Electrocardiographic Changes in Liver Cirrhosis—Clues for Cirrhotic Cardiomyopathy

Toma, Letiția; Stanciu, Adriana Mercan; Zgură, Anca; Bacalbaşa, Nicolae; Diaconu, Camelia Cristina; Iliescu, Laura

doi:10.3390/medicina56020068

Cited by 8 publications

(7 citation statements)

References 44 publications

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“…8,9,12,13 In taking a more global analytic approach, we have identified a potential role for ECG-based classification of non-cardiac disease. We additionally highlight conditions for which further study may be particularly high yield, including diseases not classically associated with ECG findings but each independently supported by prior studies (e.g., type 2 diabetes, 36,37 sleep apnea, [38][39][40][41] chronic liver disease/cirrhosis, 15,42 and renal failure 43 ), as well as diseases with previously undescribed associations.…”

Section: Discussionmentioning

confidence: 73%

Deep Learning of Electrocardiograms Enables Scalable Human Disease Profiling

Venn

Wang

Friedman

et al. 2022

Preprint

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The electrocardiogram (ECG) is an inexpensive and widely available diagnostic tool, and therefore has great potential to facilitate disease detection in large-scale populations. Both cardiac and noncardiac diseases may alter the appearance of the ECG, though the extent to which diseases across the human phenotypic landscape can be detected on the ECG remains unclear. We developed a deep learning variational autoencoder model that encodes and reconstructs ECG waveform data within a multidimensional latent space. We then systematically evaluated whether associations between ECG encodings and a broad range of disease phenotypes could be detected using the latent space model by deriving disease vectors and projecting individual ECG encodings onto the vectors. We developed models for both 12- and single-lead ECGs, akin to those used in wearable ECG technology. We leveraged phecodes to generate disease labels using International Classification of Disease (ICD) codes for about 1,600 phenotypes in three different datasets linked to electronic health record data. We tested associations between ECG encodings and disease phenotypes using a phenome-wide association study approach in each dataset, and meta-analyzed the results. We observed that the latent space ECG model identified associations for 645 (40%) diseases tested in the 12-lead model. Associations were enriched for diseases of the circulatory (n=140, 82% of category-specific diseases), respiratory (n=53, 62%), and endocrine/metabolic (n=73, 45%) systems, with additional associations evident across the human phenome; results were similar for the single-lead models. The top ECG latent space association was with hypertension in the 12-lead ECG model, and cardiomyopathy in the single-lead ECG model (p<2.2x10-308 for each). The ECG latent space model demonstrated a greater number of associations than ECG models using standard ECG intervals alone, and generally resulted in improvements in discrimination of diseases compared to models comprising only age, sex, and race. We further demonstrate how a latent space model can be used to generate disease-specific ECG waveforms and facilitate disease profiling for individual patients.

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Section: Discussionmentioning

confidence: 73%

Deep Learning of Electrocardiograms Enables Scalable Human Disease Profiling

Venn

Wang

Friedman

et al. 2022

Preprint

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“…[32,33] Toma et al and Wu et al showed that prolonged QT intervals and T accentuated deceleration of the T wave correlated with hypoalbuminemia. [13,34] In this study, the DLM could predict the development of hypoalbuminemia. The DLM may detect structural and physiological changes that affect the vulnerability of hypoalbuminemia.…”

Section: Discussionmentioning

confidence: 85%

Deep Learning Model for Detection of Hypoalbuminemia Using Electrocardiography

Kwon¹,

Lee²,

Lee³

et al. 2021

Preprint

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Background: Albumin has a pivotal role in the homeostasis of osmotic pressure and is associated with cardiovascular, nephrotic, hepatic, and nutritional diseases. The detection of hypoalbuminemia is a cornerstone for diagnosis of hidden diseases and patient deterioration. We developed and validated a deep-learning-based model (DLM) for detection of hypoalbuminemia using electrocardiography (ECG). Methods: This historical cohort study included data from consecutive patients from two hospitals. The patient data in one hospital were divided into development (82,499 ECGs from 54,248 patients) and internal validation (20,664 ECGs from 20,664 patients) datasets, whereas the patient data in the other hospital were included in only an external validation (37,421 ECGs from 37,421 patients) dataset. An DLM was developed using a 12-lead ECG signal, age, and sex from the development dataset. The endpoint was hypoalbuminemia, defined by serum albumin concentration below 3.5 g/dL. Results: During the internal and external validations, the areas under the receiver operating characteristic curve of the DLM for the detection of hypoalbuminemia were 0.887 (0.877–0.897) and 0.888 (0.880–0.896), respectively. Among the 27,400 individuals without hypoalbuminemia at the initial laboratory exam, those identified by the DLM as higher-risk patients had a significantly larger change in developing hypoalbuminemia than those in the low-risk group (7.09% vs. 1.01%, p < 0.001) during 24 months. The sensitivity map showed that the DLM focused on the T wave and QRS complex for the detection of hypoalbuminemia. Conclusions: The DLM exhibited a high accuracy for hypoalbuminemia detection and prediction using 12-, 6-, and single-lead ECGs.

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“…There is also evidence that low serum albumin can lead to changes in QRS amplitudes ( 31 ). Hypoalbuminemia was also associated with prolonged QT intervals and T waves with a deceleration accentuated by T waves ( 16 , 32 ). Therefore, patients with low serum albumin may be detected by the related ECG changes.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence-Enabled Electrocardiography Detects Hypoalbuminemia and Identifies the Mechanism of Hepatorenal and Cardiovascular Events

Lee

Lin

Fang

et al. 2022

Front. Cardiovasc. Med.

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BackgroundAlbumin, an important component of fluid balance, is associated with kidney, liver, nutritional, and cardiovascular diseases (CVD) and is measured by blood tests. Since fluid balance is associated with electrocardiography (ECG) changes, we established a deep learning model (DLM) to estimate albumin via ECG.ObjectiveThis study aimed to develop a DLM to estimate albumin via ECG and explored its contribution to future complications.Materials and MethodsA DLM was trained for estimating ECG-based albumin (ECG-Alb) using 155,078 ECGs corresponding to albumin from 79,111 patients, and another independent 13,335 patients from an academic medical center and 11,370 patients from a community hospital were used for internal and external validation. The primary analysis focused on distinguishing patients with mild to severe hypoalbuminemia, and the secondary analysis aimed to provide additional prognostic value from ECG-Alb for future complications, which included mortality, new-onset hypoalbuminemia, chronic kidney disease (CKD), new onset hepatitis, CVD mortality, new-onset acute myocardial infarction (AMI), new-onset stroke (STK), new-onset coronary artery disease (CAD), new-onset heart failure (HF), and new-onset atrial fibrillation (Afib).ResultsThe AUC to identify hypoalbuminemia was 0.8771 with a sensitivity of 56.0% and a specificity of 90.7% in the internal validation set, and the Pearson correlation coefficient was 0.69 in the continuous analysis. The most important ECG features contributing to ECG-Alb were ordered in terms of heart rate, corrected QT interval, T wave axis, sinus rhythm, P wave axis, etc. The group with severely low ECG-Alb had a higher risk of all-cause mortality [hazard ratio (HR): 2.45, 95% CI: 1.81–3.33] and the other hepatorenal and cardiovascular events in the internal validation set. The external validation set yielded similar results.ConclusionHypoalbuminemia and its complications can be predicted using ECG-Alb as a novel biomarker, which may be a non-invasive tool to warn asymptomatic patients.

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Electrocardiographic Changes in Liver Cirrhosis—Clues for Cirrhotic Cardiomyopathy

Cited by 8 publications

References 44 publications

Deep Learning of Electrocardiograms Enables Scalable Human Disease Profiling

Deep Learning of Electrocardiograms Enables Scalable Human Disease Profiling

Deep Learning Model for Detection of Hypoalbuminemia Using Electrocardiography

Artificial Intelligence-Enabled Electrocardiography Detects Hypoalbuminemia and Identifies the Mechanism of Hepatorenal and Cardiovascular Events

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