Detection Of Left Ventricular Hypertrophy Research Articles

Incidence and risk factors for development of left ventricular hypertrophy in Fabry disease

Abstract Background Left ventricular hypertrophy (LVH) is the principal cardiac manifestation of Fabry disease (FD). This study aimed to determine the incidence and predictors of LVH development in a contemporary cohort of patients with FD and no LVH at baseline evaluation. Methods Consecutively referred adult (age ≥16 years) patients with FD were enrolled into an observational cohort study. Patients were prospectively followed in a specialist cardiomyopathy centre and the primary endpoint was the first detection of LVH (left ventricular mass index (LVMi) ≥115 g/m2 in men and ≥95 g/m2 in women). Results From a cohort of 393 patients, 214 (age 35.8 ±13.8 years; 61 [29%] males) had no LVH at first evaluation. During a median follow-up of 9.4 years (interquartile range [IQR] 4.7-12.7), 55 patients (24.6%) developed LVH. The estimated incidence of LVH was 11.3% (95% confidence interval [CI] 6.5-16.1) at 5 years, 29.1% (95%CI 21.5-36.7) at 10 years, and 45.0% (95%CI 33.8-62.4) at 15 years of follow-up. On multivariable analysis, independent predictors for LVH development were age (hazard ratio [HR] 1.04 [95%CI 1.02-1.06] per 1 year increase, p-value <0.001), male sex (HR 2.90 [95%CI 1.66-5.09], p-value <0.001), and an abnormal ECG (HR 3.10 [95%CI 1.72-5.57], p-value <0.001). The annual rate of change in LVMi was +2.77 (IQR 1.45-4.62) g/m2/year in males and +1.38 (IQR 0.09-2.85) g/m2/year in females (p-value <0.001). Conclusions Approximately one quarter of FD patients developed LVH during follow-up. Age, male sex, and ECG abnormalities were associated with a higher risk of developing LVH in patients with FD.Central Illustration

Exaggerated blood pressure response to submaximal exercise must be considered relative to fitness: strong associations with hypertensive target organ damage

Abstract Exaggerated systolic blood pressure (SBP) during submaximal exercise is associated with increased cardiovascular (CV) risk. However, findings are mixed and new evidence indicates that cardiorespiratory fitness should be considered for proper clinical interpretation of exercise SBP responses. This study aimed to determine the relationship between SBP response to submaximal effort corrected for fitness and abnormalities of cardiac structure and function. Methods Each of 231 participants (age 54±12 years; 53% females) with controlled clinic BP, no evidence for ischemic heart disease, valvular heart disease or heart failure underwent cardiopulmonary exercise testing (Bruce protocol), resting and exercise echocardiography and 24h ABPM. Submaximal exercise SBP (measured at the 2nd stage of Bruce protocol) was corrected for two fitness variables: 1) peak VO2 and 2) maximal workload in METs (SBP/peakVO2 and SBP/METs). Results There were no, or weak, associations with exercise SBP and target organ damage. However, there was a progressive deterioration of cardiac function and structure parameters across the exercise SBP tertiles corrected for fitness (Table 1). Both SBP/peakVO2 and SBP/METs significantly correlated with left ventricular (LV) mass, LV diastolic and systolic, and left atrial parameters (Table 2). ROC analysis revealed good performance of corrected SBP response to submaximal exercise in detection of LV hypertrophy and diastolic dysfunction (AUC 0.792 and 0.808, and 0.771 and 0.802 for SBP/peakVO2 and SBP/METs, respectively; Figure). Conclusions Fitness-corrected SBP responses to submaximal exercise can identify more profound target organ damage with respect to cardiac function and structure even among patients with controlled clinic BP. Exaggerated BP response to exercise must be considered relative to fitness for proper clinical interpretation of BP responses to exercise testing.Figure

Cardiac magnetic resonance (CMR) height-based allometric scaling in Southeast Asians; implications in the diagnosis and prognosis of left ventricular hypertrophy (LVH): the REMODEL study

Abstract Background Left ventricular hypertrophy (LVH) is a strong predictor of adverse cardiovascular outcomes (1). Although normalizing LV mass (LVM) to height exponents has been shown to reduce variability from body size, specific recommendations for height exponents are lacking due to the scarcity of normal cohorts to define appropriate height exponents (2). Cardiac magnetic resonance (CMR) is increasingly used in assessing LVM, and the existing body of research is based primarily on the MESA cohort (3). While the MESA cohort is ethnically diverse, the intrinsic effects of ethnicity may not fully be discerned if certain ethnic groups are under-represented. Purpose We aimed to demonstrate the diagnostic and prognostic implications of establishing height exponents specific to sex in a Southeast Asian cohort. Methods Non-overweight/obese Southeast Asian healthy volunteers (n=416) were used to establish appropriate height exponents and normal reference ranges of LVM. The impact of these height exponents was examined in a separate cohort of Southeast Asians with hypertension (n=878). All individuals underwent standardized cardiovascular magnetic resonance imaging (CMR). Primary outcome was a composite of acute coronary syndrome, heart failure hospitalization, stroke, and cardiovascular mortality. Cox regression analysis of various LVH measures were adjusted for 24-hour systolic blood pressure, diabetes, and hyperlipidemia. Results The height exponents for healthy females and males were 1.57 and 2.33, respectively. In patients with hypertension, sex-specific height exponents significantly increased the detection of LVH compared with body surface area (BSA) indexing (47% [n=409/878] versus 27% [n= 236/878], p<0.001). Most individuals re-classified to LVH with sex-specific height exponents were overweight (33.1%, n=58/175) or obese (64.6%, n=113/175). There were 37 primary events over 60 [37-73] months of follow-up. Regardless of indexing method, LVH was independently associated with increased adverse events (sex-specific height exponent hazard ratio (HR): 2.8 [1.25-6.29], P=0.013). The prediction ability of LVH diagnosis by sex-specific height exponents was similar to BSA indexing (HR: 5.43 [2.49-11.8], P<0.001). Conclusions Reference ranges specific to the ethnicity, sex and imaging modality are necessary to establish appropriate height exponents. Although utilizing sex-specific height exponents to index LVM resulted in significantly more LVH reclassification when compared with BSA indexing, this did not translate to a notable improvement in event prediction. Further research into alternative methods of LVM indexing is warranted.Abstract SummaryPredictive Ability of LVH Measures

Revolutionizing LVH detection using artificial intelligence: the AI heartbeat project.

Many studies have shown the utility and promise of artificial intelligence (AI), for the diagnosis of left ventricular hypertrophy (LVH). The aim of the present study was to conduct a meta-analysis to compare the accuracy of AI tools to electrocardiographic criteria, including Sokolow-Lyon and the Cornell, most commonly used for the detection of LVH in clinical practice. Nine studies meeting the inclusion criteria were selected, comprising a sample size of 31 657 patients in the testing and 100 271 in the training datasets. Meta-analysis was performed using a hierarchal model, calculating the pooled sensitivity, specificity, accuracy, along with the 95% confidence intervals (95% CIs). To ensure that the results were not skewed by one particular study, a sensitivity analysis using the 'leave-out-one approach' was adopted for all three outcomes. AI was associated with greater pooled estimates; accuracy, 80.50 (95% CI: 80.4-80.60), sensitivity, 89.29 (95% CI: 89.25-89.33) and specificity, 93.32 (95% CI: 93.26-93.38). Adjusting for weightage of individual studies on the outcomes, the results showed that while accuracy and specificity were unchanged, the adjusted pooled sensitivity was 53.16 (95% CI: 52.92-53.40). AI demonstrates higher diagnostic accuracy and sensitivity compared with conventional ECG criteria for LVH detection. AI holds promise as a reliable and efficient tool for the accurate detection of LVH in diverse populations. Further studies are needed to test AI models in hypertensive populations, particularly in low resource settings.

Hybrid LSTM models-based detection of left ventricular hypertrophy in electrocardiogram signals

This research investigates various deep learning techniques to automatically classify Left Ventricular Hypertrophy (LVH) from electrocardiogram (ECG) signals. LVH frequently results from persistently high blood pressure, causing the heart pump harder and thicken the ventricular walls. It is associated with an increased risk of heart attacks, heart failure, stroke, and sudden cardiac death. The significance of this research lies in the early and precise detection of LVH, facilitating timely interventions and ultimately improving patient health. The non-invasive nature of ECG monitoring, integrated with the efficiency of deep learning models, contributes to faster and more accessible to enhance diagnostic accuracy and efficiency in identifying LVH. The objective of this research is to assess and compare the performance of GRU3Net, Double-Bilayer LSTM, and Conv2LSTM, Dual-LSTM models in the classification of Left Ventricular Hypertrophy (LVH) based on electrocardiogram (ECG) signals, utilizing a dataset sourced from the PTB Diagnostic ECG Database. The implemented deep learning models yielded noteworthy results. Specifically, the GRU3Net model achieved a high accuracy of 96.1%, showcasing an optimal configuration for overall accuracy. The Double-Bilayer LSTM model followed with an accuracy of 91.7%. However, a decline in accuracy was observed in both the Dual-LSTM and Conv2LSTM models, with the former registering an accuracy of 90.8% and the latter decreasing further to 87.3%.

Diagnostic accuracy of artificial intelligence in detecting left ventricular hypertrophy by electrocardiograph: a systematic review and meta-analysis

Several studies suggested the utility of artificial intelligence (AI) in screening left ventricular hypertrophy (LVH). We hence conducted systematic review and meta-analysis comparing diagnostic accuracy of AI to Sokolow–Lyon’s and Cornell’s criteria. Our aim was to provide a comprehensive overview of the newly developed AI tools for diagnosing LVH. We searched MEDLINE, EMBASE, and Cochrane databases for relevant studies until May 2023. Included were observational studies evaluating AI’s accuracy in LVH detection. The area under the receiver operating characteristic curves (ROC) and pooled sensitivities and specificities assessed AI’s performance against standard criteria. A total of 66,479 participants, with and without LVH, were included. Use of AI was associated with improved diagnostic accuracy with summary ROC (SROC) of 0.87. Sokolow–Lyon’s and Cornell’s criteria had lower accuracy (0.68 and 0.60). AI had sensitivity and specificity of 69% and 87%. In comparison, Sokolow–Lyon’s specificity was 92% with a sensitivity of 25%, while Cornell’s specificity was 94% with a sensitivity of 19%. This indicating its superior diagnostic accuracy of AI based algorithm in LVH detection. Our study demonstrates that AI-based methods for diagnosing LVH exhibit higher diagnostic accuracy compared to conventional criteria, with notable increases in sensitivity. These findings contribute to the validation of AI as a promising tool for LVH detection.

Searching for the Best Machine Learning Algorithm for the Detection of Left Ventricular Hypertrophy from the ECG: A Review.

Background: Left ventricular hypertrophy (LVH) is a powerful predictor of future cardiovascular events. Objectives: The objectives of this study were to conduct a systematic review of machine learning (ML) algorithms for the identification of LVH and compare them with respect to the classical features of test sensitivity, specificity, accuracy, ROC and the traditional ECG criteria for LVH. Methods: A search string was constructed with the operators "left ventricular hypertrophy, electrocardiogram" AND machine learning; then, Medline and PubMed were systematically searched. Results: There were 14 studies that examined the detection of LVH utilizing the ECG and utilized at least one ML approach. ML approaches encompassed support vector machines, logistic regression, Random Forest, GLMNet, Gradient Boosting Machine, XGBoost, AdaBoost, ensemble neural networks, convolutional neural networks, deep neural networks and a back-propagation neural network. Sensitivity ranged from 0.29 to 0.966 and specificity ranged from 0.53 to 0.99. A comparison with the classical ECG criteria for LVH was performed in nine studies. ML algorithms were universally more sensitive than the Cornell voltage, Cornell product, Sokolow-Lyons or Romhilt-Estes criteria. However, none of the ML algorithms had meaningfully better specificity, and four were worse. Many of the ML algorithms included a large number of clinical (age, sex, height, weight), laboratory and detailed ECG waveform data (P, QRS and T wave), making them difficult to utilize in a clinical screening situation. Conclusions: There are over a dozen different ML algorithms for the detection of LVH on a 12-lead ECG that use various ECG signal analyses and/or the inclusion of clinical and laboratory variables. Most improved in terms of sensitivity, but most also failed to outperform specificity compared to the classic ECG criteria. ML algorithms should be compared or tested on the same (standard) database.

Intelligent detection of left ventricular hypertrophy from pediatric echocardiography videos

AbstractLeft ventricular hypertrophy (LVH) is a common clinical manifestation associated with cardiovascular adverse events. Relying solely on the subjective judgment, it is challenging to promptly and accurately diagnose mild LVH. Quantitatively measuring the interventricular septum and left ventricular posterior wall to diagnose mild LVH is a time‐consuming and labor‐intensive process that is prone to errors. To propose a novel method for rapid and automatic end‐to‐end diagnosis of mild LVH. We propose a novel end‐to‐end automated method for detecting mild LVH. This method achieves rapid end‐to‐end detection of mild LVH in echocardiographic videos without the need for quantitative measurements. Initially, representative frames are extracted from echocardiographic videos, and these frames are then automatically diagnosed by the proposed network to detect LVH. The network architecture primarily consists of three key components: a feature extractor, bidirectional LSTM, and attention module. The Vit‐b model achieved 88% video classification accuracy in the experiment where 32 frames are extracted, and the ViT‐l model achieves 92% video classification accuracy in the experiment where 16 frames are extracted. It is shown experimentally that extracting fewer video frames can diagnose LVH more accurately. The experiments illustrate the superior performance and competitiveness of this method compared to other approaches, potentially applicable to the clinic.

Prevention and treatment of hypertensive left ventricular hypertrophy.

Left ventricular (LV) hypertrophy (LVH) is a well recognized target organ adaptation to longstanding uncontrolled hypertension and other cardiovascular risk factors. It is also a strong and independent predictor of many cardiovascular disorders. This focused review explores the current concepts in screening, diagnosis, prevention, and treatment of LVH in patients with hypertension. Currently, the primary screening and diagnostic tools for LVH are ECG and 2D echocardiography. Implementing machine learning in the diagnostic modalities can improve sensitivity in the detection of LVH. Lifestyle modifications, blood pressure control with antihypertensive therapy, and management of comorbidities aid in preventing and reversing LV remodeling. LVH is a common and often silent complication of hypertension. Prevention and reversal of LV remodeling are crucial for cardiovascular risk reduction in patients with hypertension.

One-shot screening: Utilization of a two-dimensional convolutional neural network for automatic detection of left ventricular hypertrophy using electrocardiograms

Background and ObjectiveLeft ventricular hypertrophy (LVH) can impair ejection function and elevate the risk of heart failure. Therefore, early detection through screening is crucial. This study aimed to propose a novel method to enhance LVH detection using 12-lead electrocardiogram (ECG) waveforms with a two-dimensional (2D) convolutional neural network (CNN). MethodsUtilizing 42,127 pairs of ECG-transthoracic echocardiogram data, we pre-processed raw data into single-shot images derived from each ECG lead and conducted lead selection to optimize LVH diagnosis. Our proposed one-shot screening method, implemented during pre-processing, enables the superimposition of waveform source data of any length onto a single-frame image, thereby addressing the limitations of the one-dimensional (1D) approach. We developed a deep learning model with a 2D-CNN structure and machine learning models for LVH detection. To assess our method, we also compared our results with conventional ECG criteria and those of a prior study that used a 1D-CNN approach, utilizing the same dataset from the University of Tokyo Hospital for LVH diagnosis. ResultsFor LVH detection, the average area under the receiver operating characteristic curve (AUROC) was 0.916 for the 2D-CNN model, which was significantly higher than that obtained using logistic regression and random forest methods, as well as the two conventional ECG criteria (AUROC of 0.766, 0.790, 0.599, and 0.622, respectively). Incorporating additional metadata, such as ECG measurement data, further improved the average AUROC to 0.921. The model's performance remained stable across two different annotation criteria and demonstrated significant superiority over the performance of the 1D-CNN model used in a previous study (AUROC of 0.807). ConclusionsThis study introduces a robust and computationally efficient method that outperforms 1D-CNN models utilized in previous studies for LVH detection. Our method can transform waveforms of any length into fixed-size images and leverage the selected lead of the ECG, ensuring adaptability in environments with limited computational resources. The proposed method holds promise for integration into clinical practice as a tool for early diagnosis, potentially enhancing patient outcomes by facilitating earlier treatment and management.

A fast, novel technique for detection of left ventricular hypertrophy by electrocardiography

Abstract Background Electrocardiography (ECG) is a widely used non-invasive tool for detecting left ventricular hypertrophy (LVH), but existing techniques suffer from complex rules and limited sensitivity. Purpose The goal of this study was to design and evaluate a new technique for rapid assessment of LVH by ECG. Methods The WS (Witteles-Somani) technique for LVH detection is based on recognizing the visual overlap between the S-wave of a precordial lead and the R-wave of any subsequent precordial lead. To evaluate its performance, ECGs from all patients at Stanford Healthcare with associated transthoracic echocardiograms (TTEs) between 2006 and 2018 were collected for analysis. ECGs with pre-existing bundle branch blocks or paced rhythms were excluded. A multiple adaptive thresholding technique was used to segment all QRS complexes from each ECG, with which algorithms to implement LVH detection criteria (i.e. WS, Sokolow-Lyon, Cornell, Peguero-Lo Presti) assessed performance over all ECGs. Associations between raw WS score (e.g. maximum S-wave and R-wave pair overlap value) and cardiovascular mortality, incident cardiovascular disease, and incident heart failure using age- and gender-adjusted Cox regression was also investigated. Results A total of 123,021 ECGs and 35,293 TTEs from 30,645 patients were collected. Of these ECGs, 36.1% belonged to patients with TTE-diagnosed LVH. The WS technique was more sensitive (46.1%) and had a higher F1-score (0.443) than other clinical criteria (Table 1), although with comparable calibration (AUROC 0.56 for WS vs. 0.54 for Sokolow-Lyon, 0.60 for Cornell, and 0.61 for Peguero-Lo Presti). Higher WS scores were associated with all-cause mortality (HR 1.08 [95% CI, 1.06, 1.11], P<0.005), incident cardiovascular disease (HR 1.15 [95% CI, 1.14, 1.17], P<0.005), and incident heart failure (HR 1.17 [95% CI, 1.16, 1.18], P<0.005). Conclusion The proposed WS technique is a much simpler and more sensitive technique than standard metrics to detect LVH from ECG, with important clinical implications in the development of future cardiovascular disease.Cumulative Incidence of DiseaseCriteria Classification Performance

Development and validation of a rapid visual technique for left ventricular hypertrophy detection from the electrocardiogram.

Left ventricular hypertrophy (LVH) detection techniques on by electrocardiogram (ECG) are cumbersome to remember with modest performance. This study validated a rapid technique for LVH detection and measured its performance against other techniques. This was a retrospective cohort study of patients at Stanford Health Care who received ECGs and resting transthoracic echocardiograms (TTE) from 2006 through 2018. The novel technique, Witteles-Somani (WS), assesses for S- and R-wave overlap on adjacent precordial leads. The WS, Sokolow-Lyon, Cornell, and Peguero-Lo Presti techniques were algorithmically implemented on ECGs. Classification metrics, receiver-operator curves, and Pearson correlations measured performance. Age- and sex-adjusted Cox proportional hazard models evaluated associations between incident cardiovascular outcomes and each technique. A total of 53,333 ECG-TTE pairs from 18,873 patients were identified. Of all ECG-TTE pairs, 21,638 (40.6%) had TTE-diagnosed LVH. The WS technique had a sensitivity of 0.46, specificity of 0.66, and AUROC of 0.56, compared to Sokolow-Lyon (AUROC 0.55), Cornell (AUROC 0.63), and Peguero-Lo Presti (AUROC 0.63). Patients meeting LVH by WS technique had a higher risk of cardiovascular mortality [HR 1.18, 95% CI (1.12, 1.24), P < 0.001] and a higher risk of developing any cardiovascular disease [HR 1.29, 95% CI (1.22, 1.36), P < 0.001], myocardial infarction [HR 1.60, 95% CI (1.44, 1.78), P < 0.005], and heart failure [HR 1.24, 95% CI (1.17, 1.32), P < 0.001]. The WS criteria is a rapid visual technique for LVH detection with performance like other LVH detection techniques and is associated with incident cardiovascular outcomes.

Automatic ECG-based detection of left ventricular hypertrophy and its predictive value in haemodialysis patients

Objective. Left ventricular hypertrophy (LVH) is one of the most severe risk factors in patients with end-stage kidney disease (ESKD) regarding all-cause and cardiovascular mortality. It contributes to the risk of sudden cardiac death which accounts for approximately 25% of deaths in ESKD patients. Electrocardiography (ECG) is the least expensive way to assess whether a patient has LVH, but manual annotation is cumbersome. Thus, an automated approach has been developed to derive ECG-based LVH parameters. The aim of the current study is to compare automatic to manual measurements and to investigate their predictive value for cardiovascular and all-cause mortality. Approach. From the 12-lead 24 h ECG measurements of 301 ESKD patients undergoing haemodialysis, three different LVH parameters were calculated. Peguero-Lo Presti voltage, Cornell voltage, and Sokolow–Lyon voltage were automatically derived and compared to the manual annotations. To determine the agreement between manual and automatic measurements and their predictive value, Bland–Altman plots were created and Cox regression analysis for cardiovascular and all-cause mortality was performed. Main results. The median values for the automatic assessment were: Peguero-Lo Presti voltage 1.76 mV (IQR 1.29–2.55), Cornell voltage 1.14 mV (IQR 0.721–1.66), and Sokolow–Lyon voltage 1.66 mV (IQR 1.08–2.23). The mean differences when compared to the manual measurements were −0.027 mV (0.21 SD), 0.027 mV (0.13 SD) and −0.025 mV (0.24 SD) for Peguero-Lo Presti, Cornell, and Sokolow–Lyon voltage, respectively. The categorial LVH detection based on pre-defined thresholds differed in only 13 cases for all indices between manual and automatic assessment. Proportional hazard ratios only differed slightly in categorial LVH detection between manually and automatically determined LVH parameters; no differences could be found for continuous parameters. Significance. This study provides evidence that automatic algorithms can be as reliable in LVH parameter assessment and risk prediction as manual measurements in ESKD patients undergoing haemodialysis.

Diagnostic Performance of Electrocardiographic Criteria Compared With Echocardiographic Diagnosis of Left Ventricular Hypertrophy in Patients at the Outpatient Department

INTRODUCTION: Several electrocardiographic (ECG) criteria have previously been suggested to diagnose left ventricular hypertrophy (LVH). Studies on diagnostic performance of each criterion in Asian population were limited and this study was done to determine the diagnostic performance of the six different ECG criteria, including the newly developed Peguero–Lo Presti criterion, in diagnosing LVH in Filipino patients. METHODOLOGY: A single-center retrospective cohort study was conducted. The comparison of ECG to echocardiographic diagnosis of LVH was assessed by Spearman ρ correlation. The area under the curve analysis was used to evaluate discrimination ability of ECG-LVH criteria to identify echocardiography-LVH. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the six criteria were described with 95% confidence interval, with P &lt; 0.05 considered statistically significant. RESULTS: A total of 325 patients were included in the final analysis; 56.61% had LVH, 23.07% of which were both ECG-based and echocardiography-based LVH. The Peguero–Lo Presti criterion had the highest sensitivity (53.1%), a lower specificity (75.5%), and a lower accuracy (68.6%), compared with the other criteria. Sokolow-Lyon index had highest specificity (97.2%) and positive predictive value (75.0%). Cornell voltage had relatively better discriminative performance (area under the curve, 0.73). CONCLUSION: Having a higher sensitivity, the Peguero–Lo Presti criterion can be used as a screening tool for LVH more than the Sokolow-Lyon and Cornell voltage. Cornell voltage criterion has higher correlation with left ventricular mass index and better discriminative ability for the detection of LVH. Further studies with the possibility of combining different ECG criteria are suggested to increase the sensitivity of the ECG criteria. KEYWORDS: Cornell voltage, electrocardiographic LVH criteria, left ventricular mass index, Peguero–Lo Presti criteria, Sokolow-Lyon voltage

Are Electrocardiographic Criteria Reliable for Left Ventricular Hypertrophy Detection in Indian Adults?

Left ventricular hypertrophy (LVH) detection is vital to the risk stratification of adults at risk of adverse cardiovascular events such ascoronary heart disease, cerebrovascular disease, and aortic aneurysms. Electrocardiogram (ECG), a non-invasive, cost-effective instrument has been widely used as a screening tool for LVH. The objective of this study was to determine the diagnostic accuracy of seven frequently used ECG criteria in high-risk Indian adults in comparison with echocardiography. ECG and transthoracic echocardiography were performed in adults older than 18 years with at least one cardiac risk factor (chronic hypertension, obesity, ischemic heart disease, and type 2 diabetes mellitus). Precision and accuracy were calculated for the various ECG criteria against LVH based on left ventricular mass index (LVMI) and cardiac remodeling by echocardiography. A total of 220 participants were enrolled. Of these, 96 had LVH by echocardiography. There was marked variability in LVH detection by the different ECG criteria: 28 by Sokolow-Lyon criteria, 26 by Cornell criteria, 24 by Lewis criteria, 46 by Scott criteria, eight by Romhilt-Estes criteria, six by Modified Cornell criteria, and only two by Roberts criteria. Agreement statistics between ECG criteria and LVMI showed that none of them had a good agreement for LVH detection. None of the ECG criteria were sensitive enough to rule out ventricular hypertrophy. In the context of cardiac remodeling, the ECG criteria had high sensitivity but low specificity and, hence, limited clinical relevance.

Association of cystatin C with changes of left ventricular structure and function in individuals with different cardiovascular risk

Aim. This study aims to investigate the association of cystatin C with changes of left ventricular structure and function in individuals with different cardiovascular risk (CVR).Material and methods. 267 patients with low-moderate (group I, n=58), high (group II, n=80) and extremely high (group III, n=129) CVR were examined. The level of serum cystatin C, creatinine and blood lipid spectrum, filtration rate of the kidneys and echocardiography indicators were estimated.Results. Among all the study participants (n=267), 194 patients (72,6% of cases) had the increased level of serum cystatin C; 165 patients (61,7% of cases) showed the signs of the left ventricular hypertrophy (LVH). The increased level of serum cystatin C was observed in 51,7% of cases in group I; 75,0% — in group II and 80,6% — in group III. The values of glomerular filtration rate (GFR) calculated using the CKD-EPI and F. Hoek formula were the following: 100,2±17,0 ml/min/1,73 m2 and 84,8±15,5 ml/min/1,73 m2, p&lt;0,05 in group I; 81,2±21,6 ml/min/1,73 m2 and 63,1±18,3 ml/min/1,73 m2, p&lt;0,05 in group II; 63,0 (32,0;93,0) ml/min/1,73 m2 and 55,1 (22,1;70,7) ml/min/1,73 m2 — in group III. The LVH detection increased with the increase of the CVR degree (43,1% — in group I; 66,2% — in group II and 67,4% in group III). Relative wall thickness (RWT, units) increased significantly from the patients of group I (0,34±0,04 units) to the patients of group II (0,37±0,08 units) and III (0,38±0,06 units). Eccentric variant of LVH significantly prevailed in all the groups. On one side, it was found that the level of serum cystatin C was in direct correlation with left ventricular mass index (LVMI, r=0,268, p&lt;0,05) and left ventricular RWT (r=0,190, p&lt;0,05), and on the other side, the inverse relationship between LVMI and GFR for cystostatin C was observed (r=-0,324, p&lt;0,05).Conclusion. The results of the study showed that the level of serum cystatin C and LVMI value significantly increase with the increase of the CVR degree. The high levels of serum cystatin C are closely associated with the increase of LVMI and the changes in the RWT value. In turn, the increase of LVMI negatively correlated with filtration rate of the kidneys in patients with different CVR. Concerning the structural changes in the left ventricle, eccentric HLV prevailed in all the three groups.

Papillary-Muscle-Derived Radiomic Features for Hypertrophic Cardiomyopathy versus Hypertensive Heart Disease Classification.

Purpose: This study aimed to assess the value of radiomic features derived from the myocardium (MYO) and papillary muscle (PM) for left ventricular hypertrophy (LVH) detection and hypertrophic cardiomyopathy (HCM) versus hypertensive heart disease (HHD) differentiation. Methods: There were 345 subjects who underwent cardiovascular magnetic resonance (CMR) examinations that were analyzed. After quality control and manual segmentation, the 3D radiomic features were extracted from the MYO and PM. The data were randomly split into training (70%) and testing (30%) datasets. Feature selection was performed on the training dataset. Five machine learning models were evaluated using the MYO, PM, and MYO+PM features in the detection and differentiation tasks. The optimal differentiation model was further evaluated using CMR parameters and combined features. Results: Six features were selected for the MYO, PM, and MYO+PM groups. The support vector machine models performed best in both the detection and differentiation tasks. For LVH detection, the highest area under the curve (AUC) was 0.966 in the MYO group. For HCM vs. HHD differentiation, the best AUC was 0.935 in the MYO+PM group. Comparing the radiomics models to the CMR parameter models for the differentiation tasks, the radiomics models achieved significantly improved the performance (p = 0.002). Conclusions: The radiomics model with the MYO+PM features showed similar performance to the models developed from the MYO features in the detection task, but outperformed the models developed from the MYO or PM features in the differentiation task. In addition, the radiomic models performed better than the CMR parameters' models.

Artificial intelligence–enabled classification of hypertrophic heart diseases using electrocardiograms

Differentiating among cardiac diseases associated with left ventricular hypertrophy (LVH) informs diagnosis and clinical care. To evaluate if artificial intelligence-enabled analysis of the 12-lead electrocardiogram (ECG) facilitates automated detection and classification of LVH. We used a pretrained convolutional neural network to derive numerical representations of 12-lead ECG waveforms from patients in a multi-institutional healthcare system who had cardiac diseases associated with LVH (n = 50,709), including cardiac amyloidosis (n = 304), hypertrophic cardiomyopathy (n = 1056), hypertension (n = 20,802), aortic stenosis (n = 446), and other causes (n = 4766). We then regressed LVH etiologies relative to no LVH on age, sex, and the numerical 12-lead representations using logistic regression ("LVH-Net"). To assess deep learning model performance on single-lead data analogous to mobile ECGs, we also developed 2 single-lead deep learning models by training models on lead I ("LVH-Net Lead I") or lead II ("LVH-Net Lead II") from the 12-lead ECG. We compared the performance of the LVH-Net models to alternative models fit on (1) age, sex, and standard ECG measures, and (2) clinical ECG-based rules for diagnosing LVH. The areas under the receiver operator characteristic curve of LVH-Net by specific LVH etiology were cardiac amyloidosis 0.95 [95% CI, 0.93-0.97], hypertrophic cardiomyopathy 0.92 [95% CI, 0.90-0.94], aortic stenosis LVH 0.90 [95% CI, 0.88-0.92], hypertensive LVH 0.76 [95% CI, 0.76-0.77], and other LVH 0.69 [95% CI 0.68-0.71]. The single-lead models also discriminated LVH etiologies well. An artificial intelligence-enabled ECG model is favorable for detection and classification of LVH and outperforms clinical ECG-based rules.

ECG for Detection of Left Ventricular Hypertrophy in patients with Chronic Kidney Disease

Background: Left ventricular hypertrophy is the most common cardiac event associated with chronic kidney disease. Aim: To detect LVH by electrocardiography in patients with CKD. Study design: Observational study. Methodology: It was an observational study with total of 62 CKD patients enrolled through convenience sampling. Their blood 3ml was drawn aseptically for estimation of serum creatinine. All enrolled subjects underwent electrocardiographic examination. Patients with low GFR (&lt;60ml/min/1.73m2) and high serum creatinine concentration (&gt;1.5mg/dl) were included. Data was evaluated by using SPSS vr 24. The results were presented as counts, means and standard deviation as appropriate. Results: Left ventricular hypertrophy was present in 50% of enrolled patients as depicted by ECG recording. The calculated sensitivity and specificity of ECG for LVH were 75% and 100% respectively. Almost 38.7% patients were 56-65 years of age. There were 33.9% patients in the age of 35-45 years and 27.4% was in the age of 46-55 years. The median (IQR) age was 54.50 (41.75-60) years. In our study population, median (IQR) of height was 152cm (150-156). The mean weight was 65.85 kg with standard deviation of 8.42 kg i.e., 65.85 ±8.42kg. Mean body mass index was 27.85. Practical Implication: Current project helped health providers to diagnose cardiac event at an early stage among patients of kidney fail¬ure on ECG findings thus the adverse outcomes could be prevented or delayed. Conclusion: ECG abnormalities are common in hospitalized CKD patients in local population. All hospitalized CKD patients should undergo ECG to screen for cardiovascular disease. It was thus concluded that ECG can be used as a sensitive diagnostic tool in combination with different ECG indices to detect LVH. Key words: Electrocardiography, Chronic Kidney Disease, Left Ventricular Hypertrophy and Sokolow-Lyon Voltage Index

Detection of left ventricular hypertrophy on the ECG through machine learning with a focus on obesity

Abstract Background Cardiac remodeling, an important aspect of cardiovascular disease (CVD) progression, is emerging as a significant therapeutic target. The electrocardiogram (ECG) is of paramount importance in the initial evaluation of a patient. However, the ECG is not a sensitive method of detecting left ventricular hypertrophy (LVH), and as far as we know, it cannot detect changes in left ventricular geometry (LVG) at early stages, especially before LVH is present. Its sensitivity is particularly low for obese patients. Purpose To use a machine learning (ML) classifier to detect abnormal LVG from ECG parameters/markers, even before it becomes LVH, and to propose some indicative markers useful for practitioners. We also looked at the results of our model for obese patients to test the markers in this population. Methods We enrolled consecutive subjects, aged 30 years or older (mean age: 61.6±12 years old) with and without essential hypertension and no indications of CVD. All patients underwent a full echocardiographic evaluation and were classified into 2 groups; those with normal geometry (NG) vs. those with concentric remodeling (CR) or LVH defined as concentric hypertrophy (CH) and eccentric hypertrophy (EH). Abnormal LVG was identified as increased relative wall thickness (RWT) and/or left ventricular mass index (LVMi). We analyzed the EKG waveforms deduced to single beat averages for each lead using custom software and extracted 70 markers. We then trained a Random Forest machine learning model to classify subjects with abnormal LVG and calculated SHAP values to perform feature importance and interaction. Results After screening 1120 individuals, we enrolled 594 subjects, aged 30 years or older (mean age: 61.6±12 years old). The percentage of women was 56.5%, while 71.3% of all patients were hypertensive. Hypertension, age, body mass index divided by the Sokolow-Lyon voltage (BMI/S-L), QRS-T angle, and QTc duration were among the most important parameters (Figure, left panel) identified by the model as being predictive of abnormal LVG (AUC/ROC = 0.84, sensitivity = 0.94, specificity 0.61). Specifically for obese patients, whose prevalence in our population was 60.3%, our model performed well (sensitivity = 0.71, specificity = 0.92. When we tried our model without the the BMI/S-L parameter, the specificity dropped to 0.88. We also found that a cut-off point of 18 for the BMI/S-L marker predicted the patients who were more probable to have developed abnormal LVG. Conclusions This study is the first to demonstrate the promising potential of ML modeling for the efficient and cost-effective diagnostic screening of abnormal LVG and cardiac remodeling through ECG. We found specific clinical and ECG parameters that can predict early pathological changes of LVG in patients without established CVD and detect the population who will benefit from a detailed echocardiographic evaluation. Funding Acknowledgement Type of funding sources: None.