Interrelations Between Hypertension and Electrocardiographic Left Ventricular Hypertrophy and Their Associations With Cardiovascular Mortality

Cao, Xiangkun Elvis; Broughton, Stephen T; Waits, George S.; Nguyen, Thong; Li, Yabing; Soliman, Elsayed Z.

doi:10.1016/j.amjcard.2018.10.006

Cited by 21 publications

(16 citation statements)

References 27 publications

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“… 33 More recently, Cao et al showed that each 19 mmHg increase in SBP led to a 49% higher risk of LVH, emphasizing the importance of ECG as a predictor of LVH. 34 Regarding microalbuminuria, DM and TG were predictors of this subclinical lesion, compatible with studies in diabetics 35 and non-diabetics. 36 Additionally, TG can be a predictor of decline in renal function.…”

Section: Discussionsupporting

confidence: 81%

Predictive Factors for Target Organ Injuries in Hypertensive Individuals

Landim¹,

Cosenso-Martin²,

Santos³

et al. 2021

IBPC

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Background The causal relationship between systemic arterial hypertension and target organ damage (TOD) is well known, as well as the association with cardiovascular risk factors (CV). Ambulatory blood pressure monitoring (ABPM) is important in monitoring hypertension and assessing the risk of TOD. Objective To evaluate the relationship between blood pressure (BP) and clinical and biochemical parameters in the development of TOD in hypertensive patients. Methods This was a retrospective cohort study with 162 hypertensive patients followed for an average period of 13 years. The TOD investigated were left ventricular hypertrophy (LVH), microalbuminuria, coronary artery disease (CAD) and stroke. Blood pressure was assessed by ABPM and LVH using echocardiogram and electrocardiogram, respectively. Biochemical-metabolic tests and 24-hour microalbuminuria were performed at baseline and follow-up. The P-value <0.05 was considered significant. Results The average age was 69±11.8 years, with a predominance of women (64.8%), white ethnicity (79.6%) and diabetics (78.4%). ABPM showed a significant reduction in BP values during follow-up, although without association with TOD (microalbuminuria, stroke, and CAD), except for LVH that showed a correlation with sleep BP ≥120/70 mmHg (P=0.044). The most frequent TODs were LVH (29.6%), microalbuminuria (26.5%), CAD (19.8%) and stroke (17.3%). In the follow-up, there was an association between LVH and diabetes; microalbuminuria was associated with diabetes and triglycerides; stroke was associated with HDL-cholesterol (HDL-c), microalbuminuria and carotid disease. CAD showed a relationship with age and HDL-c. Conclusion Predictive factors for TOD are age, microalbuminuria, diabetes, HDL-c, triglycerides and carotid disease. Nocturnal BP is correlated with LVH. The absence of a relationship between ABPM and other TODs can be explained by the use of effective drugs, improvement of metabolic and blood pressure parameters.

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Section: Discussionsupporting

confidence: 81%

Predictive Factors for Target Organ Injuries in Hypertensive Individuals

Landim¹,

Cosenso-Martin²,

Santos³

et al. 2021

IBPC

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“…ECG‐LVH criteria, such as Cornell voltage LVH, Cornell voltage product LVH, and Sokolow–Lyon LVH, predict risk for cardiovascular disease and mortality (Bacharova et al, ; Bang et al, ; Okin, Hille, Kjeldsen, & Devereux, ; Porthan et al, ; Rautaharju & Soliman, ). ECG‐LVH has similar associations with increasing systolic blood pressure and hypertension as seen with imaged‐based assessment of LVH (Cao et al, ), supporting the use of ECG‐LVH in clinical and population‐based studies.…”

Section: Introductionmentioning

confidence: 66%

“…Hille, Kjeldsen, & Devereux, 2017;Porthan et al, 2019;Rautaharju & Soliman, 2014). ECG-LVH has similar associations with increasing systolic blood pressure and hypertension as seen with imaged-based assessment of LVH (Cao et al, 2019), supporting the use of ECG-LVH in clinical and population-based studies.…”

mentioning

confidence: 89%

Short‐term repeatability of electrocardiographic criteria of left ventricular hypertrophy

Meyer

Soliman

Drager

et al. 2019

Noninvasive Electrocardiol

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Background Left ventricular hypertrophy (LVH) is a marker of cardiac end‐organ damage and a risk factor for cardiovascular morbidity and mortality. Although clinical trials and cohort studies commonly use the electrocardiogram (ECG) for LVH assessment, the repeatability of ECG‐LVH criteria has not been sufficiently examined. Therefore, we evaluated the repeatability of ECG‐LVH criteria. Methods Participants (n = 63) underwent two standard ECGs at each of two visits, two weeks apart. The ECGs were processed centrally to calculate Cornell voltage (CV) LVH, Cornell voltage product (CVP) LVH, Sokolow–Lyon (SL) LVH, and Sokolow–Lyon product (SLP) LVH. We also used the waveforms measurements contributing to these LVH criteria as continuous variables, referred to here as CV‐index, CVP‐index, and SL‐index. We calculated the intraclass correlation coefficient (ICC), minimal detectable change (95% confidence), and the prevalence‐adjusted bias‐adjusted kappa (PABAK). Results ICCs (95% confidence intervals (CI)) were 0.97 (0.96, 0.98) for CV‐index, 0.97 (0.95, 0.98) for CVP‐index, and 0.93 (0.90, 0.96) for log of SL‐index. Minimal detectable change between repeat measures of CV‐index, CVP‐index, and log of SL‐index were ≥236.7 mV, ≥26.7 mV, and ≥0.09 mV, respectively. The within‐visit PABAK was 1 for all ECG‐LVH criteria, except for the first visit SLP‐LVH (PABAK = 0.93). Between‐visit PABAK ranged from 0.83 to 0.97 across LVH criteria. Conclusions CV, CVP, and SL ECG‐LVH as continuous variables have excellent repeatability, and as binary variables have excellent within‐visit agreement and good between‐visit agreement. These results alleviate concerns about the repeatability the ECG‐LVH use in clinical trials and epidemiologic studies.

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“…For example, the presence of a fragmented QRS complex (fQRS) is indicative of myocardial scarring correlating with high event rates of future heart failure and sudden cardiac death [30]. Also, ECG evidence of left ventricular hypertrophy (LVH) and asymptomatic left ventricular diastolic dysfunction (ALVD) are likely predictors of longterm mortality [31,32] [10].…”

Section: Machine Learning In Ecgmentioning

confidence: 99%

Machine Learning in Electrocardiography and Echocardiography: Technological Advances in Clinical Cardiology

2020

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Purpose of Review Electrocardiography (ECG) and echocardiography are the most widely used diagnostic tools in clinical cardiology. This review focuses on recent advancements in applying machine learning (ML) in ECG and echocardiography and potential synergistic ML integration of ECG and echocardiography. Recent Findings ML algorithms have been used in ECG for technical quality assurance, arrhythmia identification, and prognostic predictions, and in echocardiography to recognize image views, quantify measurements, and identify pathologic patterns. Synergistic application of ML in ECG and echocardiograph has demonstrated the potential to optimize therapeutic response, improve risk stratification, and generate new disease classification. There is mounting evidence that ML potentially outperforms in disease diagnoses and outcome prediction with ECG and echocardiography when compared with trained healthcare professionals. Summary The applications of ML in ECG and echocardiography are playing increasingly greater roles in medical research and clinical practice, particularly for their contributions to developing novel diagnostic/prognostic prediction models. The automation in data acquisition, processing, and interpretation help streamline the workflows of ECG and echocardiography in contemporary cardiology practice.

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Interrelations Between Hypertension and Electrocardiographic Left Ventricular Hypertrophy and Their Associations With Cardiovascular Mortality

Cited by 21 publications

References 27 publications

Predictive Factors for Target Organ Injuries in Hypertensive Individuals

Predictive Factors for Target Organ Injuries in Hypertensive Individuals

Short‐term repeatability of electrocardiographic criteria of left ventricular hypertrophy

Machine Learning in Electrocardiography and Echocardiography: Technological Advances in Clinical Cardiology

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