Comparative performance of stroke risk scores in patients with atrial fibrillation with low stroke risk

Prediction of Incident Atrial Fibrillation and Association With Outcomes Using Routine Electronic Health Records in a Western Pacific Population.

A supervised machine learning algorithm for AF in a Western Pacific population was derived and demonstrated that higher risk was associated with hospitalisation for other cardio-renal diseases and death.

Stroke risk in diabetic patients with concomitant cardiovascular disease the CARDIAB-Stroke study.

BACKGROUND Patients with type 2 diabetes mellitus (T2DM) and atherosclerotic cardiovascular disease (ASCVD) are at high risk of stroke and transient ischemic attack (TIA), yet many remain undertreated with cardioprotective therapies. We assessed stroke risk, its predictors, and the impact of sodium-glucose cotransporter-2 inhibitors (SGLT2-I) and glucagon-like peptide-1 receptor agonists (GLP-1RA). METHODS The nationwide CARdiovascular and DIABetes (CARDIAB) cohort included patients with T2DM and ASCVD. Stroke predictors and associations between SGLT2-I andGLP-1RA use and outcomes were evaluated. RESULTS A total of 138,397 patients (60% male; median age 72 years) were followed for a median of 5.5 years. Overall, 28,676 patients (20.7%) experienced stroke or TIA. Higher risk was associated with smoking, prior stroke or TIA, chronic kidney disease, older age, male sex, and Arab ethnicity. Follow-up in cardiology or endocrinology clinics and treatment with aspirin, statins, SGLT2-I, and GLP-1RA were protective. Patients treated with both SGLT2-I and GLP-1RA had the lowest event rates (361 per 10,000 patient-years), corresponding to a 22% relative risk reduction (HR 0.78; 95% CI, 0.75-0.81). CONCLUSION Patients with T2DM and ASCVD face a markedly elevated stroke risk. SGLT2-I and GLP-1RA use was associated with significant risk reduction, particularly when combined. Greater uptake of these therapies and comprehensive risk factor management are needed to mitigate stroke burden in this high-risk group.

Anticoagulation for Atrial Fibrillation with Intermediate Stroke Risk.

Refining Residual Ischemic Stroke Risk in Anticoagulated Atrial Fibrillation Using Index Outpatient LA Diameter: The CHA2DS2-VALa Score.

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.

Predictive value of acute atrial fibrillation for short-term mortality after ischemic stroke

Background Atrial fibrillation (AF) is an important risk factor for ischemic stroke. However, the prognostic impact of acute atrial fibrillation (AAF) at the onset of acute ischemic stroke (AIS) remains unclear. Methods This retrospective study categorized 417 patients with AIS into the AAF (n = 72), other AF (n = 142), and non-AF (n = 203) groups. Multivariate logistic regression analysis of associations with 30-day all-cause mortality and severe early neurological deficit (7-day NIHSS ≥16). Results The AAF group demonstrated significantly worse outcomes than the other AF and non-AF groups. In the multivariable analysis, AAF was identified as an independent risk predictor for severe 7-day neurological deficit [odds ratio (OR): 10.09; 95% CI: 3.87−27.36; P < 0.001] and all-cause mortality within 30 days (OR: 4.11; 95% CI: 2.28−7.43; P < 0.001). Conclusions AAF at the onset of AIS is an independent risk predictor for early neurological deterioration and short-term mortality, establishing it as a crucial prognostic indicator that warrants vigilant management.

Development and Validation of the GLORIA-AF Stroke Weighted Risk Score for Patients with Atrial Fibrillation.

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.