P-Wave Indices and Risk of Ischemic Stroke

He, Jinli; Tse, Gary; Korantzopoulos, Panagiotis; Letsas, Κonstantinos P.; Ali-Hasan-Al-Saegh, Sadeq; Kamel, Hooman; Li, Guangping; Lip, Gregory Y.H.; Liu, Tong

doi:10.1161/strokeaha.117.017293

Cited by 107 publications

(42 citation statements)

References 42 publications

(44 reference statements)

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“…P-wave dispersion was defined as the maximum difference in PWD. P-wave terminal force in V1 (PTFV1) was defined as the area subtended by the terminal negative component of a biphasic P-wave in lead V1, with the area calculated by multiplication of the duration and depth of the waveform (He et al, 2017). The primary endpoint of this study was new onset persistent or permanent atrial fibrillation (AF).…”

Section: Definitions Data Extraction Electrocardiographic Measuremementioning

confidence: 99%

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P-Wave Area Predicts New Onset Atrial Fibrillation in Mitral Stenosis: A Machine Learning Approach

Tse

Lakhani²,

Zhou

et al. 2020

Front. Bioeng. Biotechnol.

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Introduction: Mitral stenosis is associated with an atrial cardiomyopathic process, leading to abnormal atrial electrophysiology, manifesting as prolonged P-wave duration (PWD), larger P-wave area, increased P-wave dispersion (PWD max-PWD min), and/or higher P-wave terminal force on lead V1 (PTFV1) on the electrocardiogram. Methods: This was a single-center retrospective study of Chinese patients, diagnosed with mitral stenosis in sinus rhythm at baseline, between November 2009 and October 2016. Automated ECG measurements from raw data were determined. The primary outcome was incident atrial fibrillation (AF). Results: A total 59 mitral stenosis patients were included (age 59 [54-65] years, 13 (22%) males). New onset AF was observed in 27 patients. Age (odds ratio [OR]: 1.08 [1.01-1.16], P = 0.017), systolic blood pressure (OR: 1.03 [1.00-1.07]; P = 0.046), mean P-wave area in V3 (odds ratio: 3.97 [1.32-11.96], P = 0.014) were significant predictors of incident AF. On multivariate analysis, age (OR: 1.08 [1.00-1.16], P = 0.037) and P-wave area in V3 (OR: 3.64 [1.10-12.00], P = 0.034) remained significant predictors of AF. Receiver-operating characteristic (ROC) analysis showed that the optimum cutoff for P-wave area in V3 was 1.45 Ashman units (area under the curve: 0.65) for classification of new onset AF. A decision tree learning model with individual and non-linear interaction variables with age achieved the best performance for outcome prediction (accuracy = 0.84, precision = 0.84, recall = 0.83, F-measure = 0.84). Conclusion: Atrial electrophysiological alterations in mitral stenosis can detected on the electrocardiogram. Age, systolic blood pressure, and P-wave area in V3 predicted new onset AF. A decision tree learning model significantly improved outcome prediction.

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Section: Definitions Data Extraction Electrocardiographic Measuremementioning

confidence: 99%

“…P-wave terminal force in V1 (PTFV1), P-wave duration (PWD), and P-wave area. Adapted from (He et al, 2017) with permission. stenosis severity), log(age * P-wave area in v3).…”

Section: Statistical Analysis Non-linear Variables and Decision Trementioning

confidence: 99%

P-Wave Area Predicts New Onset Atrial Fibrillation in Mitral Stenosis: A Machine Learning Approach

Tse

Lakhani²,

Zhou

et al. 2020

Front. Bioeng. Biotechnol.

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“…Very long P-wave duration in the top fifth percentile is associated with doubling of risk of AF ( 23 ). A recent pooled meta-analysis showed that prolonged maximum PWD (evaluated as a categorical variable) increases the risk of ischemic stroke ( 24 ). In particular, a previous study demonstrated that in patients with advanced interatrial block there is 1.7-fold increase in the risk of ischemic stroke ( 39 ) and this association resulted not attenuated by incident AF events: this evidence suggests that left atrial disease evaluated by means of P wave duration, is possibly an independent risk factor for ischemic stroke.…”

Section: Ecg Markers Of Atrial Cardiopathy and Influences Of Autonomimentioning

confidence: 99%

“…PWA is considered a marker of abnormal atrial structure and left atrial enlargement. In the ARIC study, mean P wave area was a predictor of AF ( 49 ), in a recent pooled meta-analysis, higher maximal PWA increases of 10% the risk of incident stroke ( 24 ).…”

Section: Ecg Markers Of Atrial Cardiopathy and Influences Of Autonomimentioning

confidence: 99%

“…A recent metanalysis ( 24 ) showed that higher PTFV1 values increase of 59% the risk of ischemic stroke and the association between increased PTFV1 and embolic stroke of undetermined cause ( 52 ) suggested the hypothesis that left atrial cardiopathy could be involved in the pathogenesis of cryptogenic stroke even in the absence of recognized AF ( 53 ).…”

Section: Ecg Markers Of Atrial Cardiopathy and Influences Of Autonomimentioning

confidence: 99%

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

2018

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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.

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Multi‐modality machine learning approach for risk stratification in heart failure with left ventricular ejection fraction ≤ 45%

Tse

Zhou

Woo³

et al. 2020

ESC Heart Failure

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Aims Heart failure (HF) involves complex remodelling leading to electrical and mechanical dysfunction. We hypothesized that machine learning approaches incorporating data obtained from different investigative modalities including atrial and ventricular measurements from electrocardiography and echocardiography, blood inflammatory marker [neutrophil-to-lymphocyte ratio (NLR)], and prognostic nutritional index (PNI) will improve risk stratification for adverse outcomes in HF compared to logistic regression. Methods and results Consecutive Chinese patients referred to our centre for transthoracic echocardiography and subsequently diagnosed with HF, between 1 January 2010 and 31 December 2016, were included in this study. Two machine learning techniques, multilayer perceptron and multi-task learning, were compared with logistic regression for their ability to predict incident atrial fibrillation (AF), transient ischaemic attack (TIA)/stroke, and all-cause mortality. This study included 312 HF patients [mean age: 64 (55-73) years, 75% male]. There were 76 cases of new-onset AF, 62 cases of incident TIA/stroke, and 117 deaths during follow-up. Univariate analysis revealed that age, left atrial reservoir strain (LARS) and contractile strain (LACS) were significant predictors of new-onset AF. Age and smoking predicted incident stroke. Age, hypertension, type 2 diabetes mellitus, chronic kidney disease, mitral or aortic regurgitation, P-wave terminal force in V1, the presence of partial inter-atrial block, left atrial diameter, ejection fraction, global longitudinal strain, serum creatinine and albumin, high NLR, low PNI, and LARS and LACS predicted all-cause mortality. Machine learning techniques achieved better prediction performance than logistic regression. Conclusions Multi-modality assessment is important for risk stratification in HF. A machine learning approach provides additional value for improving outcome prediction.

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P-Wave Indices and Risk of Ischemic Stroke

Cited by 107 publications

References 42 publications

P-Wave Area Predicts New Onset Atrial Fibrillation in Mitral Stenosis: A Machine Learning Approach

P-Wave Area Predicts New Onset Atrial Fibrillation in Mitral Stenosis: A Machine Learning Approach

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

Multi‐modality machine learning approach for risk stratification in heart failure with left ventricular ejection fraction ≤ 45%

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