The future of valvular heart disease assessment and therapy.

Valvular heart disease (VHD) is becoming more prevalent in an ageing population, leading to challenges in diagnosis and management. This two-part Series offers a comprehensive review of changing concepts in VHD, covering diagnosis, intervention timing, novel management strategies, and the current state of research. The first paper highlights the remarkable progress made in imaging and transcatheter techniques, effectively addressing the treatment paradox wherein populations at the highest risk of VHD often receive the least treatment. These advances have attracted the attention of clinicians, researchers, engineers, device manufacturers, and investors, leading to the exploration and proposal of treatment approaches grounded in pathophysiology and multidisciplinary strategies for VHD management. This Series paper focuses on innovations involving computational, pharmacological, and bioengineering approaches that are transforming the diagnosis and management of patients with VHD. Artificial intelligence and digital methods are enhancing screening, diagnosis, and planning procedures, and the integration of imaging and clinical data is improving the classification of VHD severity. The emergence of artificial intelligence techniques, including so-called digital twins-eg, computer-generated replicas of the heart-is aiding the development of new strategies for enhanced risk stratification, prognostication, and individualised therapeutic targeting. Various new molecular targets and novel pharmacological strategies are being developed, including multiomics-ie, analytical methods used to integrate complex biological big data to find novel pathways to halt the progression of VHD. In addition, efforts have been undertaken to engineer heart valve tissue and provide a living valve conduit capable of growth and biological integration. Overall, these advances emphasise the importance of early detection, personalised management, and cutting-edge interventions to optimise outcomes amid the evolving landscape of VHD. Although several challenges must be overcome, these breakthroughs represent opportunities to advance patient-centred investigations.

A deep patient-similarity learning framework for the assessment of diastolic dysfunction in elderly patients.

AIMS: Age-related changes in cardiac structure and function are well recognized and make the clinical determination of abnormal left ventricular (LV) diastolic dysfunction (LVDD) particularly challenging in the elderly. We investigated whether a deep neural network (DeepNN) model of LVDD, previously validated in a younger cohort, can be implemented in an older population to predict incident heart failure (HF). METHODS AND RESULTS: A previously developed DeepNN was tested on 5596 older participants (66-90 years; 57% female; 20% Black) from the Atherosclerosis Risk in Communities Study. The association of DeepNN predictions with HF or all-cause death for the American College of Cardiology Foundation/American Heart Association Stage A/B (n = 4054) and Stage C/D (n = 1542) subgroups was assessed. The DeepNN-predicted high-risk compared with the low-risk phenogroup demonstrated an increased incidence of HF and death for both Stage A/B and Stage C/D (log-rank P < 0.0001 for all). In multi-variable analyses, the high-risk phenogroup remained an independent predictor of HF and death in both Stages A/B {adjusted hazard ratio [95% confidence interval (CI)] 6.52 [4.20-10.13] and 2.21 [1.68-2.91], both P < 0.0001} and Stage C/D [6.51 (4.06-10.44) and 1.03 (1.00-1.06), both P < 0.0001], respectively. In addition, DeepNN showed incremental value over the 2016 American Society of Echocardiography/European Association of Cardiovascular Imaging (ASE/EACVI) guidelines [net re-classification index, 0.5 (CI 0.4-0.6), P < 0.001; C-statistic improvement, DeepNN (0.76) vs. ASE/EACVI (0.70), P < 0.001] overall and maintained across stage groups. CONCLUSION: Despite training with a younger cohort, a deep patient-similarity-based learning framework for assessing LVDD provides a robust prediction of all-cause death and incident HF for older patients.

A novel breakthrough in wrist-worn transdermal troponin-I-sensor assessment for acute myocardial infarction.

Aims: Clinical differentiation of acute myocardial infarction (MI) from unstable angina and other presentations mimicking acute coronary syndromes (ACS) is critical for implementing time-sensitive interventions and optimizing outcomes. However, the diagnostic steps are dependent on blood draws and laboratory turnaround times. We tested the clinical feasibility of a wrist-worn transdermal infrared spectrophotometric sensor (transdermal-ISS) in clinical practice and assessed the performance of a machine learning algorithm for identifying elevated high-sensitivity cardiac troponin-I (hs-cTnI) levels in patients hospitalized with ACS. Methods and results: We enrolled 238 patients hospitalized with ACS at five sites. The final diagnosis of MI (with or without ST elevation) and unstable angina was adjudicated using electrocardiography (ECG), cardiac troponin (cTn) test, echocardiography (regional wall motion abnormality), or coronary angiography. A transdermal-ISS-derived deep learning model was trained (three sites) and externally validated with hs-cTnI (one site) and echocardiography and angiography (two sites), respectively. The transdermal-ISS model predicted elevated hs-cTnI levels with areas under the receiver operator characteristics of 0.90 [95% confidence interval (CI), 0.84-0.94; sensitivity, 0.86; and specificity, 0.82] and 0.92 (95% CI, 0.80-0.98; sensitivity, 0.94; and specificity, 0.64), for internal and external validation cohorts, respectively. In addition, the model predictions were associated with regional wall motion abnormalities [odds ratio (OR), 3.37; CI, 1.02-11.15; P = 0.046] and significant coronary stenosis (OR, 4.69; CI, 1.27-17.26; P = 0.019). Conclusion: A wrist-worn transdermal-ISS is clinically feasible for rapid, bloodless prediction of elevated hs-cTnI levels in real-world settings. It may have a role in establishing a point-of-care biomarker diagnosis of MI and impact triaging patients with suspected ACS.

Development and preliminary validation of infrared spectroscopic device for transdermal assessment of elevated cardiac troponin.

Background: The levels of circulating troponin are principally required in addition to electrocardiograms for the effective diagnosis of acute coronary syndrome. Current standard-of-care troponin assays provide a snapshot or momentary view of the levels due to the requirement of a blood draw. This modality further restricts the number of measurements given the clinical context of the patient. In this communication, we present the development and early validation of non-invasive transdermal monitoring of cardiac troponin-I to detect its elevated state. Methods: Our device relies on infrared spectroscopic detection of troponin-I through the dermis and is tested in stepwise laboratory, benchtop, and clinical studies. Patients were recruited with suspected acute coronary syndrome. Results: We demonstrate a significant correlation (r = 0.7774, P < 0.001, n = 52 biologically independent samples) between optically-derived data and blood-based immunoassay measurements with and an area under receiver operator characteristics of 0.895, sensitivity of 96.3%, and specificity of 60% for predicting a clinically meaningful threshold for defining elevated Troponin I. Conclusion: This preliminary work introduces the potential of a bloodless transdermal measurement of troponin-I based on molecular spectroscopy. Further, potential pitfalls associated with infrared spectroscopic mode of inquiry are outlined including requisite steps needed for improving the precision and overall diagnostic value of the device in future studies.

Cardiac Computed Tomography Angiography for Device-Related Thrombus Assessment After WATCHMAN FLX™ Occluder Device Implantation: A Single-Center Retrospective Observational Study.

BACKGROUND/PURPOSE: Device-related thrombosis (DRT) is one of the greatest challenges of transcatheter left atrial appendage device occlusion. Due to the invasive nature of transesophageal echocardiography (TEE), cardiac computed tomography angiography (CCTA) is being increasingly utilized in several centers for assessing adequate left atrial appendage closure and monitoring for DRT. There is a paucity of data regarding the standardized definition of DRT on CCTA for the WATCHMAN FLX™ device. METHODS/MATERIALS: A retrospective review was conducted on 43 patients receiving WATCHMAN FLX™ device implantation with CCTA performed at the first follow-up at our institution. A comparative review of DRT predictors was performed on 10 patients who had both CCTA and TEE at the time of follow-up. RESULTS: Hypoattenuated thickening (HAT) was a common finding on CCTA and was noted to be present in 95.35% of the patients. The combination of a large device size, peridevice gap >4 mm, and HAT located on the device gutter and 1 shoulder were characteristics present on CCTA observed in 2 patients with confirmed DRT on TEE. CONCLUSION: CCTA is a noninvasive imaging modality for DRT monitoring, with guidelines still in development. We report potential predictors of DRT on CCTA. Additional studies are needed to further determine standardized parameters for DRT detection on CCTA and the significance of HAT with multimodality correlation.

Deep-Learning Models for the Echocardiographic Assessment of Diastolic Dysfunction.

OBJECTIVES: The authors explored a deep neural network (DeepNN) model that integrates multidimensional echocardiographic data to identify distinct patient subgroups with heart failure with preserved ejection fraction (HFpEF). BACKGROUND: The clinical algorithms for phenotyping the severity of diastolic dysfunction in HFpEF remain imprecise. METHODS: The authors developed a DeepNN model to predict high- and low-risk phenogroups in a derivation cohort (n = 1,242). Model performance was first validated in 2 external cohorts to identify elevated left ventricular filling pressure (n = 84) and assess its prognostic value (n = 219) in patients with varying degrees of systolic and diastolic dysfunction. In 3 National Heart, Lung, and Blood Institute-funded HFpEF trials, the clinical significance of the model was further validated by assessing the relationships of the phenogroups with adverse clinical outcomes (TOPCAT [Aldosterone Antagonist Therapy for Adults With Heart Failure and Preserved Systolic Function] trial, n = 518), cardiac biomarkers, and exercise parameters (NEAT-HFpEF [Nitrate's Effect on Activity Tolerance in Heart Failure With Preserved Ejection Fraction] and RELAX-HF [Evaluating the Effectiveness of Sildenafil at Improving Health Outcomes and Exercise Ability in People With Diastolic Heart Failure] pooled cohort, n = 346). RESULTS: The DeepNN model showed higher area under the receiver-operating characteristic curve than 2016 American Society of Echocardiography guideline grades for predicting elevated left ventricular filling pressure (0.88 vs. 0.67; p = 0.01). The high-risk (vs. low-risk) phenogroup showed higher rates of heart failure hospitalization and/or death, even after adjusting for global left ventricular and atrial longitudinal strain (hazard ratio [HR]: 3.96; 95% confidence interval [CI]: 1.24 to 12.67; p = 0.021). Similarly, in the TOPCAT cohort, the high-risk (vs. low-risk) phenogroup showed higher rates of heart failure hospitalization or cardiac death (HR: 1.92; 95% CI: 1.16 to 3.22; p = 0.01) and higher event-free survival with spironolactone therapy (HR: 0.65; 95% CI: 0.46 to 0.90; p = 0.01). In the pooled RELAX-HF/NEAT-HFpEF cohort, the high-risk (vs. low-risk) phenogroup had a higher burden of chronic myocardial injury (p < 0.001), neurohormonal activation (p < 0.001), and lower exercise capacity (p = 0.001). CONCLUSIONS: This publicly available DeepNN classifier can characterize the severity of diastolic dysfunction and identify a specific subgroup of patients with HFpEF who have elevated left ventricular filling pressures, biomarkers of myocardial injury and stress, and adverse events and those who are more likely to respond to spironolactone.

CT assessment of the left atrial appendage post-transcatheter occlusion - A systematic review and meta analysis.

BACKGROUND: Transesophageal echocardiography (TEE) is the standard imaging modality used to assess the left atrial appendage (LAA) after transcatheter device occlusion. Cardiac computed tomography angiography (CCTA) offers an alternative non-invasive modality in these patients. We aimed to conduct a comparison of the two modalities. METHODS: We performed a comprehensive systematic review of the current literature pertaining to CCTA to establish its usefulness during follow-up for patients undergoing LAA device closure. Studies that reported the prevalence of inadequate LAA closure on both CCTA and TEE were further evaluated in a meta-analysis. 19 studies were used in the systematic review, and six studies were used in the meta-analysis. RESULTS: The use of CCTA was associated with a higher likelihood of detecting LAA patency than the use of TEE (OR, 2.79, 95% CI 1.34-5.80, p â€‹= â€‹0.006, I2 â€‹= â€‹70.4%). There was no significant difference in the prevalence of peridevice gap ≥5 â€‹mm (OR, 3.04, 95% CI 0.70-13.17, p â€‹= â€‹0.13, I2 â€‹= â€‹0%) between the two modalities. Studies that reported LAA assessment in early and delayed phase techniques detected a 25%-50% higher prevalence of LAA patency on the delayed imaging. CONCLUSION: CCTA can be used as an alternative to TEE for LAA assessment post occlusion. Standardized CCTA acquisition and interpretation protocols should be developed for clinical practice.

Usefulness of Semisupervised Machine-Learning-Based Phenogrouping to Improve Risk Assessment for Patients Undergoing Transcatheter Aortic Valve Implantation.

Semisupervised machine-learning methods are able to learn from fewer labeled patient data. We illustrate the potential use of a semisupervised automated machine-learning (AutoML) pipeline for phenotyping patients who underwent transcatheter aortic valve implantation and identifying patient groups with similar clinical outcome. Using the Transcatheter Valve Therapy registry data, we divided 344 patients into 2 sequential cohorts (cohort 1, n = 211, cohort 2, n = 143). We investigated patient similarity analysis to identify unique phenogroups of patients in the first cohort. We subsequently applied the semisupervised AutoML to the second cohort for developing automatic phenogroup labels. The patient similarity network identified 5 patient phenogroups with substantial variations in clinical comorbidities and in-hospital and 30-day outcomes. Cumulative assessment of patients from both cohorts revealed lowest rates of procedural complications in Group 1. In comparison, Group 5 was associated with higher rates of in-hospital cardiovascular mortality (odds ratio [OR] 35, 95% confidence interval [CI] 4 to 309, p = 0.001), in-hospital all-cause mortality (OR 9, 95% CI 2 to 33, p = 0.002), 30-day cardiovascular mortality (OR 18, 95% CI 3 to 94, p <0.001), and 30-day all-cause mortality (OR 3, 95% CI 1.2 to 9, p = 0.02) . For 30-day cardiovascular mortality, using phenogroup data in conjunction with the Society of Thoracic Surgeon score improved the overall prediction of mortality versus using the Society of Thoracic Surgeon scores alone (AUC 0.96 vs AUC 0.8, p = 0.02). In conclusion, we illustrate that semisupervised AutoML platforms identifies unique patient phenogroups who have similar clinical characteristics and overall risk of adverse events post-transcatheter aortic valve implantation.

Machine Learning Assessment of Left Ventricular Diastolic Function Based on Electrocardiographic Features.

BACKGROUND: Left ventricular (LV) diastolic dysfunction is recognized as playing a major role in the pathophysiology of heart failure; however, clinical tools for identifying diastolic dysfunction before echocardiography remain imprecise. OBJECTIVES: This study sought to develop machine-learning models that quantitatively estimate myocardial relaxation using clinical and electrocardiography (ECG) variables as a first step in the detection of LV diastolic dysfunction. METHODS: A multicenter prospective study was conducted at 4 institutions in North America enrolling a total of 1,202 subjects. Patients from 3 institutions (n = 814) formed an internal cohort and were randomly divided into training and internal test sets (80:20). Machine-learning models were developed using signal-processed ECG, traditional ECG, and clinical features and were tested using the test set. Data from the fourth institution was reserved as an external test set (n = 388) to evaluate the model generalizability. RESULTS: Despite diversity in subjects, the machine-learning model predicted the quantitative values of the LV relaxation velocities (e') measured by echocardiography in both internal and external test sets (mean absolute error: 1.46 and 1.93 cm/s; adjusted R2 = 0.57 and 0.46, respectively). Analysis of the area under the receiver operating characteristic curve (AUC) revealed that the estimated e' discriminated the guideline-recommended thresholds for abnormal myocardial relaxation and diastolic and systolic dysfunction (LV ejection fraction) the internal (area under the curve [AUC]: 0.83, 0.76, and 0.75) and external test sets (0.84, 0.80, and 0.81), respectively. Moreover, the estimated e' allowed prediction of LV diastolic dysfunction based on multiple age- and sex-adjusted reference limits (AUC: 0.88 and 0.94 in the internal and external sets, respectively). CONCLUSIONS: A quantitative prediction of myocardial relaxation can be performed using easily obtained clinical and ECG features. This cost-effective strategy may be a valuable first clinical step for assessing the presence of LV dysfunction and may potentially aid in the early diagnosis and management of heart failure patients.

A low-cost texture-based pipeline for predicting myocardial tissue remodeling and fibrosis using cardiac ultrasound.

BACKGROUND: Maturation of ultrasound myocardial tissue characterization may have far-reaching implications as a widely available alternative to cardiac magnetic resonance (CMR) for risk stratification in left ventricular (LV) remodeling. METHODS: We extracted 328 texture-based features of myocardium from still ultrasound images. After we explored the phenotypes of myocardial textures using unsupervised similarity networks, global LV remodeling parameters were predicted using supervised machine learning models. Separately, we also developed supervised models for predicting the presence of myocardial fibrosis using another cohort who underwent cardiac magnetic resonance (CMR). For the prediction, patients were divided into a training and test set (80:20). FINDINGS: Texture-based tissue feature extraction was feasible in 97% of total 534 patients. Interpatient similarity analysis delineated two patient groups based on the texture features: one group had more advanced LV remodeling parameters compared to the other group. Furthermore, this group was associated with a higher incidence of cardiac deaths (p = 0.001) and major adverse cardiac events (p < 0.001). The supervised models predicted reduced LV ejection fraction (<50%) and global longitudinal strain (<16%) with area under the receiver-operator-characteristics curves (ROC AUC) of 0.83 and 0.87 in the hold-out test set, respectively. Furthermore, the presence of myocardial fibrosis was predicted from only ultrasound myocardial texture with an ROC AUC of 0.84 (sensitivity 86.4% and specificity 83.3%) in the test set. INTERPRETATION: Ultrasound texture-based myocardial tissue characterization identified phenotypic features of LV remodeling from still ultrasound images. Further clinical validation may address critical barriers in the adoption of ultrasound techniques for myocardial tissue characterization. FUNDING: None.

Usefulness of Speckle Tracking Strain Echocardiography for Assessment of Risk of Ventricular Arrhythmias After Placement of a Left Ventricular Assist Device.

Patients with continuous-flow left ventricular assist devices (LVADs) are at elevated risk of developing ventricular arrhythmias (VA), which can result in right ventricular dysfunction and abnormal LVAD function. Predictors of postoperative VA after LVAD placement are unclear. We hypothesized that global left ventricular circumferential strain (LVCS), a marker of transmural impairment in myocardial function, would independently predict postoperative VA in patients who underwent LVAD implantation. We studied 98 consecutive patients (57 ± 11 years, 83% men) who underwent HeartMate II axial flow LVAD placement. Speckle tracking-derived global circumferential strain was assessed from mid-left ventricular short-axis images. The primary composite end point was defined as any ventricular tachycardia that required intervention (anti-arrhythmic medication, cardioversion, implantable cardioverter defibrillator placement, implantable cardioverter defibrillator shock) or any ventricular fibrillation. A total of 33 patients (34%) experienced the primary end point (median follow-up: 7 months). Reduced LVCS was statistically significantly related to the primary end point (hazard ratio 1.77, 95% confidence interval 1.09 to 2.87 per 1 standard deviation reduction in LVCS, p = 0.02). LVCS above a cut-off value of -9.7% was associated with significantly reduced arrhythmia-free survival (log-rank p = 0.001). In conclusion, global LVCS is an independent predictor of ventricular arrhythmias after LVAD placement.

Precision Phenotyping in Heart Failure and Pattern Clustering of Ultrasound Data for the Assessment of Diastolic Dysfunction.

OBJECTIVES: The aim of this study was to investigate whether cluster analysis of left atrial and left ventricular (LV) mechanical deformation parameters provide sufficient information for Doppler-independent assessment of LV diastolic function. BACKGROUND: Medical imaging produces substantial phenotyping data, and superior computational analyses could allow automated classification of repetitive patterns into patient groups with similar behavior. METHODS: The authors performed a cluster analysis and developed a model of LV diastolic function from an initial exploratory cohort of 130 patients that was subsequently tested in a prospective cohort of 44 patients undergoing cardiac catheterization. Patients in both study groups had standard echocardiographic examination with Doppler-derived assessment of diastolic function. Both the left ventricle and the left atrium were tracked simultaneously using speckle-tracking echocardiography (STE) for measuring simultaneous changes in left atrial and ventricular volumes, volume rates, longitudinal strains, and strain rates. Patients in the validation group also underwent invasive measurements of pulmonary capillary wedge pressure and LV end diastolic pressure immediately after echocardiography. The similarity between STE and conventional 2-dimensional and Doppler methods of diastolic function was investigated in both the exploratory and validation cohorts. RESULTS: STE demonstrated strong correlations with the conventional indices and independently clustered the patients into 3 groups with conventional measurements verifying increasing severity of diastolic dysfunction and LV filling pressures. A multivariable linear regression model also allowed estimation of E/e' and pulmonary capillary wedge pressure by STE in the validation cohort. CONCLUSIONS: Tracking deformation of the left-sided cardiac chambers from routine cardiac ultrasound images provides accurate information for Doppler-independent phenotypic characterization of LV diastolic function and noninvasive assessment of LV filling pressures.

Multimodality Imaging Strategies for the Assessment of Aortic Stenosis: Viewpoint of the Heart Valve Clinic International Database (HAVEC) Group.

Aortic stenosis is the most frequent valvular heart disease. In aortic stenosis, therapeutic decision essentially depends on symptomatic status, stenosis severity, and status of left ventricular systolic function. Surgical aortic valve replacement or transcatheter aortic valve implantation is the sole effective therapy in symptomatic patients with severe aortic stenosis, whereas the management of asymptomatic patients remains controversial and is mainly based on individual risk stratification. Imaging is fundamental for the initial diagnostic work-up, follow-up, and selection of the optimal timing and type of intervention. The present review provides specific recommendations for utilization of multimodality imaging to optimize risk stratification and therapeutic decision-making processes in aortic stenosis.

LV mechanics in mitral and aortic valve diseases: value of functional assessment beyond ejection fraction.

The assessment of myocardial function in the context of valvular heart disease remains highly challenging. The myocardium deforms simultaneously in 3 dimensions, and global left ventricular (LV) function parameters such as volume and ejection fraction may remain compensated despite the changes in myocardial deformation properties. Current guidelines recommend valve replacement/repair in the presence of symptoms or reduced LV ejection fraction, but the resolution of symptoms or recovery of LV function post-surgery may not be reliably predicted. A wealth of evidence currently suggests that LV dysfunction is frequently subclinical despite normal ejection fraction. It may precede the onset of symptoms and portend a poor outcome due to progressive myocardial remodeling and dysfunction during the post-operative period. The advent of novel tissue-tracking echocardiography techniques has unleashed new opportunities for the clinical identification of early abnormalities in LV function. This review gathers and summarizes current evidence regarding the use of these techniques to assess myocardial deformation in patients with valvular heart disease.

Feasibility of intercity and trans-Atlantic telerobotic remote ultrasound: assessment facilitated by a nondedicated bandwidth connection.

We discuss the concept of ultrasound imaging at a distance by presenting the evaluation of a customized, lightweight, human-safe robotic arm for low-force, long-distance, telerobotic ultrasonography. We undertook intercity and trans-Atlantic telerobotic ultrasound simulation from master stations located in New York, New York and Munich, Germany, and imaged a phantom and a human volunteer located at a slave station in Burlington, Massachusetts, using standard Internet bandwidth <100 Mbps and <50 Mbps, respectively. The data from the robotic arm were tracked for understanding the time efficiency of the human interactions at the master stations. Comparison of a beginner in ultrasound operation with a professional sonographer revealed that although proficiency in using ultrasound was not a prerequisite for operating the robotic arm, previous experience in using clinical ultrasound was associated with progressively lower probe maneuvering time and speed due to an enhanced ability of the veteran operator in adjusting the finer angular motions of the probe. These results suggest that long-distance telerobotic echocardiography over a local nondedicated Internet bandwidth is feasible and can be rapidly learned by sonographers for cost-effective resource utilization.

Assessment of longitudinal myocardial mechanics in patients with degenerative mitral valve regurgitation predicts postoperative worsening of left ventricular systolic function.

BACKGROUND: The optimal timing of mitral valve repair (MVr) in patients with chronic severe degenerative mitral regurgitation (MR) remains controversial and is broadly based on either measurable loss of systolic function, as determined by left ventricular (LV) ejection fraction (LVEF) and/or LV chamber remodeling. The aim of this study was to test the hypothesis that the assessment of LV deformation by speckle-tracking echocardiography might uncover subclinical changes for predicting reduction of LVEF after MVr. METHODS: One hundred thirty patients (mean age, 57 ± 14 years; 85 men) who underwent MVr for chronic severe degenerative MR were retrospectively identified. Baseline and immediate postoperative transthoracic echocardiography was used to assess global longitudinal strain (GLS), global radial strain, and global circumferential strain before and after MVr. RESULTS: In comparison with baseline, MVr resulted in significant reductions in LVEF (P < .0001) and in GLS (P < .0001). Postoperative change in LVEF was related to the changes in GLS (r = -0.71, P < .0001) and global circumferential strain (r = -0.22, P = .01) but not global radial strain. For the entire group, the presence of a high preoperative GLS magnitude predicted a postoperative reduction in LVEF of >10% (odds ratio, 0.80; P < .001). Furthermore, GLS showed diagnostic value in predicting a reduction in LVEF of >10% with a resulting postoperative LVEF of <50% (area under the curve, 0.93; P < .001). CONCLUSIONS: In chronic severe degenerative MR, disproportionately higher LV global longitudinal strain signifies a maladaptive preload-related change that is associated with substantial loss of LVEF immediately after MVr. Preoperative assessment of longitudinal strain may be potentially useful for optimizing the timing of MVr for degenerative MR.

Intelligent platforms for disease assessment: novel approaches in functional echocardiography.

Accelerating trends in the dynamic digital era (from 2004 onward) has resulted in the emergence of novel parametric imaging tools that allow easy and accurate extraction of quantitative information from cardiac images. This review principally attempts to heighten the awareness of newer emerging paradigms that may advance acquisition, visualization and interpretation of the large functional data sets obtained during cardiac ultrasound imaging. Incorporation of innovative cognitive software that allow advanced pattern recognition and disease forecasting will likely transform the human-machine interface and interpretation process to achieve a more efficient and effective work environment. Novel technologies for automation and big data analytics that are already active in other fields need to be rapidly adapted to the health care environment with new academic-industry collaborations to enrich and accelerate the delivery of newer decision making tools for enhancing patient care.