A simple and easily implemented risk model to predict 1-year ischemic stroke and systemic embolism in Chinese patients with atrial fibrillation

[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.

New score for predicting thromboembolic events in patients with atrial fibrillation using direct oral anticoagulants

Determinants of thrombotic events remain uncertain in patients with atrial fibrillation treated with direct oral anticoagulants (DOACs). Our aim was to identify risk factors associated with thromboembolism in patients with at atrial fibrillation on DOACs and to construct and externally validate a predictive model that would provide a validated tool for clinical assessment of thromboembolism. In the development cohort, prediction model was built by logistic regression, the area under the curve (AUC), and Nomogram. External validation and calibration of the model using AUC and Hosmer-Lemeshow test. This national multicenter retrospective study included 3263 patients with atrial fibrillation treated with DOACs. The development cohort consisted of 2390 patients from three centers and the external validation cohort consisted of 873 patients from 13 centers. Multifactorial analysis showed that heavy drinking, hypertension, prior stroke/transient ischemic attack (TIA), cerebral infarction during hospitalization were independent risk factors for thromboembolism. The Alfalfa-TE risk score was constructed using these four factors (AUC = 0.84), and in the external validation cohort, the model showed good discriminatory power (AUC = 0.74) and good calibration (Hosmer-Lemeshow test P value of 0.649). Based on four factors, we derived and externally validated a predictive model for thromboembolism with DOACs in patients with atrial fibrillation (Alfalfa-TE risk score). The model has good predictive value and may be an effective tool to help reduce the occurrence of thromboembolism in patients with DOACs.

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.

Impact of baseline blood pressure on all-cause mortality in patients with atrial fibrillation: results from a multicenter registry study

BACKGROUND

The ideal blood pressure (BP) target for patients with atrial fibrillation (AF) is still unclear. The present study aimed to assess the effect of the baseline BP on all-cause mortality in patients with AF.

METHODS

This registry study included 20 emergency centers across China and consecutively enrolled patients with AF from 2008 to 2011. All participants were followed for 1 year ± 1 month. The primary endpoint was all-cause mortality.

RESULTS

During the follow-up, 276 (13.9%) all-cause deaths occurred. Kaplan-Meier curves showed that a systolic blood pressure (SBP) ≤110 mmHg or >160 mmHg was associated with a higher risk of all-cause mortality (log-rank test, P  = 0.014), and a diastolic blood pressure (DBP) <70 mmHg was associated with the highest risk of all-cause mortality (log-rank test, P  = 0.002). After adjusting for confounders, the multivariable Cox regression model suggested that the risk of all-cause mortality was increased in the group with SBP ≤110 mmHg (hazard ratio [HR], 1.963; 95% confidence interval [CI], 1.306-2.951), and DBP <70 mmHg (HR, 1.628; 95% CI, 1.163-2.281). In the restricted cubic splines, relations between baseline SBP or DBP and all-cause mortality showed J-shaped associations (non-linear P <0.001 and P  = 0.010, respectively). The risk of all-cause mortality notably increased at a lower baseline SBP and DBP.

CONCLUSIONS

Having a baseline SBP ≤110 mmHg or DBP <70 mmHg was associated with a significantly higher risk of all-cause mortality in patients with AF. An excessively low BP may not be an optimal target for patients with AF.

Prevalence and clinical characteristics of atrial fibrillation in hospitalized patients with coronary artery disease and hypertension: a cross-sectional study from 2008 to 2018

BACKGROUND

The clinical characteristics of patients with the comorbidities of hypertension and coronary artery disease (HT-CAD) and atrial fibrillation (AF) are largely unknown. This study aimed to investigate the prevalence of AF in patients with HT-CAD and clinical characteristics of patients with both HT-CAD and AF.

METHODS

This cross-sectional study was conducted in Chinese People's Liberation Army General Hospital in Beijing, China, and included 20,747 inpatients with HT-CAD with or without AF from August 2008 to July 2018. We examined the overall prevalence, clinical characteristics, comorbidity profiles, treatment patterns, and blood pressure (BP) control of patients with both HT-CAD and AF. Multivariate logistic regression was used to investigate the associations of cardiovascular risk factors with AF in patients with HT-CAD.

RESULTS

The overall prevalence of AF in patients with HT-CAD was 4.87% (1011/20,747), and this increased with age; to be specific, the prevalence in women and men increased from 0.78% (2/255) and 1.02% (26/2561) at the age of <50 years to 8.73% (193/2210) and 10.28% (298/2900) at the age of ≥70 years, respectively. HT-CAD patients who had AF had a higher prevalence of cardiovascular-related comorbidities than those without AF. Multivariate logistic regression showed that age, gender (male), body mass index, heart failure, and chronic kidney disease were independently associated with the risk of AF in patients with HT-CAD. For those with both HT-CAD and AF, 73.49% (743/1011) had a CHA 2 DS 2 -VASc score of ≥4, and only about half of them had the BP controlled at <140/90 mmHg, which indicated a high risk of thromboembolism and stroke. The use of oral anticoagulation increased during the study period (10.00% [20/200] in 2008 to 2011 vs. 30.06% [159/529] in 2015 to 2018, P  < 0.01), but remained at a relatively low level.

CONCLUSIONS

AF is highly prevalent among patients with HT-CAD. Patients with both HT-CAD and AF have a higher prevalence of cardiovascular-related comorbidities, lower BP control rate, and lower use of oral anticoagulation.

Comprehensive comparison of stroke risk score performance: a systematic review and meta-analysis among 6 267 728 patients with atrial fibrillation

AIMS

Multiple risk scores to predict ischaemic stroke (IS) in patients with atrial fibrillation (AF) have been developed. This study aims to systematically review these scores, their validations and updates, assess their methodological quality, and calculate pooled estimates of the predictive performance.

METHODS AND RESULTS

We searched PubMed and Web of Science for studies developing, validating, or updating risk scores for IS in AF patients. Methodological quality was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). To assess discrimination, pooled c-statistics were calculated using random-effects meta-analysis. We identified 19 scores, which were validated and updated once or more in 70 and 40 studies, respectively, including 329 validations and 76 updates-nearly all on the CHA2DS2-VASc and CHADS2. Pooled c-statistics were calculated among 6 267 728 patients and 359 373 events of IS. For the CHA2DS2-VASc and CHADS2, pooled c-statistics were 0.644 [95% confidence interval (CI) 0.635-0.653] and 0.658 (0.644-0.672), respectively. Better discriminatory abilities were found in the newer risk scores, with the modified-CHADS2 demonstrating the best discrimination [c-statistic 0.715 (0.674-0.754)]. Updates were found for the CHA2DS2-VASc and CHADS2 only, showing improved discrimination. Calibration was reasonable but available for only 17 studies. The PROBAST indicated a risk of methodological bias in all studies.

CONCLUSION

Nineteen risk scores and 76 updates are available to predict IS in patients with AF. The guideline-endorsed CHA2DS2-VASc shows inferior discriminative abilities compared with newer scores. Additional external validations and data on calibration are required before considering the newer scores in clinical practice.

CLINICAL TRIAL REGISTRATION

ID CRD4202161247 (PROSPERO).

GSTCNet: Gated spatio-temporal correlation network for stroke mortality prediction

Stroke continues to be the most common cause of death in China. It has great significance for mortality prediction for stroke patients, especially in terms of analyzing the complex interactions between non-negligible factors. In this paper, we present a gated spatio-temporal correlation network (GSTCNet) to predict the one-year post-stroke mortality. Based on the four categories of risk factors: vascular event, chronic disease, medical usage and surgery, we designed a gated correlation graph convolution kernel to capture spatial features and enhance the spatial correlation between feature categories. Bi-LSTM represents the temporal features of five timestamps. The novel gated correlation attention mechanism is then connected to the Bi-LSTM to realize the comprehensive mining of spatio-temporal correlations. Using the data on 2275 patients obtained from the neurology department of a local hospital, we constructed a series of sequential experiments. The experimental results show that the proposed model achieves competitive results on each evaluation metric, reaching an AUC of 89.17%, a precision of 97.75%, a recall of 95.33% and an F1-score of 95.19%. The interpretability analysis of the feature categories and timestamps also verified the potential application value of the model for stroke.