P‐wave durations from automated electrocardiogram analysis to predict atrial fibrillation and mortality in heart failure

Zhou, Jiandong; Li, Andrew; Tan, Martin; Lam, Matthew Chung Yan; Hung, Lok Tin; Siu, Ronald Wing Hei; Lee, Sharen; Lakhani, Ishan; Chan, Jeffrey Shi Kai; Waleed, Khalid Bin; Liu, Tong; Jeevaratnam, Kamalan; Zhang, Qingpeng; Tse, Gary

doi:10.1002/ehf2.14230

Cited by 4 publications

(1 citation statement)

References 44 publications

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“…Although less is known about the correlates of abnormally short PWD, in certain instances, short PWD may be considered an earlier and more subtle feature than prolonged PWD. It has been hypothesized that shortened atrial repolarization and refractory periods could indicate changes in atrial electrical properties preceding more overt abnormalities associated with prolonged PWD, which can eventually result in arrhythmogenesis (Nielsen et al 2015, Zhou et al 2023). Recent research has started to underscore that shorter PWD is also associated with adverse outcomes, including AF, heart failure, stroke, and dementia (Ostrowska et al 2022, Ostrowska et al 2022, Chen et al 2022, Zhou et al 2023).…”

Section: Discussionmentioning

confidence: 99%

Subclinical variations on ECG and their associations with structural brain aging networks

Haddad,

Matloff,

Park

et al. 2024

Preprint

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Impaired cardiac function is associated with cognitive impairment and brain imaging features of aging. Cardiac arrhythmias, including atrial fibrillation, are implicated in clinical and subclinical brain injuries. Even in the absence of a clinical diagnosis, subclinical or prodromal substrates of arrhythmias, including an abnormally long or short P-wave duration (PWD), a measure associated with atrial abnormalities, have been associated with stroke and cognitive decline. However, the extent to which PWD has subclinical influences on overall aging patterns of the brain is not clearly understood. Here, using neuroimaging and ECG data from the UK Biobank, we use a novel regional “brain age” method to identify the brain aging networks associated with abnormal PWD. We find that PWD is inversely associated with accelerated brain aging in the sensorimotor, frontoparietal, ventral attention, and dorsal attention networks, even in the absence of overt cardiac diseases. These findings suggest that detrimental aging outcomes may result from subclinically abnormal PWD.

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Section: Discussionmentioning

confidence: 99%

Subclinical variations on ECG and their associations with structural brain aging networks

Haddad,

Matloff,

Park

et al. 2024

Preprint

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P‐wave durations from automated electrocardiogram analysis to predict atrial fibrillation and mortality in heart failure

Zhou

Tan

et al. 2022

ESC Heart Failure

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Background P-wave indices have been used to predict incident atrial fibrillation (AF), stroke, and mortality. However, such indices derived from automated ECG measurements have not been explored for their predictive values in heart failure (HF). We investigated whether automated P-wave indices can predict adverse outcomes in HF. Methods This study included consecutive Chinese patients admitted to a single tertiary centre, presenting with HF but without prior AF, and with at least one baseline ECG, between 1 January 2010 and 31 December 2016, with last follow-up of 31 December 2019. Results A total of 2718 patients were included [median age: 77.4, interquartile range (IQR): (66.9-84.3) years; 47.9 males]. After a median follow-up of 4.8 years (IQR: 1.9-9.0 years), 1150 patients developed AF (8.8/year), 339 developed stroke (2.6/ year), 563 developed cardiovascular mortality (4.3/year), and 1972 had all-cause mortality (15.1/year). Compared with 101-120 ms as a reference, maximum P-wave durations predicted new-onset AF at ≤90 ms [

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PowerAI-Diabetes: Review of glycemic and lipid variability to predict cardiovascular events in Chinese diabetic population

Lee,

Liu,

Chung

et al. 2024

npj Metab Health Dis

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The aim of this study is to review the predictive value of visit-to-visit variability in glycaemic or lipid tests for forecasting major adverse cardiovascular events (MACE) in diabetes mellitus. Data from existing studies suggests that such variability is an independent predictor of adverse outcomes in this patient cohort. This understanding is then applied to the development of PowerAI-Diabetes, a Chinese-specific artificial intelligence-enhanced predictive model for predicting the risks of major adverse cardiovascular events and diabetic complications. The model integrates an amalgam of variables including demographics, laboratory and medication information to assess the risk of MACE. Future efforts should focus on the incorporation of treatment effects and non-traditional cardiovascular risk factors, such as social determinants of health variables, to improve the performance of predictive models.

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P‐wave durations from automated electrocardiogram analysis to predict atrial fibrillation and mortality in heart failure

Cited by 4 publications

References 44 publications

Subclinical variations on ECG and their associations with structural brain aging networks

Subclinical variations on ECG and their associations with structural brain aging networks

P‐wave durations from automated electrocardiogram analysis to predict atrial fibrillation and mortality in heart failure

PowerAI-Diabetes: Review of glycemic and lipid variability to predict cardiovascular events in Chinese diabetic population

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