Detection Of Left Ventricular Dysfunction Research Articles

The possibilities of echocardiography for detecting cardiotoxic effects of antineoplastic medications

Background. In recent decades, advancements in cancer treatment have resulted in enhanced patient survival rates, yet concurrently, escalated morbidity and mortality due to therapy-induced side effects, with cardiovascular complications (CVC) at the forefront. The risk of cardiovascular disease (CVD) during chemotherapy lies in the potentiality of interrupting or discontinuing the treatment of the underlying ailment, thereby diminishing the patient’s prospects of survival. Regrettably, delayed manifestations of cardiotoxic effects subsequent to the completion of anticancer therapy further diminish the patients’ quality of life and escalate mortality rates. Consequently, early diagnosis and timely identification of cardiotoxic effects induced by anticancer drugs have emerged as paramount objectives, shaping a novel domain in medicine termed cardio-oncology. Purpose. Assess the condition and modern ideas of the development of echocardiography in the assessment of cardiac dysfunction in the context of chemotherapy. Materials and Methods. References were analyzed using English-language biomedical databases (MEDLINE (Pubmed), Web of Science (Web of Knowledge) using the following keywords: «cardiotoxicity», «echocardiography», «chemotherapy», «ventricular dysfunction», «anticancer therapy», «risk stratification» for 2019–2024. Results. Cardiotoxicity arising as a consequence of anticancer therapy can manifest in various forms, including left ventricular (LV) myocardial dysfunction, heart failure (HF), pulmonary hypertension, thromboembolic complications, arterial hypertension, coronary heart disease, pericardial disease, valvular heart disease, and others. Detecting the subclinical variant of cardiotoxicity, which precedes the onset of HF, is of paramount importance. Presently, echocardiography remains the preferred method for evaluating the structural and functional status of the LV myocardium. However, studies have demonstrated that measuring LV ejection fraction (EF) via transthoracic echocardiography may not effectively detect LV dysfunction in its early stages. In recent years, novel myocardial imaging techniques have emerged for this purpose, with speckle-tracking echocardiography (STE) in both two-dimensional and three-dimensional modes showing promise. Соnclusion. The early detection of cardiotoxic effects induced by anticancer drugs emerges as a critical concern in cardiology practice. Evaluation of global myocardial strain derived from two-dimensional LV images provides valuable insights into diagnosing early LV dysfunction, especially in patients undergoing chemotherapy.

The role of polycystic ovary syndrome in preclinical left ventricular diastolic dysfunction: an echocardiographic approach: a systematic review and meta-analysis.

Polycystic ovary syndrome (PCOS) is a common endocrine disorder affecting women of childbearing age, causing hormonal imbalances, reproductive issues, and metabolic disturbances. Women with PCOS have an increased risk of cardiovascular disease due to insulin resistance, obesity, and hyperandrogenism. Detecting impaired left ventricular (LV) function is important in managing this condition. Echocardiography, a non-invasive imaging technique, can effectively detect LV dysfunction. The goal of this systematic review was to assess whether there are any variations in echocardiographic measures between women with PCOS and those without the condition in order to determine the potential impact of PCOS on LV function. This review followed the PRISMA reporting guidelines. A thorough search of databases including PubMed, Scopus, Web of Science, and Cochrane was conducted. The quality of the selected studies was assessed using the Joanna Briggs Institute appraisal instruments. After applying strict eligibility criteria, data were extracted and organized in Microsoft Excel sheets. Review Manager (RevMan) software was used for the analysis. Analysis of 29 studies revealed significant differences in echocardiographic measures related to diastolic function between women with PCOS and healthy controls. However, there were no significant differences in measures of systolic function. These findings indicate that PCOS may be linked to impaired LV function, thereby increasing the risk of cardiovascular disease. Further research is necessary to better understand this association and its clinical implications. Early detection and management of PCOS could potentially help prevent cardiovascular complications in affected women.

Focused cardiac ultrasound with mitral annular plane systolic excursion (MAPSE) detection of left ventricular dysfunction

ObjectivesDetecting reduced left ventricular ejection fraction (LVEF) by an emergency physician (EP) is an important skill. The subjective ultrasound assessment of LVEF by EPs correlates with comprehensive echocardiogram (CE) results. Mitral annular plane systolic excursion (MAPSE) is an ultrasound measure of vertical movement of the mitral annulus, which correlates to LVEF in the cardiology literature, but has not been studied when measured by an EP. Our objective is to determine whether EP measured MAPSE can accurately predict LVEF <50% on CE. MethodsThis is a prospective observational single center study using a convenience sample to evaluate the use of a focused cardiac ultrasound (FOCUS) for patients with possible decompensated heart failure. The FOCUS included standard cardiac views to estimate LVEF, MAPSE, and E-point septal separation (EPSS). Abnormal MAPSE was defined as <8 mm and abnormal EPSS as >10 mm. The primary outcome assessed was the ability of an abnormal MAPSE to predict an LVEF <50% on CE. MAPSE also was compared to EP estimated LVEF and EPSS. Inter-rater reliability was determined by two investigators performing independent blinded review. ResultsWe enrolled 61 subjects, 24 (39%) had an LVEF <50% on a CE. MAPSE <8 mm had a 42% sensitivity (95% CI 22–63), 89% specificity (95% CI 75–97), and accuracy of 71% for detecting LVEF <50%. MAPSE demonstrated lower sensitivity than EPSS (79% sensitivity [95% CI 58–93], and 76% specificity [95% CI 59–88]) and higher specificity than estimated LVEF (100% sensitivity [95% CI 86–100], 59% specificity [95% CI 42–75]). PPV and NPV for MAPSE was 71% (95% CI 47–88) and 70% (95% CI 62–77) respectively. The ROC for MAPSE <8 mm is 0.79 (95% CI 0.68–0.9). MAPSE measurement interrater reliability was 96%. ConclusionsIn this exploratory study evaluating MAPSE measurements by EPs, we found the measurement was easy to perform with excellent agreement across users with minimal training. A MAPSE value <8 mm had moderate predictive value for LVEF <50% on CE and was more specific for reduced LVEF than qualitative assessment. MAPSE had high specificity for LVEF <50%. Further studies are needed to validate these results on a larger scale.

Smartwatch detection of left ventricular dysfunction.

Smartwatch detection of left ventricular dysfunction.

Prospective evaluation of smartwatch-enabled detection of left ventricular dysfunction.

Although artificial intelligence (AI) algorithms have been shown to be capable of identifying cardiac dysfunction, defined as ejection fraction (EF) ≤ 40%, from 12-lead electrocardiograms (ECGs), identification of cardiac dysfunction using the single-lead ECG of a smartwatch has yet to be tested. In the present study, a prospective study in which patients of Mayo Clinic were invited by email to download a Mayo Clinic iPhone application that sends watch ECGs to a secure data platform, we examined patient engagement with the study app and the diagnostic utility of the ECGs. We digitally enrolled 2,454 unique patients (mean age 53 ± 15 years, 56% female) from 46 US states and 11 countries, who sent 125,610 ECGs to the data platform between August 2021 and February 2022; 421 participants had at least one watch-classified sinus rhythm ECG within 30 d of an echocardiogram, of whom 16 (3.8%) had an EF ≤ 40%. The AI algorithm detected patients with low EF with an area under the curve of 0.885 (95% confidence interval 0.823-0.946) and 0.881 (0.815-0.947), using the mean prediction within a 30-d window or the closest ECG relative to the echocardiogram that determined the EF, respectively. These findings indicate that consumer watch ECGs, acquired in nonclinical environments, can be used to identify patients with cardiac dysfunction, a potentially life-threatening and often asymptomatic condition.

Artificial intelligence-enabled electrocardiographic algorithm for the detection of left ventricular dysfunction in emergency department patients undergoing high-sensitivity cardiac troponin T

Abstract Background Artificial intelligence-augmented electrocardiogram (AI-ECG) algorithms have been developed from the standard 12-lead ECG and validated for the recognition of left ventricular systolic dysfunction (LVSD), defined as LV ejection fraction (LVEF)≤35%. Whether AI-ECG facilitates identification of LVSD and is associated with adverse outcomes in emergency department (ED) patients undergoing high-sensitivity cardiac troponin (hs-cTnT) testing is uncertain. Purpose To investigate the diagnostic and prognostic performance of AI-ECG in ED patients undergoing hs-cTnT measurement. Methods Observational US cohort study of ED patients undergoing hs-cTnT measurement. Cases with hs-cTnT increases &amp;gt;sex-specific 99th percentiles were adjudicated following the Fourth Universal Definition of Myocardial Infarction (MI). Post-discharge major adverse cardiac events (MACE) included death, MI, heart failure (HF) hospitalization, stroke or transient ischemic attack, and new onset atrial fibrillation/flutter during 2-years follow-up. The AI-ECG network output, which is a continuous number between 0–1, that provides a probability of LVSD, was obtained for each patient from the first ECG during the index presentation. An AI-ECG threshold of ≥0.256 indicates a positive screen that correlates with a high probability of LVSD. Results Among 1977 patients, 1729 (87%) had a negative AI-ECG screen, while 248 (13%) had a positive AI-ECG screen. Patients with a positive AI-ECG screen were older and had more comorbidities. As compared to patients with hs-cTnT≤99th percentile in whom AI-ECG was positive in 5.8%, those with hs-cTnT&amp;gt;99th percentile had a positive AI-ECG in 22% of cases (p&amp;lt;0.0001). Based on adjudicated diagnoses, the frequency of a positive AI-ECG was 20% in myocardial injury, 38% in type 1 MI, and 20% in type 2 MI. At 2-years follow-up, as compared to patients with a negative AI-ECG, those with a positive AI-ECG had a higher risk for MACE (48% vs. 21%, p&amp;lt;0.0001, adjusted HR 1.39, 95% CI 1.11–1.75) (Figure 1), mainly because of more deaths (43% vs. 30%, p=0.004) and HF hospitalizations (36% vs. 13%, p&amp;lt;0.0001). A positive AI-ECG was associated with a higher risk for MACE (60% vs. 41%, p&amp;lt;0.0001, adjusted HR 1.30, 95% CI 1.02–1.64) in those with hs-cTnT increases &amp;gt;99th percentile, but not in those without hs-cTnT increases. Among patients with an echocardiogram during index presentation or within 30-days (n=452), the diagnostic accuracy of AI-ECG for LVEF ≤35% was 81.4% (95% CI 77.5, 84.9) with a negative predictive value of 96.5% (95% CI 94.0, 98.2). A normal LVEF (&amp;gt;50%) was observed in 87% of those with a negative AI ECG, whereas in those with a positive AI-ECG LVEF was reduced (&amp;lt;50%) in 60%. Conclusions Among ED patients evaluated with hs-cTnT, a positive AI-ECG screen for LVSD identifies patients at high risk of MACE. These findings are largely because of more deaths and HF hospitalizations in those with hs-cTnT increases &amp;gt;sex-specific 99th percentiles. Funding Acknowledgement Type of funding sources: None.

Myocardial Strain Imaging Using Feature Tracking Method of Cardiac MRI: Our Initial Experience of This Novel Parameter as an Additional Diagnostic Tool.

Background Left ventricular ejection fraction (LVEF) is used as quantitative parameter to evaluate myocardial function. However, interobserver variation, limited reproducibility, and dependence on pre-load and after-load reduces its accuracy. The fall in LVEF occurs very late, when myocardial dysfunction is already advanced. Myocardial strain measurements (especially global longitudinal strain) is a new parameter to detect myocardial dysfunction before derangements in LVEF. The aim of this article is to share our experience of this novel diagnostic tool. Methods Feature tracking method of strain assessment is performed using routine long and short axis cine images of cardiac MRI (CMR). Dedicated post-processing CMR software can perform off-line analysis and provide results in form of color-coded maps, percentage values as well as strain over time curve for each myocardial segments. Results Global longitudinal strain (GLS) is more sensitive than LVEF and can identify sub-clinical left ventricular (LV) dysfunction in various cardiomyopathies. It is also an important prognostic marker in serial assessment of heart failure patients. Regional differences in strain parameters can provide clues in hypertrophic cardiomyopathy as well as amyloidosis. GLS is recommended as routine measurement in patients undergoing chemotherapy to detect LV dysfunction prior to fall in LVEF. Strain imaging can be applied to guide placement of the LV pacing lead in patients receiving cardiac resynchronization therapy. More clinical data is needed to evaluate its role in ischemic heart disease. Conclusion Strain imaging can identify LV dysfunction earlier than conventional methods and this opens a new perspective in risk stratification and therapeutic decision-making of various cardiac pathologies.

A Novel Non-Invasive Device For Screening And Optimized Management To Improve Heart Failure Outcomes In Patients With Diabetes Mellitus

<h3>Background</h3> Evidence shows diabetes mellitus (DM) is major independent risk factor for several cardiovascular disorders including heart failure (HF). Large, randomized clinical trials for screening and revascularization of stable macrovascular disease in diabetics, have failed to demonstrate a significant reduction in cardiac events and HF episodes. Studies have demonstrated that early detection of left ventricular dysfunction and prevention of microvascular complications through glycemic control in diabetes patients is a critical mechanism for reducing the incidence and severity of left ventricular dysfunction and HF. There is a need for a non-invasive, affordable, accurate, absolute and actionable method to facilitate optimized management of diabetes patients across the continuity of care. HEMOTAG, is a small portable device that uses micro-sensors to capture cardiac vibrations and electrocardiogram, transduced via thoracic electrodes. It is a viable option to measure cardiac time intervals (CTIs), surrogate markers for measurements of left ventricular dysfunction. <h3>Methods</h3> In 98 diabetes patients, the predictability value of HEMOTAG-derived CTIs- systolic time ratio index (STRi) = aortic valve opening/ (aortic valve opening to aortic valve closing) normalized by heart rate, aortic valve closing (AVC) and mitral valve opening (MVO) were assessed as valid markers for left ventricular dysfunction measured simultaneously at the time of transthoracic echocardiogram; to determine reduced ejection fraction (EF) <50%, <35%; elevated E/e'>14; and elevated left atrial volume index (LAVI) > 34 ml/m². Linear correlation analysis was performed. Sensitivity, specificity, positive, and negative predictive values were calculated. <h3>Results</h3> Mean age was 70.3 ± 11.6 years, mean EF was 59.0 ± 12.7%, mean E/e' was 17.9 ± 8.5, mean LAVI was 35.4 ± 16.1 ml/m², 30.2% were women. The following was noted with a paired dichotomous interpretation; for STRi and EF <35%: sensitivity 100%, specificity 88%, AUC 0.94; for STRi and EF <50%: sensitivity 94%, specificity 87%, AUC 0.93; for MVO and E/e' >14: sensitivity 92%, specificity 45%, AUC 0.63; for AVC and LAVI>34 ml/m²: sensitivity 92%, specificity 53%, AUC 0.65. <h3>Conclusions</h3> HEMOTAG detected systolic and diastolic dysfunction with high sensitivity and high specificity. This preliminary study has demonstrated the feasibility of estimating left ventricular dysfunction using a portable, non-invasive device that can facilitate screening and optimized management to improve HF outcomes in patients with DM. Further studies are required to validate HEMOTAG as a point-of-care non-invasive assessment for patients with DM. HEMOTAG® development and the clinical study is supported by NHLBI SBIR grant # R44HL149561.

Detection of left ventricular dysfunction on axial non-contrast chest CT

PurposeTo evaluate the performance of axial non-contrast CT images in detecting systolic and diastolic left ventricular (LV) dysfunction. MethodThis single-center retrospective study assessed 178 participants who underwent transthoracic ultrasonography and had non-contrast chest CT data within three months of ultrasonography. The patients were divided into LV systolic dysfunction (<52% ejection fraction in men and <54% in women), LV diastolic dysfunction (at least three of the following four criteria were met: average E/e' ratio >14; septal e' <7 cm/s or lateral e' <10 cm/s; peak tricuspid regurgitation velocity >2.8 m/s; or left atrial maximum volume index >34 ml/m2), and normal LV function groups. CT parameters were evaluated as predictive factors for LV dysfunction. These parameters were: I, maximum minor axis diameter of the LV lumen; II, I plus myocardial wall thickness; III, maximum left atrium anteroposterior diameter; IV, maximum transverse cardiac diameter; V, myocardial wall thickness; I-IV divided by maximum medial thoracic diameter; and I-IV divided by anteroposterior thoracic diameter. All parameters were measured on axial images: diameters were maximized. ResultsLV systolic dysfunction was indicated when parameter IV exceeded 131.2 mm with sensitivity and specificity of 71.8% and 77.0%, respectively. Moreover, LV diastolic dysfunction was indicated when parameter III divided by anteroposterior thoracic diameter exceeded 0.165 with sensitivity and specificity of 77.8% and 83.0%, respectively. ConclusionsAxial non-contrast chest CT is useful for detecting LV dysfunction. Even CT scans for other purposes provide LV function information that may lead to appropriate examination.

Value of Speckle Tracking Echocardiography for Early Detection of Left Ventricular Dysfunction in Patients with Systemic Lupus Erythematosus

Value of Speckle Tracking Echocardiography for Early Detection of Left Ventricular Dysfunction in Patients with Systemic Lupus Erythematosus

The Effectiveness of a Deep Learning Model to Detect Left Ventricular Systolic Dysfunction from Electrocardiograms.

Deep learning models can be applied to electrocardiograms (ECGs) to detect left ventricular (LV) dysfunction. We hypothesized that applying a deep learning model may improve the diagnostic accuracy of cardiologists in predicting LV dysfunction from ECGs. We acquired 37,103 paired ECG and echocardiography data records of patients who underwent echocardiography between January 2015 and December 2019. We trained a convolutional neural network to identify the data records of patients with LV dysfunction (ejection fraction < 40%) using a dataset of 23,801 ECGs. When tested on an independent set of 7,196 ECGs, we found the area under the receiver operating characteristic curve was 0.945 (95% confidence interval: 0.936-0.954). When 7 cardiologists interpreted 50 randomly selected ECGs from the test dataset of 7,196 ECGs, their accuracy for predicting LV dysfunction was 78.0% ± 6.0%. By referring to the model's output, the cardiologist accuracy improved to 88.0% ± 3.7%, which indicates that model support significantly improved the cardiologist diagnostic accuracy (P = 0.02). A sensitivity map demonstrated that the model focused on the QRS complex when detecting LV dysfunction on ECGs. We developed a deep learning model that can detect LV dysfunction on ECGs with high accuracy. Furthermore, we demonstrated that support from a deep learning model can help cardiologists to identify LV dysfunction on ECGs.

Abstract 12899: CMR Derived Intraventricular Pressure Gradients Analysis Shows Impaired Left Ventricular Function in Precapillary Pulmonary Hypertension

Introduction: Precapillary pulmonary hypertension (pPH) predominantly harms the right ventricle. Still, due to ventricular interdependence, it also affects left ventricular (LV) function. Since LV ejection fraction (EF) is often preserved, more sensitive techniques may be better suited to detect LV dysfunction, such as LA strain or estimation of LV intraventricular pressure gradients (IVPG). However, the value of these techniques in pPH is still unknown. Hypothesis: Cardiovascular magnetic resonance (CMR) derived LA phasic function and LV-IVPG can detect LV dysfunction in pPH patients. Methods: pPH patients (n=31) and healthy controls (n=10), age- and sex-matched, underwent CMR imaging. LA phasic function and the LV-IVPG, calculating the pressure gradient between apex and base throughout the cardiac cycle, were analyzed using standard long-axis cines. Results: LV systolic function, both LVEF as well as the IVPG positive systolic ejection force, was preserved in pPH patients compared to healthy controls (p=0.90, p=0.08 resp.). However, diastolic function in pPH patients was impaired, depicted by 1. lower LA reservoir (patients vs. controls 26% ±9 vs. 37% ±7, p=0.001) and conduit strain (15% ±8 vs. 26% ±7, p=0.001), and 2. an impaired diastolic suction force, and decreased E-wave decelerative force, indicating a weaker passive filling in early to mid-diastole ( see Table ). Also, 11 pPH patients (35%) showed reversal of IVPG at systolic-diastolic transition, meaning that LV pressure temporarily exceeds LA pressure, whereas none of the healthy controls showed this pattern (p=0.04). Conclusions: pPH impacts LV function by altering diastolic function, demonstrated by an impairment of LA phasic function and LV-IVPG analysis. These parameters could therefore potentially be used as early markers for LV functional decline in pPH patients.

Multimodality imaging approach to left ventricular dysfunction in diabetes: an expert consensus document from the European Association of Cardiovascular Imaging.

Heart failure (HF) is among the most important and frequent complications of diabetes mellitus (DM). The detection of subclinical dysfunction is a marker of HF risk and presents a potential target for reducing incident HF in DM. Left ventricular (LV) dysfunction secondary to DM is heterogeneous, with phenotypes including predominantly systolic, predominantly diastolic, and mixed dysfunction. Indeed, the pathogenesis of HF in this setting is heterogeneous. Effective management of this problem will require detailed phenotyping of the contributions of fibrosis, microcirculatory disturbance, abnormal metabolism, and sympathetic innervation, among other mechanisms. For this reason, an imaging strategy for the detection of HF risk needs to not only detect subclinical LV dysfunction (LVD) but also characterize its pathogenesis. At present, it is possible to identify individuals with DM at increased risk HF, and there is evidence that cardioprotection may be of benefit. However, there is insufficient justification for HF screening, because we need stronger evidence of the links between the detection of LVD, treatment, and improved outcome. This review discusses the options for screening for LVD, the potential means of identifying the underlying mechanisms, and the pathways to treatment.

Sensitivity and specificity of strain rate for the early detection of left ventricular dysfunction in breast cancer patients receiving adjuvant treatment with adriamycin with or without trastuzumab

Abstract Background Medical literature states that a global longitudinal strain rate (GLS) &amp;lt;−19% and further reductions in relation with baseline GLS may predict long term left ventricular dysfunction in breast cancer patients receiving adjuvant treatment with Adriamycin (A) with or without trastuzumab (T). However, there is not strong evidence to support the use of this technique in daily clinical practice. Purpose To analyze the sensitivity (S) and specificity (Sp) of GLS &amp;lt;−19% or its relative 10, 15 and 20% reduction form baseline, for the early detection of left ventricular dysfunction in breast cancer patients receiving adjuvant treatment with A with or without T. Methods Patients were prospectively assessed with a cardiac echoDoppler + GLS at baseline, every 3 months during the first year and every 4 months thereafter. We assessed the S and Sp of GLS &amp;lt;−19% or its relative &amp;gt;10%, &amp;gt;15% and &amp;gt;20% reduction form baseline after treatment with A with or without T as predictors of cardiotoxicity (defined as a &amp;gt;10 percentage points decrease of left ventricular ejection fraction to a value below the lower normal limit with respect to baseline). Results 136 out of 884 patients (16%) had GLS measured at each visit: 83 patients (60%) and 53 patients (40%) treated with A or A+T respectively. Patients were followed up to 31±6 months. Mean age was 51±12 years. The table shows the baseline characteristics of both groups. 10 out of 83 patients in group A (12%) and 18 out of 53 P in group A+T (34%) developed cardiotoxicity. Baseline left ventricular ejection fraction and GLS were 61±4,7% and −20,18±2,6% in group A, and 58,7±5,8% and −19,5±2,6 post treatment. In this group, GLS S and Sp were 50 and 80%, respectively. In the group A+T, the best GLS cutoff was −19%, with a 60% sensitivity and 78% specificity. Conclusions In our non-selected cohort of patients, nor GLS or its percentage reductions along follow up predicted the reduction of left ventricular ejection fraction and the developing of cardiotoxic ventricular dysfunction in breast cancer patients receiving adjuvant treatment with A with or without T. The systematic use of this tool should be further investigated and validated at each center to help establish its usefulness in daily practice. Funding Acknowledgement Type of funding sources: None. Baseline characteristics

Application of artificial intelligence to the electrocardiogram.

Artificial intelligence (AI) has given the electrocardiogram (ECG) and clinicians reading them super-human diagnostic abilities. Trained without hard-coded rules by finding often subclinical patterns in huge datasets, AI transforms the ECG, a ubiquitous, non-invasive cardiac test that is integrated into practice workflows, into a screening tool and predictor of cardiac and non-cardiac diseases, often in asymptomatic individuals. This review describes the mathematical background behind supervised AI algorithms, and discusses selected AI ECG cardiac screening algorithms including those for the detection of left ventricular dysfunction, episodic atrial fibrillation from a tracing recorded during normal sinus rhythm, and other structural and valvular diseases. The ability to learn from big data sets, without the need to understand the biological mechanism, has created opportunities for detecting non-cardiac diseases as COVID-19 and introduced challenges with regards to data privacy. Like all medical tests, the AI ECG must be carefully vetted and validated in real-world clinical environments. Finally, with mobile form factors that allow acquisition of medical-grade ECGs from smartphones and wearables, the use of AI may enable massive scalability to democratize healthcare.

Role of Two-Dimensional Speckle-Tracking Echocardiography in Early Detection of Left Ventricular Dysfunction in Dogs.

Simple SummaryTwo-dimensional speckle-tracking echocardiography represents an advanced imaging technique that allows the analysis of global and regional myocardial function, cardiac rotation and synchronicity using deformation imaging. It has gained growing importance over the last decade, especially in human medicine as a method of evaluating myocardial function. This review aims to give an overview of the current understanding of this technique and its clinical applicability in the field of veterinary medicine with a focus on early detection of left ventricular dysfunction in dogs.Two-dimensional speckle-tracking echocardiography (2D–STE) is an advanced echocardiographic technique based on deformation imaging that allows comprehensive evaluation of the myocardial function. Clinical application of 2D–STE holds great potential for its ability to provide valuable information on both global and regional myocardial function and to quantify cardiac rotation and synchronicity, which are not readily possible with the conventional echocardiography. It has gained growing importance over the past decade, especially in human medicine, and its application includes assessment of myocardial function, detection of subclinical myocardial dysfunction and serving as a prognostic indicator. This review illustrates the fundamental concepts of deformation analysis and gives an overview of the current understanding and its clinical application of this technique in veterinary medicine, with a focus on early detection of left ventricular (LV) dysfunction in dogs.

Effects of cardiac medications on ventricular function in patients with Duchenne muscular dystrophy–related cardiomyopathy

The DMD Care Considerations Working Group Guidelines 2010 recommended treating cardiac dystrophinopathy with angiotensin-converting enzyme-inhibitor (ACEi) and beta-blocker (BB) therapy to prevent the progressive decline in left ventricular function expected from earlier, natural history studies. The aim of this research was to audit change in measures of left ventricular function over 8 years to 4 years before and 4 years after deploying an ACEi/BB combination systematically at a dedicated "cardiology-muscle" clinic. This is an institutionally registered, retrospective, case-file-based audit of serial echocardiographic measures of left ventricular fractional shortening accumulated over the period 1995 to 2015. Data from 104 genetically confirmed Duchenne muscular dystrophy (DMD) patients, aged 22.2 ± 5.3 years at data censure, were included. Mean age at first detection of left ventricular dysfunction was 15.1± 4.2 years, but older in those on maintenance steroid therapy (16.8± 4.2 vs 14.5± 4.1 years; P= .04). Group mean fractional shortening fell by 1.5%/year over the 4 years before therapy, but this decreased to 0.9%/year over the first 4 years after starting therapy. Analysis of limited left ventricular ejection fraction measures showed similar but nonsignificant changes. Neither age at detection of left ventricular dysfunction nor fractional shortening percent at time of therapy initiation affected the beneficial response. The results support the international DMD recommendations of the time. This combination of cardiac medications helps stabilize heart function. For the best long-term effects, therapy needs to be initiated no later than on first detection left ventricular impairment.

Predicting cardiac disease from interactions of simultaneously-acquired hemodynamic and cardiac signals

Background and ObjectiveCoronary artery disease (CAD) and heart failure are the most common cardiovascular diseases. Non-invasive diagnostic testing for CAD requires radiation, heart rate acceleration, and imaging infrastructure. Early detection of left ventricular dysfunction is critical in heart failure management, the best measure of which is an elevated left ventricular end-diastolic pressure (LVEDP) that can only be measured using invasive cardiac catheterization. There exists a need for non-invasive, safe, and fast diagnostic testing for CAD and elevated LVEDP. This research employs nonlinear dynamics to assess for significant CAD and elevated LVEDP using non-invasively acquired photoplethysmographic (PPG) and three-dimensional orthogonal voltage gradient (OVG) signals. PPG (variations of the blood volume perfusing the tissue) and OVG (mechano-electrical activity of the heart) signals represent the dynamics of the cardiovascular system. MethodsPPG and OVG were simultaneously acquired from two cohorts, (i) symptomatic subjects that underwent invasive cardiac catheterization, the gold standard test (408 CAD positive with stenosis≥ 70% and 186 with LVEDP≥ 20 mmHg) and (ii) asymptomatic healthy controls (676). A set of Poincaré-based synchrony features were developed to characterize the interactions between the OVG and PPG signals. The extracted features were employed to train machine learning models for CAD and LVEDP. Five-fold cross-validation was used and the best model was selected based on the average area under the receiver operating characteristic curve (AUC) across 100 runs, then assessed using a hold-out test set. ResultsThe Elastic Net model developed on the synchrony features can effectively classify CAD positive subjects from healthy controls with an average validation AUC=0.90±0.03 and an AUC= 0.89 on the test set. The developed model for LVEDP can discriminate subjects with elevated LVEDP from healthy controls with an average validation AUC=0.89±0.03 and an AUC=0.89 on the test set. The feature contributions results showed that the selection of a proper registration point for Poincaré analysis is essential for the development of predictive models for different disease targets. ConclusionsNonlinear features from simultaneously-acquired signals used as inputs to machine learning can assess CAD and LVEDP safely and accurately with an easy-to-use, portable device, utilized at the point-of-care without radiation, contrast, or patient preparation.

Artificial intelligence-enhanced electrocardiography in cardiovascular disease management.

The application of artificial intelligence (AI) to the electrocardiogram (ECG), a ubiquitous and standardized test, is an example of the ongoing transformative effect of AI on cardiovascular medicine. Although the ECG has long offered valuable insights into cardiac and non-cardiac health and disease, its interpretation requires considerable human expertise. Advanced AI methods, such as deep-learning convolutional neural networks, have enabled rapid, human-like interpretation of the ECG, while signals and patterns largely unrecognizable to human interpreters can be detected by multilayer AI networks with precision, making the ECG a powerful, non-invasive biomarker. Large sets of digital ECGs linked to rich clinical data have been used to develop AI models for the detection of left ventricular dysfunction, silent (previously undocumented and asymptomatic) atrial fibrillation and hypertrophic cardiomyopathy, as well as the determination of a person’s age, sex and race, among other phenotypes. The clinical and population-level implications of AI-based ECG phenotyping continue to emerge, particularly with the rapid rise in the availability of mobile and wearable ECG technologies. In this Review, we summarize the current and future state of the AI-enhanced ECG in the detection of cardiovascular disease in at-risk populations, discuss its implications for clinical decision-making in patients with cardiovascular disease and critically appraise potential limitations and unknowns.

Three-dimensional speckle tracking echocardiography for early detection of left ventricular dysfunction in children with non-alcoholic fatty liver diseases.

To detect early left ventricular dysfunction in children with non-alcoholic fatty liver disease using three-dimensional speckle tracking echocardiography. Forty obese children with non-alcoholic fatty liver disease were included as group I. Another 40 obese children without non-alcoholic fatty liver disease of matched age, sex, and weight were included as group II. Forty healthy controls of matched age and sex served as a control group. Anthropometric measurements, laboratory investigations, and echocardiographic examinations including three-dimensional speckle tracking echocardiography were measured for all included children. Abnormal lipid profile was detected in children with non-alcoholic fatty liver disease. Troponin I levels were significantly higher in children with non-alcoholic fatty liver disease compared to obese children without non-alcoholic fatty liver disease and to healthy controls. Three-dimensional speckle tracking echocardiography examination revealed a significant reduction of left ventricular global longitudinal strain, circumferential strain, radial strain, and area strain in children with non-alcoholic fatty liver disease inspite of normal left ventricular fraction shortening measured by conventional echocardiography. All strains were negatively correlated with the grade of non-alcoholic fatty liver disease. Non-alcoholic fatty liver disease is associated with subclinical left ventricular dysfunction. Three-dimensional speckle tracking echocardiography can be helpful in identifying early left ventricular dysfunction in children with non-alcoholic fatty liver disease even in the presence of normal left ventricular ejection fraction.