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Technetium-99 pyrophosphate scintigraphy (99mTc-PYP) provides qualitative and semiquantitative diagnosis of ATTR cardiac amyloidosis (ATTR-CA) using the Perugini scoring system and heart/contralateral heart ratio (H/CL) on planar imaging. Standardized uptake values (SUV) with quantitative single photon emission computed tomography (xSPECT/CT) can offer superior diagnostic accuracy and quantification through precise myocardial contouring that enhances assessment of ATTR-CA burden. We examined the correlation of xSPECT/CT SUVs with Perugini score and H/CL ratio. We also assessed SUV correlation with cardiac magnetic resonance (CMR), echocardiographic, and baseline clinical characteristics. Retrospective review of 78 patients with suspected ATTR-CA that underwent 99mTc-PYP scintigraphy with xSPECT/CT. Patients were grouped off Perugini score (Grade 0-1 and Grade 2-3), H/CL ratio (≥ 1.5 and1.5). Two cohorts were also created: myocardium SUV

Computed tomography (CT) is emerging as a prominent diagnostic modality in the field of cardiovascular imaging. Artificial intelligence (AI) is making significant strides in the field of information technology, the commercial industry, and health care. Machine learning (ML), a branch of AI, can optimize the performance of CT and augment the assessment of coronary artery disease. These ML platforms can automate multiple tasks, perform calculations, and integrate information from a variety of data sources. In this review article, we explore the ML in CT imaging.

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 ​= ​0.006, I2 ​= ​70.4%). There was no significant difference in the prevalence of peridevice gap ≥5 ​mm (OR, 3.04, 95% CI 0.70–13.17, p ​= ​0.13, I2 ​= ​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.

Examine the impact of acute changes in left heart strain and volumes with percutaneous edge-to-edge MitraClip repair on improvement in health status assessed using Kansas City Cardiomyopathy Questionnaire-12 (KCCQ-12) score.Changes in left atrial strain, left ventricular (LV) global longitudinal strain (LVGLS), LV end-systolic volume (LVESV), and end-diastolic volume (LVEDV) were evaluated in 50 patients undergoing MitraClip repair for symptomatic primary mitral regurgitation (PMR) and secondary mitral regurgitation (SMR) on transthoracic echocardiography before and 1 month after MitraClip. Multivariable regression was used to evaluate changes in left heart strain and volumes as predictors of change in KCCQ-12 scores, adjusting for baseline clinical and echocardiographic characteristics.Both PMR and SMR patients had significant increase in LVGLS and reduction in LVEDV and LVESV ( P 0.05) after MitraClip, reduction trend in left atrial conduit strain (PMR P = 0.053; SMR P = 0.12) but no significant change in LV ejection fraction. KCCQ-12 score improved significantly in both PMR ( P 0.001) and SMR cohorts ( P 0.001). Higher delta KCCQ-12 tertiles were associated with greater reduction in LVEDV ( P = 0.022) after MitraClip. On multiple regression analysis, lower preprocedural Society of Thoracic Surgeons for Mitral Valve Replacement and KCCQ-12 score, and greater reduction in LVESV and left atrial strain conduit phase were associated with KCCQ-12 score improvement ( P 0.001).There is a significant increase in LVGLS and reduction in LVEDV, LVESV and left atrial strain conduit after edge-to-edge MitraClip repair in both PMR and SMR. Lower preprocedural Society of Thoracic Surgeons for Mitral Valve Replacement and KCCQ-12 score, and greater reduction in LVESV and left atrial conduit strain were associated with KCCQ-12 score improvement after MitraClip. Further studies are warranted to understand the mechanism and significance of our findings.

In this digital era, artificial intelligence (AI) is establishing a strong foothold in commercial industry and the field of technology. These effects are trickling into the healthcare industry, especially in the clinical arena of cardiology. Machine learning (ML) algorithms are making substantial progress in various subspecialties of cardiology. This will have a positive impact on patient care and move the field towards precision medicine. In this review article, we explore the progress of ML in cardiovascular imaging, electrophysiology, heart failure, and interventional cardiology.

Background Time of day has been associated with adverse outcomes in certain surgical pathologies. Because acute type A aortic dissection typically mandates immediate repair, relatively little attention has been paid to the potential impact of the day–night timing of the operation itself. We sought to determine whether patients with acute dissection treated during typical working hours demonstrated a difference in outcomes compared with those who required surgery after hours. Methods We undertook a comprehensive review of our prospectively collected database from July 2014 to October 2018. A total of 164 consecutive patients underwent primary repair of an acute type A dissection. Based on the procedure start time, patients were divided into 2 groups: working hours (7 am to 4 pm , Monday to Friday; n = 60), and after hours (all other times, including weekends and holidays; n = 104). We propensity-matched 58 pairs of patients and analyzed perioperative data and short-term clinical outcomes. Results Thirty-day mortality for all 164 patients was 10.4% (17 deaths), which was not significantly different between the matched groups (working-hours: 8 deaths [13.8%] versus after hours: 4 deaths [6.9%]; P = .36). Perfusion, cross-clamp, and circulatory arrest times did not differ between groups, nor did the types of aortic repairs performed. Postoperative complications were also comparable, including stroke, reoperation for bleeding, and new-onset renal failure requiring dialysis. Conclusions Thirty-day mortality and major morbidity after acute type A dissection repair are independent of when the operation is performed. Expeditious surgical intervention is recommended for all primary acute type A dissection, irrespective of time of day.

Background Valve-in-valve (ViV) transcatheter aortic valve replacement (TAVR) has become a valuable option in patients with bioprosthetic failure. However, potential issues with ViV TAVR may occur in patients with high-risk anatomy for coronary obstruction and patients with baseline smaller bioprosthetic valves at risk for patient prosthesis mismatch. The purpose of this study was, therefore, to use preoperative electrocardiography-gated, multidetector computed tomography (MDCT) in patients undergoing isolated surgical aortic valve replacement (SAVR) to (1) identify which would be high risk for coronary occlusion with ViV TAVR, and (2) predict intraoperative SAVR sizing. Methods Among 223 patients from our institutions' database that underwent SAVR for aortic insufficiency (AI) or aortic stenosis (AS) between January 2012 and January 2020, 48 patients had MDCT imaging before surgery (AI; n = 31, AS; n = 17). Of all patients, 67% (n = 32) were bicuspid morphology. Results With the use of virtual valve implantation, all patients with AI and bicuspid AS had feasible anatomy for ViV TAVR, while 38% of patients with tricuspid AS were high risk for coronary obstruction. There was a strong correlation between actual valve size implanted and preoperative MDCT measurements using annulus average diameter, area, and/or perimeter. Conclusion Preoperative MDCT in patients undergoing SAVR is a useful tool for lifetime management, particularly in patients with tricuspid AS. Decisions for surgical management may change based on MDCT's ability to predict intraoperative SAVR size and determine which patients may be high-risk candidates for future ViV TAVR due to coronary artery obstruction.

Heart failure with preserved ejection fraction (HFpEF) is a complex clinical entity associated with significant morbidity and mortality. Common comorbidities including hypertension, coronary artery disease, diabetes, chronic kidney disease, obesity, and increasing age predispose to preclinical diastolic dysfunction that often progresses to frank HFpEF. Clinical HFpEF is typically associated with some degree of diastolic dysfunction, but can occur in the absence of many conventional diastolic dysfunction indices. The exact biologic links between risk factors, structural changes, and clinical manifestations are not clearly apparent. Innovative approaches including deformation imaging have enabled deeper understanding of HFpEF cardiac mechanics beyond conventional metrics. Furthermore, predictive analytics through data-driven platforms have allowed for a deeper understanding of HFpEF phenotypes. This review focuses on the changes in cardiac mechanics that occur through preclinical myocardial dysfunction to clinically apparent HFpEF.

BACKGROUND Valve-in-valve implantation (ViV) has become a valid option for the treatment of bioprosthetic valve failure. We describe the first in-man transfemoral transcatheter aortic valve replacement (TAVR) or "turducken" in a patient with previous TAVR within surgical aortic valve replacement with preprocedural guidance utilizing three-dimensional (3D) printing and intraprocedural guidance with fusion imaging. CASE SUMMARY A 65-year-old male with a previous valve-in-valve with a transcatheter Melody valve presented with symptomatic severe valvular and paravalvular aortic incompetence. Using 3D printing for preprocedural guidance and computed tomography angiography-fluoroscopy fusion imaging for intraprocedural guidance we successfully placed a 29 mm self-expanding valve with minimal residual gradients. The patient continued to be asymptomatic at 1- and 5-year follow-up. DISCUSSION We describe the first in man aortic valve-in-valve-in-valve using preprocedural guidance with 3D printing and intraprocedural guidance with fusion imaging. The valve has continued to demonstrate good function up to 5 years postprocedure suggesting that transcatheter aortic valve-in-valve-in-valve may be feasible with the proper pre- and intraprocedural guidance.

For systemic diseases like rheumatoid arthritis, systemic lupus erythematosus (SLE), systemic sclerosis, systemic vasculitis, myopathies, and mixed connective tissue diseases, cardiac disease is a major contributing factor for morbidity and mortality. The cardiovascular manifestations are the result of various pathophysiological components, which complicate management. Furthermore, the signs and symptoms can be subtle and missed due to the complex nature of the underlying condition. As a result, various imaging approaches play an imperative role in diagnosis and prognosis. The evolving role of these modalities could lead to risk stratification and improved therapies in the future. In conclusion, our review article will highlight the role of cardiac imaging in the evaluation of cardiac involvement for systemic diseases.

Pre-procedural chronic kidney disease (CKD) and in-hospital acute kidney injury (AKI) are associated with worse outcomes following transcatheter aortic valve replacement (TAVR). We tested the feasibility of reducing overall AKI by avoiding pre-procedural cardiac CT angiography (CCTA) by using direct 3D-TEE guidance in TAVR patients with known CKD.An institutional TAVR database was examined from January 2016 to June 2020 to identify 396 patients in whom CCTA sizing was performed and 54 patients with creatinine (Cr) of1.6 mg/dL in whom direct 3D-TEE, without prior CCTA, was used for TAVR guidance. Baseline demographics, procedural, echocardiographic, and clinical endpoints were compared as defined by the Valve Academic Research Consortium-2 criteria.Baseline demographics and risk factors were similar in both groups other than the creatinine level in CCTA vs. TEE groups (1.33 ± 1.1 vs 1.76 ± 0.7 mg/dL, p = 0.005). Procedural contrast volume was significantly lower in the TEE group compared to the CCTA group. No differences were noted in echocardiographic and clinical endpoints for both groups. Despite higher baseline Cr, patents in the TEE group experienced a similar pattern of changes in Cr compared to the CCTA group, with an overall renal improvement noted at the time of discharge for both groups.In patients with baseline CKD, careful avoidance of large contrast loads associated with CCTA and intra-procedural aortography by using TEE guidance may help reduce AKI following TAVR.

Galactorrhea, or nonpuerperal lactation, is a rare complication that can occur after trauma to the chest wall. Although galactorrhea has been reported after thoracic surgery, it has not been previously noted as a potential outcome following cardiac surgery. We present a unique case of hyperprolactinemic galactorrhea experienced by a 39-year-old nongravid female patient after having undergone reoperative mitral valve replacement via a right minithoracotomy. To the best of our knowledge, this is the first reported case of spontaneous lactation occurring after cardiac surgery.

BACKGROUND There is, as yet, no broad consensus regarding the optimal surgical approach for patients requiring reoperative mitral valve surgery. Consequently, we sought to evaluate the perioperative outcomes for patients undergoing redo mitral surgery via right mini thoracotomy as compared with traditional resternotomy. METHODS A comprehensive retrospective review of our prospectively collected database was undertaken from January 2011 to December 2017. We propensity matched 90 patients who underwent reoperative mitral valve surgery via right mini thoracotomy with a concurrent cohort of patients who had redo median sternotomy. Intraoperative data and short-term clinical outcomes were analyzed. RESULTS The 30-day mortality was 3.3% (six deaths) in the entire cohort, not significantly different between redo sternotomy and mini thoracotomy groups. Patients who had their procedure via right mini thoracotomy had reduced intensive care unit (P = .029) and overall hospital (P

TOPIC: Chest Infections TYPE: Original Investigations PURPOSE: Covid-19 caused by the novel SARS-CoV-2 has emerged as a global health crisis with various clinical complications. Covid-19 related respiratory manifestations have been reported as mild, moderate to severe including acute lung injury and acute respiratory distress syndrome necessitating non-invasive forms of oxygenation to mechanical ventilation (MV). MV patients frequently undergo prolonged hospitalizations with substantial morbidity and mortality. We sought to evaluate risk factors for MV in our cohort. METHODS: We conducted a retrospective cohort study of patients admitted in our institution from March 1st to June 21st2020, to assess risk factors for Covid-19 related respiratory failure requiring MV. The original cohort encompassed 166 MV and 503 non MV patients. Information from our hospital medical records was extracted, which included demographics, presenting symptoms, past medical history, vital signals, and laboratory data and need for MV. We propensity matched 166 MV with a concurrent cohort of non MV patients in our institution. Covariates applied in matching included age, gender, race, and body mass index (BMI). The admission clinical attributes and laboratory parameters were analyzed, along with outcomes. RESULTS: The mean age of our matched cohort was 63.8 years. Mechanically Ventilated patients had a higher incidence of tachycardia (heart rate > 125) (p 24 cycles per minute (p 97.8 F (Temperature > (p =.037), shortness of breath (p =.001), and headaches (p =.005). In addition, mechanically ventilated patients had a lower serum albumin (g/dl) ≤ 3 units (p 6.6 units (p =.004), serum lactic acid (mmol/L) > 1.7 units (p =.003), serum LDH U/L > 465 U/L (p .305 units (p 8.4 k/ul (p

Objectives This study sought to explore the spectrum of cardiac abnormalities in student athletes who returned to university campus in July 2020 with uncomplicated coronavirus disease 2019 (COVID-19). Background There is limited information on cardiovascular involvement in young individuals with mild or asymptomatic COVID-19. Methods Screening echocardiograms were performed in 54 consecutive student athletes (mean age 19 years; 85% male) who had positive results of reverse transcription polymerase chain reaction nasal swab testing of the upper respiratory tract or immunoglobulin G antibodies against severe acute respiratory syndrome coronavirus type 2. Sequential cardiac magnetic resonance imaging was performed in 48 (89%) subjects. Results A total of 16 (30%) athletes were asymptomatic, whereas 36 (66%) and 2 (4%) athletes reported mild and moderate COVID-19 related symptoms, respectively. For the 48 athletes completing both imaging studies, abnormal findings were identified in 27 (56.3%) individuals. This included 19 (39.5%) athletes with pericardial late enhancements with associated pericardial effusion. Of the individuals with pericardial enhancements, 6 (12.5%) had reduced global longitudinal strain and/or an increased native T1. One patient showed myocardial enhancement, and reduced left ventricular ejection fraction or reduced global longitudinal strain with or without increased native T1 values was also identified in an additional 7 (14.6%) individuals. Native T2 findings were normal in all subjects, and no specific imaging features of myocardial inflammation were identified. Hierarchical clustering of left ventricular regional strain identified 3 unique myopericardial phenotypes that showed significant association with the cardiac magnetic resonance findings (p = 0.03). Conclusions More than 1 in 3 previously healthy college athletes recovering from COVID-19 infection showed imaging features of a resolving pericardial inflammation. Although subtle changes in myocardial structure and function were identified, no athlete showed specific imaging features to suggest an ongoing myocarditis. Further studies are needed to understand the clinical implications and long-term evolution of these abnormalities in uncomplicated COVID-19., Central Illustration

Imaging technology has diminished the reliance on cardiac auscultation as a definitive diagnostic tool. However, it retains relevance in its immediacy, minimal preparation, and power source independence. We present a case of clinically detected continuous murmur raising specific diagnostic possibilities not accounted for advanced imaging. Further testing revealed a large supracristal ventricular septal defect (VSD) and aortic regurgitation (AR), allowing the surgeon to anticipate combined septal and valvular surgery. This report highlights the value of cardiac auscultation as a guide and validation for imaging. The absence of lesions on imaging is not proof of lesion absence.

Cardiovascular disease is the leading cause of mortality in Western countries and leads to a spectrum of complications that can complicate patient management. The emergence of artificial intelligence (AI) has garnered significant interest in many industries, and the field of cardiovascular imaging is no exception. Machine learning (ML) especially is showing significant promise in various diagnostic imaging modalities. As conventional statistics are reaching their apex in computational capabilities, ML can explore new possibilities and unravel hidden relationships. This will have a positive impact on diagnosis and prognosis for cardiovascular imaging. In this in-depth review, we highlight the role of AI and ML for various cardiovascular imaging modalities.

The emergence of artificial intelligence (AI) has sparked tremendous interest in the academic community, commercial industries, and society with wide-ranging applications from self-driving cars to automated voice recognition. With the explosive progress of AI, the field of cardiovascular imaging is no exception. Cardiovascular disease is one of the leading causes of mortality and morbidity, various diagnostic modalities play a paramount role in patient evaluation. As data originating form imaging and health care is becoming increasing complex for conventional statistics, this places us at a delicate crossroads. Machine learning (ML), a subset of AI, has shown significant promise and will help cardiovascular imaging transcend to new heights. In this review, we herein describe the role of ML to date in various imaging modalities for in cardiovascular disease.

Objective Debate continues as to the optimal minimally invasive treatment modality for complex disease of the left anterior descending coronary artery, with advocates for both robotic-assisted minimally invasive direct coronary artery bypass and percutaneous coronary intervention with a drug-eluting stent. We analyzed the midterm outcomes of patients with isolated left anterior descending disease, revascularized by minimally invasive direct coronary artery bypass or drug-eluting stent percutaneous coronary intervention, focusing on those with complex lesion anatomy. Methods A retrospective review was undertaken of all patients who underwent coronary revascularization between January 2008 and December 2016. From this population, 158 propensity-matched pairs of patients were generated from 158 individuals who underwent minimally invasive direct coronary artery bypass for isolated complex left anterior descending disease and from 373 patients who underwent percutaneous coronary intervention using a second-generation drug-eluting stent. Midterm survival and incidence of repeat left anterior descending intervention were analyzed for both patient groups. Results Overall 9-year survival was not significantly different between patient groups both before and after propensity matching. Midterm mortality in the matched minimally invasive direct coronary artery bypass group was low, irrespective of patient risk profile. By contrast, advanced age (hazard ratio, 1.10; P = .012) and obesity (hazard ratio, 1.09; P = .044) predicted increased late death after drug-eluting stent percutaneous coronary intervention among matched patients. Patients who underwent minimally invasive direct coronary artery bypass were significantly less likely to require repeat left anterior descending revascularization than those who had percutaneous coronary intervention, both before and after propensity matching. Smaller stent diameter in drug-eluting stent percutaneous coronary intervention was associated with increased left anterior descending reintervention (hazard ratio, 3.53; P = .005). Conclusions In patients with complex disease of the left anterior descending artery, both minimally invasive direct coronary artery bypass and percutaneous coronary intervention are associated with similar excellent intermediate-term survival, although reintervention requirements are lower after surgery.

In this current digital landscape, artificial intelligence (AI) has established itself as a powerful tool in the commercial industry and is an evolving technology in healthcare. Cutting-edge imaging modalities outputting multi-dimensional data are becoming increasingly complex. In this era of data explosion, the field of cardiovascular imaging is undergoing a paradigm shift toward machine learning (ML) driven platforms. These diverse algorithms can seamlessly analyze information and automate a range of tasks. In this review article, we explore the role of ML in the field of cardiovascular imaging.

Prosthetic valve endocarditis (PVE) after surgical aortic valve replacement and transcatheter aortic valve replacement (TAVR) carries significant morbidity/mortality. Our review aims to compare incidence, predisposing factors, microbiology, diagnosis, management, and outcomes of PVE in surgical aortic valve replacement/TAVR patients. We searched PubMed and Embase to identify published studies from January 1, 2015 to March 13, 2020. Key words were indexed for original reports, clinical studies, and reviews. Reports were evaluated by 2 authors against a priori inclusion/exclusion criteria. Studies were included if they reported incidence and outcomes related to surgical aortic valve replacement/TAVR PVE and excluded if they were published pre‐2015 or included a small population. We followed the Cochrane methodology and Preferred Reporting Items for Systematic Reviews and Meta‐Analyses guidelines for all stages of the design and implementation. Study quality was based on the Newcastle‐Ottawa Scale. Thirty‐three studies with 311 to 41 025 patients contained relevant information. The majority found no significant difference in incidence of surgical aortic valve replacement/TAVR PVE (reported as 0.3%–1.2% per patient‐year versus 0.6%–3.4%), but there were key differences in pathogenesis. TAVR has a specific set of infection risks related to entry site, procedure, and device, including nonstandardized protocols for infection control, valve crimping injury, paravalvular leak, neo‐leaflet stress, intact/calcified native leaflets, and intracardiac hardware. With the expansion of TAVR to lower risk and younger patients, a better understanding of pathogenesis, patient presentation, and guideline‐directed treatment is paramount. When operative intervention is necessary, mortality remains high at 20% to 30%. Unique TAVR infection risks present opportunities for PVE prevention, therefore, further investigation is imperative.

Echocardiography is an indispensable tool in diagnostic cardiology and is fundamental to clinical care. Significant advances in cardiovascular imaging technology paralleled by rapid growth in electronic medical records, miniaturized devices, real-time monitoring, and wearable devices using body sensor network technology have led to the development of complex data. The intricate nature of these data can be overwhelming and exceed the capabilities of current statistical software. Machine learning (ML), a branch of artificial intelligence (AI), can help health care providers navigate through this complex labyrinth of information and unravel hidden discoveries. Furthermore, ML algorithms can help automate several tasks in echocardiography and clinical care. ML can serve as a valuable diagnostic tool for physicians in the field of echocardiography. In addition, it can help expand the capabilities of research and discover alternative pathways in medical management. In this review article, we describe the role of AI and ML in echocardiography.

Background Hybrid coronary revascularization (HCR) constitutes a left internal mammary artery graft to the left anterior descending (LAD) coronary artery, coupled with percutaneous coronary intervention (PCI) for non-LAD lesions. This management strategy is not commonly offered to patients with complex multivessel disease. Our objective was to evaluate 8-year survival in patients with triple-vessel disease (TVD) treated by HCR, compared with that of concurrent matched patients managed by traditional coronary artery bypass grafting (CABG) or multivessel PCI. Methods A retrospective review was undertaken of 4805 patients with TVD who presented between January 2009 and December 2016. A cohort of 100 patients who underwent HCR were propensity-matched with patients treated by CABG or multivessel PCI. The primary endpoint was all-cause mortality at 8 years. Results Patients with TVD who underwent HCR had similar 8-year mortality (5.0%) as did those with CABG (4.0%) or multivessel PCI (9.0%). A composite endpoint of death, repeat revascularization, and new myocardial infarction, was not significantly different between patient groups (HCR 21.0% vs CABG 15.0%, P = .36; HCR 21.0% vs PCI 25.0%, P = .60). Despite a higher baseline synergy between percutaneous coronary intervention with taxus and cardiac surgery(SYNTAX) score, HCR was able to achieve a lower residual SYNTAX score than multivessel PCI (P = .001). Conclusions In select patients with TVD, long-term survival and FREEDOM from major adverse cardiovascular events after HCR are similar to that seen after traditional CABG or multivessel PCI. HCR should be considered for patients with multivessel disease, presuming a low residual SYNTAX score can be achieved.

Diagnostic imaging plays an indispensable role in the modern management of cardiovascular diseases. Despite significant advances in imaging modalities, the workflow in clinical imaging requires a great deal of human input. With increasing demands in patient care, current workflows in cardiovascular imaging face a number of challenges which include insufficient number of specialists, time constraints, learning curves, and inter-observer variability. Automated techniques using revolutionary approaches with machine learning can fundamentally alter our interpretation process and produce smarter, faster, and efficient results. With the emergence of big data and deep learning techniques which enable direct inference of image data without intensive feature engineering or can process raw images directly. Machine learning has been rapidly adopted in the field of medicine and has shown its fast and reproducible automatic inference in various cardiovascular imaging modalities, including echocardiography, nuclear imaging, cardiac computed tomography, and cardiac magnetic resonance. While deep domain knowledge of cardiovascular disease remains the cornerstone throughout the process, physicians may be relieved from mundane tasks using automation and be able to spend more time on decision-making and communication with patients and other medical staff in the new paradigm brought by machine learning. In this review article, we explore the role of machine in the clinical inference from cardiac imaging data recently.

Artificial Intelligence (AI) is the simulation of human intelligence in machines so they can perform various actions and execute decision-making. Machine learning (ML), a branch of AI, can analyse information from data and discover novel patterns. AI and ML are rapidly gaining prominence in healthcare as data become increasingly complex. These algorithms can enhance the role of cardiovascular imaging by automating many tasks or calculations, find new patterns or phenotypes in data and provide alternative diagnoses. In interventional cardiology, AI can assist in intraprocedural guidance, intravascular imaging and provide additional information to the operator. AI is slowly expanding its boundaries into interventional cardiology and can fundamentally alter the field. In this review, the authors discuss how AI can enhance the role of cardiovascular imaging and imaging in interventional cardiology.

Left bundle branch block is a pattern of altered ventricular depolarization and subsequently affects repolarization. These obscure patterns can affect the traditional ST segment shift criteria for the electrocardiographic detection of coronary insufficiency syndromes. Previously, patients with coronary ischemic pain and LBBB judged to be “new” (not previously documented) were considered to have ST elevation myocardial infarction (STEMI) warranting acute thrombolytic therapy. Current STEMI management favors emergent invasive angiography; however, recent data suggests the prevalence of coronary obstructive pathology may be as low as 50%. The application of more specific, less-sensitive Sgarbossa electrocardiographic criteria may reduce angiographic assessment in an otherwise high-risk population unlikely to tolerate further myocardial injury. We present a case that may facilitate a more nuanced EKG-based approach to distinguish those who may benefit from acute invasive angiography while reducing the frequency of unnecessary angiographic evaluation.

BACKGROUND Software-based beamforming which utilizes delay and standard beamforming is a signal processing technique that temporarily stores data from each probe element to improve specular reflections to improve the image resolution. We compared a software algorithm which uses delay and standard beamforming with delay and sum beamforming in standard, hardware to evaluate endocardial borders and need for echo contrast. METHODS In this prospective study, eligible participants were ≥18 years of age referred clinically for transthoracic echocardiograms. A limited study consisting of three views (apical 4, apical 3, and apical 2 chamber) was performed with the software-based beamforming and standard platform. Number and quality of segments visualized were evaluated using a 17-segment model. Quality of segments was graded as 0 = not visualized, 1 = incompletely visualized, or 2 = completely visualized. Overall quality score for each study (0 = poor, 1 = adequate, 2 = good) was reported. The need for contrast was determined by ASE guidelines. RESULTS A total of 101 patients (mean age 61 ± 16 years, males 52%) were enrolled. Mean number of segments visualized in apical 4- (6.28 vs 5.65, P

Background New York City emerged as the Epicenter for Covid-19 due to novel Coronavirus SARS-CoV-2 soon after it was declared a Global Pandemic in early 2020 by the WHO. Covid-19 presents with a wide spectrum of illness from asymptomatic to severe respiratory failure, shock, multiorgan failure and death. Although the overall fatality rate is low, there is significant mortality among hospitalized patients. There is limited information exploring the impact of Covid-19 in community hospital settings in ethnically diverse populations. We aimed to identify risk factors for Covid-19 mortality in our institution. Methods We conducted a retrospective cohort study of hospitalized in our institution for Covid 19 from March 1st to June 21st 2020. It comprised of 425 discharged patients and 245 expired patients. Information was extracted from our EMR which included demographics, presenting symptoms, and laboratory data. We propensity matched 245 expired patients with a concurrent cohort of discharged patients. Statistically significant covariates were applied in matching, which included age, gender, race, body mass index (BMI), diabetes mellitus, and hypertension. The admission clinical attributes and laboratory parameters and outcomes were analyzed. Results The mean age of the matched cohort was 66.9 years. Expired patients had a higher incidence of dyspnea (P < 0.001) and headache (0.031). In addition, expired patients had elevated CRP- hs (mg/dl) ≥ 123 (< .0001), SGOT or AST (IU/L) ≥ 54 (p < 0.001), SGPT or ALT (IU/L) ≥ 41 (p < 0.001), and creatinine (mg/dl) ≥ 1.135 (0.001), lower WBC counts (k/ul) ≥ 8.42 (0.009). Furthermore, on multivariate logistic regression, dyspnea (OR = 2.56, P < 0.001), creatinine ≥ 1.135 (OR = 1.79, P = 0.007), LDH(U/L) > 465 (OR = 2.18, P = 0.001), systolic blood pressure < 90 mm Hg (OR = 4.28, p = .02), respiratory rate > 24 (OR = 2.88, p = .001), absolute lymphocyte percent (≤ 12%) (OR = 1.68, p = .001) and procalcitonin (ng/ml) ≥ 0.305 (OR = 1.71, P = .027) predicted in- hospital mortality in all matched patients. Conclusion Our case series provides admission clinical characteristics and laboratory parameters that predict in- hospital mortality in propensity Covid 19 matched patients with a large Hispanic population. These risk factors will require further validation. Disclosures All Authors: No reported disclosures