P-wave dispersion and left atrial indices as predictors of paroxysmal atrial fibrillation in patients with non hemorrhagic cerebrovascular strokes and transient ischemic attacks

Prediction of atrial fibrillation after a stroke event: A systematic review with meta-analysis

BACKGROUND

Detecting atrial fibrillation (AF) after stroke is a key component of secondary prevention, but indiscriminate prolonged cardiac monitoring is costly and burdensome. Multivariable prediction models could be used to inform selection of patients.

OBJECTIVE

This study aimed to determine the performance of available models for predicting AF after a stroke.

METHODS

We searched for studies of multivariable models that were derived, validated, or augmented for prediction of AF in patients with a stroke, using MEDLINE and Embase from inception through September 20, 2024. Discrimination measures for tools with C statistic data from ≥3 cohorts were pooled by bayesian meta-analysis, with heterogeneity assessed through a 95% prediction interval. The risk of bias was assessed with the Prediction model Risk Of Bias Assessment tool (PROBAST).

RESULTS

We included 75 studies with 58 prediction models; 66% had a high risk of bias. Fifteen multivariable models were eligible for meta-analysis. Three models showed excellent discrimination: SAFE (C statistic, 0.856; 95% confidence interval [CI], 0.796-0.916), SURF (0.815; 95% CI, 0.728-0.893), and iPAB (0.888; 95% CI, 0.824-0.957). Excluding high-bias studies, only SAFE showed excellent discrimination (0.856; 95% CI 0.800-0.915). No model showed excellent discrimination when limited to external validation or studies with ≥100 AF events. No clinical impact studies were found.

CONCLUSION

Three of the 58 identified multivariable prediction models for AF after stroke demonstrated excellent statistical performance on meta-analysis. However, prospective validation is required to understand the effectiveness of these models in clinical practice before they can be recommended for inclusion in clinical guidelines.

Artificial Intelligence-Enabled ECG: Physiologic and Pathophysiologic Insights and Implications

Advancements in machine learning and computing methods have given new life and great excitement to one of the most essential diagnostic tools to date-the electrocardiogram (ECG). The application of artificial intelligence-enabled ECG (AI-ECG) has resulted in the ability to identify electrocardiographic signatures of conventional and unique variables and pathologies, giving way to tremendous clinical potential. However, what these AI-ECG models are detecting that the human eye is missing remains unclear. In this article, we highlight some of the recent developments in the field and their potential clinical implications, while also attempting to shed light on the physiologic and pathophysiologic features that enable these models to have such high diagnostic yield. © 2022 American Physiological Society. Compr Physiol 12:3417-3424, 2022.

The prognostic value of P-wave dispersion and left atrial functions assessed with three-dimensional echocardiography in patients with cirrhosis

BACKGROUND

Cirrhotic cardiomyopathy (CCM) is a well-known entity. The aim of this study was to compare left atrial three-dimensional (3D) volume and P-wave dispersion (PWd) in patients with cirrhosis and a healthy population. The secondary purpose was to assess the left phasic volumes and reservoir functions with 3D echocardiography for the prediction of an increased risk of poor outcomes in patients with cirrhosis.

METHODS

The study included 50 patients with cirrhosis and 43 healthy control subjects without atrial fibrillation. All patients were assessed with two-dimensional (2D), 3D, and tissue Doppler transthoracic echocardiography. The PWd was calculated using a 12-lead surface electrocardiogram (ECG). Cirrhotic patients were followed up for 2.5 years for the evaluation of poor outcomes and the development of atrial fibrillation.

RESULTS

Patients with cirrhosis were observed to have significantly higher left atrial phasic volumes such as minimal left atrial volume (3D-LAVmin, P = 0.004) and indexed LAVmin (3D-LAVImin, P = 0.0001), and significantly decreased left atrial reservoir functions such as left atrial emptying volume (3D-LAEV, P = 0,001), left atrial ejection fraction (3D-LAEF, P = 0,001) on 3D echocardiography. PWd was determined to be significantly longer in the cirrhotic group compared with the control group (P = 0.003). In the 2.5-year follow-up period, poor outcomes occurred in 34 patients (22 patients died, six patients had liver transplantation, six patients developed atrial fibrillation/AHRE episodes). In Cox regression analysis, the MELD score (HR, 1.16 (1.06-1.26), P = 0.001) and 3D-LAVImin (HR, 0.95 (0.86-1.00), P = 0.040) were significantly associated with all-cause mortality. Cirrhotic patients with LAVImin of >15 ml/m2 were seen to have poor survival (long rank P = 0.033).

CONCLUSION

The results of this study showed that patients with cirrhosis had higher left atrial volume, longer PWd and worse diastolic functions compared with the control group. Higher disease severity scores were associated with left atrial function and volume. In addition, left atrial volume measured with 3DE was a strong predictor of future adverse events, and minimal left atrial volumes had a higher prognostic value than any other left atrial function indices.

Atrial Cardiopathy and Sympatho-Vagal Imbalance in Cryptogenic Stroke: Pathogenic Mechanisms and Effects on Electrocardiographic Markers

Recently, atrial cardiopathy has emerged as possible pathogenic mechanism in cryptogenic stroke and many electrocardiographic (ECG) markers have been proposed in order to detect an altered atrial substrate at an early stage. The autonomic nervous system (ANS) plays a well-known role in determining significant and heterogeneous electrophysiological changes of atrial cardiomyocytes, that promote atrial fibrillation episodes in cardioembolic stroke. Conversely, the role of ANS in atrial cardiopathy and cryptogenic stroke is less known, as well as ANS effects on ECG markers of atrial dysfunction. In this paper, we review the evidence linking ANS dysfunction and atrial cardiopathy as a possible pathogenic factor in cryptogenic stroke.