Broadening Perspectives of Artificial Intelligence in Echocardiography

Echocardiography frequently serves as the first-line treatment of diagnostic imaging for several pathological entities in cardiology. Artificial intelligence (AI) has been growing substantially in information technology and various commercial industries. Machine learning (ML), a branch of AI, has been shown to expand the capabilities and potential of echocardiography. ML algorithms expand the field of echocardiography by automated assessment of the ejection fraction and left ventricular function, integrating novel approaches such as speckle tracking or tissue Doppler echocardiography or vector flow mapping, improved phenotyping, distinguishing between cardiac conditions, and incorporating information from mobile health and genomics. In this review article, we assess the impact of AI and ML in echocardiography.

Cardiac arrest in the setting of probable catastrophic antiphospholipid syndrome in young patient with a history of COVID infection and polyglandular disorder-Case report

Antiphospholipid syndrome is an autoimmune disorder characterized by arterial and venous thrombosis and recurrent spontaneous abortions due to the persistent presence of antiphospholipid antibodies. Probable Catastrophic antiphospholipid (Catastrophic antiphospholipid-like syndrome) is a life-threatening presentation of antiphospholipid syndrome which manifests as intravascular thrombosis, leading to rapid onset of symptoms and involvement of multiple organ systems. We present a case of a 28-year-old woman with a history of polyglandular autoimmune syndrome, systemic lupus erythematosus, provoked bilateral deep vein thrombosis in the setting of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection 2 years prior, and hypothyroidism who presents with a cardiac arrest in the setting of an acute ST-elevation myocardial infarction with thromboembolic occlusion of two coronary arteries simultaneously in the setting of noncompliance with anticoagulation for the past 1 week. Her presentation was further complicated by acute hypoxic respiratory failure due to diffuse alveolar hemorrhage during the hospital course with progressive multiorgan failure and eventual death. Catastrophic antiphospholipid is associated with high morbidity and mortality, thus a timely diagnosis and multidisciplinary approach to management is needed for evaluation and management.

Integrating Echocardiography Parameters With Explainable Artificial Intelligence for Data-Driven Clustering of Primary Mitral Regurgitation Phenotypes

BACKGROUND

Primary mitral regurgitation (MR) is a heterogeneous clinical disease requiring integration of echocardiographic parameters using guideline-driven recommendations to identify severe disease.

OBJECTIVES

The purpose of this preliminary study was to explore novel data-driven approaches to delineate phenotypes of MR severity that benefit from surgery.

METHODS

The authors used unsupervised and supervised machine learning and explainable artificial intelligence (AI) to integrate 24 echocardiographic parameters in 400 primary MR subjects from France (n = 243; development cohort) and Canada (n = 157; validation cohort) followed up during a median time of 3.2 years (IQR: 1.3-5.3 years) and 6.8 (IQR: 4.0-8.5 years), respectively. The authors compared the phenogroups' incremental prognostic value over conventional MR profiles and for the primary endpoint of all-cause mortality incorporating time-to-mitral valve repair/replacement surgery as a covariate for survival analysis (time-dependent exposure).

RESULTS

High-severity (HS) phenogroups from the French cohort (HS: n = 117; low-severity [LS]: n = 126) and the Canadian cohort (HS: n = 87; LS: n = 70) showed improved event-free survival in surgical HS subjects over nonsurgical subjects (P = 0.047 and P = 0.020, respectively). A similar benefit of surgery was not seen in the LS phenogroup in both cohorts (P = 0.70 and P = 0.50, respectively). Phenogrouping showed incremental prognostic value in conventionally severe or moderate-severe MR subjects (Harrell C statistic improvement; P = 0.480; and categorical net reclassification improvement; P = 0.002). Explainable AI specified how each echocardiographic parameter contributed to phenogroup distribution.

CONCLUSIONS

Novel data-driven phenogrouping and explainable AI aided in improved integration of echocardiographic data to identify patients with primary MR and improved event-free survival after mitral valve repair/replacement surgery.

Non-Calcified Coronary Artery Plaque on Coronary Computed Tomography Angiogram: Prevalence and Significance

OBJECTIVE

We aimed to assess the prevalence of non-calcified plaque (NCP) on computed tomography angiography (CCTA) in symptomatic and asymptomatic individuals. In addition, we seek to compare plaque assessment on CCTA with intravascular ultrasound-virtual histology (IVUS-VH) and to assess the prognostic value of non-calcified plaques (NCPs).

BACKGROUND

The CCTA can characterize coronary plaques and help quantify burden. Furthermore, it can provide additional prognostic information which can enable further risk stratification of patients.

METHODS

We performed a broad comprehensive review of the current literature pertaining to CCTA and primarily isolated NCP in symptomatic and asymptomatic patients. In addition, our review included studies correlating plaque on CT with IVUS-VH.

CONCLUSIONS

NCP is the initial precursor of calcified plaque and serves as a prominent marker of early coronary atherosclerosis. By detecting NCP during early stages, several measures can be implemented which can alter the evolutionary course of the underlying disease. This can potentially lead to a lower incidence of cardiovascular events.

Lung Metastasis From Uterine Leiomyosarcoma: An Asymptomatic Presentation for a Rare Tumor

Uterine leiomyosarcoma (ULC) is an uncommon neoplasm characterized by poor prognosis, it can predispose to distant metastasis, causing various symptomatic presentations. We present a unique case of a large heterogeneous mass in the lung cavity arising from a ULC, with complete absence of pulmonary symptoms and with concurrent coronavirus disease 2019 (COVID-19) infection. A high degree of clinical suspicion is required for ULC with accompanying metastasis.

A Case Report on Uremic Toxins and Their Effects on Cardiac Rhythm: Understanding Junctional Ventricular Escape Rhythm in Renal Failure

Understanding the reasons behind junctional ventricular escape rhythm is crucial for guiding the clinical management of patients. Various factors such as acidosis, hyperkalemia, metabolic toxins, digoxin toxicity, and BRASH syndrome (comprising bradycardia, renal failure, atrioventricular (AV) nodal blockade, shock, and hyperkalemia) should be considered when dealing with a symptomatic unstable patient in a hospital. In this case, we present an example where metabolic toxins, specifically uremia, in combination with other factors, lead the patient to enter a ventricular escape rhythm, ultimately resulting in cardiogenic shock. The main objective of this case study is to illustrate how uremic metabolic acidosis contributes to AV nodal blockade, leading to a junctional ventricular escape rhythm within the context of BRASH.

Evolution of Stress Echocardiogram in the Era of CT Angiography

The ideal diagnostic modality for acute chest pain is a highly debated topic in the cardiovascular community. With the rapid rise of coronary computed tomography angiography (CTA) and the fall of functional testing, stress echocardiography (SE) is at a delicate crossroads. Though there are many advantages of coronary CTA, it is not without its flaws. The exact realm of SE needs to be clearly defined, as well as which patients need diagnostic testing. The emergence of additional parameters will propel the evolution of modern SE. In this review article, we explore the role of SE, guidelines, comparison of SE versus CTA, and additional parameters in the coronary CTA era.

Role of Deep Learning in Computed Tomography

Computed tomography has played an instrumental role in the understanding of the pathophysiology of atherosclerosis in coronary artery disease. It enables visualization of the plaque obstruction and vessel stenosis in a comprehensive manner. As technology for computed tomography is constantly evolving, coronary applications and possibilities are constantly expanding. This influx of information can overwhelm a physician's ability to interpret information in this era of big data. Machine learning is a revolutionary approach that can help provide limitless pathways in patient management. Within these machine algorithms, deep learning has tremendous potential and can revolutionize computed tomography and cardiovascular imaging. In this review article, we highlight the role of deep learning in various aspects of computed tomography.

Quantitative single photon emission computed tomography derived standardized uptake values on 99mTc-PYP scan in patients with suspected ATTR cardiac amyloidosis

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 and < 1.5). Two cohorts were also created: myocardium SUVmax > 1.88 and ≤ 1.88 at 1-hour based off an AUC curve with 1.88 showing the greatest sensitivity and specificity. Cardiac SUV retention index was calculated as [SUVmax myocardium/SUVmax vertebrae] × SUVmax paraspinal muscle. Primary outcome was myocardium SUVmax at 1-hour correlation with Perugini grades, H/CL ratio, CMR, and echocardiographic data. Higher Perugini Grades corresponded with higher myocardium SUVmax values, especially when comparing Perugini Grade 3 to Grade 2 and 1 (3.03 ± 2.1 vs 0.59 ± 0.97 and 0.09 ± 0.2, P < 0.001). Additionally, patients with H/CL ≥ 1.5 had significantly higher myocardium SUVmax compared to patients with H/CL ≤ 1.5 (2.92 ± 2.18 vs 0.35 ± 0.60, P < 0.01). Myocardium SUVmax at 1-hour strongly correlated with ECV (r = 0.91, P = 0.001), pre-contrast T1 map values (r = 0.66, P = 0.037), and left ventricle mass index (r = 0.80, P = 0.002) on CMR. SUVs derived from 99mTc-PYP scintigraphy with xSPECT/CT provides a discriminatory and quantitative method to diagnose and assess ATTR-CA burden. These findings strongly correlate with CMR.

Acute effect of edge-to-edge repair of mitral regurgitation on left heart mechanics and health status

AIMS

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.

METHODS

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.

RESULTS

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).

CONCLUSION

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.

Community-Acquired Pneumonia Progressing to Necrotizing Pneumonia Due to Congenital Anatomical Abnormalities in the Lung

Pneumonia is generally a treatable disease but there are instances when physicians are faced with rare circumstances such as congenital structural abnormalities. Structural abnormalities in the lungs may predispose to pneumonia and other complications. We present a patient with pneumonia, which progressed to necrotizing pneumonia. A diagnostic bronchoscopy was performed and identified multiple accessory lobes in the right lung. Multiple accessory lobes are not easily identifiable by diagnostic imaging such as X-rays or computed tomography scans. As a result, treating pneumonia in patients with such structural anomalies can further complicate management. Currently, there is limited information that correlates pneumonia and accessory lobes with necrotizing pneumonia.

The Role of Cardiac Imaging in the Evaluation of Cardiac Involvement in Systemic Diseases

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.

1441. Significant Decrease in the Incidence Rate of Common Outpatient Upper Respiratory Tract Infection Diagnoses per Clinic Visit in the First Respiratory Season of October 2020 to March 2021 During the Covid-19 Pandemic. A Report From an Outpatient Antimicrobial Stewardship Program at a community hospital in Brooklyn

Background

As part of our outpatient Antimicrobial Stewardship Program, we do surveillance of diagnoses and antibiotic use for common upper respiratory tract infections such as acute upper respiratory tract infection, acute bronchitis, sinusitis, and pharyngitis. We sought to evaluate the impact of the Covid-19 pandemic on the incidence rate of upper respiratory tract infection diagnoses per clinic visit during October 2020 to March 2021 season compared to the three prior respiratory seasons. We also sought to reflect of impact of increase in televisits and overlapping symptoms of COVID 19 and upper respiratory tract infections.

Methods

Our cohort study extending from October 2017 to March 2021. We collected number of diagnoses of upper respiratory infections and number of unique clinic visits during four consecutive respiratory seasons at our primary care sites via electronic health records.

Results

During the recent October 2020 to March 2021 respiratory season which coincided with the second NYC Covid-19 wave, we had 11569 unique clinic visits and 39 diagnoses of an upper respiratory tract infection - incident rate of 1.29. In the three prior respiratory seasons combined, we had 40939 unique clinic visits and 833 diagnoses of an upper respiratory tract infection – incident rate of 1.49. The incident rates showed a dramatic decline using the test based method and the chi square-statistic p&lt; 0.0001 with an incident rate ratio using a poisson exact method of 6.0359. Statistical comparisons of the current season to each prior individual season yielded similar results. The percentage of Tele-visits during the current season was 19% compared to 0% in the 3 prior seasons.

Conclusion

During the first respiratory season from October 2020 to March 2021 in midst of the Covid-19 pandemic which also coincided with the second Covid-19 wave in New York, we saw a statistically significant decrease in incidence of common upper respiratory tract infection diagnoses per clinic visit compared to the three prior respiratory seasons. Overlapping signs and symptoms of upper respiratory tract infections and Covid-19 with the added percentage in Tele-visits did not cause an increase in incidence rates of upper respiratory tract infection diagnoses. Covid-19 related mitigation efforts may have played a role.

Disclosures

All Authors: No reported disclosures

312. Clinical Attributes and Risk Factors for In- Hospital Mortality among Covid-19 Patients in a Community Hospital Setting: A Propensity Matched Analysis

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 &lt; 0.001) and headache (0.031). In addition, expired patients had elevated CRP- hs (mg/dl) ≥ 123 (&lt; .0001), SGOT or AST (IU/L) ≥ 54 (p &lt; 0.001), SGPT or ALT (IU/L) ≥ 41 (p &lt; 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 &lt; 0.001), creatinine ≥ 1.135 (OR = 1.79, P = 0.007), LDH(U/L) &gt; 465 (OR = 2.18, P = 0.001), systolic blood pressure &lt; 90 mm Hg (OR = 4.28, p = .02), respiratory rate &gt; 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

Cardiovascular Imaging and Intervention Through the Lens of Artificial Intelligence

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.

Artificial intelligence and machine learning in cardiovascular computed tomography

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.

High Prevalence of Pericardial Involvement in College Student Athletes Recovering From COVID-19

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.

Case of Supracristal Ventricular Septal Defect and Aortic Regurgitation Detected by Cardiac Auscultation but Missed by Diagnostic Imaging

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.

Artificial Intelligence and Machine Learning in Cardiovascular Imaging

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 Role of Artificial Intelligence in Cardiovascular Imaging: State of the Art Review

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.

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 ​= ​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.

Genetic gains with rapid-cycle genomic selection for combined drought and waterlogging tolerance in tropical maize (Zea mays L.)

Rapid cycle genomic selection (RC-GS) helps to shorten the breeding cycle and reduce the costs of phenotyping, thereby increasing genetic gains in terms of both cost and time. We implemented RC-GS on two multi-parent yellow synthetic (MYS) populations constituted by intermating ten elite lines involved in each population, including four each of drought and waterlogging tolerant donors and two commercial lines, with proven commercial value. Cycle 1 (C₁ ) was constituted based on phenotypic selection and intermating of the top 5% of 500 S₂ families derived from each MYS population, test-crossed and evaluated across moisture regimes. C₁ was advanced to the next two cycles (C₂ and C₃ ) by intermating the top 5% selected individuals with high genomic estimated breeding values (GEBVs) for grain yield under drought and waterlogging stress. To estimate genetic gains, population bulks from each cycle were test-crossed and evaluated across locations under different moisture regimes. Results indicated that the realised genetic gain under drought stress was 0.110 t ha^-1 yr^-1 and 0.135 t ha^-1 yr^-1 , respectively, for MYS-1 and MYS-2. The gain was less under waterlogging stress, where MYS-1 showed 0.038 t ha^-1 yr^-1 and MYS-2 reached 0.113 t ha^-1 yr^-1 . Genomic selection for drought and waterlogging tolerance resulted in no yield penalty under optimal moisture conditions. The genetic diversity of the two populations did not change significantly after two cycles of GS, suggesting that RC-GS can be an effective breeding strategy to achieve high genetic gains without losing genetic diversity.

Preoperative multidetector computed tomography for isolated surgical aortic valve replacement: Planning for future transcatheter options

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.

Infective Endocarditis After Surgical and Transcatheter Aortic Valve Replacement: A State of the Art Review

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.

The Role of Artificial Intelligence in Echocardiography

PURPOSE OF REVIEW

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.

RECENT FINDINGS

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.

Cardiac mechanics in heart failure with preserved ejection fraction

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.

Transcatheter aortic valve-in-valve-in-valve implantation with three-dimensional printing guidance: A case report

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.

Galactorrhea Following Minimally Invasive Reoperative Mitral Valve Replacement: An Unexpected Complication

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.

Acute Type A Aortic Dissection Repair After Hours: Does It Influence Outcomes?

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.

Artificial Intelligence in Cardiac Imaging

Machine learning (ML), a subset of artificial intelligence, is showing promising results in cardiology, especially in cardiac imaging. ML algorithms are allowing cardiologists to explore new opportunities and make discoveries not seen with conventional approaches. This offers new opportunities to enhance patient care and open new gateways in medical decision-making. This review highlights the role of ML in cardiac imaging for precision phenotyping and prognostication of cardiac disorders.

Cardiac magnetic resonance imaging: the future is bright

Over the last 15 years, cardiovascular magnetic resonance (CMR) imaging has progressively evolved to become an indispensable tool in cardiology. It is a non-invasive technique that enables objective and functional assessment of myocardial tissue. Recent innovations in magnetic resonance imaging scanner technology and parallel imaging techniques have facilitated the generation of T1 and T2 parametric mapping to explore tissue characteristics. The emergence of strain imaging has enabled cardiologists to evaluate cardiac function beyond conventional metrics. Significant progress in computer processing capabilities and cloud infrastructure has supported the growth of artificial intelligence in CMR imaging. In this review article, we describe recent advances in T1/T2 mapping, myocardial strain, and artificial intelligence in CMR imaging.

Application of mobile health, telemedicine and artificial intelligence to echocardiography

The intersection of global broadband technology and miniaturized high-capability computing devices has led to a revolution in the delivery of healthcare and the birth of telemedicine and mobile health (mHealth). Rapid advances in handheld imaging devices with other mHealth devices such as smartphone apps and wearable devices are making great strides in the field of cardiovascular imaging like never before. Although these technologies offer a bright promise in cardiovascular imaging, it is far from straightforward. The massive data influx from telemedicine and mHealth including cardiovascular imaging supersedes the existing capabilities of current healthcare system and statistical software. Artificial intelligence with machine learning is the one and only way to navigate through this complex maze of the data influx through various approaches. Deep learning techniques are further expanding their role by image recognition and automated measurements. Artificial intelligence provides limitless opportunity to rigorously analyze data. As we move forward, the futures of mHealth, telemedicine and artificial intelligence are increasingly becoming intertwined to give rise to precision medicine.

Artificial Intelligence in Cardiovascular Medicine

PURPOSE OF REVIEW

The ripples of artificial intelligence are being felt in various sectors of human life. Machine learning, a subset of artificial intelligence, extracts information from large databases of information and is gaining traction in various fields of cardiology. In this review, we highlight noteworthy examples of machine learning utilization in echocardiography, nuclear cardiology, computed tomography, and magnetic resonance imaging over the past year.

RECENT FINDINGS

In the past year, machine learning (ML) has expanded its boundaries in cardiology with several positive results. Some studies have integrated clinical and imaging information to further augment the accuracy of these ML algorithms. All the studies mentioned in this review have clearly demonstrated superior results of ML in relation to conventional approaches for identifying obstructions or predicting major adverse events in reference to conventional approaches. As the influx of data arriving from gradually evolving technologies in health care and wearable devices continues to be more complex, ML may serve as the bridge to transcend the gap between health care and patients in the future. In order to facilitate a seamless transition between both, a few issues must be resolved for a successful implementation of ML in health care.

Beamforming algorithms for endocardial border detection

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 < .001), apical 3- (6.27 vs 5.54, P < .001), and apical 2-chamber views (6.26 vs 5.72 P < .001) was higher with the software vs standard platform. The average overall score for image quality was significantly better for the software platform vs standard (1.4 vs 0.9, P =< .001). With the software platform, 23% were judged as requiring contrast as compared with 45% for the standard platform (P < .001).

CONCLUSIONS

Delay and standard beamforming in software platform identified more segments with better image quality when compared to the standard high-end platform, decreasing the need for contrast usage.

Minimally Invasive Direct Coronary Artery Bypass for the management of Anomalous Left Coronary Artery from the Right Coronary Sinus

OBJECTIVE

Anomalous left coronary artery from the right coronary sinus (ALCA) is a known cause of sudden death. Surgical correction is recommended for all patients with interarterial course. We describe two patients who underwent surgical correction through an off pump- minimally invasive direct coronary artery bypass (MIDCAB) approach with good short- and mid-term results.

Latent myopathy is more pronounced in patients with low flow versus normal flow aortic stenosis with normal left ventricular ejection fraction who are undergoing surgical aortic valve replacement: Multicenter study with a brief review of the literature

BACKGROUND

Midwall fibrosis and low stroke volume are independent predictors of mortality in severe aortic stenosis (AS) with preserved LV ejection fraction (LVEF). The role of speckle tracking echocardiography (STE) to identify latent myopathy pre- and post- aortic valve replacement (AVR) in high risk AS patients with normal LVEF is limited.

METHODS

Demographic, 2D echocardiographic, and STE data were analyzed in patients with severe AS and preserved LVEF who underwent tissue AVR. Velocity vector imaging (VVI) was used to assess regional and global peak systolic longitudinal strain (GLS). Low flow (LF) was defined as an indexed LV stroke volume <35 mL/m² .

RESULTS

Between December 2008 and May 2011, 37 patients (75 ± 9 years, 51% male) had both pre- and post-AVR echos within 6.6 ± 6.5 months (median = 4 months; range = 2.5-9.5) of surgery. Compared with pre-AVR, GLS (-6.9 ± 4.9% vs -11.1 ± 4.1%; P < .001) and strain rate (-0.72 ± 0.3s^-1 vs -0.87 ± 0.3s^-1 ; P = .01) improved post-AVR. Pre-AVR mid-segments showed a similar myopathy as the basal segments (-9.5 ± 4.3% vs -9.0 ± 4.2%;P = .3). The 16 (43%) LF patients in this study had lower pre- and post-AVR strain compared to NF patients (GLS Pre-AVR:LF vs NF: -5.1 ± 4.1% vs -8.4 ± 4.9% (P = .04) and GLS Post-AVR:LF vs NF: -9.2 ± 3.7% vs -12.5 ± 3.9% (P = .01)). However, there was no difference in absolute and %change improvement in GLS post-AVR (LF vs NF:∆ -4.2 ± 3.5% vs ∆-4.1 ± 5.3% (P = .90) and 193 ± 214% vs 143 ± 230% change (P = .5)). The lowest GLS was seen in LF/HG AS followed by LF/LG, NF/LG and NF/HG AS; P = .03.

CONCLUSIONS

Latent myopathy is more pronounced in LF AS both pre- and post-AVR. Our study provides evidence of improvement in myopathy in LF AS despite a persistent worse myopathy compared to NF patients post-AVR.