Feb 2026· Digital Health· Vol 12· 0 citations· 28 references
Medicine
TL;DR
In patients with cryptogenic stroke receiving an ICM, the ECG-AI score showed modest discrimination for AF detection, outperforming CHA2DS2-VA and HAVOC, but not Brown ESUS-AF, which indicates a possible role for AI-driven ECG analysis in risk stratification.
Abstract
Background and Aims Detecting subclinical atrial fibrillation (AF) and initiating anticoagulation therapy are critical for secondary stroke prevention after cryptogenic stroke. This study aimed to evaluate the effectiveness of traditional AF risk scores commonly used in clinical practice and compare with an artificial intelligence (AI)-driven ECG-derived AF prediction score. Methods A retrospective tertiary care center study identified all cryptogenic stroke/TIA patients admitted between 2017-2023 who received an implantable cardiac monitor (ICM). The European Society of Cardiology (ESC)-recommended risk factors and risk score components (i.e.,CHA2DS2-VA, Brown ESUS-AF, and HAVOC) from the 2024 guidelines for AF management, were analyzed using multivariate logistic regression to predict AF within one-year post-ICM insertion. The predictive performance of these risk scores and an AI-driven ECG algorithm for AF detection was assessed and compared. Results 230 cryptogenic stroke/TIA patients underwent ICM insertion. Only age (OR: 1.033, 95%CI 1.002-1.066) was associated with an increased likelihood of AF detection within one-year post-ICM insertion. The ECG-AI score demonstrated higher predictive power over CHA2DS2-VA and HAVOC (p=.002), and was the only score exceeding an AUROC value of 0.7, but did not outperform Brown ESUS-AF (p=.210). Patients with high scores had a more than two-fold increased likelihood of AF detection (HR: 2.7, 95%CI 1.2-5.8, p=.01). Conclusions In patients with cryptogenic stroke receiving an ICM, the ECG-AI score showed modest discrimination for AF detection, outperforming CHA2DS2-VA and HAVOC, but not Brown ESUS-AF. Age was the only significant clinical predictor. These findings indicate a possible role for AI-driven ECG analysis in risk stratification.
Background Atrial fibrillation detected after stroke (AFDAS) is clinically important, but AFDAS-specific risk tools for patients without known atrial fibrillation (AF) remain limited. We developed and temporally validated the Prediction of AF in Ischemic Stroke (PAFIS) score. Methods We retrospectively analyzed ischemic-stroke patients from the National Taiwan University Hospital Integrative Medical Data Center. The development cohort included patients hospitalized in 2010–2020 (n = 3406), and the temporal validation cohort included those hospitalized in 2021–2023 (n = 1366). Known AF (KAF) was defined as AF documented before stroke or within 14 days after stroke; AFDAS was defined as newly documented AF beyond 14 days among patients without KAF. Multivariable logistic regression restricted to KAF-free patients was used for score derivation. Discrimination, calibration, and time-to-AFDAS risk stratification were assessed. Results Among KAF-free patients, AFDAS was detected during routine clinical follow-up in 176 of 2175 (8.1%) in the development cohort and 148 of 1366 (10.8%) in the validation cohort. The final PAFIS score included age ≥ 75 years, female sex, valvular heart disease, left atrial diameter ≥ 40 mm, and tricuspid regurgitation peak gradient ≥30 mmHg. AUCs were 0.72 (95% CI, 0.68–0.76) in development and 0.65 (95% CI, 0.60–0.70) in validation. PAFIS outperformed CHA₂DS₂-VASc, HAVOC, and AF-ESUS, but not Brown ESUS-AF. Observed AFDAS detection rates increased across risk groups in both cohorts. Conclusions PAFIS provides a simple AFDAS-specific tool for selective post-stroke rhythm monitoring. Because AF ascertainment was based on routine clinical care without standardized prolonged monitoring, PAFIS predicts AF detection under routine practice rather than true AFDAS incidence. Prospective multicenter validation with standardized monitoring is warranted.
J. Hsu, Ting-Chuan Wang, Yen-Yun Yang et al.· International Journal of Car...· 1 citation
The CHA2DS2-VALa score significantly improves stroke risk stratification in AF by integrating LA diameter into conventional scoring, as echocardiographic measurement is widely available and reproducible.
S. Ömür, Emin Koyun, G. Genc Tapar et al.· Cardiovascular Electrophysio...· 0 citations
Background Artificial intelligence–enabled electrocardiography (AI-ECG) has emerged as a potential method for identifying atrial fibrillation (AF) from sinus rhythm. However, its clinical utility and interpretability in routine practice remain uncertain. Objectives The objective of the study was to assess the performance and explainability of an AI-integrated ECG system for AF risk stratification in a multicenter real-world cohort. Methods We enrolled 665 patients aged ≥40 years who underwent 12-lead ECGs using an AI-enabled electrocardiograph (FCP-9900). The device automatically assigned AF risk into 4 categories (low, mid-low, mid-high, and high). Machine-learning models—support vector machine, adaptive boosting, and artificial neural networks—were developed using clinical and ECG-derived variables. Internal validation used stratified 10-fold cross-validation, and external validation used an independent cohort. Feature contributions were assessed with SHapley Additive exPlanations. Results AF prevalence increased across AI-ECG risk categories, with significantly higher odds in the mid-high and high groups vs low. Model 2, which incorporated CHADS2 and CHA2DS2-VASc scores, achieved strong discrimination in internal and external validation (support vector machine AUC 1.00; adaptive boosting 0.97-0.98; artificial neural network 0.89-0.95), outperforming AI-ECG alone (area under the receiver operating characteristic curve: 0.64-0.69). SHapley Additive exPlanations analysis showed CHA2DS2-VASc as the most influential predictor, whereas AI-ECG provided modest incremental value. Conclusions AI-ECG provides rapid, low-cost AF risk estimation from a single sinus rhythm ECG; however, its predictive performance is modest compared with clinical scores. At present, AI-ECG may complement, but not replace, traditional risk stratification.
Kouki Matsuo, Y. Sobue, T. Miyake et al.· JACC: Advances· 0 citations
BACKGROUND AND AIMS
Accurate stroke-risk stratification is central to anticoagulation decision-making in patients with atrial fibrillation (AF), but conventional scores may not fully capture risk heterogeneity. We aimed to develop and externally validate an interpretable weighted score using a time-to-event framework.
METHODS
GLORIA-AF Phase II/III data were used to evaluate 17 baseline predictors using LASSO-penalized Cox regression with stability selection; coefficients were converted into integer weights. Performance was assessed using discrimination, calibration, integrated discrimination improvement (IDI), continuous net reclassification improvement (NRI), and decision-curve analysis. External validation was performed in EORP-AF and APHRS-AF registries.
RESULTS
Among 20,517 patients included in the derivation cohort (mean [SD] age, 69.9 [10.3] years; 9,196 women [44.8%]), 487 (2.4%) had stroke, and 17,397 (84.8%) were receiving anticoagulation at baseline. Ten selected predictors formed a 0-23-point score. The derived score achieved a C-index of 0.661 (95% CI, 0.636-0.685), higher than CHA2DS2-VA (0.626; P < 0.001), CHA2DS2-VASc (0.615; P < 0.001), and the unweighted score (P = 0.016), with no significant difference from the full Cox or machine learning models. In external validation (8,309 patients; 147 strokes), the C-index was 0.652 (95% CI, 0.614-0.690) versus 0.616 for CHA2DS2-VA (95%CI, 0.598-0.634; P < 0.001). IDI/NRI, calibration, and decision-curve analyses supported improved risk differentiation, close calibration, and generally greater net benefit than CHA2DS2-VA. The score retained higher discrimination than CHA2DS2-VA among patients without baseline anticoagulation (P < 0.001).
CONCLUSION
The GLORIA-AF Stroke Weighted Risk Score provides risk refinement beyond CHA2DS2-VA while retaining discrimination consistent with more complex models. External validation supports its transportability and potential adjunctive role in guideline-directed thromboembolic risk assessment.
The occurrence of NOAF was associated with increased in-hospital mortality, which was 2–3 times higher in patients with arrhythmia, and most NOAF prediction models developed specifically in STEMI cohorts undergoing PCI demonstrated higher discriminative ability.
R. L. Pak, B. I. Geltser, E. Kokarev et al.· Siberian Journal of Clinical...· 0 citations
Atrial fibrillation (AF) is frequently asymptomatic and often remains undetected until complications arise. Although artificial intelligence (AI)-enabled electrocardiography (ECG) can predict incident AF from sinus rhythm ECGs, its influence on physician risk assessment in simulated clinical settings remains uncertain. We developed a deep learning model to predict multi-day AF risk using non-AF 12-lead ECGs. For ECG-labeled outcomes, the model achieved an AUROC of 0.79 in the internal EUMC cohort and 0.74 in the external BIDMC cohort. For Holter-labeled outcomes, AUROC values reached 0.87 in the internal EUMC subset and 0.75 in the prospective PROVISION-AF cohort. To assess decision-support utility, a multinational survey of 70 physicians evaluated how AI-derived risk estimates influenced physician risk assessment and follow-up decisions in structured simulated cases. AI assistance significantly improved physicians' AF risk discrimination (AUROC 0.573 to 0.650) and negative predictive value (0.764 to 0.839), with significant net reclassification improvement for non-AF cases (NRI 0.127, p < 0.001). Performance gains were most notable among non-electrophysiologist cardiologists. In conclusion, AI-derived risk estimates improved physician risk discrimination in a structured simulated survey, particularly in non-specialist settings, supporting their potential role as a digital decision-support tool. Further real-world implementation studies are needed to determine whether these effects translate into improved clinical outcomes or healthcare efficiency.
Yeji Kim, Bogeun Kim, J. Yoon et al.· npj Digital Medicine· 0 citations