The electrical determinants of increased wall thickness and mass in left ventricular hypertrophy

Maanja, Maren; Schlegel, Todd T.; Kozor, Rebecca; Lundin, Magnus; Wieslander, Björn; Wong, Timothy C.; Schelbert, Erik B.

doi:10.1016/j.jelectrocard.2019.09.024

Cited by 12 publications

(19 citation statements)

References 86 publications

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“…However, further prospective evaluation in future studies is also required. [15] 99 [13] 109 [14] 114 [15] <0.001…”

Section: Discussionmentioning

confidence: 99%

“…Moving beyond but not excluding basic ECG measurements such as heart rate and waveform amplitudes and durations, the diagnostic output from the ECG can be further substantially improved by using combinations of advanced ECG measures from 12-lead-ECG-derived vectorcardiography and waveform complexity [15][16][17] . An accurate ECG Heart Age using advanced, 5-minute, 12-lead ECG was developed in 2014, based on Bayesian statistics 9 .…”

Section: Introductionmentioning

confidence: 99%

“…Moving beyond but not excluding basic ECG measurements such as heart rate and waveform amplitudes and durations, the diagnostic output from the ECG can be further substantially improved by also utilizing combinations of advanced ECG measures, for example those from 12-lead-ECG-derived vectorcardiography , waveform complexity, and measures of beat-tobeat waveform variablility [15][16][17] . In 2014, such advanced measures were used along with 5minute, high-fidelity 12-lead ECGs and a Bayesian statistical approach, to develop an accurate estimate of ECG-based Heart Age.…”

Section: Introductionmentioning

confidence: 99%

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Heart age estimated using explainable advanced electrocardiography

Lindow

Palencia-Lamela

Schlegel

2021

Preprint

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Background: Electrocardiographic (ECG) Heart Age conveying cardiovascular risk has been estimated by both Bayesian and artificial intelligence approaches. We hypothesized that explainable measures from the 10-second 12-lead ECG could successfully predict Bayesian ECG Heart Age. Methods: Advanced analysis was performed on ECGs from healthy subjects and patients with cardiovascular risk or proven heart disease. Regression models were used to predict a Bayesian 5-minute ECG Heart Age from the standard resting 10-second 12-lead ECG. The difference between 10-second ECG Heart Age and chronological age was compared. Results: In total, 2,771 subjects were included (n=1682 healthy volunteers, n=305 with cardiovascular risk factors, n=784 with cardiovascular disease). Overall, 10-second Heart Age showed strong agreement with the 5-minute Heart Age (R2=0.94, p<0.001, mean±SD bias 0.0±5.1 years). The difference between 10-second ECG Heart Age and chronological age was 0.0±5.7 years in healthy individuals, 7.4±7.3 years in subjects with cardiovascular risk factors (p<0.001), and 14.3±9.2 years for patients with cardiovascular disease (p<0.001). Conclusions: ECG Heart Age can be accurately estimated from a 10-second 12-lead ECG in a transparent and explainable fashion based on known ECG measures, without artificial intelligence techniques. The difference between ECG Heart Age and chronological age increases markedly with cardiovascular risk and disease.

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“…However, further prospective evaluation in future studies is also required. [15] 99 [13] 109 [14] 114 [15] <0.001…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Moving beyond but not excluding basic ECG measurements such as heart rate and waveform amplitudes and durations, the diagnostic output from the ECG can be further substantially improved by also utilizing combinations of advanced ECG measures, for example those from 12-lead-ECG-derived vectorcardiography , waveform complexity, and measures of beat-tobeat waveform variablility [15][16][17] . In 2014, such advanced measures were used along with 5minute, high-fidelity 12-lead ECGs and a Bayesian statistical approach, to develop an accurate estimate of ECG-based Heart Age.…”

Section: Introductionmentioning

confidence: 99%

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Heart age estimated using explainable advanced electrocardiography

Lindow

Palencia-Lamela

Schlegel

2021

Preprint

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“…The copyright holder for this preprint this version posted October 17, 2022. ; https://doi.org/10.1101/2022.10. 16.22280561 doi: medRxiv preprint performance for LV mass, GT, and several other wall thickness-related measures, and (c) to classify LVH based on the measures with the highest prognostic associations.…”

Section: (Which Was Not Certified By Peer Review)mentioning

confidence: 99%

Left ventricular mass and global wall thickness – prognostic utility and characterization of left ventricular hypertrophy

Lundin

Heiberg

Nordlund

et al. 2022

Preprint

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AIMS: Cardiovascular magnetic resonance (CMR) can accurately measure left ventricular (LV) mass, which is associated with morbidity and mortality. Several measures related to LV wall thickness exist, with uncertain relative prognostic strength. We hypothesized that prognosis can be used to select an optimal measure of wall thickness for characterizing LV hypertrophy. METHODS AND RESULTS: Subjects having undergone CMR were studied (cardiac patients, n=2543; healthy volunteers, n=100). A new measure, global wall thickness (GT, GTI if indexed to body surface area), and related measures, including mass/volume and mass/volume2/3, were calculated. GT was accurately calculated from LV mass and LV end-diastolic volume. Among patients with follow-up (n=1575, median follow-up 5.4 years), the most predictive measure of death or hospitalization for heart failure was LV mass index (LVMI) (hazard ratio (HR)[95% confidence interval] 1.16[1.12–1.20], p<0.001), followed by GTI (HR 1.14[1.09–1.19], p<0.001). Among patients with normal mass, volume, systolic function and absence of scar (n=326, median follow-up 5.8 years), the most predictive measure was GT (HR 1.62[1.35–1.94], p<0.001). GT and LVMI could characterize patients as having a normal LV mass and wall thickness, or concentric remodeling, concentric hypertrophy, or eccentric hypertrophy, and the three abnormal groups had worse prognosis than the normal group (p<0.05 for all). CONCLUSIONS: LV mass is highly prognostic when mass is elevated (advanced disease), but global wall thickness is easily and accurately calculated, and adds value and discrimination amongst those with normal mass (early disease). The combination of these two measures can be used to characterize hypertrophy.

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“…Moving beyond but not excluding basic ECG measurements such as heart rate and waveform amplitudes and durations, the diagnostic output from the ECG can be further substantially improved by also utilizing combinations of advanced ECG measures, for example those from 12-lead-ECG-derived vectorcardiography , waveform complexity, and measures of beat-to-beat waveform variablility 17 – 19 . In 2014, such advanced measures were used along with 5-min, high-fidelity 12-lead ECGs and a Bayesian statistical approach, to develop an accurate estimate of ECG-based Heart Age.…”

Section: Introductionmentioning

confidence: 99%

Heart age estimated using explainable advanced electrocardiography

Lindow

Palencia-Lamela

Schlegel

2022

Sci Rep

Self Cite

View full text Add to dashboard Cite

Electrocardiographic (ECG) Heart Age conveying cardiovascular risk has been estimated by both Bayesian and artificial intelligence approaches. We hypothesised that explainable measures from the 10-s 12-lead ECG could successfully predict Bayesian 5-min ECG Heart Age. Advanced analysis was performed on ECGs from healthy subjects and patients with cardiovascular risk or proven heart disease. Regression models were used to predict patients’ Bayesian 5-min ECG Heart Ages from their standard, resting 10-s 12-lead ECGs. The difference between 5-min and 10-s ECG Heart Ages were analyzed, as were the differences between 10-s ECG Heart Age and the chronological age (the Heart Age Gap). In total, 2,771 subjects were included (n = 1682 healthy volunteers, n = 305 with cardiovascular risk factors, n = 784 with cardiovascular disease). Overall, 10-s Heart Age showed strong agreement with the 5-min Heart Age (R2 = 0.94, p < 0.001, mean ± SD bias 0.0 ± 5.1 years). The Heart Age Gap was 0.0 ± 5.7 years in healthy individuals, 7.4 ± 7.3 years in subjects with cardiovascular risk factors (p < 0.001), and 14.3 ± 9.2 years in patients with cardiovascular disease (p < 0.001). Heart Age can be accurately estimated from a 10-s 12-lead ECG in a transparent and explainable fashion based on known ECG measures, without deep neural network-type artificial intelligence techniques. The Heart Age Gap increases markedly with cardiovascular risk and disease.

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The electrical determinants of increased wall thickness and mass in left ventricular hypertrophy

Cited by 12 publications

References 86 publications

Heart age estimated using explainable advanced electrocardiography

Heart age estimated using explainable advanced electrocardiography

Left ventricular mass and global wall thickness – prognostic utility and characterization of left ventricular hypertrophy

Heart age estimated using explainable advanced electrocardiography

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