomerative hierarchical cluster analysis was performed using Ward's method and squared Euclidian distance to derive the clusters of patients. Cox regression analyses were used to evaluate outcomes including all-cause death, cardiovascular death, non-cardiovascular death, ischemic stroke, hospitalization for heart failure (HF) and composite of ventricular tachycardia, ventricular fibrillation and cardiac arrest (VT/VF/CA) over a mean follow-up period of 2.0 ± 2.3 years. Results: Four clusters were generated from the 12,688 patients included. Cluster 1 (n = 2375) was younger, low cardiovascular disease (CVD)-risk group with a high cancer prevalence. Clusters 2 (n = 6441) and 3 (n = 1639) depicted moderate-risk groups for CVD. Cluster 3 also had the highest degree of frailty and lung disease while Cluster 4 (n = 2233) represented a high-risk cohort for CVD. After adjusting for confounders, with cluster 1 as the reference, cluster 3 had the highest risk of all-cause death, HR 1.24 (1.09-1.41), ARD (10.3%), cardiovascular death, HR 1.56 (1.19-2.06), ARD (3.3%), non-cardiovascular death, HR 1.20 (1.04-1.38), ARD (6.9%), hospitalization for HF, HR 2.07 (1.71-2.50), ARD (9.1%) and VT/VF/CA, HR 1.74 (1.20-2.53), (ARD 1.3%). Conclusions: Four distinct clusters of AF patients were identified, discriminated by the differential presence of comorbidities. Our findings suggest that hospitalized AF patients with moderate CVD risk may have a poorer prognosis compared to hospitalized AF patients with high CVD risk in the presence of lung pathology and frailty. This subgroup of patients may require more stringent management of existing comorbidities such as chronic obstructive pulmonary disease and sleep apnea, alongside their AF.

Phenotypes of atrial fibrillation in a Taiwanese longitudinal cohort: Insights from an Asian perspective

Background

Atrial fibrillation (AF) is a condition with heterogeneous underlying causes, often involving multiple cardiovascular comorbidities. Large-scale studies examining the heterogeneity of patients with AF in the Asian population are limited.

Objectives

The purpose of this study was to identify distinct phenotypic clusters of patients with AF and evaluate their associated risks of ischemic stroke, heart failure hospitalization, cardiovascular mortality, and all-cause mortality.

Methods

We analyzed 5002 adult patients with AF from the National Taiwan University Hospital between 2014 and 2019 using an unsupervised hierarchical cluster analysis based on the CHA2DS2-VASc score.

Results

We identified 4 distinct groups of patients with AF: cluster I included diabetic patients with heart failure preserved ejection fraction as well as chronic kidney disease (CKD); cluster II comprised older patients with low body mass index and pulmonary hypertension; cluster III consisted of patients with metabolic syndrome and atherosclerotic disease; and cluster IV comprised patients with left heart dysfunction, including reduced ejection fraction. Differences in the risk of ischemic stroke across clusters (clusters I, II, and III vs cluster IV) were statistically significant (hazard ratio [HR] 1.87, 95% confidence interval [CI] 1.00-3.48; HR 2.06, 95% CI 1.06-4.01; and HR 1.70, 95% CI 1.02-2.01). Cluster II was independently associated with the highest risk of hospitalization for heart failure (HR 1.19, 95% CI 0.79-1.80), cardiovascular mortality (HR 2.51, 95% CI 1.21-5.22), and overall mortality (HR 2.98, 95% CI 1.21-4.2).

Conclusion

A data-driven algorithm can identify distinct clusters with unique phenotypes and varying risks of cardiovascular outcomes in patients with AF, enhancing risk stratification beyond the CHA2DS2-VASc score.

The absolute lactate levels versus clearance for prognostication of post-cardiotomy patients on veno-arterial ECMO

AIMS

Veno-arterial extracorporeal membrane oxygenation (VA-ECMO) is a life-saving procedure for supporting patients with cardiogenic shock after cardiac surgery. This work aimed to analyse the impact of changes in blood lactate levels on the survival of patients on post-cardiotomy ECMO (PC-ECMO) and whether lactate clearance (LC) performs better than absolute lactate levels.

METHODS AND RESULTS

We retrospectively analysed the data of adult patients who received PC-ECMO at our centre between 2016 and 2022. The primary outcome was the in-hospital mortality rate. Arterial lactate levels were measured at ECMO initiation, peak and 12 and 24 h after VA-ECMO support. LC was calculated at 12 and 24 h. Out of 2368 patients who received cardiac surgeries, 152 (median age, 48 years; 57.9% of them were men) received PC-ECMO. Of them, 48 (31.6%) survived and were discharged, while 104 (68.4%) died during the index hospitalization. Non-survivors had higher frequencies of atrial fibrillation (41.35% vs. 12.5%, P < 0.001), chronic kidney disease (26.9% vs. 6.3%, P = 0.004), prolonged cardiopulmonary bypass (237 vs. 192 min, P = 0.016) and aortic cross-clamping times (160 vs. 124 min, P = 0.04) than survivors. Non-survivors had a significantly higher median Sequential Organ Failure Assessment (SOFA) score at ECMO initiation (13.5 vs. 9, P < 0.001) and a lower median Survival After Veno-arterial ECMO (SAVE) score (-3 vs. 3, P < 0.001) with higher SAVE classes (P < 0.001) than survivors. After 12 h of VA-ECMO support, the blood lactate level was negatively correlated with LC in survivors (r = -0.755, P < 0.001) and non-survivors (r = -0.601, P < 0.001). After 24 h, the same negative correlation was identified between survivors (r = -0.764, P < 0.001) and non-survivors (r = -0.847, P < 0.001). Blood lactate levels measured at 12 h to determine hospital mortality [>8.2 mmol/L, area under the receiver operating characteristic curve (AUROC): 0.868] and 24 h (>2.6 mmol/L, AUROC: 0.896) had the best performance, followed by LC-T12 (<21.94%, AUROC: 0.807), LC-T24 (<40.3%, AUROC: 0.839) and peak blood lactate (>14.35 mmol/L, AUROC: 0.828). The initial pre-ECMO blood lactate (>6.25 mmol/L, AUROC: 0.731) had an acceptable ability to discriminate mortality but was less than the following measurements and clearance. Kaplan-Meier curves demonstrated that LC of <21.94% at T12 h and <40.3% at T24 h was associated with decreased survival (log-rank P < 0.001). Cox proportional hazards regression analysis for mortality revealed that LC of <21.94% at T12 h had an adjusted hazard ratio (HR) of 2.73 [95% confidence interval (CI): 1.64-5.762, P < 0.001] and LC of <40.3% at T24 h had an adjusted HR of 1.98 (95% CI: 1.46-4.173, P < 0.001). The predictors of hospital mortality after PC-ECMO were the lactate level at 12 h [odds ratio (OR): 1.67, 95% CI: 1.121-2.181, P = 0.001], initial SOFA score (OR: 1.593, 95% CI: 1.15-2.73, P < 0.001), initial blood lactate (OR: 1.21, 95% CI: 1.016-1.721, P = 0.032) and atrial fibrillation (OR: 6.17, 95% CI: 2.37-57.214, P = 0.003). Bivariate models using lactate levels and clearance at the same points revealed that blood lactate levels performed better than the clearance percentage.

CONCLUSIONS

Serial measurements of arterial blood lactate and LC help in obtaining early prognostic guidance in adult patients supported by VA-ECMO after cardiac surgery. Absolute lactate levels, compared with LC at the same time points, demonstrated better performance in differentiating mortality.

AI-based derivation of atrial fibrillation phenotypes in the general and critical care populations

BACKGROUND

Atrial fibrillation (AF) is the most common heart arrhythmia worldwide and is linked to a higher risk of mortality and morbidity. To predict AF and AF-related complications, clinical risk scores are commonly employed, but their predictive accuracy is generally limited, given the inherent complexity and heterogeneity of patients with AF. By classifying different presentations of AF into coherent and manageable clinical phenotypes, the development of tailored prevention and treatment strategies can be facilitated. In this study, we propose an artificial intelligence (AI)-based methodology to derive meaningful clinical phenotypes of AF in the general and critical care populations.

METHODS

Our approach employs generative topographic mapping, a probabilistic machine learning method, to identify micro-clusters of patients with similar characteristics. It then identifies macro-cluster regions (clinical phenotypes) in the latent space using Ward's minimum variance method. We applied it to two large cohort databases (UK-Biobank and MIMIC-IV) representing general and critical care populations.

FINDINGS

The proposed methodology showed its ability to derive meaningful clinical phenotypes of AF. Because of its probabilistic foundations, it can enhance the robustness of patient stratification. It also produced interpretable visualisation of complex high-dimensional data, enhancing understanding of the derived phenotypes and their key characteristics. Using our methodology, we identified and characterised clinical phenotypes of AF across diverse patient populations.

INTERPRETATION

Our methodology is robust to noise, can uncover hidden patterns and subgroups, and can elucidate more specific patient profiles, contributing to more robust patient stratification, which could facilitate the tailoring of prevention and treatment programs specific to each phenotype. It can also be applied to other datasets to derive clinically meaningful phenotypes of other conditions.

FUNDING

This study was funded by the DECIPHER project (LJMU QR-PSF) and the EU project TARGET (10113624).

The Application of Artificial Intelligence in Atrial Fibrillation Patients: From Detection to Treatment

Atrial fibrillation (AF) is the most prevalent arrhythmia worldwide. Although the guidelines for AF have been updated in recent years, its gradual onset and associated risk of stroke pose challenges for both patients and cardiologists in real-world practice. Artificial intelligence (AI) is a powerful tool in image analysis, data processing, and for establishing models. It has been widely applied in various medical fields, including AF. In this review, we focus on the progress and knowledge gap regarding the use of AI in AF patients and highlight its potential throughout the entire cycle of AF management, from detection to drug treatment. More evidence is needed to demonstrate its ability to improve prognosis through high-quality randomized controlled trials.

Developing a Chatbot to Support Individuals With Neurodevelopmental Disorders: Tutorial

Families of individuals with neurodevelopmental disabilities or differences (NDDs) often struggle to find reliable health information on the web. NDDs encompass various conditions affecting up to 14% of children in high-income countries, and most individuals present with complex phenotypes and related conditions. It is challenging for their families to develop literacy solely by searching information on the internet. While in-person coaching can enhance care, it is only available to a minority of those with NDDs. Chatbots, or computer programs that simulate conversation, have emerged in the commercial sector as useful tools for answering questions, but their use in health care remains limited. To address this challenge, the researchers developed a chatbot named CAMI (Coaching Assistant for Medical/Health Information) that can provide information about trusted resources covering core knowledge and services relevant to families of individuals with NDDs. The chatbot was developed, in collaboration with individuals with lived experience, to provide information about trusted resources covering core knowledge and services that may be of interest. The developers used the Django framework (Django Software Foundation) for the development and used a knowledge graph to depict the key entities in NDDs and their relationships to allow the chatbot to suggest web resources that may be related to the user queries. To identify NDD domain-specific entities from user input, a combination of standard sources (the Unified Medical Language System) and other entities were used which were identified by health professionals as well as collaborators. Although most entities were identified in the text, some were not captured in the system and therefore went undetected. Nonetheless, the chatbot was able to provide resources addressing most user queries related to NDDs. The researchers found that enriching the vocabulary with synonyms and lay language terms for specific subdomains enhanced entity detection. By using a data set of numerous individuals with NDDs, the researchers developed a knowledge graph that established meaningful connections between entities, allowing the chatbot to present related symptoms, diagnoses, and resources. To the researchers' knowledge, CAMI is the first chatbot to provide resources related to NDDs. Our work highlighted the importance of engaging end users to supplement standard generic ontologies to named entities for language recognition. It also demonstrates that complex medical and health-related information can be integrated using knowledge graphs and leveraging existing large datasets. This has multiple implications: generalizability to other health domains as well as reducing the need for experts and optimizing their input while keeping health care professionals in the loop. The researchers' work also shows how health and computer science domains need to collaborate to achieve the granularity needed to make chatbots truly useful and impactful.

Patterns of comorbidities in patients with atrial fibrillation and impact on management and long-term prognosis: an analysis from the Prospective Global GLORIA-AF Registry

BACKGROUND

Clinical complexity, as the interaction between ageing, frailty, multimorbidity and polypharmacy, is an increasing concern in patients with AF. There remains uncertainty regarding how combinations of comorbidities influence management and prognosis of patients with atrial fibrillation (AF). We aimed to identify phenotypes of AF patients according to comorbidities and to assess associations between comorbidity patterns, drug use and risk of major outcomes.

METHODS

From the prospective GLORIA-AF Registry, we performed a latent class analysis based on 18 diseases, encompassing cardiovascular, metabolic, respiratory and other conditions; we then analysed the association between phenotypes of patients and (i) treatments received and (ii) the risk of major outcomes. Primary outcome was the composite of all-cause death and major adverse cardiovascular events (MACE). Secondary exploratory outcomes were also analysed.

RESULTS

32,560 AF patients (mean age 70.0 ± 10.5 years, 45.4% females) were included. We identified 6 phenotypes: (i) low complexity (39.2% of patients); (ii) cardiovascular (CV) risk factors (28.2%); (iii) atherosclerotic (10.2%); (iv) thromboembolic (8.1%); (v) cardiometabolic (7.6%) and (vi) high complexity (6.6%). Higher use of oral anticoagulants was found in more complex groups, with highest magnitude observed for the cardiometabolic and high complexity phenotypes (odds ratio and 95% confidence interval CI): 1.76 [1.49-2.09] and 1.57 [1.35-1.81], respectively); similar results were observed for beta-blockers and verapamil or diltiazem. We found higher risk of the primary outcome in all phenotypes, except the CV risk factor one, with highest risk observed for the cardiometabolic and high complexity groups (hazard ratio and 95%CI: 1.37 [1.13-1.67] and 1.47 [1.24-1.75], respectively).

CONCLUSIONS

Comorbidities influence management and long-term prognosis of patients with AF. Patients with complex phenotypes may require comprehensive and holistic approaches to improve their prognosis.

A Comparison of the Impact of Pharmacological Treatments on Cardioversion, Rate Control, and Mortality in Data-Driven Atrial Fibrillation Phenotypes in Critical Care

Critical care physicians are commonly faced with patients exhibiting atrial fibrillation (AF), a cardiac arrhythmia with multifaceted origins. Recent investigations shed light on the heterogeneity among AF patients by uncovering unique AF phenotypes, characterized by differing treatment strategies and clinical outcomes. In this retrospective study encompassing 9401 AF patients in an intensive care cohort, we sought to identify differences in average treatment effects (ATEs) across different patient groups. We extract data from the MIMIC-III database, use hierarchical agglomerative clustering to identify patients' phenotypes, and assign them to treatment groups based on their initial drug administration during AF episodes. The treatment options examined included beta blockers (BBs), potassium channel blockers (PCBs), calcium channel blockers (CCBs), and magnesium sulfate (MgS). Utilizing multiple imputation and inverse probability of treatment weighting, we estimate ATEs related to rhythm control, rate control, and mortality, approximated as hourly and daily rates (%/h, %/d). Our analysis unveiled four distinctive AF phenotypes: (1) postoperative hypertensive, (2) non-cardiovascular mutlimorbid, (3) cardiovascular multimorbid, and (4) valvulopathy atrial dilation. PCBs showed the highest cardioversion rates across phenotypes, ranging from 11.6%/h (9.35-13.3) to 7.69%/h (5.80-9.22). While CCBs demonstrated the highest effectiveness in controlling ventricular rates within the overall patient cohort, PCBs and MgS outperformed them in specific phenotypes. PCBs exhibited the most favorable mortality outcomes overall, except for the non-cardiovascular multimorbid cluster, where BBs displayed a lower mortality rate of 1.33%/d [1.04-1.93] compared to PCBs' 1.68%/d [1.10-2.24]. The results of this study underscore the significant diversity in ATEs among individuals with AF and suggest that phenotype-based classification could be a valuable tool for physicians, providing personalized insights to inform clinical decision making.

Phenotyping of atrial fibrillation with cluster analysis and external validation

OBJECTIVES

Atrial fibrillation (AF) is a heterogeneous condition. We performed a cluster analysis in a cohort of patients with AF and assessed the prognostic implication of the identified cluster phenotypes.

METHODS

We used two multicentre, prospective, observational registries of AF: the SAKURA AF registry (Real World Survey of Atrial Fibrillation Patients Treated with Warfarin and Non-vitamin K Antagonist Oral Anticoagulants) (n=3055, derivation cohort) and the RAFFINE registry (Registry of Japanese Patients with Atrial Fibrillation Focused on anticoagulant therapy in New Era) (n=3852, validation cohort). Cluster analysis was performed by the K-prototype method with 14 clinical variables. The endpoints were all-cause mortality and composite cardiovascular events.

RESULTS

The analysis subclassified derivation cohort patients into five clusters. Cluster 1 (n=414, 13.6%) was characterised by younger men with a low prevalence of comorbidities; cluster 2 (n=1003, 32.8%) by a high prevalence of hypertension; cluster 3 (n=517, 16.9%) by older patients without hypertension; cluster 4 (n=652, 21.3%) by the oldest patients, who were mainly female and with a high prevalence of heart failure history; and cluster 5 (n=469, 15.3%) by older patients with high prevalence of diabetes and ischaemic heart disease. During follow-up, the risk of all-cause mortality and composite cardiovascular events increased across clusters (log-rank p<0.001, p<0.001). Similar results were found in the external validation cohort.

CONCLUSIONS

Machine learning-based cluster analysis identified five different phenotypes of AF with unique clinical characteristics and different clinical outcomes. The use of these phenotypes may help identify high-risk patients with AF.

Identification of atrial fibrillation phenotypes at low risk of stroke in patients with CHA2DS2-VASc ≥2: Insight from the China-AF study

OBJECTIVE

Patients with atrial fibrillation (AF) are highly heterogeneous, and current risk stratification scores are only modestly good at predicting an individual's stroke risk. We aim to identify distinct AF clinical phenotypes with cluster analysis to optimize stroke prevention practices.

METHODS

From the prospective Chinese Atrial Fibrillation Registry cohort study, we included 4337 AF patients with CHA2 DS2 -VASc≥2 for males and 3 for females who were not treated with oral anticoagulation. We randomly split the patients into derivation and validation sets by a ratio of 7:3. In the derivation set, we used outcome-driven patient clustering with metric learning to group patients into clusters with different risk levels of ischemic stroke and systemic embolism, and identify clusters of patients with low risks. Then we tested the results in the validation set, using the clustering rules generated from the derivation set. Finally, the survival decision tree was applied as a sensitivity analysis to confirm the results.

RESULTS

Up to the follow-up of 1 year, 140 thromboembolic events (ischemic stroke or systemic embolism) occurred. After supervised metric learning from six variables involved in CHA2 DS2 -VASc scheme, we identified a cluster of patients (255/3035, 8.4%) at an annual thromboembolism risk of 0.8% in the derivation set. None of the patients in the low-risk cluster had prior thromboembolism, heart failure, diabetes, or age older than 70 years. After applying the regularities from metric learning on the validation set, we also identified a cluster of patients (137/1302, 10.5%) with an incident thromboembolism rate of 0.7%. Sensitivity analysis based on the survival decision tree approach selected a subgroup of patients with the same phenotypes as the metric-learning algorithm.

CONCLUSIONS

Cluster analysis identified a distinct clinical phenotype at low risk of stroke among high-risk [CHA2 DS2 -VASc≥2 (3 for females)] patients with AF. The use of the novel analytic approach has the potential to prevent a subset of AF patients from unnecessary anticoagulation and avoid the associated risk of major bleeding.

Atrial fibrillation in vascular surgery: a systematic review and meta-analysis on prevalence, incidence and outcome implications

AIMS

To know the prevalence of atrial fibrillation (AF), as well as the incidence of postoperative AF (POAF) in vascular surgery for arterial diseases and its outcome implications.

METHODS

We performed a systematic review and meta-analysis following the PRISMA statement.

RESULTS

After the selection process, we analyzed 44 records (30 for the prevalence of AF history and 14 for the incidence of POAF).The prevalence of history of AF was 11.5% [95% confidence interval (CI) 1-13.3] with high heterogeneity (I2 = 100%). Prevalence was higher in the case of endovascular procedures. History of AF was associated with a worse outcome in terms of in-hospital death [odds ratio (OR) 3.29; 95% CI 2.66-4.06; P < 0.0001; I2 94%] or stroke (OR 1.61; 95% CI 1.39-1.86; P < 0.0001; I2 91%).The pooled incidence of POAF was 3.6% (95% CI 2-6.4) with high heterogeneity (I2 = 100%). POAF risk was associated with older age (mean difference 4.67 years, 95% CI 2.38-6.96; P = 0.00007). The risk of POAF was lower in patients treated with endovascular procedures as compared with an open surgical procedure (OR 0.35; 95% CI 0.13-0.91; P = 0.03; I2 = 61%).

CONCLUSIONS

In the setting of vascular surgery for arterial diseases a history of AF is found overall in 11.5% of patients, more frequently in the case of endovascular procedures, and is associated with worse outcomes in terms of short-term mortality and stroke.The incidence of POAF is overall 3.6%, and is lower in patients treated with an endovascular procedure as compared with open surgery procedures. The need for oral anticoagulants for preventing AF-related stroke should be evaluated with randomized clinical trials.

Phenotypes and outcomes in non-anticoagulated patients with atrial fibrillation: An unsupervised cluster analysis

BACKGROUND

Patients with atrial fibrillation are characterized by great clinical heterogeneity and complexity. The usual classifications may not adequately characterize this population. Data-driven cluster analysis reveals different possible patient classifications.

AIMS

To identify different clusters of patients with atrial fibrillation who share similar clinical phenotypes, and to evaluate the association between identified clusters and clinical outcomes, using cluster analysis.

METHODS

An agglomerative hierarchical cluster analysis was performed in non-anticoagulated patients from the Loire Valley Atrial Fibrillation cohort. Associations between clusters and a composite outcome comprising stroke/systemic embolism/death and all-cause death, stroke and major bleeding were evaluated using Cox regression analyses.

RESULTS

The study included 3434 non-anticoagulated patients with atrial fibrillation (mean age 70.3±17 years; 42.8% female). Three clusters were identified: cluster 1 was composed of younger patients, with a low prevalence of co-morbidities; cluster 2 included old patients with permanent atrial fibrillation, cardiac pathologies and a high burden of cardiovascular co-morbidities; cluster 3 identified old female patients with a high burden of cardiovascular co-morbidities. Compared with cluster 1, clusters 2 and 3 were independently associated with an increased risk of the composite outcome (hazard ratio 2.85, 95% confidence interval 1.32-6.16 and hazard ratio 1.52, 95% confidence interval 1.09-2.11, respectively) and all-cause death (hazard ratio 3.54, 95% confidence interval 1.49-8.43 and hazard ratio 1.88, 95% confidence interval 1.26-2.79, respectively). Cluster 3 was independently associated with an increased risk of major bleeding (hazard ratio 1.72, 95% confidence interval 1.06-2.78).

CONCLUSION

Cluster analysis identified three statistically driven groups of patients with atrial fibrillation, with distinct phenotype characteristics and associated with different risks for major clinical adverse events.

Exploring phenotypes of deep vein thrombosis in relation to clinical outcomes beyond recurrence

BACKGROUND

Deep vein thrombosis (DVT) is a multifactorial disease with several outcomes, but current classifications solely stratify it based on recurrence risk.

OBJECTIVES

We aimed to identify DVT phenotypes and assess their relation to recurrent venous thromboembolism (VTE), postthrombotic syndrome, arterial events, and cancer.

PATIENTS/METHODS

Hierarchical clustering was performed on a DVT cohort with a follow-up of up to 5 years using 23 baseline characteristics. Phenotypes were summarized by discriminative characteristics. Hazard ratios (HRs) were calculated using Cox regression; the recurrence risk was adjusted for the anticoagulant therapy duration. The study was carried out in accordance with the Declaration of Helsinki and approved by the medical ethics committee.

RESULTS

In total, 825 patients were clustered into 4 phenotypes: 1. women using estrogen therapy (n = 112); 2. patients with a cardiovascular risk profile (n = 268); 3. patients with previous VTE (n = 128); and 4. patients without discriminant characteristics (n = 317). Overall, the risks of recurrence, postthrombotic syndrome, arterial events, and cancer were low in phenotype 1 (reference), intermediate in phenotype 4 (HR: 4.6, 1.2, 2.2, 1.8), and high in phenotypes 2 (HR: 6.1, 1.6, 4.5, 2.9) and 3 (HR: 5.7, 2.5, 2.3, 3.7).

CONCLUSIONS

This study identified 4 distinct phenotypes among patients with DVT that are not only associated with the increasing recurrence risk but also with outcomes beyond recurrence. Our results thereby highlight the limitations of current risk stratifications that stratify based on the predictors of the recurrence risk only. Overall, risks were lowest in women using estrogen therapy and highest in patients with a cardiovascular risk profile. These findings might inform a more personalized approach to clinical management.

DIGItal Health Literacy after COVID-19 Outbreak among Frail and Non-Frail Cardiology Patients: The DIGI-COVID Study

BACKGROUND

Telemedicine requires either the use of digital tools or a minimum technological knowledge of the patients. Digital health literacy may influence the use of telemedicine in most patients, particularly those with frailty. We aimed to explore the association between frailty, the use of digital tools, and patients' digital health literacy.

METHODS

We prospectively enrolled patients referred to arrhythmia outpatient clinics of our cardiology department from March to September 2022. Patients were divided according to frailty status as defined by the Edmonton Frail Scale (EFS) into robust, pre-frail, and frail. The degree of digital health literacy was assessed through the Digital Health Literacy Instrument (DHLI), which explores seven digital skill categories measured by 21 self-report questions.

RESULTS

A total of 300 patients were enrolled (36.3% females, median age 75 (66-84)) and stratified according to frailty status as robust (EFS ≤ 5; 70.7%), pre-frail (EFS 6-7; 15.7%), and frail (EFS ≥ 8; 13.7%). Frail and pre-frail patients used digital tools less frequently and accessed the Internet less frequently compared to robust patients. In the logistic regression analysis, frail patients were significantly associated with the non-use of the Internet (adjusted odds ratio 2.58, 95% CI 1.92-5.61) compared to robust and pre-frail patients. Digital health literacy decreased as the level of frailty increased in all the digital domains examined.

CONCLUSIONS

Frail patients are characterized by lower use of digital tools compared to robust patients, even though these patients would benefit the most from telemedicine. Digital skills were strongly influenced by frailty.

Impact of anthropometric factors on outcomes in atrial fibrillation patients: analysis on 10 220 patients from the European Society of Cardiology (ESC)-European Heart Rhythm Association (EHRA) EurObservational Research Programme on Atrial Fibrillation (EORP-AF) general long-term registry

AIM

To investigate the association of anthropometric parameters [height, weight, body mass index (BMI), body surface area (BSA), and lean body mass (LBM)] with outcomes in atrial fibrillation (AF).

METHODS AND RESULTS

Ten-thousand two-hundred twenty patients were enrolled [40.3% females, median age 70 (62-77) years, followed for 728 (interquartile range 653-745) days]. Sex-specific tertiles were considered for the five anthropometric variables. At the end of follow-up, survival free from all-cause death was worse in the lowest tertiles for all the anthropometric variables analyzed. On multivariable Cox regression analysis, an independent association with all-cause death was found for the lowest vs. middle tertile when body weight (hazard ratio [HR] 1.66, 95%CI 1.23-2.23), BMI (HR 1.65, 95%CI 1.23-2.21), and BSA (HR 1.49, 95%CI 1.11-2.01) were analysed in female sex, as well as for body weight in male patients (HR 1.61, 95%CI 1.25-2.07). Conversely, the risk of MACE was lower for the highest tertile (vs. middle tertile) of BSA and LBM in males and for the highest tertile of weight and BSA in female patients. A higher occurrence of haemorrhagic events was found for female patients in the lowest tertile of height [odds ratio (OR) 1.90, 95%CI 1.23-2.94] and LBM (OR 2.13, 95%CI 1.40-3.26).

CONCLUSIONS

In AF patients height, weight, BMI, BSA, and LBM were associated with clinical outcomes, with all-cause death being higher for patients presenting lower values of these variables, i.e. in the lowest tertiles of distribution. The anthropometric variables independently associated with other outcomes were also different between male and female subjects.

Morbid liver manifestations are intrinsically bound to metabolic syndrome and nutrient intake based on a machine-learning cluster analysis

Metabolic syndrome (MetS) is one of the most important medical problems around the world. Identification of patient´s singular characteristic could help to reduce the clinical impact and facilitate individualized management. This study aimed to categorize MetS patients using phenotypical and clinical variables habitually collected during health check-ups of individuals considered to have high cardiovascular risk. The selected markers to categorize MetS participants included anthropometric variables as well as clinical data, biochemical parameters and prescribed pharmacological treatment. An exploratory factor analysis was carried out with a subsequent hierarchical cluster analysis using the z-scores from factor analysis. The first step identified three different factors. The first was determined by hypercholesterolemia and associated treatments, the second factor exhibited glycemic disorders and accompanying treatments and the third factor was characterized by hepatic enzymes. Subsequently four clusters of patients were identified, where cluster 1 was characterized by glucose disorders and treatments, cluster 2 presented mild MetS, cluster 3 presented exacerbated levels of hepatic enzymes and cluster 4 highlighted cholesterol and its associated treatments Interestingly, the liver status related cluster was characterized by higher protein consumption and cluster 4 with low polyunsaturated fatty acid intake. This research emphasized the potential clinical relevance of hepatic impairments in addition to MetS traditional characterization for precision and personalized management of MetS patients.

Comparing atrial fibrillation guidelines: Focus on stroke prevention, bleeding risk assessment and oral anticoagulant recommendations

Clinical practice in atrial fibrillation (AF) patient management is constantly evolving. In the past 3 years, various new AF guidelines or focused updates have been published, given this rapidly evolving field. In 2019, the American College of Cardiology/American Heart Association published a focused update of the 2014 guidelines. In 2020, both the European Society of Cardiology and the Canadian Cardiovascular Society released their new guidelines. Finally, the most recent guidelines were those published in 2021 by the Asian Pacific Heart Rhythm Society, which updates their 2017 version and the 2021 National Institute for Health and Care Excellence (NICE) guidelines. In the present narrative review, we compare these guidelines, emphasizing similarities and differences in the following mainstay elements of patient care: thromboembolic risk assessment, oral anticoagulants (OACs) prescription, bleeding risk evaluation, and integrated patient management. A formal evaluation of baseline thromboembolic and bleeding risks and their reassessment during follow-up is evenly recommended, although some differences in using risk stratification scores. OACs prescription is highly encouraged where appropriate, and prescription algorithms are broadly similar. The importance of an integrated and multidisciplinary approach to patient care is emerging, aiming to address several different aspects of a multifaceted disease.

Rhythm- or rate-control strategies according to 4S-AF characterization scheme and long-term outcomes in atrial fibrillation patients: the FAMo (Fibrillazione Atriale in Modena) cohort

The 4S-AF scheme [Stroke risk, Symptom severity, Severity of atrial fibrillation (AF) burden, Substrate severity] was recently proposed to characterize AF patients. In this post hoc analysis we evaluated the agreement between the therapeutic strategy (rate or rhythm control, respectively), as suggested by the 4S-AF scheme, and the actual strategy followed in a patients cohort. Outcomes of interest were as follows: all-cause death, a composite of all-cause death/any thromboembolism/acute coronary syndrome, and a composite of all-cause death, any thrombotic/ischemic event, and major bleeding (net clinical outcome). We enrolled 615 patients: 60.5% male, median age 74 [interquartile range (IQR) 67-80] years; median CHA₂DS₂VASc 4 and median HAS-BLED 2. The 4S-AF score would have suggested a rhythm-control strategy in 351 (57.1%) patients while a rate control in 264 (42.9%). The strategy adopted was concordant with the 4S-AF suggestions in 342 (55.6%) cases, and non-concordant in 273 (44.4%). After a median follow-up of 941 days (IQR 365-1282), 113 (18.4%) patients died, 158 (25.7%) had an event of the composite endpoint. On adjusted Cox regression analysis, when 4S-AF score suggested rate control, disagreement with that suggestion was not associated with a worse outcome. When 4S-AF indicated rhythm control, disagreement was associated with a higher risk of all-cause death (HR 7.59; 95% CI 1.65-35.01), and of the composite outcome (HR 2.69; 95% CI 1.19-6.06). The 4S-AF scheme is a useful tool to comprehensively evaluate AF patients and aid the decision-making process. Disagreement with the rhythm control suggestion of the 4S-AF scheme was associated with adverse clinical outcomes.

Atrial Fibrillation in the Setting of Acute Pneumonia: Not a Secondary Arrhythmia

Atrial fibrillation (AF) is the most common arrhythmia in the setting of critically ill patients. Pneumonia, and in particular community-acquired pneumonia, is one of the most common causes of illness and hospital admission worldwide. This article aims to review the association between AF and acute diseases, with specific attention to pneumonia, from the pathophysiology to its clinical significance. Even though the relationship between pneumonia and AF has been known for years, it was once considered a transient bystander. In recent years there has been growing knowledge on the clinical significance of this arrhythmia in acute clinical settings, in which it holds a prognostic role which is not so different as compared to that of the so-called "primary" AF. AF is a distinct entity even in the setting of pneumonia, and acute critical illnesses in general, and it should therefore be managed with a guidelines-oriented approach, including prescription of anticoagulants in patients at thromboembolic risk, always considering patients' individuality. More data on the significance of the arrhythmia in this setting will help clinicians to give patients the best possible care.

Bimodal distribution of atrial fibrillation burden in 3 distinct cohorts: What is 'paroxysmal' atrial fibrillation?

BACKGROUND

Burden of atrial fibrillation (AF), as a continuous measure, is an emerging alternative classification often assumed to increase linearly with progression of disease. Yet there are no descriptions of AF burden distributions across populations.

METHODS

We examined patterns of AF burden (% time in AF) across 3 different cohorts: outpatients with AF undergoing Holter monitoring in a national registry (ORBIT-AF II), routine outpatients undergoing Holter monitoring in a tertiary healthcare system (UHealth), and patients >= 65 years with cardiac implantable electronic devices (Merlin.netTM linked to Medicare).

RESULTS

We included 2,058 ORBIT-AF II patients, 4,537 UHealth patients, and 39,710 from Merlin.net. Mean age ranged from 56 to 77 years, sex ranged from 40% to 61% male, and mean CHA2DS2-VASc scores ranged from 2.2 to 4.9. Across all cohorts, AF burden demonstrated skewed frequency towards the extremes, with the vast majority of patients having either very low or very high AF burden. This bimodal distribution was consistent across cohorts, across clinically-documented AF types (paroxysmal v persistent), patients with or without a known AF diagnosis, and among patients with different types of cardiac implantable electronic devices.

CONCLUSIONS

Across 3 broad, diverse cohorts with continuous monitoring, distribution of AF burden was consistently skewed towards the extremes without an even, linear distribution or progression. As AF burden is increasingly recognized as a descriptor and potential risk-stratifier, these findings have important implications for future research and patient care.

Impact of clinical phenotypes on management and outcomes in European atrial fibrillation patients: a report from the ESC-EHRA EURObservational Research Programme in AF (EORP-AF) General Long-Term Registry

BACKGROUND

Epidemiological studies in atrial fibrillation (AF) illustrate that clinical complexity increase the risk of major adverse outcomes. We aimed to describe European AF patients' clinical phenotypes and analyse the differential clinical course.

METHODS

We performed a hierarchical cluster analysis based on Ward's Method and Squared Euclidean Distance using 22 clinical binary variables, identifying the optimal number of clusters. We investigated differences in clinical management, use of healthcare resources and outcomes in a cohort of European AF patients from a Europe-wide observational registry.

RESULTS

A total of 9363 were available for this analysis. We identified three clusters: Cluster 1 (n = 3634; 38.8%) characterized by older patients and prevalent non-cardiac comorbidities; Cluster 2 (n = 2774; 29.6%) characterized by younger patients with low prevalence of comorbidities; Cluster 3 (n = 2955;31.6%) characterized by patients' prevalent cardiovascular risk factors/comorbidities. Over a mean follow-up of 22.5 months, Cluster 3 had the highest rate of cardiovascular events, all-cause death, and the composite outcome (combining the previous two) compared to Cluster 1 and Cluster 2 (all P < .001). An adjusted Cox regression showed that compared to Cluster 2, Cluster 3 (hazard ratio (HR) 2.87, 95% confidence interval (CI) 2.27-3.62; HR 3.42, 95%CI 2.72-4.31; HR 2.79, 95%CI 2.32-3.35), and Cluster 1 (HR 1.88, 95%CI 1.48-2.38; HR 2.50, 95%CI 1.98-3.15; HR 2.09, 95%CI 1.74-2.51) reported a higher risk for the three outcomes respectively.

CONCLUSIONS

In European AF patients, three main clusters were identified, differentiated by differential presence of comorbidities. Both non-cardiac and cardiac comorbidities clusters were found to be associated with an increased risk of major adverse outcomes.