Clinical Risk Score for the Prediction of Incident Atrial Fibrillation: Derivation in 7 220 654 Taiwan Patients With 438 930 Incident Atrial Fibrillations During a 16-Year Follow-Up

Impact of shared decision-making in Taiwanese patients with atrial fibrillation eligible for novel oral anticoagulant therapy

BACKGROUND/PURPOSE

Shared decision-making (SDM) promotes patient awareness about medical conditions and treatments, facilitating patient involvement in care decisions. This two-stage multicenter study evaluated impacts of SDM in Taiwanese adults with atrial fibrillation (AF) eligible for novel oral anticoagulant (NOAC) therapy.

METHODS

Participants were NOAC-naïve (part I) or dabigatran-experienced (part II). During Stage I, part I participants (n = 124) completed a semi-structured survey (understanding evaluation sections only) before and after viewing SDM materials on stroke prevention for AF. Surveys collected data on anxiety about AF, confidence in healthcare professionals, usefulness of the SDM materials, and perception of different NOACs. During Stage II, part I participants after being prescribed NOACs, and part II participants completed another survey to compare impacts of SDM.

RESULTS

During Stage I, dabigatran was the preferred NOAC after viewing the SDM materials among 90% of part I participants. During Stage II, both part I (n = 87) and part II participants (n = 104) completed another survey. Fewer part I participants were anxious about AF (p < 0.01), and more had confidence in healthcare professionals (p < 0.01) after viewing SDM materials than before. Most part I participants (≥90%) rated the SDM materials as "very helpful". In Stage II, participants viewing SDM before initiating dabigatran had lower anxiety (part I, 43%; part II, 53%; p < 0.01) and a higher trust (part I, 92%; part II, 84%; p < 0.01).

CONCLUSION

In conclusion, SDM reduced anxiety and improved trust in healthcare professionals among NOAC-naïve participants with AF.

Association between antithrombotic drug regimen changes and clinical outcomes after stroke in atrial fibrillation

BACKGROUND

The impact of post-stroke antithrombotic regimen in atrial fibrillation is uncertain.

OBJECTIVE

This study aimed to describe antithrombotic therapy prescribing patterns after ischemic stroke and the impact on outcomes.

METHODS

A total of 23,165 patients with atrial fibrillation experiencing ischemic stroke were identified. Subsequent post-stroke events included recurrent ischemic stroke, intracranial hemorrhage, major bleeding, mortality, and composite outcomes.

RESULTS

Of those who were nonanticoagulated before a stroke, 33.5% remained nonanticoagulated and 39.2% were prescribed only antiplatelet agents (APs) after a stroke. Compared with non-vitamin K antagonist oral anticoagulants (NOACs) after stroke, there was a significant increase in ischemic stroke and mortality in nonanticoagulated patients (adjusted hazard ratio [aHR], 2.09 and 3.92) and AP users (aHR, 1.32 and 1.28). Post-stroke warfarin was associated with a significantly increased risk of major bleeding compared with NOACs (aHR, 1.23). Of 769 patients receiving NOACs before stroke and continuing NOACs after stroke, those switching to a different NOAC were associated with significantly higher risk of ischemic stroke (aHR, 2.07) and composite outcomes (aHR, 1.36-1.85) with no difference in intracranial hemorrhage, major bleeding, or mortality compared with those receiving the same NOAC after stroke. Of patients receiving NOACs before stroke, the risks of clinical events were similar between patients taking NOACs alone and those taking NOAC plus AP after stroke.

CONCLUSION

NOAC alone after stroke was associated with a better clinical outcome compared with nonanticoagulation, AP, or warfarin. Of patients already taking NOACs before stroke, the addition of AP did not confer additional benefits compared with NOACs alone. A change of NOAC types after stroke was associated with a 2-fold higher risk of ischemic stroke and composite outcomes.

External validation of the FAscore scale to evaluate the risk of atrial fibrillation in patients with arterial hypertension

BACKGROUND AND AIM

To use a risk scale or predictive model outside the population of origin, it is necessary to evaluate the predictive indicators through external validation. The aim was to validate the FAscore, originally constructed in hypertensive patients in primary care in the Valencian Region, in an external cohort with hypertension in primary care in the Basque Country.

METHODS

A retrospective cohort study was designed to perform an external validation of the FAscore app in patients affiliated with 26 health centers in the municipality of Bilbao. The area under the ROC curve and predictive indicators were calculated with their 95% confidence intervals.

RESULTS

Thirty-six thousand eight hundred nine patients were included: 53.6% (n=19,719) were women, the mean age was 75.1 years, 41.8% (n=15,381). Over the four-year follow-up period, 1420 patients were diagnosed with AF (cumulative incidence 3.9%). The median risk estimated by FAscore was 4.5%, and the 5th, 25th, 75th, and 95th percentiles were 1.0%, 2.5%, 6.1%, and 14.8%, respectively. The ROC curve for the risk estimated by FAscore and the cases of atrial fibrillation observed was AUC 0.715 (95% CI 0.703-0.727). The 5% risk cutoff provides a sensitivity of 70.8%, specificity of 61.0%, positive predictive value of 6.8%, negative predictive value of 98.1%, and positive and negative likelihood ratios of 1.82 and 0.48, respectively.

CONCLUSION

This study reports on the external validation of the atrial fibrillation risk scale in hypertensive patients, which shows an acceptable predictive capacity. The best-performing risk cutoff, providing good predictive indicators, can be set at 5%.

Role of the Electrocardiogram for Identifying the Development of Atrial Fibrillation

Atrial fibrillation (AF), a prevalent cardiac arrhythmia, is associated with increased morbidity and mortality worldwide. Stroke, the leading cause of serious disability in the United States, is among the important complications of this arrhythmia. Recent studies have demonstrated that certain clinical variables can be useful in the prediction of AF development in the future. The electrocardiogram (ECG) is a simple and cost-effective technology that is widely available in various healthcare settings. An emerging body of evidence has suggested that ECG tracings preceding the development of AF can be useful in predicting this arrhythmia in the future. Various variables on ECG especially different P wave parameters have been investigated in the prediction of new-onset AF and found to be useful. Several risk models were also introduced using these variables along with the patient's clinical data. However, current guidelines do not provide a clear consensus regarding implementing these prediction models in clinical practice for identifying patients at risk of AF. Also, the role of intensive screening via ECG or implantable devices based on this scoring system is unclear. The purpose of this review is to summarize AF and various related terminologies and explain the pathophysiology and electrocardiographic features of this tachyarrhythmia. We also discuss the predictive electrocardiographic features of AF, review some of the existing risk models and scoring system, and shed light on the role of monitoring device for screening purposes.

Risk calculator for incident atrial fibrillation across a range of prediction horizons

BACKGROUND

The increasing burden of atrial fibrillation (AF) emphasizes the need to identify high-risk individuals for enrolment in clinical trials of AF screening and primary prevention. We aimed to develop prediction models to identify individuals at high-risk of AF across prediction horizons from 6-months to 10-years.

METHODS

We used secondary-care linked primary care electronic health record data from individuals aged ≥30 years without known AF in the UK Clinical Practice Research Datalink-GOLD dataset between January 2, 1998 and November 30, 2018; randomly divided into derivation (80%) and validation (20%) datasets. Models were derived using logistic regression from known AF risk factors for incident AF in prediction periods of 6 months, 1-year, 2-years, 5-years, and 10-years. Performance was evaluated using in the validation dataset with bootstrap validation with 200 samples, and compared against the CHA2DS2-VASc and C2HEST scores.

RESULTS

Of 2,081,139 individuals in the cohort (1,664,911 in the development dataset, 416,228 in the validation dataset), the mean age was 49.9 (SD 15.4), 50.7% were women, and 86.7% were white. New cases of AF were 7,386 (0.4%) within 6 months, 15,349 (0.7%) in 1 year, 38,487 (1.8%) in 5 years, and 79,997 (3.8%) by 10 years. Valvular heart disease and heart failure were the strongest predictors, and association of hypertension with AF increased at longer prediction horizons. The optimal risk models incorporated age, sex, ethnicity, and 8 comorbidities. The models demonstrated good-to-excellent discrimination and strong calibration across prediction horizons (AUROC, 95%CI, calibration slope: 6-months, 0.803, 0.789-0.821, 0.952; 1-year, 0.807, 0.794-0.819, 0.962; 2-years, 0.815, 0.807-0.823, 0.973; 5-years, 0.807, 0.803-0.812, 1.000; 10-years 0.780, 0.777-0.784, 1.010), and superior to the CHA2DS2-VASc and C2HEST scores. The models are available as a web-based FIND-AF calculator.

CONCLUSIONS

The FIND-AF models demonstrate high discrimination and calibration across short- and long-term prediction horizons in 2 million individuals. Their utility to inform trial enrolment and clinical decisions for AF screening and primary prevention requires further study.

The optimal antithrombotic strategy for post-stroke patients with atrial fibrillation and extracranial artery stenosis-a nationwide cohort study

BACKGROUND

In post-stroke atrial fibrillation (AF) patients who have indications for both oral anticoagulant (OAC) and antiplatelet agent (AP), e.g., those with carotid artery stenosis, there is debate over the best antithrombotic strategy. We aimed to compare the risks of ischemic stroke, composite of ischemic stroke/major bleeding and composite of ischemic stroke/intracranial hemorrhage (ICH) between different antithrombotic strategies.

METHODS

This study included post-stroke AF patients with and without extracranial artery stenosis (ECAS) (n = 6390 and 28,093, respectively) identified from the Taiwan National Health Insurance Research Database. Risks of clinical outcomes and net clinical benefit (NCB) with different antithrombotic strategies were compared to AP alone.

RESULTS

The risk of recurrent ischemic stroke was higher for patients with ECAS than those without (12.72%/yr versus 10.60/yr; adjusted hazard ratio [aHR] 1.104, 95% confidence interval [CI] 1.052-1.158, p < 0.001). For patients with ECAS, when compared to AP only, non-vitamin K antagonist oral anticoagulant (NOAC) monotherapy was associated with lower risks for ischaemic stroke (aHR 0.551, 95% CI 0.454-0.669), the composite of ischaemic stroke/major bleeding (aHR 0.626, 95% CI 0.529-0.741) and the composite of ischaemic stroke/ICH (aHR 0.577, 95% CI 0.478-0.697), with non-significant difference for major bleeding and ICH. When compared to AP only, warfarin monotherapy was associated with higher risks of major bleeding (aHR 1.521, 95% CI 1.231-1.880), ICH (aHR 2.045, 95% CI 1.329-3.148), and the composite of ischaemic stroke and major bleeding. With combination of AP plus warfarin, there was an increase in ischaemic stroke, major bleeding, and the composite outcomes, when compared to AP only. NOAC monotherapy was the only approach associated with a positive NCB, while all other options (warfarin, combination of AP-OAC) were associated with negative NCB.

CONCLUSIONS

For post-stroke AF patients with ECAS, NOAC monotherapy was associated with lower risks of adverse outcomes and a positive NCB. Combination of AP with NOAC or warfarin did not offer any benefit, but more bleeding especially with AP-warfarin combination therapy.

Risk of Incident Atrial Fibrillation and Subsequent Use of Oral Anticoagulants in Patients with Dementia

BACKGROUND

 Dementia and atrial fibrillation (AF) have many shared risk factors. Besides, patients with dementia are under-represented in randomized trials, and even if AF is present, oral anticoagulants (OACs) are not prescribed frequently. This study aimed to report the incidence of newly diagnosed AF in dementia patients, and the impacts of use of vitamin K antagonist (VKA; e.g., warfarin) and non-VKA OAC (NOACs) on stroke and bleeding outcomes.

METHODS

 Our study utilized the Taiwan National Health Insurance Research Database. A total of 554,074 patients with dementia were compared with 554,074 age- and sex-matched patients without dementia regarding the risk of incident AF. Among patients with dementia who experienced incident AF, the risks of clinical events of patients treated with warfarin or NOACs were compared with those without OACs (reference group).

RESULTS

 The risk of incident AF was greater for patients with dementia compared with those without (adjusted hazard ratio [aHR]: 1.054; 95% confidence interval [CI]: 1.040-1.068 for all types of dementia, aHR: 1.035; 95% CI: 1.020-1.051 for presenile/senile dementia, and aHR: 1.125; 95% CI: 1.091-1.159 for vascular dementia). Among patients with dementia and experienced incident AF, warfarin use was associated with a higher risk of ischemic stroke (aHR: 1.290; 95% CI: 1.156-1.440), intracranial hemorrhage (ICH; aHR: 1.678; 95% CI: 1.346-2.090), and major bleeding (aHR: 1.192; 95% CI: 1.073-1.323) compared with non-OACs. NOAC use was associated with a lower risk of ischemic stroke (aHR: 0.421; 95% CI: 0.352-0.503) and composite risk of ischemic stroke or major bleeding (aHR: 0.544; 95% CI: 0.487-0.608) compared with non-OACs. These results were consistent among the patients after the propensity matching.

CONCLUSION

 In this large nationwide cohort, the risk of newly diagnosed AF was higher in patients with dementia (all dementia, presenile/senile dementia, and vascular dementia) compared with those without dementia. For patients with dementia who experienced incident AF, NOAC use was associated with a better clinical outcome compared with non-OAC. Patients with dementia require a holistic approach to their care and management, including the use of NOACs to reduce the risks of clinical events.

Performance of Different Risk Scores for the Detection of Atrial Fibrillation Among Patients With Cryptogenic Stroke

BACKGROUND

Atrial fibrillation (AF) is a frequent underlying cause of cryptogenic stroke (CS) and its detection can be increased using implantable cardiac monitoring (ICM). We sought to evaluate different risk scores and assess their diagnostic ability in identifying patients with CS with underlying AF on ICM.

METHODS

Patients with CS, being admitted to a single tertiary stroke center between 2017 and 2022 and receiving ICM, were prospectively evaluated. The CHA2DS2-VASc, HAVOC, Brown ESUS-AF, and C2HEST scores were calculated at baseline. The primary outcome of interest was the detection of AF, which was defined as at least 1 AF episode on ICM lasting for 2 consecutive minutes or more. The diagnostic accuracy measures and the net reclassification improvement were calculated for the 4 risk scores. Stroke recurrence was evaluated as a secondary outcome.

RESULTS

A total of 250 patients with CS were included, and AF was detected by ICM in 20.4% (n=51) during a median monitoring period of 16 months. Patients with CS with AF detection were older compared with the rest (P=0.045). The median HAVOC, Brown ESUS-AF, and C2HEST scores were higher among the patients with AF compared with the patients without AF (all P<0.05), while the median CHA2DS2-VASc score was similar between the 2 groups. The corresponding C statistics for CHA2DS2-VASc, HAVOC, Brown ESUS-AF, and C2HEST for AF prediction were 0.576 (95% CI, 0.482-0.670), 0.612 (95% CI, 0.523-0.700), 0.666 (95% CI, 0.587-0.746), and 0.770 (95% CI, 0.699-0.839). The C2HEST score presented the highest diagnostic performance based on C statistics (P<0.05 after correction for multiple comparisons) and provided significant improvement in net reclassification for AF detection (>70%) compared with the other risk scores. Finally, stroke recurrence was documented in 5.6% of the study population, with no difference regarding the 4 risk scores between patients with and without recurrent stroke.

CONCLUSIONS

The C2HEST score was superior to the CHA2DS2-VASc, HAVOC, and Brown ESUS-AF scores for discriminating patients with CS with underlying AF using ICM.

Higher Risk of Incident Hyperthyroidism in Patients With Atrial Fibrillation

BACKGROUND

Atrial fibrillation (AF) has been linked to increased hyperthyroidism risk, but contributing factors are unclear.

OBJECTIVE

We aimed to investigate whether AF could predict hyperthyroidism and related risk factors.

METHODS

This retrospective cohort study was conducted in a tertiary medical institution and included patients aged 18 years or older with AF but without hyperthyroidism at diagnosis. The endpoint was defined as newly diagnosed hyperthyroidism during the follow-up period.

RESULTS

The study cohort included 8552 participants. Patients who developed new hyperthyroidism were younger and the proportion of females was higher. They had fewer comorbidities, including diabetes (26% vs 29%, P = .121), hypertension (51% vs 58%, P < .001), coronary artery disease (17% vs 25%, P < .001), stroke (16% vs 22%, P < .001), and end-stage renal disease (ESRD) (6% vs 10%, P = .001). The CHADS2 score was lower in patients with hyperthyroidism (1.74 vs 2.05, P = .031), but there was no statistically significant difference in the CHA2DS2-VASc and HAS-BLED score. Cox regression analysis identified younger age, female gender, history of congestive heart failure, hypertension, diabetes, non-ESRD status, and lower CHADS2 score but not CHA2DS2-VASc as independent predictors of incident hyperthyroidism during follow-up. We also propose a novel, simple risk stratification score (SAD HEC2 score) with excellent predictive power for incident hyperthyroidism during follow-up.

CONCLUSION

Our results provide insight into clinical risk factors for the development of hyperthyroidism in AF patients, as identified by the novel SAD HEC2 score. AF appears to be a common precursor of hyperthyroidism.

A Discussion of the Contemporary Prediction Models for Atrial Fibrillation

Atrial Fibrillation is a complex disease state with many environmental and genetic risk factors. While there are environmental factors that have been shown to increase an individual's risk of atrial fibrillation, it has become clear that atrial fibrillation has a genetic component that influences why some patients are at a higher risk of developing atrial fibrillation compared to others. This review will first discuss the clinical diagnosis of atrial fibrillation and the corresponding rhythm atrial flutter. We will then discuss how a patients' risk of stroke can be assessed by using other clinical co-morbidities. We will then review the clinical risk factors that can be used to help predict an individual patient's risk of atrial fibrillation. Many of the clinical risk factors have been used to create several different risk scoring methods that will be reviewed. We will then discuss how genetics can be used to identify individuals who are at higher risk for developing atrial fibrillation. We will discuss genome-wide association studies and other sequencing high-throughput sequencing studies. Finally, we will touch on how genetic variants derived from a genome-wide association studies can be used to calculate an individual's polygenic risk score for atrial fibrillation. An atrial fibrillation polygenic risk score can be used to identify patients at higher risk of developing atrial fibrillation and may allow for a reduction in some of the complications associated with atrial fibrillation such as cerebrovascular accidents and the development of heart failure. Finally, there is a brief discussion of how artificial intelligence models can be used to predict which patients will develop atrial fibrillation.

Non-vitamin K antagonist oral anticoagulants in atrial fibrillation patients without previous oral anticoagulants or stable under warfarin: a nationwide cohort study

AIMS

Investigations on non-VKA oral anticoagulants (NOACs) for atrial fibrillation (AF) patients without taking any oral anticoagulants (OACs) or staying well on warfarin were limited. We aimed to investigate the associations between stroke prevention strategies and clinical outcomes among AF patients who were previously well without taking any OACs or stayed well on warfarin for years.

METHODS AND RESULTS

The retrospective analysis included a total of 54 803 AF patients who did not experience an ischaemic stroke or intra-cranial haemorrhage (ICH) for years after AF was diagnosed. Among these patients, 32 917 patients who did not receive OACs were defined as the 'original non-OAC cohort' (group 1), and 8007 patients who continuously received warfarin were defined as the 'original warfarin cohort' (group 2). In group 1, compared to non-OAC, warfarin showed no significant difference in ischaemic stroke (aHR 0.979, 95%CI 0.863-1.110, P = 0.137) while those initiated NOACs were associated with lower risk (aHR 0.867, 95%CI 0.786-0.956, P = 0.043). When compared to warfarin, the composite of 'ischaemic stroke or ICH' and 'ischaemic stroke or major bleeding' was significantly lower in the NOAC initiator with an aHR of 0.927 (95%CI 0.865-0.994; P = 0.042) and 0.912 (95%CI 0.837-0.994; P < 0.001), respectively. In group 2, when compared to warfarin, those shifted to NOACs were associated with a lower risk of ischaemic stroke (aHR 0.886, 95%CI 0.790-0.993, P = 0.002) and major bleeding (aHR 0.849, 95%CI 0.756-0.953, P < 0.001).

CONCLUSIONS

The NOACs should be considered for AF patients who were previously well without taking OACs and those who were free of ischaemic stroke and ICH under warfarin for years.

Blood pressure, hypertension and the risk of atrial fibrillation: a systematic review and meta-analysis of cohort studies

Elevated blood pressure and hypertension have been associated with increased risk of atrial fibrillation in a number of epidemiological studies, however, the strength of the association has differed between studies. We conducted a systematic review and meta-analysis of the association between blood pressure and hypertension and atrial fibrillation. PubMed and Embase databases were searched for studies of hypertension and blood pressure and atrial fibrillation up to June 6th 2022. Cohort studies reporting adjusted relative risk (RR) estimates and 95% confidence intervals (CIs) of atrial fibrillation associated with hypertension or blood pressure were included. A random effects model was used to estimate summary RRs. Sixty eight cohort studies were included in the meta-analysis. The summary RR was 1.50 (95% CI: 1.42-1.58, I2 = 98.1%, n = 56 studies) for people with hypertension compared to those without hypertension (1,080,611 cases, 30,539,230 participants), 1.18 (95% CI: 1.16-1.21, I2 = 65.9%, n = 37 studies) per 20 mmHg increase in systolic blood pressure (346,471 cases, 14,569,396 participants), and 1.07 (95% CI: 1.03-1.11, I2 = 91.5%, n = 22 studies) per 10 mmHg increase in diastolic blood pressure (332,867 cases, 14,354,980 participants). There was evidence of a nonlinear association between diastolic blood pressure and atrial fibrillation with a steeper increase in risk at lower levels of diastolic blood pressure, but for systolic blood pressure the association appeared to be linear. For both systolic and diastolic blood pressure, the risk increased even within the normal range of blood pressure and persons at the high end of systolic and diastolic blood pressure around 180/110 mmHg had a 1.8-2.3 fold higher risk of atrial fibrillation compared to those with a blood pressure of 90/60 mmHg. These results suggest that elevated blood pressure and hypertension increases the risk of atrial fibrillation and there is some increase in risk even within the normal range of systolic and diastolic blood pressure.

Stroke prevention in rural residents: development of a simplified risk assessment tool with artificial intelligence

BACKGROUND

Limited studies have focused on the risk assessment of stroke in rural regions. Moreover, the application of artificial intelligence in stroke risk scoring system is still insufficient. This study aims to develop a simplified and visualized risk score with good performance and convenience for rural stroke risk assessment, which is combined with a machine learning (ML) algorithm.

METHODS

Participants of the Henan Rural Cohort were enrolled in this study. The total participants (n = 38,322) were randomly split into a train set and a test set in the ratio of 7:3. An ML algorithm was used to select variables and the logistic regression was then applied to construct the scoring system. The C-statistic and the Brier score (BS) were used to evaluate the discrimination and calibration. The Framingham stroke risk profile (FSRP) and the self-reported stroke risk function (SRSRF) were chosen to be compared.

RESULTS

The Rural Stroke Risk Score (RSRS) was produced in this study, including age, drinking status, triglyceride, type 2 diabetes mellitus, hypertension, waist circumference, and family history of stroke. On validation, the C-statistic was 0.757 (95% CI 0.749-0.765) and the BS was 0.058 in the test set. In addition, the discrimination of RSRS was 6.02% and 7.34% higher than that of the FSRP and SRSRF, respectively.

CONCLUSIONS

A well-performed scoring system for assessing stroke risk in rural residents was developed in this study. This risk score would facilitate stroke screening and the prevention of cardiovascular disease in economically underdeveloped areas.

PAGln, an Atrial Fibrillation-Linked Gut Microbial Metabolite, Acts as a Promoter of Atrial Myocyte Injury

Phenylacetylglutamine (PAGln), a gut microbiota (GM)-derived metabolite, is associated with cardiovascular disease. Studies have shown that disordered GM participated in the progression of atrial fibrillation (AF), but the relationship between PAGln and AF is unclear. This study investigated the characteristics of PAGln in AF patients and its impact on atrial myocytes. Based on our previous metagenomic data, the relative abundance of porA, a critical bacterial enzyme for PAGln synthesis, exhibited an increased tendency in AF. In an independent cohort consisting of 42 controls without AF and 92 AF patients, plasma PAGln levels were higher in AF patients than in controls (p < 0.001) by immunoassay. Notably, PAGln exerted a predictive potential of AF with an AUC of 0.774 (p < 0.001), and a predictive model constructed based on the PAGln and Taiwan AF score further improved the predictive potential. Furthermore, a positive correlation was determined between PAGln and LA diameter. Subsequently, the effect of PAGln intervention was examined on HL-1 cells in vitro, revealing that PAGln increased apoptosis, reactive oxygen species (ROS) production, CaMKII and RyR2 activation and decreased cell viability. In conclusion, increased PAGln was associated with AF, and PAGln might contribute to the AF pathogenesis by promoting oxidative stress and apoptosis in atrial myocytes.

Stroke Prevention in Atrial Fibrillation: A Scientific Statement of JACC: Asia (Part 1)

Atrial fibrillation is the most common sustained cardiac arrhythmia and is associated with substantial increases in the risk of stroke and systemic thromboembolism. With the successful introduction of the first non-vitamin K antagonist direct oral anticoagulant (NOAC) in 2009, the role of vitamin K antagonists has been replaced in most clinical settings except in a few conditions when NOACs are contraindicated. Data for the use of NOACs in different clinical scenarios have been accumulating in the recent decade, and a more sophisticated strategy for atrial fibrillation patients is now warranted. JACC: Asia recently appointed a working group to summarize the most updated information regarding stroke prevention in AF. This statement aimed to provide possible treatment option 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 the part 1 of the whole statement.

Association between Age of Onset of Hypertension and Incident Atrial Fibrillation

We investigated whether age at hypertension (HTN) onset was associated with the risk of atrial fibrillation (AF) in the general population. This prospective longitudinal community-based cohort study included 9892 participants without AF at baseline, who underwent biennial electrocardiography for a median duration of 11.5 years. The participants were divided into five groups, consisting of a normotensive group (Group-N) and four HTN groups based on HTN onset age: <45 years (Group-H1); 45−54 years (Group-H2); 55−64 years (Group-H3); and ≥65 years (Group-H4). A multivariate Cox proportional hazards model showed that the presence of HTN at baseline was associated with higher AF risk (hazard ratio [HR], 1.93; 95% confidence interval [CI] 1.32−2.80). The participants in Group-H1 had the highest risk of AF (HR 3.18; CI 1.74−5.82), and the risk of AF decreased as HTN onset age increased across the four HTN groups (p for trend = 0.014). The AF onset age was significantly younger in participants in Group-H1 than in Groups-H2−H4. Early-onset HTN was associated with an increased risk of AF, and younger onset of AF in the general population. Surveillance for AF should be considered at a younger age in individuals with HTN.

Risk of Ischemic Bowel Disease in Patients With Atrial Fibrillation Receiving Warfarin or Non-vitamin K Antagonist Oral Anticoagulants

Background

Although atrial fibrillation (AF) is a risk factor for ischemic bowel disease, data regarding the incidence of ischemic bowel disease in patients with anticoagulated AF were limited.

Methods

The present study used the Taiwan NHIRD and included newly diagnosed patients with AF aged ≥ 20 years without ischemic bowel disease from 2012 to 2018. A total of 69,549 patients taking warfarin or non-vitamin K antagonist oral anticoagulants (NOACs) constituted the final study group. We aimed to study the incidence of ischemic bowel disease in patients with AF receiving warfarin or NOACs. Secondary endpoints were also analyzed, including ischemic stroke, systemic embolism, myocardial infarction, mortality, intracranial hemorrhage (ICH), major bleeding, and composite adverse events (ischemic bowel disease or ICH or major bleeding).

Results

There were 43,787 patients taking NOACs and 25,762 patients taking warfarin. The overall incidence rate of ischemic bowel disease was 0.036% per year and increased with the CHA2DS2-VASc scores [0.013% for patients with a CHA2DS2-VASc score of 0 (men) or 1 (women), 0.022% for those with a CHA2DS2-VASc score of 1 (men) or 2 (women), and 0.039% for those with a CHA2DS2-VASc score ≥ 2 (men) or ≥ 3 (women)]. The risk of ischemic bowel disease was similar between NOAC and warfarin groups (0.036%/year vs. 0.037%/year; adjusted hazard ratio 0.802, p = 0.430), whereas the NOAC group had a significantly lower risk of secondary endpoints compared to the warfarin group.

Conclusion

We reported the incidence of ischemic bowel disease in patients with anticoagulated AF from a nationwide cohort database and observed a positive correlation between the increase of CHA2DS2-VASc scores and the incidence rate. Moreover, NOAC was as effective as warfarin for the risk of ischemic bowel disease.

Is machine learning the future for atrial fibrillation screening?

Atrial fibrillation (AF) is the most common arrhythmia and causes significant morbidity and mortality. Early identification of AF may lead to early treatment of AF and may thus prevent AF-related strokes and complications. However, there is no current formal, cost-effective strategy for population screening for AF. In this review, we give a brief overview of targeted screening for AF, AF risk score models used for screening and describe the different screening tools. We then go on to extensively discuss the potential applications of machine learning in AF screening.

Exploring and Identifying Prognostic Phenotypes of Patients with Heart Failure Guided by Explainable Machine Learning

Identifying patient prognostic phenotypes facilitates precision medicine. This study aimed to explore phenotypes of patients with heart failure (HF) corresponding to prognostic condition (risk of mortality) and identify the phenotype of new patients by machine learning (ML). A unsupervised ML was applied to explore phenotypes of patients in a derivation dataset (n = 562) based on their medical records. Thereafter, supervised ML models were trained on the derivation dataset to classify these identified phenotypes. Then, the trained classifiers were further validated on an independent validation dataset (n = 168). Finally, Shapley additive explanations were used to interpret decision making of phenotype classification. Three patient phenotypes corresponding to stratified mortality risk (high, low, and intermediate) were identified. Kaplan−Meier survival curves among the three phenotypes had significant difference (pairwise comparison p < 0.05). Hazard ratio of all-cause mortality between patients in phenotype 1 (n = 91; high risk) and phenotype 3 (n = 329; intermediate risk) was 2.08 (95%CI 1.29−3.37, p = 0.003), and 0.26 (95%CI 0.11−0.61, p = 0.002) between phenotype 2 (n = 142; low risk) and phenotype 3. For phenotypes classification by random forest, AUCs of phenotypes 1, 2, and 3 were 0.736 ± 0.038, 0.815 ± 0.035, and 0.721 ± 0.03, respectively, slightly better than the decision tree. Then, the classifier effectively identified the phenotypes for new patients in the validation dataset with significant difference on survival curves and hazard ratios. Finally, age and creatinine clearance rate were identified as the top two most important predictors. ML could effectively identify patient prognostic phenotypes, facilitating reasonable management and treatment considering prognostic condition.

Modified Taiwan Atrial Fibrillation Score for the Prediction of Incident Atrial Fibrillation

Background

We have proposed the Taiwan AF score consisting of age, male sex, hypertension, heart failure, coronary artery disease, end-stage renal disease, and alcoholism to predict incident atrial fibrillation (AF) in Asian population. We hypothesized that the modified Taiwan AF score (mTaiwan AF score) excluding alcoholism remained useful for predicting new onset AF.

Methods

A total of 7,220,654 subjects aged ≥ 40 years without a past history of cardiac arrhythmia were identified from a national cohort, and 438,930 incident AF occurred during a 16-year follow-up with an incidence of 0.42 per 100 person-years. The mTaiwan AF score ranging between −2 and 14 and its predictive accuracy of incident AF was analyzed.

Results

The areas under the receiver operating characteristic curve (AUCs) of the mTaiwan AF scores in predicting AF are 0.861 for 1-year follow-up, 0.829 for 5-year follow-up, 0.795 for 10-year follow-up, and 0.751 for 16-year follow-up. The risk of incident AF increased from 0.05%/year for patients with a score of −2 to 6.98%/year for those having a score of 14. Patients were classified into three groups based on the tertile values of the mTaiwan AF scores—group 1 (score −2-3), group 2 (score 4-9) and group 3 (score 10-14). The annual risks of incident AF were 0.20, 1.33, and 3.36% for group 1, 2, and 3, respectively. Compared to patients in group 1, the hazard ratios of incident AF were 5.79 [95% confidence interval (CI) 3.75-7.75] for group 2 and 8.93 (95% CI 6.47-10.80) for group 3.

Conclusions

We demonstrated that the mTaiwan AF score based on age and clinical comorbidities could be used to predict incident AF in Asian population.