Closing the Last Mile Gap in Access to Multimodality Imaging in Rural Settings: Design of the Imaging Core of the Risk Underlying Rural Areas Longitudinal Study.

Achieving optimal cardiovascular health in rural populations can be challenging for several reasons including decreased access to care with limited availability of imaging modalities, specialist physicians, and other important health care team members. Therefore, innovative solutions are needed to optimize health care and address cardiovascular health disparities in rural areas. Mobile examination units can bring imaging technology to underserved or remote communities with limited access to health care services. Mobile examination units can be equipped with a wide array of assessment tools and multiple imaging modalities such as computed tomography scanning and echocardiography. The detailed structural assessment of cardiovascular and lung pathology, as well as the detection of extracardiac pathology afforded by computed tomography imaging combined with the functional and hemodynamic assessments acquired by echocardiography, yield deep phenotyping of heart and lung disease for populations historically underrepresented in epidemiological studies. Moreover, by bringing the mobile examination unit to local communities, innovative approaches are now possible including engagement with local professionals to perform these imaging assessments, thereby augmenting local expertise and experience. However, several challenges exist before mobile examination unit-based examinations can be effectively integrated into the rural health care setting including standardizing acquisition protocols, maintaining consistent image quality, and addressing ethical and privacy considerations. Herein, we discuss the potential importance of cardiac multimodality imaging to improve cardiovascular health in rural regions, outline the emerging experience in this field, highlight important current challenges, and offer solutions based on our experience in the RURAL (Risk Underlying Rural Areas Longitudinal) cohort study.

The Use of Artificial Intelligence Guidance for Rheumatic Heart Disease Screening by Novices.

INTRODUCTION: A novel technology utilizing artificial intelligence (AI) to provide real-time image-acquisition guidance, enabling novices to obtain diagnostic echocardiographic images, holds promise to expand the reach of echo screening for rheumatic heart disease (RHD). We evaluated the ability of nonexperts to obtain diagnostic-quality images in patients with RHD using AI guidance with color Doppler. METHODS: Novice providers without prior ultrasound experience underwent a 1-day training curriculum to complete a 7-view screening protocol using AI guidance in Kampala, Uganda. All trainees then scanned 8 to 10 volunteer patients using AI guidance, half RHD and half normal. The same patients were scanned by 2 expert sonographers without the use of AI guidance. Images were evaluated by expert blinded cardiologists to assess (1) diagnostic quality to determine presence/absence of RHD and (2) valvular function and (3) to assign an American College of Emergency Physicians score of 1 to 5 for each view. RESULTS: Thirty-six novice participants scanned a total of 50 patients, resulting in a total of 462 echocardiogram studies, 362 obtained by nonexperts using AI guidance and 100 obtained by expert sonographers without AI guidance. Novice images enabled diagnostic interpretation in >90% of studies for presence/absence of RHD, abnormal MV morphology, and mitral regurgitation (vs 99% by experts, P ≤ .001). Images were less diagnostic for aortic valve disease (79% for aortic regurgitation, 50% for aortic stenosis, vs 99% and 91% by experts, P < .001). The American College of Emergency Physicians scores of nonexpert images were highest in the parasternal long-axis images (mean, 3.45; 81% ≥ 3) compared with lower scores for apical 4-chamber (mean, 3.20; 74% ≥ 3) and apical 5-chamber images (mean, 2.43; 38% ≥ 3). CONCLUSIONS: Artificial intelligence guidance with color Doppler is feasible to enable RHD screening by nonexperts, performing significantly better for assessment of the mitral than aortic valve. Further refinement is needed to optimize acquisition of color Doppler apical views.

Percutaneous MitraClip Device or Surgical Mitral Valve Repair in Patients With Primary Mitral Regurgitation Who Are Candidates for Surgery: Design and Rationale of the REPAIR MR Trial.

Background The current standard of care for the treatment of patients with primary mitral regurgitation (MR) is surgical mitral valve repair. Transcatheter edge-to-edge repair with the MitraClip device provides a less invasive treatment option for patients with both primary and secondary MR. Worldwide, >150 000 patients have been treated with the MitraClip device. However, in the United States, MitraClip is approved for use only in primary patients with MR who are at high or prohibitive risk for mitral valve surgery. The REPAIR MR (Percutaneous MitraClip Device or Surgical Mitral Valve Repair in Patients With Primary Mitral Regurgitation Who Are Candidates for Surgery) trial is designed to compare early and late outcomes associated with transcatheter edge-to-edge repair with the MitraClip and surgical repair of primary MR in older or moderate surgical risk patients. Methods and Results The REPAIR MR trial is a prospective, randomized, parallel-controlled, open-label multicenter, noninferiority trial for the treatment of severe primary MR (verified by an independent echocardiographic core laboratory). Patients with severe MR and indications for surgery because of symptoms (New York Heart Association class II-IV), or without symptoms with left ventricular ejection fraction ≤60%, pulmonary artery systolic pressure >50 mm Hg, or left ventricular end-systolic diameter ≥40 mm are eligible for the trial provided they meet the moderate surgical risk criteria as follows: (1) ≥75 years of age, or (2) if <75 years of age, then the subject has a Society of Thoracic Surgeons Predicted Risk Of Mortality score of ≥2% for mitral repair (or Society of Thoracic Surgeons replacement score of ≥4%), or the presence of a comorbidity that may introduce a surgery-specific risk. The local surgeon must determine that the mitral valve can be surgically repaired. Additionally, an independent eligibility committee will confirm that the MR can be reduced to mild or less with both the MitraClip and surgical mitral valve repair with a high degree of certainty. A total of 500 eligible subjects will be randomized in a 1:1 ratio to receive the MitraClip device or to undergo surgical mitral valve repair (control group). There are 2 co-primary end points for the trial, both of which will be evaluated at 2 years. Each subject will be followed for 10 years after enrollment. The study has received approval from both the Food and Drug Administration and the Centers for Medicare and Medicaid Services, and enrolled its first subject in July 2020. Conclusions The REPAIR MR trial will determine the safety and effectiveness of transcatheter edge-to-edge repair with the MitraClip in patients with primary MR who are at moderate surgical risk and are candidates for surgical MV repair. The trial will generate contemporary comparative clinical evidence for the MitraClip device and surgical MV repair. Registration https://clinicaltrials.gov/ct2/show/NCT04198870; NCT04198870.

Independent Predictors of Mortality in COVID-19 Myocardial Injury: The Role of Troponin Levels, GRACE Score, SOFA Score, and TIMI Score.

Background Coronavirus disease 2019 (COVID-19) infection is associated with troponin elevation, which is associated with increased mortality. However, it is not clear if troponin elevation is independently linked to increased mortality in COVID-19 patients. Although there is considerable literature on risk factors for mortality in COVID-19-associated myocardial injury, the Global Registry of Acute Coronary Events (GRACE), Thrombolysis in Myocardial Infarction (TIMI), and Sequential Organ Failure Assessment (SOFA) scores have not been studied in COVID-19-related myocardial injury. This data is important in risk-stratifying COVID-19 myocardial injury patients. Methodology Of the 1,500 COVID-19 patients admitted to our hospitals, 217 patients who had troponin levels measured were included. Key variables were collected manually, and univariate and multivariate cox regression analysis was done to determine the predictors of mortality in COVID-19-associated myocardial injury. The differences in clinical profiles and outcomes of COVID-19 patients with and without troponin elevation were compared. Results Mortality was 26.5% in the normal troponin group and 54.6% in the elevated troponin group. Patients with elevated troponins had increased frequency of hypotension (p = 0.01), oxygen support (p < 0.01), low absolute lymphocyte (p < 0.01), elevated blood urea nitrogen (p < 0.01), higher C-reactive protein (p < 0.01), higher D-dimer (p < 0.01), higher lactic acid (p < 0.01), and higher Quick SOFA (qSOFA), SOFA, TIMI, and GRACE (all scores p < 0.01). On univariate cox regression, troponin elevation (hazard ratio (HR) = 1.85, 95% confidence interval (CI) = 1.18-2.88, p < 0.01), TIMI score >3 (HRv = 1.79, 95% CI = 1.11-2.75, p = 0.01), and GRACE score >140 (HR = 2.27, 95% CI = 1.45-3.55, p < 0.01) were highly associated with mortality, whereas cardiovascular disease (HR = 1.40, 95% CI = 0.89-2.21, p = 0.129) and cardiovascular risk factors (HR = 1.15, 95% CI = 0.73-1.81, p = 0.52) were not. After adjusting for age, use of a non-rebreather or high-flow nasal cannula, hemoglobin <8.5 g/dL, suspected or confirmed source of infection, and qSOFA and SOFA scores (HR = 1.18, 95% CI = 1.07-1.29, p < 0.01) were independently associated with mortality, whereas troponin (HR = 1.08, 95% CI = 0.63-1.85, p = 0.76), TIMI score (HR = 1.02, 95% CI = 0.99-1.06, p = 0.12) and GRACE scores (HR = 1.01, 95% CI = 0.99-1.02, p = 0.10) were not associated with mortality. Conclusions Our study shows that troponin, GRACE score, and TIMI score are not independent predictors of mortality in COVID-19 myocardial injury. This may be because troponin elevation in COVID-19 patients may be related to demand ischemia rather than acute coronary syndrome-related. This was shown by the association of troponin with a higher degree of systemic inflammation and end-organ dysfunction. Therefore, we recommend SOFA scores in risk-stratifying COVID-19 patients with myocardial injury.

Left anterior descending artery myocardial bridge manifesting as episodic, symptomatic exertional non-sustained ventricular tachycardia.

A 48-year-old man presented to the emergency department (ED) with exertional chest pressure associated with palpitations and lightheadedness. He was found to have non-sustained ventricular tachycardia (NSVT) in the ED, which resolved spontaneously. Given his history of hyperlipidaemia, unknown family history due to being adopted and episode of NSVT in the ED, he underwent cardiac catheterisation, which showed non-obstructive coronary artery disease and distal left anterior descending artery myocardial bridge (MB). The patient subsequently underwent ECG treadmill stress test with reproduction of chest pressure and NSVT. The patient was referred to cardiac surgery for definitive management of symptomatic MB and underwent resection of MB.

Deep Learning-Based Automated Echocardiographic Quantification of Left Ventricular Ejection Fraction: A Point-of-Care Solution.

BACKGROUND: We have recently tested an automated machine-learning algorithm that quantifies left ventricular (LV) ejection fraction (EF) from guidelines-recommended apical views. However, in the point-of-care (POC) setting, apical 2-chamber views are often difficult to obtain, limiting the usefulness of this approach. Since most POC physicians often rely on visual assessment of apical 4-chamber and parasternal long-axis views, our algorithm was adapted to use either one of these 3 views or any combination. This study aimed to (1) test the accuracy of these automated estimates; (2) determine whether they could be used to accurately classify LV function. METHODS: Reference EF was obtained using conventional biplane measurements by experienced echocardiographers. In protocol 1, we used echocardiographic images from 166 clinical examinations. Both automated and reference EF values were used to categorize LV function as hyperdynamic (EF>73%), normal (53%-73%), mildly-to-moderately (30%-52%), or severely reduced (<30%). Additionally, LV function was visually estimated for each view by 10 experienced physicians. Accuracy of the detection of reduced LV function (EF<53%) by the automated classification and physicians' interpretation was assessed against the reference classification. In protocol 2, we tested the new machine-learning algorithm in the POC setting on images acquired by nurses using a portable imaging system. RESULTS: Protocol 1: the agreement with the reference EF values was good (intraclass correlation, 0.86-0.95), with biases <2%. Machine-learning classification of LV function showed similar accuracy to that by physicians in most views, with only 10% to 15% cases where it was less accurate. Protocol 2: the agreement with the reference values was excellent (intraclass correlation=0.84) with a minimal bias of 2.5±6.4%. CONCLUSIONS: The new machine-learning algorithm allows accurate automated evaluation of LV function from echocardiographic views commonly used in the POC setting. This approach will enable more POC personnel to accurately assess LV function.

Utility of a Deep-Learning Algorithm to Guide Novices to Acquire Echocardiograms for Limited Diagnostic Use.

Importance: Artificial intelligence (AI) has been applied to analysis of medical imaging in recent years, but AI to guide the acquisition of ultrasonography images is a novel area of investigation. A novel deep-learning (DL) algorithm, trained on more than 5 million examples of the outcome of ultrasonographic probe movement on image quality, can provide real-time prescriptive guidance for novice operators to obtain limited diagnostic transthoracic echocardiographic images. Objective: To test whether novice users could obtain 10-view transthoracic echocardiographic studies of diagnostic quality using this DL-based software. Design, Setting, and Participants: This prospective, multicenter diagnostic study was conducted in 2 academic hospitals. A cohort of 8 nurses who had not previously conducted echocardiograms was recruited and trained with AI. Each nurse scanned 30 patients aged at least 18 years who were scheduled to undergo a clinically indicated echocardiogram at Northwestern Memorial Hospital or Minneapolis Heart Institute between March and May 2019. These scans were compared with those of sonographers using the same echocardiographic hardware but without AI guidance. Interventions: Each patient underwent paired limited echocardiograms: one from a nurse without prior echocardiography experience using the DL algorithm and the other from a sonographer without the DL algorithm. Five level 3-trained echocardiographers independently and blindly evaluated each acquisition. Main Outcomes and Measures: Four primary end points were sequentially assessed: qualitative judgement about left ventricular size and function, right ventricular size, and the presence of a pericardial effusion. Secondary end points included 6 other clinical parameters and comparison of scans by nurses vs sonographers. Results: A total of 240 patients (mean [SD] age, 61 [16] years old; 139 men [57.9%]; 79 [32.9%] with body mass indexes >30) completed the study. Eight nurses each scanned 30 patients using the DL algorithm, producing studies judged to be of diagnostic quality for left ventricular size, function, and pericardial effusion in 237 of 240 cases (98.8%) and right ventricular size in 222 of 240 cases (92.5%). For the secondary end points, nurse and sonographer scans were not significantly different for most parameters. Conclusions and Relevance: This DL algorithm allows novices without experience in ultrasonography to obtain diagnostic transthoracic echocardiographic studies for evaluation of left ventricular size and function, right ventricular size, and presence of a nontrivial pericardial effusion, expanding the reach of echocardiography to clinical settings in which immediate interrogation of anatomy and cardiac function is needed and settings with limited resources.

Imaging the mitral valve: a primer for the interventional surgeon.

Transcatheter mitral valve interventions (TMVI) have evolved over the past decade as alternatives to open surgical repair for the therapeutic management of patients with severe mitral regurgitation (MR). Concurrent with the development of these technologies, quality multi-modality cardiac imaging has become essential in patient selection and procedural guidance. The former involves assessments of the pathophysiologic mechanisms of regurgitation, valvular anatomy and morphology, as well as objective quantification of the severity of MR. Both transthoracic and transesophageal echocardiography (TEE) are crucial and serve as the gateway to diagnosis and management of mitral valvular disease. Along with multi-detector computed tomography (CT) and cardiac magnetic resonance imaging (CMR), echocardiography plays an important role for preprocedural planning and evaluation of the spatial relationships of the mitral valvular complex with the coronary sinus, circumflex coronary artery and left ventricular (LV) outflow tract. Procedures that target mitral leaflets (e.g., MitraClip, PASCAL) or annulus (e.g., Cardioband, Carillon), or provide chordal (e.g., NeoChord, Harpoon) or valvular replacement, tend to be guided by TEE and assisted by fluoroscopy. As newer devices become available and outcomes of TMVI improve, cardiac imaging will undoubtedly continue to play an essential role in the success of percutaneous mitral valve repair (MVr) and replacement. The interventional surgeon of the future must therefore have a thorough understanding of the various imaging modalities while synthesizing and integrating novel concepts (e.g., neo-LV outflow tract) as applicable to assessing valvular function and pathology.

Novel Multiphase Assessment for Predicting Left Ventricular Outflow Tract Obstruction Before Transcatheter Mitral Valve Replacement.

OBJECTIVES: This study proposes a physiologic assessment of left ventricular outflow tract obstruction (LVOTO) that accommodates changes in systolic flow and accounts for the dynamic neo-left ventricular outflow tract (LVOT). BACKGROUND: Patients considered for transcatheter mitral valve replacement trials often screen-fail because of the perceived risk of LVOTO. In the Intrepid Global Pilot Study, assumed risk of LVOTO was based on computed tomography estimates of the neo-LVOT area computed at end-systole. However, this may overestimate actual risk. METHODS: Retrospective analyses were performed for screen-failed patients for potential LVOTO (n = 33) and treated patients (n = 29) with available dynamic computed tomography. A multiphase assessment of the neo-LVOT area was performed and represented as: 1) multiphase average; and 2) early systolic value. Prospective evaluation was performed in 9 patients approved for enrollment with multiphase and early systole methods that would have previously screen-failed with the end-systolic approach. RESULTS: Of 166 patients screened for possible inclusion; 32 were screen-failed for nonanatomical reasons. Screen failure for assumed LVOTO risk occurred in 37 of 134 (27.6%) patients. Retrospective analysis indicated a potential enrollment increase of 11 of 33 (33.3%) and 18 of 33 (54.5%) patients using multiphase and early systolic assessment methods. In the prospective cohort, there were no clinical observations of LVOTO 30 days post-procedure, despite assumed risk based on end-systolic estimates. CONCLUSIONS: Multiphase, and specifically early systolic, assessment of the neo-LVOT may better determine risk of LVOTO with transcatheter mitral valve replacement compared with end-systolic estimates. This novel approach has the potential to significantly increase patient eligibility, with over one-half of patients previously screen-failed now eligible for treatment.

Automated Echocardiographic Quantification of Left Ventricular Ejection Fraction Without Volume Measurements Using a Machine Learning Algorithm Mimicking a Human Expert.

BACKGROUND: Echocardiographic quantification of left ventricular (LV) ejection fraction (EF) relies on either manual or automated identification of endocardial boundaries followed by model-based calculation of end-systolic and end-diastolic LV volumes. Recent developments in artificial intelligence resulted in computer algorithms that allow near automated detection of endocardial boundaries and measurement of LV volumes and function. However, boundary identification is still prone to errors limiting accuracy in certain patients. We hypothesized that a fully automated machine learning algorithm could circumvent border detection and instead would estimate the degree of ventricular contraction, similar to a human expert trained on tens of thousands of images. METHODS: Machine learning algorithm was developed and trained to automatically estimate LVEF on a database of >50 000 echocardiographic studies, including multiple apical 2- and 4-chamber views (AutoEF, BayLabs). Testing was performed on an independent group of 99 patients, whose automated EF values were compared with reference values obtained by averaging measurements by 3 experts using conventional volume-based technique. Inter-technique agreement was assessed using linear regression and Bland-Altman analysis. Consistency was assessed by mean absolute deviation among automated estimates from different combinations of apical views. Finally, sensitivity and specificity of detecting of EF ≤35% were calculated. These metrics were compared side-by-side against the same reference standard to those obtained from conventional EF measurements by clinical readers. RESULTS: Automated estimation of LVEF was feasible in all 99 patients. AutoEF values showed high consistency (mean absolute deviation =2.9%) and excellent agreement with the reference values: r=0.95, bias=1.0%, limits of agreement =±11.8%, with sensitivity 0.90 and specificity 0.92 for detection of EF ≤35%. This was similar to clinicians' measurements: r=0.94, bias=1.4%, limits of agreement =±13.4%, sensitivity 0.93, specificity 0.87. CONCLUSIONS: Machine learning algorithm for volume-independent LVEF estimation is highly feasible and similar in accuracy to conventional volume-based measurements, when compared with reference values provided by an expert panel.

Clinical Implementation of Continuous-Wave Doppler: It Made All the Difference.

Today, Doppler echocardiography is central to our ability to determine cardiovascular hemodynamics, especially in valvular heart diseases, noninvasively. Continuous-wave Doppler (CWD) plays a central diagnostic role in the diagnosis and management of patients with aortic stenosis. The development and use of CWD in aortic stenosis was due to the pioneering work of Dr. Liv Hatle and her outstanding medical and engineering colleagues in Norway. The author was fortunate to be the first to use the early CWD instruments in North America. Therefore, this article highlights key lessons learned: the importance and value of key contributions made by our engineering and young cardiology and sonographer colleagues, the key importance of the independent PEDOF CWD probe as well as use of the audio signal for accurate detection of high-velocity flows, and the value of CWD for the diagnosis and management of other cardiovascular conditions.

Echocardiographic Imaging for Transcatheter Aortic Valve Replacement.

Transcatheter aortic valve replacement has become an accepted alternative to surgery for patients with severe, symptomatic aortic stenosis who are inoperable or are at high surgical risk. Recent trials support the use of transcatheter aortic valve replacement also in patients at intermediate risk, and ongoing trials are assessing appropriateness in other patient groups. The authors review the key anatomic features integral to the transcatheter aortic valve replacement procedure and the echocardiographic imaging required for preprocedural, intraprocedural, and postprocedural assessment.

Trials Testing the Value of Imaging Use in Valve Disease and in Transcatheter Valvular Interventions.

The role of imaging in the diagnosis of valvular heart disease is well established through years of investigation and validation. However, the role of imaging in guiding the treatment decision-making for valvular heart disease is less well established, and there is a striking paucity of randomized trial data to help inform these decisions. Given this relative absence of randomized trial data, the present article highlights some of the most important knowledge gaps and defines meaningful opportunities for the field to help advance the care of patients with valvular heart disease in a cost-effective fashion. (The PARTNER 3-Trial: The Safety and Effectiveness of the SAPIEN 3 Transcatheter Heart Valve in Low Risk Patients With Aortic Stenosis [P3]; NCT02675114; Medtronic Transcatheter Aortic Valve Replacement in Low Risk Patients; NCT02701283).

Practice gaps in the care of mitral valve regurgitation: Insights from the American College of Cardiology mitral regurgitation gap analysis and advisory panel.

BACKGROUND: The revised 2014 American College of Cardiology (ACC)/American Heart Association valvular heart disease guidelines provide evidenced-based recommendations for the management of mitral regurgitation (MR). However, knowledge gaps related to our evolving understanding of critical MR concepts may impede their implementation. METHODS: The ACC conducted a multifaceted needs assessment to characterize gaps, practice patterns, and perceptions related to the diagnosis and treatment of MR. A key project element was a set of surveys distributed to primary care and cardiovascular physicians (cardiologists and cardiothoracic surgeons). Survey and other gap analysis findings were presented to a panel of 10 expert advisors from specialties of general cardiology, cardiac imaging, interventional cardiology, and cardiac surgeons with expertise in valvular heart disease, especially MR, and cardiovascular education. The panel was charged with assessing the relative importance and potential means of remedying identified gaps to improve care for patients with MR. RESULTS: The survey results identified several knowledge and practice gaps that may limit implementation of evidence-based recommendations for MR care. Specifically, half of primary care physicians reported uncertainty regarding timing of intervention for patients with severe primary or functional MR. Physicians in all groups reported that quantitative indices of MR severity were frequently not reported in clinical echocardiographic interpretations, and that these measurements were not consistently reviewed when provided in reports. In the treatment of MR, nearly 30% of primary care physician and general cardiologists did not know the volume of mitral valve repair surgeries by their reference cardiac surgeons and did not have a standard source to obtain this information. After review of the survey results, the expert panel summarized practice gaps into 4 thematic areas and offered proposals to address deficiencies and promote better alignment with the 2014 ACC/American Heart Association valvular disease guidelines. CONCLUSION: Important knowledge and skill gaps exist that may impede optimal care of the patient with MR. Focused educational and practice interventions should be developed to reduce these gaps.