High-Throughput Deep Learning Detection of Mitral Regurgitation.

BACKGROUND: Diagnosis of mitral regurgitation (MR) requires careful evaluation by echocardiography with Doppler imaging. This study presents the development and validation of a fully automated deep learning pipeline for identifying apical 4-chamber view videos with color Doppler echocardiography and detecting clinically significant (moderate or severe) MR from transthoracic echocardiograms. METHODS: A total of 58 614 transthoracic echocardiograms (2 587 538 videos) from Cedars-Sinai Medical Center were used to develop and test an automated pipeline to identify apical 4-chamber view videos with color Doppler across the mitral valve and then assess MR severity. The model was tested internally on a test set of 1800 studies (80 833 videos) from Cedars-Sinai Medical Center and externally evaluated in a geographically distinct cohort of 915 studies (46 890 videos) from Stanford Healthcare. RESULTS: In the held-out Cedars-Sinai Medical Center test set, the view classifier demonstrated an area under the curve (AUC) of 0.998 (0.998-0.999) and correctly identified 3452 of 3539 echocardiography videos as having color Doppler information across the mitral valve (sensitivity of 0.975 [0.968-0.982] and specificity of 0.999 [0.999-0.999] compared with manually curated videos). In the external test cohort from Stanford Healthcare, the view classifier correctly identified 1051 of 1055 manually curated videos with color Doppler information across the mitral valve (sensitivity of 0.996 [0.990-1.000] and specificity of 0.999 [0.999-0.999]). In the Cedars-Sinai Medical Center test cohort, MR moderate or greater in severity was detected with an AUC of 0.916 (0.899-0.932) and severe MR was detected with an AUC of 0.934 (0.913-0.953). In the Stanford Healthcare test cohort, the model detected MR moderate or greater in severity with an AUC of 0.951 (0.924-0.973) and severe MR with an AUC of 0.969 (0.946-0.987). CONCLUSIONS: In this study, a novel automated pipeline for identifying clinically significant MR from full transthoracic echocardiography studies demonstrated excellent performance across large numbers of studies and across multiple institutions. Such an approach has the potential for automated screening and surveillance of MR.

Risk of Severe Acute Respiratory Syndrome Coronavirus 2 Infection Following Prior Infection or Vaccination.

BACKGROUND: The extent to which infection versus vaccination has conferred similarly durable severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) immunity during the Omicron era remains unclear. METHODS: In a cohort of 4496 adults under continued serological surveillance throughout the first year of Omicron-predominant SARS-CoV-2 transmission, we examined incidence of new infection among individuals whose last known antigenic exposure was either recent (<90 days) or remote (≥90 days) infection or vaccination. RESULTS: We adjudicated 2053 new-onset infections occurring between 15 December 2021 through 22 December 2022. In multivariable-adjusted analyses, compared to individuals whose last known exposure was remote vaccination, those with recent vaccination (odds ratio [OR], 0.82 [95% confidence interval {CI}, .73-.93]; P = .002) or recent infection (OR, 0.14 [95% CI, .05-.45]; P = .001) had lower risk for new infection within the subsequent 90-day period. Given a significant age interaction (P = .004), we found that remote infection compared to remote vaccination was associated with significantly greater new infection risk in persons aged ≥60 years (OR, 1.88 [95% CI, 1.13-3.14]; P = .015) with no difference seen in those <60 years (1.03 [95% CI, .69-1.53]; P = .88). CONCLUSIONS: During the initial year of Omicron, prior infection and vaccination both offered protection against new infection. However, remote prior infection was less protective than remote vaccination for individuals aged ≥60 years. In older adults, immunity gained from vaccination appeared more durable than immunity gained from infection.

Machine Learning and Bias in Medical Imaging: Opportunities and Challenges.

Bias in health care has been well documented and results in disparate and worsened outcomes for at-risk groups. Medical imaging plays a critical role in facilitating patient diagnoses but involves multiple sources of bias including factors related to access to imaging modalities, acquisition of images, and assessment (ie, interpretation) of imaging data. Machine learning (ML) applied to diagnostic imaging has demonstrated the potential to improve the quality of imaging-based diagnosis and the precision of measuring imaging-based traits. Algorithms can leverage subtle information not visible to the human eye to detect underdiagnosed conditions or derive new disease phenotypes by linking imaging features with clinical outcomes, all while mitigating cognitive bias in interpretation. Importantly, however, the application of ML to diagnostic imaging has the potential to either reduce or propagate bias. Understanding the potential gain as well as the potential risks requires an understanding of how and what ML models learn. Common risks of propagating bias can arise from unbalanced training, suboptimal architecture design or selection, and uneven application of models. Notwithstanding these risks, ML may yet be applied to improve gain from imaging across all 3A's (access, acquisition, and assessment) for all patients. In this review, we present a framework for understanding the balance of opportunities and challenges for minimizing bias in medical imaging, how ML may improve current approaches to imaging, and what specific design considerations should be made as part of efforts to maximize the quality of health care for all.

Point of Care Ultrasound for Diagnosis and Management in Heart Failure: A Targeted Literature Review.

BACKGROUND: Cardiac point of care ultrasound (POCUS) has shown increasing utility as a tool for diagnosing and managing heart failure (HF). Within cardiology, intravascular volume assessment leveraging visualization of the inferior vena cava (IVC) is a central aspect of care, as IVC size correlates with central venous pressure. This targeted literature review aimed to examine the existing literature assessing the use of POCUS in diagnosis and management of HF patients utilizing POCUS-based IVC measurement either alone or in combination with secondary methods. METHODS: A targeted PubMed and Ovid database search up until August 28, 2023 using a keyword search was completed. Studies that did not include IVC assessment with POCUS in HF were excluded. RESULTS: The initial search using both PubMed and Ovid resulted in 370 journal publications. After exclusion criteria were used 15 studies were included in the review. Studies were grouped into three categories: 1) how well POCUS was able to identify HF, 2) whether POCUS-based findings correlated with other measures evaluating HF and was able to predict the effect of diuretic administration, and 3) whether POCUS-based findings served as a good prognostic indicator. The 5 studies that evaluated HF identification with POCUS found that both diagnostic sensitivity and specificity may reach 90%-100% when IVC measurement was coupled with a lung ultrasound assessing the presence of B-lines or pleural effusion. Five studies assessing POCUS findings correlating with other HF measures and diuretic effect found that IVC diameter changed significantly with diuretic administration (p<0.05). All 6 studies assessing POCUS as a predictor of long-term mortality or hospital readmission found measures that achieved statistical significance with p<0.05. CONCLUSIONS: Including POCUS as standard-of-care - both as a diagnostic tool in the emergency department and a management tool in in-patient and out-patient facilities - may improve the treatment of HF.

How Should We Approach Faculty Who Create Hostile Learning Environments for Underrepresented Students and Trainees?

Faculty members who demonstrate resistance to or lack of skill in addressing negative bias in practice and learning environments can erode safety, especially among underrepresented students. This commentary on a case suggests how educators and leaders should respond to problematic behaviors of unwilling or unskilled faculty, prevent mistreatment of students and colleagues, and facilitate continuous faculty development. This commentary also considers strategies for motivating equity and building health care cultures of accountability.

Workflow and Framework for Collecting and Implementing Point-of-Care Ultrasound Data in the Management of Heart Failure Patients.

Management and prevention of acute decompensated heart failure remain highly prevalent and challenging medical conditions. Incorporation of Point-of-Care Ultrasound (POCUS) as an adjunctive tool for assessing volume status and treatment response has shown significant promise. POCUS can be used for imaging internal anatomic structures serially and capturing these images for comparison and measurement over time. This protocol describes a scalable and standardized methodology for the serial assessment of the inferior vena cava (IVC). The methodology includes serial image collection, measurement, and presentation in the electronic medical record. A workflow for POCUS-acquired images of the IVC was created to capture the images and measure the diameter in a discrete data field for direct comparison over time and in response to clinical management. The protocol also includes the assessment of the presence or absence of pleural effusion as discrete data in the standardized workflow. By integrating POCUS into heart failure management, clinicians can improve patient outcomes through more precise and timely adjustments in treatment.

Evaluation and Assessment of the ABATE Framework to Enhance Implicit Bias Training for Virtual Interviews in Medical Schools.

Introduction: The influence of implicit biases in virtual interviews must be addressed to ensure equity within the admissions process. ABATE is a mnemonic framework of five specific categories of implicit bias (affinity-based, backdrop-based, appearance-based, technology and media-based, and enunciation-based biases) that should be anticipated and mitigated for faculty, staff, health professionals, and medical students who conduct virtual interviews at medical schools. Methods: A 60-minute workshop was developed to educate medical school admissions interviewers about the ABATE model and strategies to mitigate implicit bias during virtual interviews. Four workshops were held over 1 year totaling 217 individual attendees. The workshops were evaluated using a single-group, pre-post questionnaire designed with the Kirkpatrick evaluation model. Results: Attendees reported that they found the ABATE workshop useful and relevant to improving their ability to minimize implicit bias during virtual interviews. Significant improvements were found in attendee reactions to the utility of implicit bias training (M pre = 2.6, M post = 3.1, p = .002). Significant changes were also reported in attendees' attitudes about interviewing confidence (M pre = 3.0, M post = 3.2, p = .04), bias awareness (M pre = 3.0, M post = 3.4, p = .002), and identifying and applying bias mitigation solutions (M pre = 2.5, M post = 3.0, p = .003). Knowledge specific to backdrop-based biases also significantly increased (M pre = 3.2, M post = 3.4, p = .04). Discussion: The ABATE workshop demonstrates promise in mitigating implicit bias in virtual medical school interviews.

Early-Onset Hypertension and Sex-Specific Residual Risk for Cardiovascular Disease in Type 2 Diabetes.

OBJECTIVE: To investigate whether the sex disparities in type 2 diabetes-associated cardiovascular disease (CVD) risks may be related to early-onset hypertension that could benefit from intensive blood pressure (BP) control. RESEARCH DESIGN AND METHODS: We analyzed intensive versus standard BP control in relation to incident CVD events in women and men with type 2 diabetes, based on their age of hypertension diagnosis. RESULTS: Among 3,792 adults with type 2 diabetes (49% women), multivariable-adjusted CVD risk was increased per decade earlier age at hypertension diagnosis (hazard ratio 1.11 [1.03-1.21], P = 0.006). Excess risk associated with early-diagnosed hypertension was attenuated in the presence of intensive versus standard antihypertensive therapy in women (P = 0.036) but not men (P = 0.76). CONCLUSIONS: Women with type 2 diabetes and early-onset hypertension may represent a higher-risk subpopulation that not only contributes to the excess in diabetes-related CVD risk for women but may benefit from intensive BP control.

Postural orthostatic tachycardia syndrome after COVID-19 vaccination.

BACKGROUND: There is an association between coronavirus disease 2019 (COVID-19) mRNA vaccination and the incidence or exacerbation of postural orthostatic tachycardia syndrome (POTS). OBJECTIVE: The purpose of this study was to characterize patients reporting new or exacerbated POTS after receiving the mRNA COVID-19 vaccine. METHODS: We prospectively collected data from sequential patients in a POTS clinic between July 2021 and June 2022 reporting new or exacerbated POTS symptoms after COVID-19 vaccination. Heart rate variability (HRV) and skin sympathetic nerve activity (SKNA) were compared against those of 24 healthy controls. RESULTS: Ten patients (6 women and 4 men; age 41.5 ± 7.9 years) met inclusion criteria. Four patients had standing norepinephrine levels > 600 pg/mL. All patients had conditions that could raise POTS risk, including previous COVID-19 infection (N = 4), hypermobile Ehlers-Danlos syndrome (N = 6), mast cell activation syndrome (N = 6), and autoimmune (N = 7), cardiac (N = 7), neurological (N = 6), or gastrointestinal conditions (N = 4). HRV analysis indicated a lower ambulatory root mean square of successive differences (46.19 ±24 ms; P = .042) vs control (72.49 ± 40.8 ms). SKNA showed a reduced mean amplitude (0.97 ± 0.052 µV; P = .011) vs control (1.2 ± 0.31 µV) and burst amplitude (1.67 ± 0.16 µV; P = .018) vs control (4. 3 ± 4.3 µV). After 417.2 ± 131.4 days of follow-up, all patients reported improvement with the usual POTS care, although 2 with COVID-19 reinfection and 1 with small fiber neuropathy did have relapses of POTS symptoms. CONCLUSION: All patients with postvaccination POTS had pre-existing conditions. There was no evidence of myocardial injuries or echocardiographic abnormalities. The decreased HRV suggests a sympathetic dominant state. Although all patients improved with guideline-directed care, there is a risk of relapse.

Sex Differences in Association of Physical Activity With All-Cause and Cardiovascular Mortality.

BACKGROUND: Although physical activity is widely recommended for reducing cardiovascular and all-cause mortality risks, female individuals consistently lag behind male individuals in exercise engagement. OBJECTIVES: The goal of this study was to evaluate whether physical activity derived health benefits may differ by sex. METHODS: In a prospective study of 412,413 U.S. adults (55% female, age 44 ± 17 years) who provided survey data on leisure-time physical activity, we examined sex-specific multivariable-adjusted associations of physical activity measures (frequency, duration, intensity, type) with all-cause and cardiovascular mortality from 1997 through 2019. RESULTS: During 4,911,178 person-years of follow-up, there were 39,935 all-cause deaths including 11,670 cardiovascular deaths. Regular leisure-time physical activity compared with inactivity was associated with 24% (HR: 0.76; 95% CI: 0.73-0.80) and 15% (HR: 0.85; 95% CI: 0.82-0.89) lower risk of all-cause mortality in women and men, respectively (Wald F = 12.0, sex interaction P < 0.001). Men reached their maximal survival benefit of HR 0.81 from 300 min/wk of moderate-to-vigorous physical activity, whereas women achieved similar benefit at 140 min/wk and then continued to reach a maximum survival benefit of HR 0.76 also at ∼300 min/wk. Sex-specific findings were similar for cardiovascular death (Wald F = 20.1, sex interaction P < 0.001) and consistent across all measures of aerobic activity as well as muscle strengthening activity (Wald F = 6.7, sex interaction P = 0.009). CONCLUSIONS: Women compared with men derived greater gains in all-cause and cardiovascular mortality risk reduction from equivalent doses of leisure-time physical activity. These findings could enhance efforts to close the "gender gap" by motivating especially women to engage in any regular leisure-time physical activity.

Core Competencies of an Anti-racist Physician: Elective Course for Undergraduate Medical Students.

Introduction: Medical schools seeking to correct and reform curricula towards anti-racist perspectives need to address anti-Black forms of racism specifically and teach students critical upstander skills to interrupt manifestations of racism. We developed a course to teach preclinical medical students basic anti-racism competencies including recognition and awareness of anti-Black racism in medicine and upstander skills to advocate for patients and colleagues. Methods: In 2021 and 2022, we designed, implemented, and evaluated an elective course for second-year medical students (N = 149) to introduce competencies of anti-racism focusing on upstander skills for addressing anti-Blackness. We designed three patient cases and one student-centered case to illustrate manifestations of anti-Black racism in medicine and used these cases to stimulate small-group discussions and guide students toward recognizing and understanding ways of responding to racism. We designed pre- and postassessments to evaluate the effectiveness of the course and utilized anonymous feedback surveys. Results: Participants showed significant improvement in pre- to postassessment scores in both years of the course. The anonymous feedback survey showed that 97% of students rated the course at least somewhat effective, and the qualitative responses revealed five core themes: course timing, case complexity, learner differentiation, direct instruction, and access to resources. Discussion: This course reinforces upstander competencies necessary for advancing anti-racism in medicine. It addresses a gap in medical education by reckoning with the entrenched nature of anti-Black racism in the culture of medicine and seeks to empower undergraduate medical students to advocate for Black-identifying patients and colleagues.

Multi-Organ System Metabolic Stress and Sex-Divergent Vascular Associations.

Introduction: Women experience excess cardiovascular risk compared to men in the setting of similar metabolic disease burden. This consistent finding could be related to sex differences in the vascular response to various forms of metabolic stress. In this study we examine the association of both systemic and organ-specific metabolic stress with vascular health in women and men. Methods: We conducted an observational study of 4,299 adult participants (52% women, aged 59±13 years) of the National Health and Nutrition Examination Survey (NHANES) 2017-2018 cohort and 110,225 adult outpatients (55% women, aged 64±16 years) of the Cedars-Sinai Medical Center (CSMC) 2019 cohort. We used natural splines to examine the association of systemic and organ-specific measures of metabolic stress including body mass index (BMI), hemoglobin A1c (HbA1c), hepatic FIB-4 score, and CKD-EPI estimated glomerular filtration rate (eGFR) on systolic blood pressure (SBP). Piecewise linear models were generated using normal value thresholds (BMI <25 kg/m 2 , HbA1c <5.7%, FIB-4 <1.3, and eGFR ≥90 ml/min), which approximated observed spline breakpoints. The primary outcome was increase in SBP (relative to a sex-specific physiologic baseline SBP) in association with increase in level of each metabolic measure. Results: Women compared to men demonstrated larger magnitudes and an earlier onset of increase in SBP per increment increase across all metabolic stress measures. The slope of SBP increase per increment of each metabolic measure was greater for women than men particularly for metabolic measures within the normal range, with slope differences of 1.71 mmHg per kg/m2 of BMI, 9.61 mmHg per %HbA1c, 6.45 mmHg per FIB-4 unit, and 0.37 mmHg per ml/min decrement of eGFR in the NHANES cohort (P difference <0.05 for all). Overall results were consistent in the CSMC cohort. Conclusions: Women exhibited greater vascular sensitivity in the setting of multiple types of metabolic stress, particularly in periods representing the transition from metabolic health to disease. These findings underscore the importance of involving early metabolic health interventions as part of efforts to mitigate vascular risks in both women and men.

Using Deep learning to Predict Cardiovascular Magnetic Resonance Findings from Echocardiography Videos.

Background: Echocardiography is the most common modality for assessing cardiac structure and function. While cardiac magnetic resonance (CMR) imaging is less accessible, CMR can provide unique tissue characterization including late gadolinium enhancement (LGE), T1 and T2 mapping, and extracellular volume (ECV) which are associated with tissue fibrosis, infiltration, and inflammation. While deep learning has been shown to uncover findings not recognized by clinicians, it is unknown whether CMR-based tissue characteristics can be derived from echocardiography videos using deep learning. We hypothesized that deep learning applied to echocardiography could predict CMR-based measurements. Methods: In a retrospective single-center study, adult patients with CMRs and echocardiography studies within 30 days were included. A video-based convolutional neural network was trained on echocardiography videos to predict CMR-derived labels including wall motion abnormality (WMA) presence, LGE presence, and abnormal T1, T2 or ECV across echocardiography views. The model performance was evaluated in a held-out test dataset not used for training. Results: The study population included 1,453 adult patients (mean age 56±18 years, 42% female) with 2,556 paired echocardiography studies occurring on average 2 days after CMR (interquartile range 2 days prior to 6 days after). The model had high predictive capability for presence of WMA (AUC 0.873 [95%CI 0.816-0.922]), however, the model was unable to reliably detect the presence of LGE (AUC 0.699 [0.613-0.780]), native T1 (AUC 0.614 [0.500-0.715]), T2 0.553 [0.420-0.692], or ECV 0.564 [0.455-0.691]). Conclusions: Deep learning applied to echocardiography accurately identified CMR-based WMA, but was unable to predict tissue characteristics, suggesting that signal for these tissue characteristics may not be present within ultrasound videos, and that the use of CMR for tissue characterization remains essential within cardiology. Clinical Perspective: Tissue characterization of the heart muscle is useful for clinical diagnosis and prognosis by identifying myocardial fibrosis, inflammation, and infiltration, and can be measured using cardiac MRI. While echocardiography is highly accessible and provides excellent functional information, its ability to provide tissue characterization information is limited at this time. Our study using a deep learning approach to predict cardiac MRI-based tissue characteristics from echocardiography showed limited ability to do so, suggesting that alternative approaches, including non-deep learning methods should be considered in future research.

Opportunistic Screening of Chronic Liver Disease with Deep Learning Enhanced Echocardiography.

Importance: Chronic liver disease affects more than 1.5 billion adults worldwide, however the majority of cases are asymptomatic and undiagnosed. Echocardiography is broadly performed and visualizes the liver; but this information is not leveraged. Objective: To develop and evaluate a deep learning algorithm on echocardiography videos to enable opportunistic screening for chronic liver disease. Design: Retrospective observational cohorts. Setting: Two large urban academic medical centers. Participants: Adult patients who received echocardiography and abdominal imaging (either abdominal ultrasound or abdominal magnetic resonance imaging) with ≤30 days between tests, between July 4, 2012, to June 4, 2022. Exposure: Deep learning model predictions from a deep-learning computer vision pipeline that identifies subcostal view echocardiogram videos and detects the presence of cirrhosis or steatotic liver disease (SLD). Main Outcome and Measures: Clinical diagnosis by paired abdominal ultrasound or magnetic resonance imaging (MRI). Results: A total of 1,596,640 echocardiogram videos (66,922 studies from 24,276 patients) from Cedars-Sinai Medical Center (CSMC) were used to develop EchoNet-Liver, an automated pipeline that identifies high quality subcostal images from echocardiogram studies and detects the presence of cirrhosis or SLD. In the held-out CSMC test cohort, EchoNet-Liver was able to detect the presence of cirrhosis with an AUC of 0.837 (0.789 - 0.880) and SLD with an AUC of 0.799 (0.758 - 0.837). In a separate test cohort with paired abdominal MRIs, cirrhosis was detected with an AUC of 0.704 (0.689-0.718) and SLD was detected with an AUC of 0.726 (0.659-0.790). In an external test cohort of 106 patients (n = 5,280 videos), the model detected cirrhosis with an AUC of 0.830 (0.738 - 0.909) and SLD with an AUC of 0.768 (0.652 - 0.875). Conclusions and Relevance: Deep learning assessment of clinical echocardiography enables opportunistic screening of SLD and cirrhosis. Application of this algorithm may identify patients who may benefit from further diagnostic testing and treatment for chronic liver disease.

Impact of Measurement Noise on Genetic Association Studies of Cardiac Function.

Recent research has effectively used quantitative traits from imaging to boost the capabilities of genome-wide association studies (GWAS), providing further understanding of disease biology and various traits. However, it's important to note that phenotyping inherently carries measurement error and noise that could influence subsequent genetic analyses. The study focused on left ventricular ejection fraction (LVEF), a vital yet potentially inaccurate quantitative measurement, to investigate how imprecision in phenotype measurement affects genetic studies. Several methods of acquiring LVEF, along with simulating measurement noise, were assessed for their effects on ensuing genetic analyses. The results showed that by introducing just 7.9% of measurement noise, all genetic associations in an LVEF GWAS with almost forty thousand individuals could be eliminated. Moreover, a 1% increase in mean absolute error (MAE) in LVEF had an effect equivalent to a 10% reduction in the sample size of the cohort on the power of GWAS. Therefore, enhancing the accuracy of phenotyping is crucial to maximize the effectiveness of genome-wide association studies.

Deep Learning Phenotyping of Tricuspid Regurgitation for Automated High Throughput Assessment of Transthoracic Echocardiography.

Background and Aims: Diagnosis of tricuspid regurgitation (TR) requires careful expert evaluation. This study developed an automated deep learning pipeline for assessing TR from transthoracic echocardiography. Methods: An automated deep learning workflow was developed using 47,312 studies (2,079,898 videos) from Cedars-Sinai Medical Center (CSMC) between 2011 and 2021. The pipeline was tested on a temporally distinct test set of 2,462 studies (108,138 videos) obtained in 2022 at CSMC and a geographically distinct cohort of 5,549 studies (278,377 videos) from Stanford Healthcare (SHC). Results: In the CSMC test dataset, the view classifier demonstrated an AUC of 1.000 (0.999 - 1.000) and identified at least one A4C video with colour Doppler across the tricuspid valve in 2,410 of 2,462 studies with a sensitivity of 0.975 (0.968-0.982) and a specificity of 1.000 (1.00-1.000). In the CSMC test cohort, moderate-or-severe TR was detected with an AUC of 0.928 (0.913 - 0.943) and severe TR was detected with an AUC of 0.956 (0.940 - 0.969). In the SHC cohort, the view classifier correctly identified at least one TR colour Doppler video in 5,268 of the 5,549 studies, resulting in an AUC of 0.999 (0.998 - 0.999), a sensitivity of 0.949 (0.944 - 0.955) and specificity of 0.999 (0.999 - 0.999). The AI model detected moderate-or-severe TR with an AUC of 0.951 (0.938 - 0.962) and severe TR with an AUC of 0.980 (0.966 - 0.988). Conclusions: We developed an automated pipeline to identify clinically significant TR with excellent performance. This approach carries potential for automated TR detection and stratification for surveillance and screening. Key Question: Can an automated deep learning model assess tricuspid regurgitation severity from echocardiography? Key Finding: We developed and validated an automated tricuspid regurgitation detection algorithm pipeline across two healthcare systems with high volume echocardiography labs. The algorithm correctly identifies apical-4-chamber view videos with colour Doppler across the tricuspid valve and grades clinically significant TR with strong agreement to expert clinical readers. Take Home message: A deep learning pipeline could automate TR screening, facilitating reproducible accurate assessment of TR severity, allowing rapid triage or re-review and expand access in low-resource or primary care settings.

Exploring the underlying molecular mechanisms of acute myocardial infarction after SARS-CoV-2 infection.

An increase in acute myocardial infarction (AMI)-related deaths has been reported during the COVID-19 pandemic. Despite evidence suggesting the association between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and AMI, the underlying mechanisms remain unclear. Here, we integrated mRNA and microRNA expression profiles related to SARS-CoV-2 infection and AMI from public databases. We then performed transcriptomic analysis using bioinformatics and systems biology approaches to explore the potential molecular mechanisms of SARS-CoV-2 infection affects AMI. First, twenty-one common differentially expressed genes (DEGs) were identified from SARS-CoV-2 infection and AMI patients in endothelial cells datasets and then we performed functional analysis to predict the roles of these DEGs. The functional analysis emphasized that the endothelial cell response to cytokine stimulus due to excessive inflammation was essential in these two diseases. Importantly, the tumor necrosis factor and interleukin-17 signaling pathways appeared to be integral factors in this mechanism. Interestingly, most of these common genes were also upregulated in transcriptomic datasets of SARS-CoV-2-infected cardiomyocytes, suggesting that these genes may be shared in cardiac- and vascular-related injuries. We subsequently built a protein-protein interaction network and extracted hub genes and essential modules from this network. At the transcriptional and post-transcriptional levels, regulatory networks with common DEGs were also constructed, and some key regulator signatures were further identified and validated. In summary, our research revealed that a highly activated inflammatory response in patients with COVID-19 might be a crucial factor for susceptibility to AMI and we identified some candidate genes and regulators that could be used as biomarkers or potential therapeutic targets.