Left ventricular hypertrophy (LVH) is one of the strongest predictors of cardiovascular disease (CVD) and mortality; yet the means to diagnose LVH in resource-constrained settings remain limited. The objectives of this study were to determine LVH prevalence by transthoracic echocardiography (TTE) in a high-risk group, and compare TTE vs. electrocardiography (ECG-LVH) for LVH detection. We analyzed enrollment data from the Haiti cardiovascular disease cohort study on adults (≥ 18 years, n = 3,005) in Port-au-Prince between 2019 and 2021. All participants underwent questionnaires, vital signs, physical exams, and 12-lead ECGs. TTEs were acquired on those with hypertension or exhibiting CVD symptoms (n = 1040, 34.7%). TTE-LVH was defined according to the American Society of Echocardiography guidelines and ECG-LVH by Sokolow-Lyon, Cornell, and Limb-Lead Voltage criteria. The prevalence of TTE-LVH was 39.0% (95% CI 36.6-41.5%) and associated with older age. Only 26% of those with TTE-LVH and elevated blood pressure were on antihypertensives. Prevalence of ECG-LVH ranged from 1.9 to 5.0%, and compared to TTE-LVH had low agreement (κ < 0.20), low sensitivity (< 10%) and high specificity (> 90%). These findings indicate a high prevalence of TTE-LVH among high-risk Haitian adults, and poor detection using ECGs compared to TTEs. For those with TTE-LVH, treatment with antihypertensives may reduce the risk of adverse CVD outcomes.

Hypertension is often underrecognized, leading to preventable morbidity and mortality. Tailored data systems combined with care augmented by trained nonphysicians have the potential to improve cardiovascular care.

To determine whether previously collected cardiovascular imaging data could be harnessed to improve the detection and treatment of hypertension through a system-level intervention.

The NOTIFY-LVH trial was a 2-arm, pragmatic randomized clinical trial conducted from March 2023 through June 2024 within the Mass General Brigham health care system, a multi-institutional network serving the greater Boston, Massachusetts, area. The study included individuals with a Mass General Brigham primary care affiliation who had left ventricular hypertrophy (LVH) on a prior echocardiogram, had no established cardiomyopathy diagnosis, and were not being treated with antihypertensive medications. Patients were followed for 12 months postintervention.

Population health coordinators contacted clinicians of patients randomized to the intervention, notifying them of LVH and offering assistance with follow-up care. A clinical support pathway-including 24-hour ambulatory blood pressure monitoring or cardiology referrals-was provided to aid LVH evaluation.

The primary outcome was the initiation of an antihypertensive medication. Secondary outcomes included new hypertension and cardiomyopathy diagnoses.

A total of 648 patients were randomized-326 to the intervention and 322 to the control. Mean (SD) patient age was 59.4 (10.8) years and 248 patients (38.3%) were female. A total of 102 patients (15.7%) had a baseline diagnosis of hypertension and 109 patients (20.1%) had a mean outpatient blood pressure of 130/80 mm Hg or higher. Over 12 months, 53 patients (16.3%) in the intervention arm were prescribed an antihypertensive medication vs 16 patients (5.0%) in the control arm (adjusted odds ratio [OR], 3.76; 95% CI, 2.09-6.75; P < .001). Individuals in the intervention group were also more likely to be diagnosed with hypertension (adjusted OR, 4.43; 95% CI, 2.36-8.33; P < .001). Cardiomyopathy diagnoses did not significantly differ between groups.

In the NOTIFY-LVH randomized clinical trial, a centralized population health coordinator-led notification and clinical support pathway for individuals with LVH on prior echocardiograms increased the initial treatment of hypertension. This work highlights the potential benefit of leveraging preexisting but potentially underutilized cardiovascular data to improve health care delivery through mechanisms augmenting the traditional ambulatory care system.

ClinicalTrials.gov Identifier: NCT05713916.

Left ventricular (LV) long-axis shortening at the cardiac base is a determinant of left atrial (LA) reservoir function. Cardiac amyloidosis (CA) is characteristic of amyloid deposition predominantly in the LV basal wall. We investigated the relationship between LV basal strain and LA reservoir strain among patients with pathological LV hypertrophy and subsequently evaluated the diagnostic ability of LA reservoir strain to identify CA etiology and its predictive value for heart failure hospitalization.

We retrospectively analyzed 341 patients with LV hypertrophy. Cardiac etiologies were diagnosed by tissue biopsy, cardiac magnetic resonance imaging or 99mTc-PYP scintigraphy. LV basal strain and LA reservoir strain were analyzed.

Patients were diagnosed with CA (n = 75) and other etiologies (n = 266). LV basal strain was correlated with LA reservoir strain in the CA group (r = 0.58, p < 0.01) and the non-CA group (r = 0.44, p < 0.01). A binary logistic regression analysis showed that relative apical sparing of longitudinal strain, septal E/e' and LA reservoir strain had the ability to discriminate between the CA and non-CA groups (p < 0.01 for all). The area under the curve for relative apical sparing of longitudinal strain had a stronger ability than LA reservoir strain to discriminate CA from non-CA etiologies (0.90 versus 0.81, respectively; p < 0.01). During the follow-up period (median 2.7 years), the incidence of heart failure hospitalization was higher in the CA group than the non-CA group (35% versus 14%, respectively; p < 0.01). According to univariate Cox regression analysis, three LA factors (LA reservoir strain, E/e' and LA volume index) were associated with heart failure hospitalization in the non-CA group (p < 0.05 for all).

LA reservoir strain was associated with LV basal strain among patients with pathological LV hypertrophy. Echocardiographic assessment of LA reservoir strain might add diagnostic value to identify CA etiology in these patients.

Left ventricular hypertrophy (LVH) is a common clinical finding associated with adverse cardiovascular outcomes. Once LVH is diagnosed, defining its cause has crucial clinical implications. Artificial intelligence (AI) may allow significant progress in the automated detection of LVH and its underlying causes from cardiovascular imaging. This systematic review aims to investigate the diagnostic performance of AI models developed to diagnose LVH and its common aetiologies.

MEDLINE/PubMed, EMBASE and Cochrane databases were systematically searched to identify relevant studies on echocardiography, cardiac magnetic resonance (CMR), and cardiac computed tomography (CT).

Thirty studies were included in this review. Of them, 14 were on echocardiography, 15 on CMR, and one on cardiac CT. Regarding the AI methods applied, 79 % of studies in echocardiography utilized deep learning (DL), 64 % employed convolutional neural networks (CNNs), and 21 % applied traditional machine learning (ML) algorithms. For CMR studies, 53 % used DL, 27 % relied on CNNs, and 47 % adopted traditional ML methods. All studies showed good diagnostic performances, but those applying AI tools to determine the underlying causes of LVH demonstrated the highest accuracy metrics compared to those focused on detecting LVH itself.

AI models designed to detect and differentiate LVH on cardiac imaging are currently under development and are demonstrating promising results. Further studies focusing on real-life validation of these models, and cost-effectiveness analyses are needed.

Increased cardiac after load and multiple non-hemodynamic stimuli implicate in adverse left ventricular remodeling (LVR). This is particularly identifiable in treatment-resistant and secondary hypertension contexts, like primary hyperaldosteronism (PA), however little data exists on post-treatment residual LVR in these individuals.

Cardiac magnetic resonance (CMR) with T1 mapping were performed in 14 patients with treated PA matched with 15 treated patients with primary hypertension (PH) and 15 healthy individuals. Blood pressure (BP) control was defined as < 140 x 90mmHg.

Treated PA and PH patients had similar indexed left ventricular, extracellular matrix and intracellular masses (respectively 68 ± 12g/m2, 17 ± 3g/m2 and 52 ± 10g/m2 for PA vs 63 ± 18g/m2, 16 ± 5g/m2 and 47 ± 14g/m2 for PH, p > 0.05 for all), that were significantly higher than normal individuals (47 ± 8g/m2, 11 ± 2g/m2 and 36 ± 6g/m2, respectively, p < 0.05 for all). Patients with uncontrolled BP exhibited greater cardiomyocyte hypertrophy than those controlled (55 ± 11 g/m2 vs 43 ± 11 g/m2, p = 0.01), regardless of the cause of hypertension. PH individuals had strong correlations between BP measurements and LVR parameters of the CMR, while in PA correlations were weaker.

In treated patients with PA and PH, CMR detected similar residual tissue LVR in both groups. Uncontrolled BP was more related to the observed LVR than to the etiology of hypertension. BP levels were more strongly correlated to CMR LVR parameters in PH than PA patients.

Network represents adjacent relationships, connections, and interactions among constituent elements in complex systems but often loses critical information about spatial configurations. However, structure-function relationships in biological systems, e.g., the human heart, are highly dependent on both connectivity relationships and geometric details. Therefore, this paper presents a new self-organizing approach to derive the geometric structure from a network representation of the heart. We propose to simulate the network as a physical system, where nodes are treated as charged particles and edges as springs and then let these nodes self-organize to reconstruct geometric details. Despite random initiations, this network evolves into a steady topology when its energy is minimized. This study addresses the open question, i.e., "whether a network representation can effectively resemble spatial geometry of a biological system," thereby paving a stepstone to leverage network theory to investigate disease-altered biological functions.

The utility of standard published electrocardiographic (ECG) criteria for left ventricular hypertrophy (LVH) in patients with left bundle branch block (LBBB) is not established. We have previously shown that in ECGs demonstrating LBBB, QRS duration outperforms vectorcardiographic X, Y, Z lead and root-mean-squared (3D) amplitudes and voltage-time-integrals in diagnosing LVH and dilation. We sought to evaluate diagnostic yields of published LVH criteria versus QRS duration for ECG based diagnosis of LVH and dilation in presence of LBBB.

We included adult patients with typical LBBB having ECG and transthoracic echocardiogram performed within 3 months of each other in 2010-2020. We obtained area under receiver-operator characteristic curve (AUC) for QRS duration and each of the published ECG LVH criteria to predict increased LV mass indexed (↑LVMi, women >95 g/m2, men >115 g/m2) and LV end diastolic volume indexed (↑LVEDVi, women >61 mL/m2, men >74 mL/m2).

Among 413 adults (53 % women, age 73 ± 12 yr) with LBBB, the traditional LVH criteria performed poorly to detect ↑LVMi or ↑LVEDVi. Cornell voltage-duration product had the highest AUCs (↑LVMi 0.634, ↑LVEDVi 0.580). QRS duration had a higher AUC for diagnosing ↑LVMi (women 0.657, men 0.703) and ↑LVEDVi (women 0.668, men 0.699) compared to any other criteria.

In patients with LBBB, prolonged QRS duration (women ≥150 ms, men ≥160 ms) is a superior predictor of LVH and dilation than traditional ECG-based LVH criteria.

Traditional ECG criteria for left ventricular hypertrophy (LVH) have low diagnostic yield. Machine learning (ML) can improve ECG classification.

ECG summary features (rate, intervals, axis), R-wave, S-wave and overall-QRS amplitudes, and QRS/QRST voltage-time integrals (VTIs) were extracted from 12-lead, vectorcardiographic X-Y-Z-lead, and root-mean-square (3D) representative-beat ECGs. Latent features were extracted by variational autoencoder from X-Y-Z and 3D representative-beat ECGs. Logistic regression, random forest, light gradient boosted machine (LGBM), residual network (ResNet) and multilayer perceptron network (MLP) models using ECG features and sex, and a convolutional neural network (CNN) using ECG signals, were trained to predict LVH (left ventricular mass indexed in women >95 g/m², men >115 g/m²) on 225,333 adult ECG-echocardiogram (within 45 days) pairs. AUROCs for LVH classification were obtained in a separate test set for individual ECG variables, traditional criteria and ML models.

In the test set (n=25,263), AUROC for LVH classification was higher for ML models using ECG features (LGBM 0.790, MLP 0.789, ResNet 0.788) as compared to the best individual variable (VTIQRS-3D 0.677), the best traditional criterion (Cornell voltage-duration product 0.647) and CNN using ECG signal (0.767). Among patients without LVH who had a follow-up echocardiogram >1 (closest to 5) years later, LGBM false positives, compared to true negatives, had a 2.63 (95% CI 2.01, 3.45)-fold higher risk for developing LVH (p<0.0001).

ML models are superior to traditional ECG criteria to classify-and predict future-LVH. Models trained on extracted ECG features, including variational autoencoder latent variables, outperformed CNN directly trained on ECG signal.

Cardiovascular and cerebrovascular diseases (CCVDs), which are circulatory system diseases caused by heart defects and vascular diseases, are the major noncommunicable diseases affecting global public health. With the improvement of economic level and the change of human lifestyle, the prevalence of CCVDs continues to increase. Ginseng (Panax ginseng C. A. Mey.) was widely used in traditional diseases due to its supposed tonic properties. Ginsenoside Rb1 (G-Rb1) is the most abundant active ingredient with multiple pharmacological effects extracted from ginseng, which has been shown to have potential benefits on the cardiovascular system through a variety of mechanisms, including anti-oxidation, anti-inflammatory, regulation of vasodilation, reduction of platelet adhesion, influence of calcium ion channels, improvement of lipid distribution, involving in glucose metabolism and controlling blood sugar. This review reviewed the protective effects of G-Rb1 on CCVDs and its potential mechanisms, such as atherosclerosis (AS), hypertension, coronary heart disease (CHD), ischemic stroke (IS) and periocular microvascular retinopathy. Finally, we reviewed and reported the results of in vivo and in vitro experiments using G-Rb1 to improve CCVDs, highlighted its efficacy, safety, and limitations.

Menopause is a normal stage in the human female aging process characterized by the cessation of menstruation and the ovarian production of estrogen and progesterone hormones. Menopause is associated with an increased risk of several different diseases. Cardiovascular diseases are generally less common in females than in age-matched males. However, this female advantage is lost after menopause. Cardiac hypertrophy is a disease characterized by increased cardiac size that develops as a response to chronic overload or stress. Similar to other cardiovascular diseases, the risk of cardiac hypertrophy significantly increases after menopause. However, the exact underlying mechanisms are not yet fully elucidated. Several studies have shown that surgical or chemical induction of menopause in experimental animals is associated with cardiac hypertrophy, or aggravates cardiac hypertrophy induced by other stressors. Arachidonic acid (AA) released from the myocardial phospholipids is metabolized by cardiac cytochrome P450 (CYP), cyclooxygenase (COX), and lipoxygenase (LOX) enzymes to produce several eicosanoids. AA-metabolizing enzymes and their respective metabolites play an important role in the pathogenesis of cardiac hypertrophy. Menopause is associated with changes in the cardiovascular levels of CYP, COX, and LOX enzymes and the levels of their metabolites. It is possible that these changes might play a role in the increased risk of cardiac hypertrophy after menopause.

Cardiovascular death is the main cause of death in patients with end-stage kidney disease (ESKD). Left ventricular hypertrophy (LVH) and left atrial diameter (LAD) enlargement are frequent cardiac alterations in patients with ESKD and are major risk factors for cardiovascular events. However, it remains unclear whether there is an association between combined LAD or LVH and all-cause or cardiovascular mortality in this population.

A single-centre, retrospective cohort study including 576 haemodialysis (HD) patients was conducted. Patients were evaluated by cardiac ultrasound, and the study cohort was divided into four groups according to LAD and LVH status: low LAD and non-LVH; low LAD and LVH; high LAD and non-LVH; and high LAD and LVH. We used Kaplan-Meier analysis and Cox proportional hazard regression to analyse all-cause and cardiovascular mortality after multivariate adjustment.

LAD was associated with an increased risk of all-cause mortality (HR 2.371, 1.602-3.509; p < 0.001). No significant differences were found between LVH and the risk of all-cause mortality. Patients with high LAD and LVH had significantly greater all-cause and cardiovascular mortality than did those with low LAD and non-LVH after adjustments for numerous potential confounders (HR 3.080, 1.608-5.899; p = 0.001) (HR 4.059, 1.753-9.397; p = 0.001).

Among maintenance haemodialysis (MHD) patients, LAD was more strongly associated with mortality than was LVH. A high LAD and LVH are associated with a greater risk of mortality. Our results emphasize that the occurrence of LAD and LVH in combination provides information that may be helpful in stratifying the risk of MHD patients.

Introduction: Transcatheter aortic valve replacement (TAVR) has become an efficient and safe alternative to surgical aortic valve replacement (SAVR). While severe aortic stenosis as well as severe aortic regurgitation (AR) are known to negatively impact left ventricular ejection fraction (LVEF), prior studies have shown that TAVR can lead to an improvement in LVEF. Thus far, little is known about the prognostic implication of LVEF improvement as a sole predictor of outcomes. Therefore, the aim of this study was to assess the prognostic impact of LVEF impairment before TAVR, as well as early LVEF improvement in patients undergoing TAVR. Materials and Methods: Patients undergoing TAVR in a large tertiary university hospital were consecutively included in a prospective registry. Transthoracic echocardiography (TTE) was performed at baseline, after 1 month and annually thereafter. Significant LVEF improvement was defined as a relative increase of ≥10% in LVEF at 30 days compared to baseline LVEF. The primary outcome was all-cause mortality at 1 year. Secondary outcomes were major adverse cardiovascular events (MACEs) including cardiovascular death, non-fatal myocardial infarction, stroke, bleeding and unplanned re-interventions of the aortic valve at 5 years. Results: Among 1655 patients who underwent TAVR between September 2011 and April 2024, the LVEF at baseline was available for 1556 patients. Of these, 1031 patients (66.2%) had preserved LVEF at baseline (LVEF ≥ 53%), whereas 303 patients (19.5%) had moderately reduced LVEF (40-52%) and 222 patients (14.3%) had severely reduced LVEF (<40%). Out of the patients with impaired LVEF, 155 (40.4%) patients showed a significant improvement in LVEF ≥10% after 30 days, while 229 (60.6%) patients showed no significant LVEF improvement (<10%). Patients with preserved LVEF at baseline had significantly better mortality outcomes than those with severely reduced LVEF (p < 0.001). LVEF improvement was associated with a survival benefit after 1 year (p = 0.009, HR 2.68, 0.95 CI 1.23-5.85) which diminished after 5 years (p = 0.058), but patients with LVEF improvement showed lower MACE rates at 5 years (p < 0.001). Conclusions: Preserved LVEF before TAVR is an independent predictor for improved outcomes. Additionally, early improvement in LVEF is associated with beneficial outcomes in patients undergoing TAVR.

Left ventricular hypertrophy (LVH) is a significant risk factor for cardiovascular mortality and morbidity in patients with hypertension. However, the effect of age on LVH regression or persistence and its differential prognostic value remain unclear. Therefore, we investigated the clinical implications of LVH regression in 1847 patients with hypertension and echocardiography data (at baseline and during antihypertensive treatment at an interval of 6-18 months) according to age. LVH was defined as a left ventricular mass index (LVMI) > 115 g/m2 and >95 g/m2 in men and women, respectively. LVH prevalence at baseline was not different according to age (age < 65 years: 42.6%; age ≥65 years: 45.7%; p = 0.187), but LVH regression was more frequently observed in the younger group (36.4% vs. 27.5%; p = 0.008). Spline curves and multiple linear regression analysis showed a significant relationship between reductions in systolic blood pressure and LVMI in the younger group (β = 0.425; p < 0.001), but not the elderly group (β = 0.044; p = 0.308). LVH regression was associated with a lower risk of the study outcome (composite of cardiovascular death and hospitalization for heart failure) regardless of age. In conclusion, the association between the reduction in blood pressure and LVH regression was prominent in patients with age < 65 years, but not in those with age ≥65 years. However, an association between LVH regression and lower risk of cardiovascular death and hospitalization for heart failure was observed regardless of patient age, suggesting the prognostic value of the LVH regression not only in the younger patients but also in elderly patients.

The cardiovascular risk associated with left ventricular hypertrophy (LVH) in the community and, particularly, in the hypertensive fraction of the general population, represents the rationale for its timely and accurate identification in order to implement adequate preventive strategies. Although electrocardiography (ECG) is the first-line and most economical method of diagnosing LVH its accuracy is largely suboptimal. Over the last 70 years, dozens of different ECG criteria, mostly based on measurements of QRS voltages, have been proposed. In this long journey, a few years ago Peguero et al. developed a novel ECG voltage criterion, currently recognized as Peguero-Lo Presti (PLP) suggesting that it has greater sensitivity than traditional ECG-LVH criteria. Considering that in the last 5 years numerous studies have investigated the diagnostic value of this new index, this review aimed to summarize the data published so far on this topic focusing both on the accuracy in identifying the presence of LVH compared with imaging techniques such as echocardiography (ECHO) and magnetic resonance imaging (MRI) and the value in predicting hard outcomes. The evidence in favor of the greater diagnostic accuracy of the PLP criterion in detecting LVH, phenotyped by ECHO or MRI, and in the stratification of hard outcomes compared with traditional ECG criteria does not appear to be sufficiently proven. Given that the diagnosis of LVH by all ECG criteria (including the PLP) exclusively based on the QRS amplitude is largely imprecise, the development of new multiparametric ECG criteria based on artificial intelligence could represent a real improvement in the diagnostic capacity of the ECG.

Several studies investigated the association between nighttime blood pressure (BP) and left ventricular hypertrophy (LVH) in diabetes, but since most of these studies were conducted in diabetes populations only, they did not compare differences in the impact of nighttime BP on LVH in subjects without diabetes. Moreover, data about the impact of glucose control in diabetes on the relationship between nighttime BP and LVH are sparse. We classified 1277 adults (age 64.7 ± 11.8 years) performing ambulatory BP monitoring while enrolled as part of the Japan Morning Surge Home Blood Pressure (J-HOP) study into groups according to the control status of daytime BP (systolic BP [SBP] < 135 mmHg or ≥135 mmHg), nighttime BP (SBP < 120 mmHg or ≥120 mmHg), and diabetes (HbA1c < 7.0% or ≥7.0%). LVH was assessed by echocardiography. LVH according to echocardiographic criteria was identified in 33.7% of the participants. The group with poorly controlled diabetes plus uncontrolled nighttime BP (n = 90) had a 2.1-fold higher risk of LVH compared to the group with controlled nighttime BP and non-diabetes (n = 505) (odds ratio [OR] 2.10, 95% confidence interval [CI]: 1.29-3.44). No association was observed between uncontrolled daytime BP and diabetes for LVH. In the participants with poorly controlled diabetes (n = 146), uncontrolled nighttime BP posed a 3.1-fold higher risk of LVH compared to controlled nighttime BP (OR 3.12, 95%CI: 1.47-6.62). This association was not found in controlled diabetes. Uncontrolled nighttime BP was associated with a risk of LVH, especially among individuals with poorly controlled diabetes.

To determine associations between anabolic-androgenic steroid (AAS) use-related morbidity including cardiovascular disease (CVD) and engagement to health services.

In this cross-sectional study, 90 males with at least 12 months cumulative current or former use of AAS were included. The participants were divided into a treatment-seeking group (TSG) and a non-treatment seeking group (non-TSG) based on their responses to a self-report web questionnaire. All participants were screened for symptoms that could be indicative of CVD through a clinical interview, and examined with blood samples, blood pressure measurements and transthoracic echocardiography.

In the total sample (n = 90), mean age was 39 ± 11 years with cumulative AAS use of 12 ± 9 years. Among men in the TSG with current use there were higher prevalence of dyspnoea (50% vs 7%) and reduced left ventricular ejection fraction (LVEF) in conjunction with left ventricular hypertrophy (LVH) (36 vs. 9%) and/or high blood pressure (55% vs. 19%) compared to men in the non-TSG. Among men with current AAS use and established LVEF <50% (n = 25) or LVH (n = 21), 44% (11) and 43% (9) respectively, had never engaged health services due to AAS-related adverse effects. Deviant liver- and kidney parameters were frequently observed in the total sample but without between-group differences.

Treatment-seeking behavior among current AAS users may be associated with increased levels of dyspnoea and established CVD. Despite objective signs of severe CVD among a substantial amount of study participants, it is of great concern that the majority had never sought treatment for AAS-related concerns.

Successful antihypertensive management can limit left ventricular hypertrophy (LVH) and improve the clinical prognosis. However, it remains unclear whether intensive blood pressure (BP) lowering has a greater effect on the occurrence and regression of LVH compared to standard BP lowering.

We searched the electronic databases of PubMed, EMBASE and Web of Science from inception to 2 June 2023. Relevant and eligible studies were included. A random-effects model was used to estimate the pooled odds ratio (OR) and 95% confidence intervals (CI).

Four RCTs including 20,747 patients met our inclusion criteria. The results demonstrated that intensive BP lowering was associated with a significantly lower rate of LVH (OR 0.85; 95%CI: 0.78-0.93; I2 48.6%) in patients with hypertension compared to standard BP lowering. Subgroup analysis revealed that the effect of intensive BP lowering on LVH was more pronounced in patients with high cardiovascular disease (CVD) risk factors (OR 0.82; 95%CI: 0.72-0.93; I2 57.9%). In addition, intensive BP lowering led to significant regression of LVH (OR 0.68; 95%CI: 0.52-0.88; I2 45.5%).

Our study suggests that intensive BP lowering should be instigated as soon as possible for optimal control of BP and to prevent regression of LVH, especially in patients with high risk of CVD. However, caution is warranted when treating hypertensive patients with LVH to systolic blood pressure (SBP) targets below 130 mm Hg.

Subclinical alterations in cardiac structure and function include a variety of abnormal phenotypes of established adverse prognostic significance such as left ventricular hypertrophy (LVH), alterations of LV geometry, left atrial (LA) enlargement, and aortic root (AR) dilatation. The excess cardiovascular (CV) risk associated with these phenotypes has been consistently demonstrated in different clinical settings such in patients with systemic hypertension, coronary heart disease, diabetes mellitus, chronic kidney disease, heart failure and in geneal population samples. The Pressioni Monitorate e Loro Associazioni (PAMELA), a longitudinal population-based study originally designed to assess the normality values, prognostic significance of office, home and 24-hour blood pressure, including among the many clinical and laboratory variables the collection of echocardiographic data, allowed to gather important information on the clinical prognostic significance of subclinical cardiac damage during a long follow-up period. This article summarizes the original findings provided by the PAMELA study on the clinical correlates and prognostic significance of echocardiographic markers of subclinical organa damage namely LVH, left atrial enlargement (LA) and AR dilatation at the community level.

Electronic health records contain vast amounts of cardiovascular data, including potential clues suggesting unrecognized conditions. One important example is the identification of left ventricular hypertrophy (LVH) on echocardiography. If the underlying causes are untreated, individuals are at increased risk of developing clinically significant pathology. As the most common cause of LVH, hypertension accounts for more cardiovascular deaths than any other modifiable risk factor. Contemporary healthcare systems have suboptimal mechanisms for detecting and effectively implementing hypertension treatment before downstream consequences develop. Thus, there is an urgent need to validate alternative intervention strategies for individuals with preexisting-but potentially unrecognized-LVH.

Through a randomized pragmatic trial within a large integrated healthcare system, we will study the impact of a centralized clinical support pathway on the diagnosis and treatment of hypertension and other LVH-associated diseases in individuals with echocardiographic evidence of concentric LVH. Approximately 600 individuals who are not treated for hypertension and who do not have a known cardiomyopathy will be randomized. The intervention will be directed by population health coordinators who will notify longitudinal clinicians and offer to assist with the diagnostic evaluation of LVH. Our hypothesis is that an intervention that alerts clinicians to the presence of LVH will increase the detection and treatment of hypertension and the diagnosis of alternative causes of thickened myocardium. The primary outcome is the initiation of an antihypertensive medication. Secondary outcomes include new hypertension diagnoses and new cardiomyopathy diagnoses. The trial began in March 2023 and outcomes will be assessed 12 months from the start of follow-up.

The NOTIFY-LVH trial will assess the efficacy of a centralized intervention to improve the detection and treatment of hypertension and LVH-associated diseases. Additionally, it will serve as a proof-of-concept for how to effectively utilize previously collected electronic health data to improve the recognition and management of a broad range of chronic cardiovascular conditions.

NCT05713916.

Aortic valve stenosis is associated with age, rheumatic fever and bicuspid aortic valve, but its association with other co-morbidities, such as inflammatory disease and race/ethnicity, is less known.

To investigate any association between aortic stenosis and many co-morbidities.

We used the large Nationwide Inpatient Sample database to evaluate any association between aortic stenosis and risk factors. We performed univariate and multivariable analyses, adjusting for co-morbid conditions.

Data were extracted from the first available database that used the International Classification of Diseases, Tenth Revision codes specifically coding for aortic stenosis alone, spanning from 2016 to 2020 (n=112,982,565). A total of 2,322,649 patients had aortic stenosis; the remaining 110,659,916 served as controls. We found a strong and independent significant association between aortic stenosis and coronary artery disease (odds ratio [OR]: 2.11, 95% confidence interval [CI]: 2.09-2.13), smoking (OR: 1.08, 95% CI: 1.07-1.08), diabetes mellitus (OR: 1.15, 95% CI: 1.14-1.16), hypertension (OR: 1.41, 95% CI: 1.4-1.42), hyperlipidaemia (OR: 1.31, 95% CI: 1.3-1.32), renal disease (OR: 1.3, 95% CI: 1.29-1.31), chronic obstructive pulmonary disease (OR: 1.05, 95% CI: 1.04-1.05), obesity (OR: 1.3, 95% CI: 1.29-1.32), white race/ethnicity (OR: 1.47, 95% CI: 1.42-1.52), rheumatoid arthritis (OR: 1.13, 95% CI: 1.11-1.15), scleroderma (OR: 1.93, 95% CI: 1.79-2.09), systemic connective tissue disease (OR: 1.24, 95% CI: 1.2-1.27), polyarteritis nodosa (OR: 1.5, CI: 1.24-1.81) and Raynaud's syndrome (OR: 1.16, 95% CI: 1.09-1.24) (all P<0.001), in addition to known factors, such as age, male sex and bicuspid aortic valve.

Using a very large database, we found many new associations with aortic valve stenosis, including race/ethnicity, renal disease, several inflammatory diseases, chronic obstructive pulmonary disease and obesity, in addition to many other known cardiovascular risk factors.

Left ventricular mass (LVM) is an important predictor of cardiovascular risk. In adolescence, LVM is commonly indexed to height2.7, although some evidence suggests that this may not fully account for sex differences.

We investigated appropriate allometric scaling of LVM to height, total lean mass, and body surface area, in a UK birth cohort of 2039 healthy adolescents (17±1 years). Allometric relationships were determined by linear regression stratified by sex, following log transformation of x and y variables [log(y)=a+b×log(x)], b is the allometric exponent.

Log (LVM) showed linear relationships with log(height) and log(lean mass). Biased estimates of slope resulted when the sexes were pooled. The exponents were lower than the conventional estimate of 2.7 for males (mean [95% CI]=1.66 [1.30-2.03]) and females (1.58 [1.27-1.90]). When LVM was indexed to lean mass, the exponent was 1.16 (1.05-1.26) for males and 1.07 (0.97-1.16) for females. When LVM was indexed to estimated body surface area, the exponent was 1.53 (1.40-1.66) for males and 1.34 (1.24-1.45) for females.

Allometric exponents derived from pooled data, including men and women without adjustment for sex were biased, possibly due to sex differences in body composition. We suggest that when assessing LVM, clinicians should consider body size, body composition, sex, and age. Our observations may also have implications for the identification of young individuals with cardiac hypertrophy.

To develop a prediction model for the development of hypertension in the decade following pre-eclampsia in women who were normotensive shortly after pregnancy.

This was a longitudinal cohort study of formerly pre-eclamptic women attending a university hospital in The Netherlands between 1996 and 2019. We developed a prediction model for incident hypertension using multivariable logistic regression analysis. The model was validated internally using bootstrapping techniques.

Of 259 women, 185 (71%) were normotensive at the first cardiovascular assessment, at a median of 10 (interquartile range (IQR), 6-24) months after a pre-eclamptic pregnancy, of whom 49 (26%) had developed hypertension by the second visit, at a median of 11 (IQR, 6-14) years postpartum. The prediction model, based on birth-weight centile, mean arterial pressure, total cholesterol, left ventricular mass index and left ventricular ejection fraction, had good-to-excellent discriminative ability, with an area under the receiver-operating-characteristics curve (AUC) of 0.82 (95% CI, 0.75-0.89) and an optimism-corrected AUC of 0.80. The sensitivity and specificity of our model to predict hypertension were 98% and 34%, respectively, and positive and negative predictive values were 35% and 98%, respectively.

Based on five variables, we developed a good-to-excellent predictive tool to identify incident hypertension following pre-eclampsia in women who were normotensive shortly after pregnancy. After external validation, this model could have considerable clinical utility in tackling the cardiovascular legacy of pre-eclampsia. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Standard ECG criteria for left ventricular (LV) hypertrophy rely on QRS amplitudes. However, in the setting of left bundle branch block (LBBB), ECG correlates of LV hypertrophy are not well established. We sought to evaluate quantitative ECG predictors of LV hypertrophy in the presence of LBBB.

We included adult patients with typical LBBB having ECG and transthoracic echocardiogram performed within 3 months of each other in 2010-2020. Orthogonal X, Y, Z leads were reconstructed from digital 12‑lead ECGs using Kors's matrix. In addition to QRS duration, we evaluated QRS amplitudes and voltage-time-integrals (VTIs) from all 12 leads, X, Y, Z leads and 3D (root-mean-squared) ECG. We used age, sex and BSA-adjusted linear regressions to predict echocardiographic LV calculations (mass, end-diastolic and end-systolic volumes, ejection fraction) from ECG, and separately generated ROC curves for predicting echocardiographic abnormalities.

We included 413 patients (53% women, age 73 ± 12 years). All 4 echocardiographic LV calculations were most strongly correlated with QRS duration (all p < 0.00001). In women, QRS duration ≥ 150 ms had sensitivity/specificity 56.3%/64.4% for increased LV mass and 62.7%/67.8% for increased LV end-diastolic volume. In men, QRS duration ≥ 160 ms had a sensitivity/specificity 63.1%/72.1% for increased LV mass and 58.3%/74.5% for increased LV end-diastolic volume. QRS duration was best able to discriminate eccentric hypertrophy (area under ROC curve 0.701) and increased LV end-diastolic volume (0.681).

In patients with LBBB, QRS duration (≥ 150 in women and ≥ 160 in men) is a superior predictor of LV remodeling esp. eccentric hypertrophy and dilation.

Cardiovascular disease (CVD) is a common chronic clinical condition and is the main cause of death in humans worldwide. Understanding the genetic and molecular mechanisms involved in the development of CVD is essential to develop effective prevention strategies and therapeutic measures. An increasing number of CVD‑related genetic studies have been conducted, including those on the potential roles of microRNAs (miRs). These studies have demonstrated that miR‑378 is involved in the pathological processes of CVD, including those of myocardial infarction, heart failure and coronary heart disease. Despite the potential importance of miR‑378 CVD, a comprehensive summary of the related literature is lacking. Thus, the present review aimed to summarize the findings of previous studies on the roles and mechanisms of miR‑378 in a variety of CVDs and provide an up‑to date basis for further r research targeting the prevention and treatment of CVDs.

Asian Indians are at higher risk of cardiometabolic disease compared to other ethnic groups, and the age of onset is typically younger. Cardiac structure and function are poorly characterized in this ethnic group. In this study, we describe image-acquisition methods and the reproducibility of measurements and detailed echocardiography characteristics in two large Indian population-based cohorts (the New Delhi and Vellore Birth Cohorts) from India.

The IndEcho study captured transthoracic echocardiographic measurements of cardiac structure and function from 2,322 men and women aged 43-50 years. M-mode measurements in the parasternal long axis (PLAX) and 2-dimensional (2D) short axis recordings at the mitral valve, mid-papillary and apical level were recorded. Apical 2D recordings of two- three- and four-chamber (2C, 3C and 4C) views and Doppler images (colour, pulsed and continuous) were recorded in cine-loop format. Left ventricular (LV) mass, LV hypertrophy, and indices of LV systolic and diastolic function were derived.

Echocardiographic measurements showed good/excellent technical reproducibility. Hetero-geneity across sites, sex and rural/urban differences in cardiac structure and function were observed. Overall, this cohort of South Asian Indians had smaller LV mass and normal systolic and diastolic function when compared with published data on other Asian Indians and the West, (LV mass indexed for body surface area: Delhi men: 68 g/m2, women 63.9; Vellore men: 65.8, women 61.6) but were within ethnic-specific reference ranges. The higher prevalence of obesity, diabetes and hypertension is reflected by the higher proportion of LV remodelling and lesser hypertrophy.

Our study adds to scarce population-based echocardiographic data for mid-life Asian Indians. Compared to published literature on other ethnic groups, the Asian Indian heart is characterised by smaller cardiac dimensions and normal range systolic and diastolic function on a background of a high prevalence of hypertension, diabetes and cardiac disease at a relatively young age. This data will form the basis for further analyses of lifecourse, metabolic and body composition predictors of cardiac structure and function, and echocardiographic predictors of future mortality.

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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.

Left ventricular hypertrophy (LVH) indicates subclinical organ damage, associating with the incidence of cardiovascular diseases. From the medical perspective, electrocardiogram (ECG) is a low-cost, non-invasive, and easily reproducible tool that is often used as a preliminary diagnosis for the detection of heart disease. Nowadays, there are many criteria for assessing LVH by ECG. These criteria usually include that voltage combination of RS peaks in multi-lead ECG must be greater than one or more thresholds for diagnosis. We developed a system for detecting LVH using ECG signals by two steps: firstly, the R-peak and S-valley amplitudes of the 12-lead ECG were extracted to automatically obtain a total of 24 features and ECG beats of each case (LVH or non-LVH) were segmented; secondly, a back propagation neural network (BPN) was trained using a dataset with these features. Echocardiography (ECHO) was used as the gold standard for diagnosing LVH. The number of LVH cases (of a Taiwanese population) identified was 173. As each ECG sequence generally included 8 to 13 cycles (heartbeats) due to differences in heart rate, etc., we identified 1466 ECG cycles of LVH patients after beat segmentation. Results showed that our BPN model for detecting LVH reached the testing accuracy, precision, sensitivity, and specificity of 0.961, 0.958, 0.966 and 0.956, respectively. Detection performances of our BPN model, on the whole, outperform 7 methods using ECG criteria and many ECG-based artificial intelligence (AI) models reported previously for detecting LVH.

Previous studies have found that the macrophage migration inhibitor factor is associated with endothelial dysfunction and ventricular remodelling. The aim of this study was to explore the potential relationship between plasma macrophage migration inhibitor factor levels and hypertension and hypertensive left ventricular hypertrophy. A total of 308 participants (including 187 uncomplicated hypertensive patients and 121 healthy controls) were enroled from 2017 to 2019. The association between macrophage migration inhibitor factors and hypertension and hypertensive left ventricular hypertrophy was estimated with univariate and multivariate logistic regression models. Elevated macrophage migration inhibitor factor was associated with the development of hypertension (second tertile: adjusted OR, 2.27, 95% CI, 1.24-4.16, P = 0.008; third tertile: adjusted OR, 5.43, 95% CI, 2.75-10.71, P < 0.001; compared with the first tertile). In addition, we assessed the association between macrophage migration inhibitor factor and left ventricular hypertrophy in hypertensive patients (n = 187). Plasma macrophage migration inhibitor factor was significantly correlated with hypertensive left ventricular mass index (r = 0.580, P < 0.001). In patients with hypertension, an elevated macrophage migration inhibitor factor was significantly associated with hypertensive left ventricular hypertrophy (second tertile: adjusted OR, 3.20, 95% CI, 1.17-8.78, P = 0.024; third tertile: adjusted OR, 24.95, 95% CI, 8.72-71.41, P < 0.001; compared with the first tertile). Receiver operating characteristic analysis indicated that macrophage migration inhibitor factor had reasonable predictive accuracy for the development of hypertensive left ventricular hypertrophy (area under curve 0.84, 95% CI 0.78-0.90, P < 0.001). Our data indicated that elevated macrophage migration inhibitor factor is associated with hypertension and hypertensive left ventricular hypertrophy.

A total of 69 patients were enrolled in the study, including 23 patients with hypertrophic cardiomyopathy (HCM), 26 patients with Left Ventricle (LV) enlargement comprising 16 dilated cardiomyopathy (DCM) patients and 10 ischemic cardiomyopathy (ICM) patients, and 20 control subjects. All patients underwent 2DE, contrast-enhanced 2DE (Contrast-2DE), 3DE, Contrast-3DE, and single photon emission computed tomography (SPECT) examinations. The 2DE-AL and 3DE methods measured the left ventricular mass (LVM). The results were compared with those measured by SPECT. The measured LVM of the 69 patients was systematically overestimated by 2DE-AL (177.4 ± 56.2 g), Contrast-2DE-AL (174.5 ± 55.5 g), 3DE (167.3 ± 59.2 g), and Contrast-3DE (154.2 ± 46.7 g) when compared with SPECT (148.5 ± 52.4 g) (P < 0.05), while Contrast-3DE provided the best agreement with SPECT in LVM measurement (r = 0.898, P < 0.001) and had the smallest deviation (5.7 ± 23.1 g). 3DE overestimated LVM more compared to Contrast-3DE in LV hypertrophy group (165.5 ± 37.9 g versus 153.5 ± 27.6 g, P = 0.003) and LV enlargement group (204.5 ± 69.3 g versus 183.5 ± 53.5 g, P = 0.006). For 2DE methods, there was no significant difference between the LVM obtained with or without contrast enhancement in control group (132.3 ± 23.6 g versus 128.4 ± 23.3 g), LV hypertrophy group (177.7 ± 38.6 versus 178.3 ± 30.9 g, P = 0.889), and LV enlargement group (211.9 ± 63.2 g versus 206.5 ± 66.0 g, P = 0.386). The difference between LVM measured by 2DE-AL and SPECT was the greatest (27.9 ± 34.0 g), especially in LV hypertrophy group and LV enlargement group (LV hypertrophy group 39.7 ± 26.0 g; LV enlargement group 24.2 ± 42.8 g). To conclude, Contrast-3DE and SPECT show greater consistency in LVM measurement, especially in cardiomyopathy, when compared with 2DE. Administering contrast can effectively reduce the overestimation of LVM by non-contrast DE.

Adenosine is a ubiquitous endogenous nucleoside or autacoid that affects the cardiovascular system through the activation of four G-protein coupled receptors: adenosine A₁ receptor (A₁AR), adenosine A_2A receptor (A_2AAR), adenosine A_2B receptor (A_2BAR), and adenosine A₃ receptor (A₃AR). With the rapid generation of this nucleoside from cellular metabolism and the widespread distribution of its four G-protein coupled receptors in almost all organs and tissues of the body, this autacoid induces multiple physiological as well as pathological effects, not only regulating the cardiovascular system but also the central nervous system, peripheral vascular system, and immune system. Mounting evidence shows the role of CYP450-enzymes in cardiovascular physiology and pathology, and the genetic polymorphisms in CYP450s can increase susceptibility to cardiovascular diseases (CVDs). One of the most important physiological roles of CYP450-epoxygenases (CYP450-2C & CYP2J2) is the metabolism of arachidonic acid (AA) and linoleic acid (LA) into epoxyeicosatrienoic acids (EETs) and epoxyoctadecaenoic acid (EpOMEs) which generally involve in vasodilation. Like an increase in coronary reactive hyperemia (CRH), an increase in anti-inflammation, and cardioprotective effects. Moreover, the genetic polymorphisms in CYP450-epoxygenases will change the beneficial cardiovascular effects of metabolites or oxylipins into detrimental effects. The soluble epoxide hydrolase (sEH) is another crucial enzyme ubiquitously expressed in all living organisms and almost all organs and tissues. However, in contrast to CYP450-epoxygenases, sEH converts EETs into dihydroxyeicosatrienoic acid (DHETs), EpOMEs into dihydroxyoctadecaenoic acid (DiHOMEs), and others and reverses the beneficial effects of epoxy-fatty acids leading to vasoconstriction, reducing CRH, increase in pro-inflammation, increase in pro-thrombotic and become less cardioprotective. Therefore, polymorphisms in the sEH gene (Ephx2) cause the enzyme to become overactive, making it more vulnerable to CVDs, including hypertension. Besides the sEH, ω-hydroxylases (CYP450-4A11 & CYP450-4F2) derived metabolites from AA, ω terminal-hydroxyeicosatetraenoic acids (19-, 20-HETE), lipoxygenase-derived mid-chain hydroxyeicosatetraenoic acids (5-, 11-, 12-, 15-HETEs), and the cyclooxygenase-derived prostanoids (prostaglandins: PGD₂, PGF_2α; thromboxane: Txs, oxylipins) are involved in vasoconstriction, hypertension, reduction in CRH, pro-inflammation and cardiac toxicity. Interestingly, the interactions of adenosine receptors (A_2AAR, A₁AR) with CYP450-epoxygenases, ω-hydroxylases, sEH, and their derived metabolites or oxygenated polyunsaturated fatty acids (PUFAs or oxylipins) is shown in the regulation of the cardiovascular functions. In addition, much evidence demonstrates polymorphisms in CYP450-epoxygenases, ω-hydroxylases, and sEH genes (Ephx2) and adenosine receptor genes (ADORA1 & ADORA2) in the human population with the susceptibility to CVDs, including hypertension. CVDs are the number one cause of death globally, coronary artery disease (CAD) was the leading cause of death in the US in 2019, and hypertension is one of the most potent causes of CVDs. This review summarizes the articles related to the crosstalk between adenosine receptors and CYP450-derived oxylipins in vascular, including the CRH response in regular salt-diet fed and high salt-diet fed mice with the correlation of heart perfusate/plasma oxylipins. By using A_2AAR^-/-, A₁AR^-/-, eNOS^-/-, sEH^-/- or Ephx2^-/-, vascular sEH-overexpressed (Tie2-sEH Tr), vascular CYP2J2-overexpressed (Tie2-CYP2J2 Tr), and wild-type (WT) mice. This review article also summarizes the role of pro-and anti-inflammatory oxylipins in cardiovascular function/dysfunction in mice and humans. Therefore, more studies are needed better to understand the crosstalk between the adenosine receptors and eicosanoids to develop diagnostic and therapeutic tools by using plasma oxylipins profiles in CVDs, including hypertensive cases in the future.

Left ventricular hypertrophy (LVH) is the main marker of HMOD in children and young people (CYP). We aimed to assess the prevalence of LVH and its determinants in CYP with primary hypertension (PH).

A meta-analysis of prevalence was performed. A literature search of articles reporting LVH in CYP with PH was conducted in Medline, Embase, and Cochrane databases. Studies with a primary focus on CYP (up to 21 years) with PH were included. Meta-regression was used to analyze factors explaining observed heterogeneity.

The search yielded a total of 2,200 articles, 153 of those underwent full-text review, and 47 reports were included. The reports evaluated 51 study cohorts including 5,622 individuals, 73% male subjects, and a mean age of 13.6 years. LVH was defined as left ventricle mass index (LVMI) ≥ 95th percentile in 22 (47%), fixed cut-off ≥38.6 g/m2.7 in eight (17%), sex-specific fixed cut-off values in six (13%), and miscellaneously in others. The overall prevalence of LVH was 30.5% (95% CI 27.2-33.9), while heterogeneity was high (I 2 = 84%). Subgroup analysis including 1,393 individuals (76% male subjects, mean age 14.7 years) from pediatric hypertension specialty clinics and LVH defined as LVMI ≥95th percentile only (19 study cohorts from 18 studies), reported prevalence of LVH at 29.9% (95% CI 23.9 to 36.3), and high heterogeneity (I 2 = 84%). Two studies involving patients identified through community screening (n = 1,234) reported lower LVH prevalence (21.5%). In the meta-regression, only body mass index (BMI) z-score was significantly associated with LVH prevalence (estimate 0.23, 95% CI 0.08-0.39, p = 0.004) and accounted for 41% of observed heterogeneity, but not age, male percentage, BMI, or waist circumference z-score. The predominant LVH phenotype was eccentric LVH in patients from specialty clinics (prevalence of 22% in seven studies with 779 participants) and one community screening study reported the predominance of concentric LVH (12%).

Left ventricular hypertrophy is evident in at least one-fifth of children and young adults with PH and in nearly a third of those referred to specialty clinics with a predominant eccentric LVH pattern in the latter. Increased BMI is the most significant risk association for LVH in hypertensive youth.

Cardiac magnetic resonance imaging (CMR) is a powerful diagnostic modality that provides detailed quantitative assessment of cardiac anatomy and function. Automated extraction of CMR measurements from clinical reports that are typically stored as unstructured text in electronic health record systems would facilitate their use in research. Existing machine learning approaches either rely on large quantities of expert annotation or require the development of engineered rules that are time-consuming and are specific to the setting in which they were developed.

We hypothesize that the use of pretrained transformer-based language models may enable label-efficient numerical extraction from clinical text without the need for heuristics or large quantities of expert annotations. Here, we fine-tuned pretrained transformer-based language models on a small quantity of CMR annotations to extract 21 CMR measurements. We assessed the effect of clinical pretraining to reduce labeling needs and explored alternative representations of numerical inputs to improve performance.

Our study sample comprised 99,252 patients that received longitudinal cardiology care in a multi-institutional health care system. There were 12,720 available CMR reports from 9280 patients. We adapted PRAnCER (Platform Enabling Rapid Annotation for Clinical Entity Recognition), an annotation tool for clinical text, to collect annotations from a study clinician on 370 reports. We experimented with 5 different representations of numerical quantities and several model weight initializations. We evaluated extraction performance using macroaveraged F1-scores across the measurements of interest. We applied the best-performing model to extract measurements from the remaining CMR reports in the study sample and evaluated established associations between selected extracted measures with clinical outcomes to demonstrate validity.

All combinations of weight initializations and numerical representations obtained excellent performance on the gold-standard test set, suggesting that transformer models fine-tuned on a small set of annotations can effectively extract numerical quantities. Our results further indicate that custom numerical representations did not appear to have a significant impact on extraction performance. The best-performing model achieved a macroaveraged F1-score of 0.957 across the evaluated CMR measurements (range 0.92 for the lowest-performing measure of left atrial anterior-posterior dimension to 1.0 for the highest-performing measures of left ventricular end systolic volume index and left ventricular end systolic diameter). Application of the best-performing model to the study cohort yielded 136,407 measurements from all available reports in the study sample. We observed expected associations between extracted left ventricular mass index, left ventricular ejection fraction, and right ventricular ejection fraction with clinical outcomes like atrial fibrillation, heart failure, and mortality.

This study demonstrated that a domain-agnostic pretrained transformer model is able to effectively extract quantitative clinical measurements from diagnostic reports with a relatively small number of gold-standard annotations. The proposed workflow may serve as a roadmap for other quantitative entity extraction.

A pattern of reduced basal longitudinal strain (BLS) is often observed with left ventricular (LV) hypertrophy (LVH). Whether this pattern is associated with poor outcome is unclear. We aimed to evaluate the prognostic value of regional longitudinal strain according to LV geometry.

We investigated participants in the 4th Copenhagen City Heart Study who had an echocardiogram with speckle tracking performed. Participants were stratified according to the presence of LVH (LV mass index ≥116 g/m2 for men and ≥96 g/m2 for women). The outcome was major adverse cardiovascular events (MACE) defined as a composite of myocardial infarction, heart failure, and/or cardiovascular death. The study population consisted of 1090 participants. Mean LVEF was 60% and 160 (15%) had LVH. During a median follow-up of 14.7 years, there were 137 events. Both BLS and midventricular strain, but not apical strain, became incrementally impaired in the spectrum from normal to hypertensives subjects without LVH, and to participants with hypertension and LVH. After multivariable adjustment, BLS and midventricular strain were independently associated with MACE in participants with LVH (BLS: HR 1.08, 95% CI 1.00-1.17, P = 0.041; midventricular strain: HR 1.10, 95% CI 1.00-1.21, P = 0.041) but not in participants without LVH (BLS: HR 0.96, 95% CI 0.90-1.01, P = 0.13; midventricular strain: HR 0.97, 95% CI 0.91-1.03, P = 0.36).

BLS and midventricular strain, but not apical strain, become incrementally impaired in the spectrum from normal geometry to LVH, and are independently associated with MACE in participants with LVH.