Improving dynamic stroke risk prediction in non-anticoagulated patients with and without atrial fibrillation: comparing common clinical risk scores and machine learning algorithms

Machine learning to predict stroke risk from routine hospital data: A systematic review

PURPOSE

Stroke remains a leading cause of morbidity and mortality. Despite this, current risk stratification tools such as CHA2DS2-VASc and QRISK3 are of limited accuracy, particularly in those without a diagnosis of atrial-fibrillation. Hence, there is a need for more accurate stroke risk prediction models. Machine-learning (ML) may provide a solution to this by leveraging existing routine hospital databases to build accurate stroke risk prediction models and identify novel risk factors for stroke.

AIMS

In this systematic review we appraise current research using ML to predict stroke risk from routine hospital data. Based on these findings we then highlight common methodological limitations and recommendations for future research.

METHODS

In this review we identify 49 original research (38 in the general population and 11 in AF specific populations) articles from the PUBMED database from January-2013 to December-2024 using ML and routine hospital data to predict the risk of stroke.

RESULTS

ML models were able to accurately predict stroke risk in both AF specific and general populations, with AUCs ranging from 0.64 to 0.99. Where tested, ML also consistently outperformed traditional risk stratification tool, such as CHA2DS2-VASc. ML also appeared useful in identifying several novel risk factors from electrocardiogram, laboratory test and echocardiography data. However, the quality of datasets were often limited, there was a high suspicion of overfitting and models often lacked calibration, external validation and explainability analysis.

CONCLUSION

Whilst ML has shown great potential in stroke prediction and identifying novel risk factors for stroke, improvements in study methodology is required prior to integration of ML into routine healthcare. Future research should adhere to the EQUATOR guidance on prediction models and encourage interdisciplinary collaboration between computer scientists and clinicians. Further prospective RCTs are also required to validate models in the clinical setting and the identify barriers of integrating ML into routine healthcare.

Advancing personalised care in atrial fibrillation and stroke: The potential impact of AI from prevention to rehabilitation

Atrial fibrillation (AF) is a complex condition caused by various underlying pathophysiological disorders and is the most common heart arrhythmia worldwide, affecting 2 % of the European population. This prevalence increases with age, imposing significant financial, economic, and human burdens. In Europe, stroke is the second leading cause of death and the primary cause of disability, with numbers expected to rise due to ageing and improved survival rates. Functional recovery from AF-related stroke is often unsatisfactory, leading to prolonged hospital stays, severe disability, and high mortality. Despite advances in AF and stroke research, the full pathophysiological and management issues between AF and stroke increasingly need innovative approaches such as artificial intelligence (AI) and machine learning (ML). Current risk assessment tools focus on static risk factors, neglecting the dynamic nature of risk influenced by acute illness, ageing, and comorbidities. Incorporating biomarkers and automated ECG analysis could enhance pathophysiological understanding. This paper highlights the need for personalised, integrative approaches in AF and stroke management, emphasising the potential of AI and ML to improve risk prediction, treatment personalisation, and rehabilitation outcomes. Further research is essential to optimise care and reduce the burden of AF and stroke on patients and healthcare systems.

Fair prediction of 2-year stroke risk in patients with atrial fibrillation

OBJECTIVE

This study aims to develop machine learning models that provide both accurate and equitable predictions of 2-year stroke risk for patients with atrial fibrillation across diverse racial groups.

MATERIALS AND METHODS

Our study utilized structured electronic health records (EHR) data from the All of Us Research Program. Machine learning models (LightGBM) were utilized to capture the relations between stroke risks and the predictors used by the widely recognized CHADS2 and CHA2DS2-VASc scores. We mitigated the racial disparity by creating a representative tuning set, customizing tuning criteria, and setting binary thresholds separately for subgroups. We constructed a hold-out test set that not only supports temporal validation but also includes a larger proportion of Black/African Americans for fairness validation.

RESULTS

Compared to the original CHADS2 and CHA2DS2-VASc scores, significant improvements were achieved by modeling their predictors using machine learning models (Area Under the Receiver Operating Characteristic curve from near 0.70 to above 0.80). Furthermore, applying our disparity mitigation strategies can effectively enhance model fairness compared to the conventional cross-validation approach.

DISCUSSION

Modeling CHADS2 and CHA2DS2-VASc risk factors with LightGBM and our disparity mitigation strategies achieved decent discriminative performance and excellent fairness performance. In addition, this approach can provide a complete interpretation of each predictor. These highlight its potential utility in clinical practice.

CONCLUSIONS

Our research presents a practical example of addressing clinical challenges through the All of Us Research Program data. The disparity mitigation framework we proposed is adaptable across various models and data modalities, demonstrating broad potential in clinical informatics.

Computational Medicine: What Electrophysiologists Should Know to Stay Ahead of the Curve

PURPOSE OF REVIEW

Technology drives the field of cardiac electrophysiology. Recent computational advances will bring exciting changes. To stay ahead of the curve, we recommend electrophysiologists develop a robust appreciation for novel computational techniques, including deterministic, statistical, and hybrid models.

RECENT FINDINGS

In clinical applications, deterministic models use biophysically detailed simulations to offer patient-specific insights. Statistical techniques like machine learning and artificial intelligence recognize patterns in data. Emerging clinical tools are exploring avenues to combine all the above methodologies. We review three ways that computational medicine will aid electrophysiologists by: (1) improving personalized risk assessments, (2) weighing treatment options, and (3) guiding ablation procedures. Leveraging clinical data that are often readily available, computational models will offer valuable insights to improve arrhythmia patient care. As emerging tools promote personalized medicine, physicians must continue to critically evaluate technology-driven tools they consider using to ensure their appropriate implementation.

Machine Learning-Based Predictive Models for Patients with Venous Thromboembolism: A Systematic Review

BACKGROUND

 Venous thromboembolism (VTE) is a chronic disorder with a significant health and economic burden. Several VTE-specific clinical prediction models (CPMs) have been used to assist physicians in decision-making but have several limitations. This systematic review explores if machine learning (ML) can enhance CPMs by analyzing extensive patient data derived from electronic health records. We aimed to explore ML-CPMs' applications in VTE for risk stratification, outcome prediction, diagnosis, and treatment.

METHODS

 Three databases were searched: PubMed, Google Scholar, and IEEE electronic library. Inclusion criteria focused on studies using structured data, excluding non-English publications, studies on non-humans, and certain data types such as natural language processing and image processing. Studies involving pregnant women, cancer patients, and children were also excluded. After excluding irrelevant studies, a total of 77 studies were included.

RESULTS

 Most studies report that ML-CPMs outperformed traditional CPMs in terms of receiver operating area under the curve in the four clinical domains that were explored. However, the majority of the studies were retrospective, monocentric, and lacked detailed model architecture description and external validation, which are essential for quality audit. This review identified research gaps and highlighted challenges related to standardized reporting, reproducibility, and model comparison.

CONCLUSION

 ML-CPMs show promise in improving risk assessment and individualized treatment recommendations in VTE. Apparently, there is an urgent need for standardized reporting and methodology for ML models, external validation, prospective and real-world data studies, as well as interventional studies to evaluate the impact of artificial intelligence in VTE.

The role of artificial intelligence in optimizing management of atrial fibrillation in acute ischemic stroke

Atrial fibrillation (AF) is a severe condition associated with high morbidity and mortality, including an increased risk of stroke and poor outcomes poststroke. Our understanding of the prognosis in AF remains poor. Machine learning (ML) has been applied to the diagnosis, management, and prognosis of AF in the context of stroke but remains suboptimal for clinical use. This article endeavors to provide a comprehensive overview of current ML applications to AF patients at risk of stroke, as well as poststroke patients without AF. Strategies to develop effective ML involve the validation of a variety of ML algorithms across internal and external datasets as well as exploring their predictive powers in hypothetical and realistic settings. Recent literature of this rapidly evolving field has displayed much promise. However, further testing and innovation of medical artificial intelligence are required before its imminent introduction to ensure complete patient trust within the community. Prioritizing this research is imperative for advancing the optimization of ongoing care for AF patients, as well as the management of stroke patients with AF.

Evaluating Machine Learning Models for Stroke Prognosis and Prediction in Atrial Fibrillation Patients: A Comprehensive Meta-Analysis

BACKGROUND/OBJECTIVE

Atrial fibrillation (AF) complicates the management of acute ischemic stroke (AIS), necessitating precise predictive models to enhance clinical outcomes. This meta-analysis evaluates the efficacy of machine learning (ML) models in three key areas: stroke prognosis in AF patients, stroke prediction in AF patients, and AF prediction in stroke patients. The study aims to assess the accuracy and variability of ML models in forecasting AIS outcomes and detecting AF in stroke patients, while exploring the clinical benefits and limitations of integrating these models into practice.

METHODS

We conducted a systematic search of PubMed, Embase, and Cochrane databases up to June 2024, selecting studies that evaluated ML accuracy in stroke prognosis and prediction in AF patients and AF prediction in stroke patients. Data extraction and quality assessment were performed independently by two reviewers, with random-effects modeling applied to estimate pooled accuracy metrics.

RESULTS

The meta-analysis included twenty-four studies comprising 7,391,645 patients, categorized into groups for stroke prognosis in AF patients (eight studies), stroke prediction in AF patients (thirteen studies), and AF prediction in stroke patients (three studies). The pooled AUROC was 0.79 for stroke prognosis and 0.68 for stroke prediction in AF, with higher accuracy noted in short-term predictions. The mean AUROC across studies was 0.75, with models such as Extreme Gradient Boosting (XGB) and Random Forest (RF) showing superior performance. For stroke prognosis in AF, the mean AUROC was 0.78, whereas stroke prediction yielded a mean AUROC of 0.73. AF prediction post-stroke had an average AUROC of 0.75. These findings indicate moderate predictive capability of ML models, underscoring the need for further refinement and standardization. The absence of comprehensive sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) metrics limited the ability to conduct full meta-analytic modeling.

CONCLUSIONS

While ML models demonstrate potential for enhancing stroke prognosis and AF prediction, they have yet to meet the clinical standards required for widespread adoption. Future efforts should focus on refining these models and validating them across diverse populations to improve their clinical utility.

Machine learning applications in preventive healthcare: A systematic literature review on predictive analytics of disease comorbidity from multiple perspectives

Artificial intelligence is constantly revolutionizing biomedical research and healthcare management. Disease comorbidity is a major threat to the quality of life for susceptible groups, especially middle-aged and elderly patients. The presence of multiple chronic diseases makes precision diagnosis challenging to realize and imposes a heavy burden on the healthcare system and economy. Given an enormous amount of accumulated health data, machine learning techniques show their capability in handling this puzzle. The present study conducts a review to uncover current research efforts in applying these methods to understanding comorbidity mechanisms and making clinical predictions considering these complex patterns. A descriptive metadata analysis of 791 unique publications aims to capture the overall research progression between January 2012 and June 2023. To delve into comorbidity-focused research, 61 of these scientific papers are systematically assessed. Four predictive analytics of tasks are detected: disease comorbidity data extraction, clustering, network, and risk prediction. It is observed that some machine learning-driven applications address inherent data deficiencies in healthcare datasets and provide a model interpretation that identifies significant risk factors of comorbidity development. Based on insights, both technical and practical, gained from relevant literature, this study intends to guide future interests in comorbidity research and draw conclusions about chronic disease prevention and diagnosis with managerial implications.

Machine learning approach for prediction of outcomes in anticoagulated patients with atrial fibrillation

BACKGROUND

The accuracy of available prediction tools for clinical outcomes in patients with atrial fibrillation (AF) remains modest. Machine Learning (ML) has been used to predict outcomes in the AF population, but not in a population entirely on anticoagulant therapy.

METHODS AND AIMS

Different supervised ML models were applied to predict all-cause death, cardiovascular (CV) death, major bleeding and stroke in anticoagulated patients with AF, processing data from the multicenter START-2 Register.

RESULTS

11078 AF patients (male n = 6029, 54.3%) were enrolled with a median follow-up period of 1.5 years [IQR 1.0-2.6]. Patients on Vitamin K Antagonists (VKA) were 5135 (46.4%) and 5943 (53.6%) were on Direct Oral Anticoagulants (DOAC). Using Multi-Gate Mixture of Experts, a cross-validated AUC of 0.779 ± 0.016 and 0.745 ± 0.022 were obtained, respectively, for the prediction of all-cause death and CV-death in the overall population. The best ML model outperformed CHA2DSVA2SC and HAS-BLED for all-cause death prediction (p < 0.001 for both). When compared to HAS-BLED, Gradient Boosting improved major bleeding prediction in DOACs patients (0.711 vs. 0.586, p < 0.001). A very low number of events during follow-up (52) resulted in a suboptimal ischemic stroke prediction (best AUC of 0.606 ± 0.117 in overall population). Body mass index, age, renal function, platelet count and hemoglobin levels resulted the most important variables for ML prediction.

CONCLUSIONS

In AF patients, ML models showed good discriminative ability to predict all-cause death, regardless of the type of anticoagulation strategy, and major bleeding on DOAC therapy, outperforming CHA2DS2VASC and the HAS-BLED scores for risk prediction in these populations.

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.

Prediction of atrial fibrillation and stroke using machine learning models in UK Biobank

Objective

Atrial fibrillation (AF) is the most common cardiac arrythmia, and it is associated with increased risk for ischemic stroke, which is underestimated, as AF can be asymptomatic. The aim of this study was to develop optimal ML models for prediction of AF in the population, and secondly for ischemic stroke in AF patients.

Methods

To develop ML models for prediction of 1) AF in the general population and 2) ischemic stroke in patients with AF we constructed XGBoost, LightGBM, Random Forest, Deep Neural Network, Support Vector Machine and Lasso penalised logistic regression models using UK-Biobank's extensive real-world clinical data, questionnaires, as well as biochemical and genetic data, and their predictive performances were compared. Ranking and contribution of the different features was assessed by SHapley Additive exPlanations (SHAP) analysis. The clinical tool CHA2DS2-VASc for prediction of ischemic stroke among AF patients, was used for comparison to the best performing ML model.

Findings

The best performing model for AF prediction was LightGBM, with an area-under-the-roc-curve (AUROC) of 0.729 (95% confidence intervals (CI): 0.719, 0.738). The best performing model for ischemic stroke prediction in AF patients was XGBoost with AUROC of 0.631 (95% CI: 0.604, 0.657). The improved AUROC in the XGBoost model compared to CHA2DS2-VASc was statistically significant based on DeLong's test (p-value = 2.20E-06). In addition, the SHAP analysis showed that several peripheral blood biomarkers (e.g. creatinine, glycated haemoglobin, monocytes) were associated with ischemic stroke, which are not considered by CHA2DS2-VASc.

Implications

The best performing ML models presented have the potential for clinical use, but further validation in independent studies is required. Our results endorse the incorporation of some routinely measured blood biomarkers for ischemic stroke prediction in AF patients.

Atrial fibrillation: stroke prevention

Stroke prevention is central to the management of patients with atrial fibrillation (AF) which has moved towards a more holistic or integrative care approach. The published evidence suggests that management of AF patients following such a holistic approach based on the Atrial fibrillation Better Care (ABC) pathway is associated with a lower risk of stroke and adverse events. Risk assessment, re-assessment and use of direct oral anticoagulants (DOACs) are important for stroke prevention in AF. The stroke and bleeding risks of AF patients are not static and should be re-assessed regularly. Bleeding risk assessment is to address and mitigate modifiable bleeding risk factors, and to identify high bleeding risk patients for early review and follow-up. Well-controlled comorbidities and healthy lifestyles also play an important role to achieve a better clinical outcome. Digital health solutions are increasingly relevant in the diagnosis and management of patients with AF, with the potential to improve stroke prevention. In this review, we provide an update on stroke prevention in AF, including importance of holistic management, risk assessment/re-assessment, and stroke prevention for special AF populations. Evidence-based and structured management of AF patients would reduce the risk of stroke and other adverse events.

Metabolic syndrome and ischaemic stroke in non-anticoagulated atrial fibrillation with low CHA2DS2-VASc scores

OBJECTIVE

Conflicting results have been reported on whether metabolic syndrome (MetS) confers an increased risk of ischaemic stroke in atrial fibrillation (AF). We investigated the risk of ischaemic stroke according to MetS in patients with AF who are not indicated for oral anticoagulants.

METHODS

A total of 76 015 oral anticoagulant-naïve patients with AF with low Congestive Heart Failure, Hypertension, Age ≥75 years (Doubled), Diabetes Mellitus, Stroke (Doubled), Vascular Disease, Age 65-74 years, Sex Category (Female) (CHA2DS2-VASc) score (0 and 1) were included from the National Health Information Database. The risk of ischaemic stroke was evaluated according to MetS, the number of MetS components (metabolic burden), and individual metabolic components defined by health examination data within 2 years of AF diagnosis.

RESULTS

MetS was prevalent among 21 570 (28.4%) of the entire study population (mean age 49.8±11.1 years, mean CHA2DS2-VASc score 0.7±0.5). During a mean follow-up of 5.1 years, ischaemic stroke occurred in 1395 (1.84%) patients. MetS was associated with a higher risk of ischaemic stroke (adjusted HR (aHR) 1.19, 95% CI 1.06 to 1.33, p=0.002). Patients with the highest number of MetS components (4 or 5) showed the greatest aHR of 1.38 (95% CI 1.13 to 1.69), whereas those with a single component had a marginal risk of ischaemic stroke (aHR 1.17, 95% CI 0.97 to 1.40). Elevated blood pressure and increased waist circumference were independent components associated with 1.48-fold and 1.15-fold higher risks of ischaemic stroke, respectively.

CONCLUSION

Among AF patients with CHA2DS2-VASc scores of 0 and 1 with no anticoagulation, MetS is associated with an increased risk of ischaemic stroke. Given the linear incremental association between metabolic burden and ischaemic stroke, the integrated management of metabolic derangements in AF is required.

Current and Future Use of Artificial Intelligence in Electrocardiography

Artificial intelligence (AI) is increasingly used in electrocardiography (ECG) to assist in diagnosis, stratification, and management. AI algorithms can help clinicians in the following areas: (1) interpretation and detection of arrhythmias, ST-segment changes, QT prolongation, and other ECG abnormalities; (2) risk prediction integrated with or without clinical variables (to predict arrhythmias, sudden cardiac death, stroke, and other cardiovascular events); (3) monitoring ECG signals from cardiac implantable electronic devices and wearable devices in real time and alerting clinicians or patients when significant changes occur according to timing, duration, and situation; (4) signal processing, improving ECG quality and accuracy by removing noise/artifacts/interference, and extracting features not visible to the human eye (heart rate variability, beat-to-beat intervals, wavelet transforms, sample-level resolution, etc.); (5) therapy guidance, assisting in patient selection, optimizing treatments, improving symptom-to-treatment times, and cost effectiveness (earlier activation of code infarction in patients with ST-segment elevation, predicting the response to antiarrhythmic drugs or cardiac implantable devices therapies, reducing the risk of cardiac toxicity, etc.); (6) facilitating the integration of ECG data with other modalities (imaging, genomics, proteomics, biomarkers, etc.). In the future, AI is expected to play an increasingly important role in ECG diagnosis and management, as more data become available and more sophisticated algorithms are developed.

Machine Learning and the Conundrum of Stroke Risk Prediction

Stroke is a leading cause of death worldwide. With escalating healthcare costs, early non-invasive stroke risk stratification is vital. The current paradigm of stroke risk assessment and mitigation is focused on clinical risk factors and comorbidities. Standard algorithms predict risk using regression-based statistical associations, which, while useful and easy to use, have moderate predictive accuracy. This review summarises recent efforts to deploy machine learning (ML) to predict stroke risk and enrich the understanding of the mechanisms underlying stroke. The surveyed body of literature includes studies comparing ML algorithms with conventional statistical models for predicting cardiovascular disease and, in particular, different stroke subtypes. Another avenue of research explored is ML as a means of enriching multiscale computational modelling, which holds great promise for revealing thrombogenesis mechanisms. Overall, ML offers a new approach to stroke risk stratification that accounts for subtle physiologic variants between patients, potentially leading to more reliable and personalised predictions than standard regression-based statistical associations.

Transient ischemic attack events and incident cardiovascular and non-cardiovascular complications: Observations from a large diversified multimorbid cohort

Background:

Transient ischemic attack (TIA) is a strong signal prompting the incidence of future cardiovascular and non-cardiovascular complications, in light of recent debate on the so-called “stroke-heart syndrome.” We aimed to investigate the relation of TIAs to incident clinical events.

Methods:

Patients were drawn from three health plans with a wide spectrum of age groups and a wide mix of socio-economic/disability status. Two TIA cohorts in a retrospective design were used to achieve the study specific aims: (i) to investigate the incidence of TIA and associated cardiovascular and non-cardiovascular complications within 30 and 90 days from the onset of incident TIA events; and (ii) to examine the potential risk factors for developing incident TIA events in the general population with/without a history of prior stroke.

Results:

The incident TIA cohort consisted of 53,716 patients with an average age of 64.2 years (SD 15.2) and 46.1% male. Following TIA, the incidence proportions of ischemic stroke within 30 and 90 days were 2.7% and 3.8%, respectively, and for incident acute coronary syndrome being 0.94 and 1.84, respectively. Ventricular arrhythmia had proportions of 1.2 and 2.14, respectively within 30 and 90 days, with acute heart failure having values of 0.49 and 0.923. About 45% or more of the cardiovascular and non-cardiovascular complications occurred in the first 30 days following the incident TIA cases. About one-third of the recurrent TIA cases followed the incident TIA cases within a span of 30 days. Amongst comorbidities with stroke in the comorbid history, prior stroke provided the strongest risk factor in terms of odds ratio (OR = 8.34, 95% CI 7.21–9.66) for incident TIA events. Age was strongly associated with incident TIA events. Without a prior history of stroke (ischemic stroke/transient ischemic attack/thrombo-embolic events), valvular disease was the strongest risk factor from among the comorbidities (OR-1.87, 95% CI 1.51–2.32). Age also provided strong associations with incident TIA events.

Conclusions:

Following a TIA, there was a high risk of stroke, acute coronary syndrome, ventricular arrhythmia, acute heart failure, and non-cardiovascular complications.

Risk factors for cardiovascular disease in patients with metabolic-associated fatty liver disease: a machine learning approach

BACKGROUND

Nonalcoholic fatty liver disease is associated with an increased cardiovascular disease (CVD) risk, although the exact mechanism(s) are less clear. Moreover, the relationship between newly redefined metabolic-associated fatty liver disease (MAFLD) and CVD risk has been poorly investigated. Data-driven machine learning (ML) techniques may be beneficial in discovering the most important risk factors for CVD in patients with MAFLD.

METHODS

In this observational study, the patients with MAFLD underwent subclinical atherosclerosis assessment and blood biochemical analysis. Patients were split into two groups based on the presence of CVD (defined as at least one of the following: coronary artery disease; myocardial infarction; coronary bypass grafting; stroke; carotid stenosis; lower extremities artery stenosis). The ML techniques were utilized to construct a model which could identify individuals with the highest risk of CVD. We exploited the multiple logistic regression classifier operating on the most discriminative patient's parameters selected by univariate feature ranking or extracted using principal component analysis (PCA). Receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) were calculated for the investigated classifiers, and the optimal cut-point values were extracted from the ROC curves using the Youden index, the closest to (0, 1) criteria and the Index of Union methods.

RESULTS

In 191 patients with MAFLD (mean age: 58, SD: 12 years; 46% female), there were 47 (25%) patients who had the history of CVD. The most important clinical variables included hypercholesterolemia, the plaque scores, and duration of diabetes. The five, ten and fifteen most discriminative parameters extracted using univariate feature ranking and utilized to fit the ML models resulted in AUC of 0.84 (95% confidence interval [CI]: 0.77-0.90, p < 0.0001), 0.86 (95% CI 0.80-0.91, p < 0.0001) and 0.87 (95% CI 0.82-0.92, p < 0.0001), whereas the classifier fitted over 10 principal components extracted using PCA followed by the parallel analysis obtained AUC of 0.86 (95% CI 0.81-0.91, p < 0.0001). The best model operating on 5 most discriminative features correctly identified 114/144 (79.17%) low-risk and 40/47 (85.11%) high-risk patients.

CONCLUSION

A ML approach demonstrated high performance in identifying MAFLD patients with prevalent CVD based on the easy-to-obtain patient parameters.

Clinical utility and prognostic implications of the 4S-AF scheme: Report from Asia Pacific Heart Rhythm Society Atrial Fibrillation Registry

BACKGROUND

The 4S-AF classification scheme comprises of four domains (stroke risk [St], symptoms [Sy], severity of atrial fibrillation (AF) burden [Sb] and substrate [Su]), which has been recommended in the 2020 ESC guidelines to characterize and evaluate patients with AF.

OBJECTIVES

We aimed to determine whether the 4S-AF scheme would be useful for AF characterization and provides prognostic information in a large contemporary prospective Asian registry conducted by the Asia Pacific Heart Rhythm Society (APHRS).

METHODS

Among 4666 patients enrolled in APHRS registry, 3586 of them whose data about left atrial (LA) dimension and European Heart Rhythm Association (EHRA) symptom score were available have constituted as the study population. The 4S-AF score was calculated as the sum of each domain with a maximum score of 9. The clinical endpoint was defined as the 1-year composite risk of any thromboembolic event, ischaemic stroke, heart failure, acute coronary syndrome, significant coronary artery disease requiring coronary intervention and all-cause mortality.

RESULTS

Based on the 4S-AF domains, 86.7% were 'non-low risk' for stroke; 94.3% had EHRA Class I-II, 48.5% were newly diagnosed or paroxysmal AF; and only 8.4% had no cardiovascular risk factors or LA enlargement. The risk of clinical events was higher in patients who were 'non-low risk' for stroke (aOR 2.175, 95% CI 1.060-4.461), with permanent AF (aOR 1.579, 95% CI 1.106-2.225) and increasing points for substrate (aORs 2.376-4.968 from score 2 to 4). When compared to the first tertile of 4S-AF score (0-3 points), patients in the second tertile (4-5 points) had approximately 2.5-fold increase in adverse events (OR 2.478, 95% CI 1.678-3.661, p < .001), while those in the third tertile (6-9 points), had a 3.5-fold increase (OR 3.484, 95% CI 2.322-5.226, p < .001), both without significant differences between the 5 participating countries (p for interaction > .05). If all 4S-AF domains were appropriately treated, this was associated with a lower risk of composite clinical outcomes (aOR 0.384, p < .001; p for interaction for different countries = .234).

CONCLUSIONS

Categorization according to the 4S-AF scheme can be related to the risk of the composite adverse event rate in Asian AF patients, and appropriate treatments based on the 4S-AF scheme resulted in better clinical outcomes. These observations support the characterization and management according to the 4S-AF scheme in Asian patients.

Habitual Alcohol Intake and Risk of Atrial Fibrillation in Young Adults in Korea

Importance

Guidelines recommend that all risk factors for early-onset atrial fibrillation, including lifestyle factors, be proactively managed, considering the poor prognosis of the disease. Not much is known about the association of cumulative alcohol intake with the risk of atrial fibrillation in young adults aged 20 to 39 years, especially among heavy drinkers.

Objective

To explore the association of alcohol consumption with the risk of incident atrial fibrillation in young adults.

Design, Setting, and Participants

Using the National Health Insurance Service database, a nationwide population-based cohort study of adults aged 20 to 39 years without prior atrial fibrillation who underwent 4 serial annual health examinations between 2009 and 2012 was conducted. The cumulative alcohol consumption burden over 4 years was calculated by assigning 1 point to more than moderate drinking (≥105 g of alcohol per week) each year. Additionally, a semiquantitative cumulative burden was calculated by assigning 0, 1, 2, and 3 points to non, mild (<105 g per week), moderate (105-210 g per week), and heavy (≥210 g per week) drinking, respectively. Data were analyzed from May to June 2021.

Exposure

Amount of alcohol intake in 4 years.

Main Outcomes and Measures

The primary outcome was incident atrial fibrillation during the follow-up period.

Results

A total of 1 537 836 participants (mean [SD] age 29.5 [4.1] years, 1 100 099 [71.5%] male) were included in the final analysis. According to the 4-year cumulative burden of alcohol consumption stratified by moderate to heavy drinking, 889 382 participants (57.8%) were in the burden 0 group, 203 374 participants (13.2%) in the burden 1 group, 148 087 participants (9.6%) in the burden 2 group, 144 023 participants (9.4%) in the burden 3 group, and 152 970 participants (9.9%) in the burden 4 group. During a median (IQR) follow-up of 6.13 (4.59-6.48) years, atrial fibrillation was newly diagnosed in 3066 participants (0.36 per 1000 person-years). Participants with a cumulative burden of 4 points who continued more than moderate drinking for 4 years showed a 25% higher risk of atrial fibrillation compared with 0-point participants who kept non-to-mild drinking over 4 years (adjusted HR, 1.25; 95% CI, 1.12-1.40). In a semiquantitative analysis, participants who sustained heavy drinking for 4 consecutive years were associated with a 47% higher atrial fibrillation risk than those who remained nondrinkers over 4 years (aHR, 1.47, CI 1.18-1.83).

Conclusions and Relevance

Persistent moderate to heavy drinking and higher cumulative alcohol consumption burden might increase the risk of atrial fibrillation even in young adults aged 20 to 39 years.

Stroke Prevention in Atrial Fibrillation: A Scientific Statement of JACC: Asia (Part 2)

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and is associated with substantial increases in the risk for stroke and systemic thromboembolism. With the successful introduction of the first non-vitamin K antagonistdirect oral anticoagulant agent (NOAC) in 2009, the role of vitamin K antagonists has been replaced in most clinical settings except in a few conditions for which NOACs are contraindicated. Data for the use of NOACs in different clinical scenarios have been accumulating in the past decade, and a more sophisticated strategy for patients with AF is now warranted. JACC: Asia recently appointed a working group to summarize the most updated information regarding stroke prevention in AF. The aim of this statement is to provide possible treatment options in daily practice. Local availability, cost, and patient comorbidities should also be considered. Final decisions may still need to be individualized and based on clinicians' discretion. This is part 2 of the statement.

Incident and recurrent myocardial infarction (MI) in relation to comorbidities: Prediction of outcomes using machine-learning algorithms

BACKGROUND

To date, incident and recurrent MI remains a major health issue worldwide, and efforts to improve risk prediction in population health studies are needed. This may help the scalability of prevention strategies and management in terms of healthcare cost savings and improved quality of care.

METHODS

We studied a large-scale population of 4.3 million US patients from different socio-economic and geographical areas from three health plans (Commercial, Medicare, Medicaid). Individuals had medical/pharmacy benefits for at least 30 months (2 years for comorbid history and followed up for 6 months or more for clinical outcomes). Machine-learning (ML) algorithms included supervised (logistic regression, neural network) and unsupervised (decision tree, gradient boosting) methodologies. Model discriminant validity, calibration and clinical utility were performed separately on allocated test sample (1/3 of original data).

RESULTS

In the absence of MI in comorbid history, the overall incidence rates were 0.442 cases/100 person-years and in the presence of MI history, 0.652. ML algorithms showed that supervised formulations had incrementally higher discriminant validity than unsupervised techniques (e.g., for incident MI outcome in the absence of MI in comorbid history: logistic regression "LR" - c index 0.921, 95%CI 0.920-0.922; neural network "NN" - c index 0.914, 95%CI 0.913-0.915; gradient boosting "GB" - c index 0.902, 95%CI 0.900-0.904; decision tree "DT" - c index 0.500, 95%CI 0.495-0.505). Calibration and clinical utility showed good to excellent results.

CONCLUSION

ML algorithms can substantially improve the prediction of incident and recurrent MI particularly in terms of the non-linear formulation. This approach may help with improved risk prediction, allowing implementation of cardiovascular prevention strategies across diversified sub-populations with different clusters of complexity.

Targeted vs. full population screening costs for incident atrial fibrillation and AF-related stroke for a healthy population aged 65 years in the United Kingdom

AIMS

Atrial Fibrillation (AF) is the most common sustained heart arrhythmia and a major preventable cause of stroke. Stroke accounts for a large amount of health and social care funding and over the coming years is likely to place an increasing cost burden on the wider UK health care system. We therefore need to understand how an opportunistic AF screening programme would impact on healthcare costs of AF (and AF-related stroke) for the NHS.

METHODS AND RESULTS

Using UK population forecasts and prior published data we initially calculated the number of people to be screened, newly-diagnosed and treated for Atrial Fibrillation (AF), and the associated costs of such a programme for all 65 year olds and for just a 'high risk' group. The reduction in the number of stroke cases recorded and the associated cost savings were subsequently calculated, for 2020 and the projected estimates over the following decade. The number of newly diagnosed AF patients at 65 years old for the two groups (all 65 year olds and for just a 'high risk' group) would be in 6754 and 797 in 2020, rising to 9200 and 1086 in 2030, respectively. In 2020 the cost of the screening programme for the two options would be £14.3m and £1.7m. If AF is medicated and monitored then there would be a subsequent reduction in the number of stroke cases in 2020 by 4323 or 510 depending on the group screened, with associated savings of £394.2m and £46.5m,  respectively. Focussing on 2030 and should opportunistic screenings for AF be introduced at age 65, with subsequent treatment, it is predicted to reduce the number of stroke cases over the decade by 5888 if all 65 year olds are screened and 695 if just the high risk group are screened. If the number of strokes can be reduced by treating these screened AF patients, we would substantially reduce the health and social care costs of stroke by £654.6m and £77.3m,  respectively.

CONCLUSION

The number of newly diagnosed AF patients at age 65 will rise over the decade between 2020 and 2030. Screening and treatment of AF will substantially reduce the health and social care costs of AF-related stroke in the NHS.

Incidence and Complications of Atrial Fibrillation in a Low Socioeconomic and High Disability United States (US) Population: A Combined Statistical and Machine Learning Approach

BACKGROUND

Poor socioeconomic status coupled with individual disability is significantly associated with incident atrial fibrillation (AF) and AF-related adverse outcomes, with the information currently lacking for US cohorts. We examined AF incidence/complications and the dynamic nature of associated risk factors in a large socially disadvantaged US population.

METHODS

A large population representing a combined poor socioeconomic status/disability (Medicaid program) was examined from diverse geographical regions across the US continent. The target population was extracted from administrative databases with patients possessing medical/pharmacy benefits. This retrospective cohort study was conducted from Jan 1, 2016, to Sep 30, 2021, and was limited to 18- to 80-year age group drawn from the Medicaid program. Descriptive and inferential statistics (parametric: logistic regression and neural network) were applied to all computations using a combined statistical and machine learning (ML) approach.

RESULTS

A total of 617413 individuals participated in the study, with mean age of 41.7 years (standard deviation "SD" 15.2) and 65.6% female patients. Seven distinct groups were identified with different combinations of low socioeconomic status and disability constraints. The overall crude AF incidence rate was 0.49 cases/100 person-years (95% confidence limit "CI" 0.40-0.58), with the lowest rate for the younger group (temporary assistance for needy family "TANF") (0.20, 95%CI 0.18-0.21), the highest rates for the older groups (age, blindness, or disability "ABD" duals-1.51, 95% CI 1.31-1.58; long-term services and support "LTSS" duals-1.45, 95% CI 1.31-1.58), and the remaining four other groups in between the lower and upper rates. Based on independent effects after accounting for confounders in main effect modeling, the point estimates of odds ratios for AF status with various clinical outcomes were as follows: stroke (2.69, 95% CI 2.53-2.85); heart failure (6.18, 95% CI 5.86-6.52); myocardial infarction (3.71, 95% CI 3.49-3.94); major bleeding (2.26, 95% CI 2.14-2.38); and cognitive impairment (1.74, 95% CI 1.59-1.91). A logistic regression-based ML model produced excellent discriminant validity for high-risk AF outcomes (c "concordance" index based on training data 0.91, 95%CI 0.891-0.929), together with similar measures for external validity, calibration, and clinical utility. The performance measures for the ML models predicting associated complications with high-risk AF cases were good to excellent.

CONCLUSIONS

A combination of low socioeconomic status and disability contributes to AF incidence and complications, elevating risks to higher levels relative to the general population. ML algorithms can be used to identify AF patients at high risk of clinical events. While further research is definitely in need on this socially important issue, the reported investigation is unique in which it integrates the general case about the subject due to the different ethnic groups around the world under a unified culture stemming from residing in the US.

2021 Focused update of the 2017 consensus guidelines of the Asia Pacific Heart Rhythm Society (APHRS) on stroke prevention in atrial fibrillation

The consensus of the Asia Pacific Heart Rhythm Society (APHRS) on stroke prevention in atrial fibrillation (AF) has been published in 2017 which provided useful clinical guidance for cardiologists, neurologists, geriatricians, and general practitioners in Asia-Pacific region. In these years, many important new data regarding stroke prevention in AF were reported. The Practice Guidelines subcommittee members comprehensively reviewed updated information on stroke prevention in AF, and summarized them in this 2021 focused update of the 2017 consensus guidelines of the APHRS on stroke prevention in AF. We highlighted and focused on several issues, including the importance of AF Better Care (ABC) pathway, the advantages of non-vitamin K antagonist oral anticoagulants (NOACs) for Asians, the considerations of use of NOACs for Asian patients with AF with single 1 stroke risk factor beyond gender, the role of lifestyle factors on stroke risk, the use of oral anticoagulants during the "coronavirus disease 2019" (COVID-19) pandemic, etc. We fully realize that there are gaps, unaddressed questions, and many areas of uncertainty and debate in the current knowledge of AF, and the physician's decision remains the most important factor in the management of AF.