Risultati per: Lo screening oncologico: come aumentare l’adesione

Circulation, Volume 150, Issue Suppl_1, Page A4131439-A4131439, November 12, 2024. Background:Evidence linking social isolation to cardiovascular disease morbidity and mortality has grown in recent years. Still, information on how this may manifest in real world settings and its implications for screening practices is limited. In 2019, our large national integrated health care system implemented screening for social isolation as part of a broader universal social risk assessment. This repository of screening data was joined to administrative claims to test these associations in real world data and explore differences by demographic and medical factors.Methods:Social isolation responses recorded from 2019-2022 were included for a cohort of adult health plan members with documented atherosclerotic cardiovascular disease (ASCVD). We selected a single random assessment for each member and retained any other responses for sensitivity analyses. Cohort members had at least 10 months of enrollment surrounding assessment date for use as the baseline period and were followed for 365 days. We used cox proportional hazards regression with right censoring for coverage gaps to estimate the risk of all-cause mortality conferred by social isolation. We used Poisson regression to model the rate of inpatient stays.Results:There were 881 deaths among 7,484 members (18% of those with social isolation; 11% of those without). The isolated group skewed less male (54% vs. 65%, p

Circulation, Volume 150, Issue Suppl_1, Page A4144730-A4144730, November 12, 2024. Background:A personalized, non-invasive assessment approach for evaluating the risk of obstructive coronary artery disease (CAD) is crucial for patients with an intermediate or low clinical likelihood of CAD before undergoing invasive coronary angiography (ICA). This method allows clinicians to effectively rule out the presence of obstructive CAD without the need for ICA or to determine if a referral for ICA is warranted. Emerging lipidomics biomarkers may be valuable in this process. However, technological challenges in detecting structurally similar lipids and the requirement for advanced computational tools have so far impeded the clinical application of lipidomics research.Hypothesis:Our study aims to develop an innovative non-invasive diagnostic test utilizing novel lipidomics biomarkers, potentially revolutionizing current risk classification schemes for CAD.Methods:In this post-hoc analysis of the CorLipid trial (NCT04580173), we employed extreme gradient boosting (XGBoost) machine learning to assess the predictive power of a lipidomics panel for obstructive CAD risk. Liquid chromatography-mass spectrometry analyzed lipid profiles from 146 individuals undergoing ICA. SYNTAX Score (SS) was used to define obstructive CAD as SS >0 versus non-obstructive CAD (SS=0).Results:Of the 146 participants (25% female, mean age: 61 ±11 years old), 55% had obstructive CAD (SS >0). Lipidome changes [phosphatidylinositols, (lyso-)phosphatidylethanolamine, (lyso-)phosphatidylcholine, triglycerides, diglycerides, and sphingomyelins] were investigated to identify lipids potentially associated with the phenotype and complexity of CAD. Using this information, 290 quantified serum lipid species were utilized to develop an XGBoost algorithm with 17 serum biomarkers ( consisting of sphingolipids, glycerophospholipids, triacylglycerols, galectin-3, glucose, low-density lipoprotein, and lactate dehydrogenase) with very good discriminative ability [ROC AUC: 0.875 (95%CI: 0.867-0.883)], excellent sensitivity (100%) but moderate specificity (62.1%) for the prediction of obstructive CAD.Conclusions:These findings indicate that a deep-learning-based non-invasive diagnostic test, using lipidomics serum biomarkers, could reliably rule-out obstructive CAD without necessitating ICA. To enhance generalizability, these results should be validated in larger and similar cohorts. Further research, particularly leveraging machine learning, is promising for refining risk stratification.

Circulation, Volume 150, Issue Suppl_1, Page A4139026-A4139026, November 12, 2024. Background:Familial hypercholesteremia (FH) leads to elevated low-density lipoprotein cholesterol (LDL-C) and atherosclerotic cardiovascular disease (ASCVD). Although treatable, FH is underdiagnosed. Lipid lowering therapy may mask diagnostic pretreatment LDL-C levels. Participants of ASCVD trials may be enriched for FH, so ASCVD trial enrollment may be a unique contact point to opportunistically diagnose FH.Hypothesis:The population of the ACCELERATE trial of evacetrapib and ASCVD outcomes is enriched for FH.Methods:ACCELERATE is a phase 3 cardiovascular outcomes trial which randomized 12,092 patients with high-risk vascular disease to receive evacetrapib or placebo. FH was not reported. Using participant-level data, we estimated pretreatment LDL-c using validated corrections based on type and dose of statin therapy. We defined severe hypercholesterolemia as pretreatment LDL-C ≥ 190 mg/dl and FH as severe hypercholesterolemia with total cholesterol > 290 mg/dL in a first or second degree relative, consistent with Simon Broome register criteria. We compared trial prevalence to general prevalence (severe hypercholesterolemia ~7%, FH ~0.4%). We evaluated the adjusted association of severe hypercholesterolemia with the primary trial endpoint of ASCVD events using multivariable Cox proportional hazards regression.Results:Data were available for 11,993 participants (99%). The prevalence of severe hypercholesteremia was 15% (1809/11993). The prevalence of FH was 2.1% (255/11993). Pretreatment LDL-C ≥ 190 mg/dL, as compared with pretreatment LDL-C < 190 mg/dL, was significantly associated with a higher incidence of the primary ASCVD trial endpoint (15% vs 13.5% respectively, adjusted hazard ratio 1.19; 95% CI 1.03-1.38, P=0.021;Figure).Conclusion:In a participant-level analysis of a rigorous, independently adjudicated ASCVD outcomes trial, severe hypercholesterolemia and FH were more prevalent in the trial population than the general population based on pretreatment LDL-C calculation. Severe hypercholesterolemia was significantly associated with higher ASCVD incidence. ASCVD trial enrollment may be a novel high-yield contact point for index FH case identification using simple pretreatment LDL-C calculation.

Circulation, Volume 150, Issue Suppl_1, Page A4121454-A4121454, November 12, 2024. Introduction:Recent research has shown that AI is able to assess biological aging and cardiovascular disease (CVD) risk using chest radiographs. However, the lack of explainability of such deep learning algorithms hinders clinical utility and adoption. This motivates the current study which searches for and tests the use of machine extractable quantitative features in chest radiographs to predict CVD risk in population screening.Method:Chest radiograph measurements characterizing cardiomediastinal geometry, aortic calcification and tortuosity were handpicked for development of a segmentation-based feature extraction algorithm. The algorithm was applied on the PLCO lung screening dataset for analysis. The association between measurement-based imaging features, clinical characteristics (age, sex, BMI, smoking status, hypertension, diabetes, liver disease) with CVD mortality and 10-year major adverse cardiovascular events (MACE) were analysed by using proportional hazard regression, with feature selection done by LASSO.Result:Of 29,453 eligible subjects, 5693 subjects from a single study centre were used for fitting of all models. The median follow-up time was 19 years. A total of 32 imaging features were extracted and analysed. For both 10-year MACE and CVD mortality, model using imaging features, age, and sex performed similarly to model using conventional risk factors, and a deep learning chest radiograph CVD risk model. Two imaging features, mediastinal width at valve-level [HR 1.36 (1.23-1.50)] and maximal lateral displacement of descending aorta [HR 1.29 (1.18-1.42)] were found to be prognostic. To the best of our knowledge, these features have not been reported previously.Conclusion:Quantitative imaging features can predict CVD risk in chest radiograph similar to deep learning models while providing feature interpretability and explainability. Two novel imaging features prognostic of CVD risk were found and shown to be complementary to conventional risk factors.

Circulation, Volume 150, Issue Suppl_1, Page A4140219-A4140219, November 12, 2024. Background:Poor healthcare access results in late- or non-diagnosis of rheumatic heart disease (RHD), perpetuating the burden of RHD in low-resource settings. The ADUNU program, a partnership with the Ugandan Ministry of Health in Kitgum, Uganda, aims to improve RHD case detection through decentralized screening led by primary care nurses, who independently perform and interpret brief screening echocardiograms using handheld echocardiography.Hypothesis:We hypothesized ADUNU’s simplified screening protocol would achieve sensitivity and specificity greater than 80% on confirmatory evaluation.Aim:To determine the health system impact of deploying a novel RHD screening protocol into the public health system in Uganda through a cross-sectional study of diagnostic accuracy.Methods:Primary healthcare nurses, certified to perform echocardiographic screening using a single parasternal long axis view in 2D and color Doppler, integrated screening into their clinical and outreach workflows. Community members with positive screens (mitral regurgitation jet ≥2cm or aortic regurgitation jet ≥1cm) were referred for confirmatory echocardiograms at the District Hospital. A random subset with negative screens were recruited for confirmatory echocardiograms at the time of screening as well. Sensitivity, specificity, predictive values, likelihood ratios, accuracy, and agreement (Cohen’s kappa) were calculated between the screening protocol and the confirmatory results.Results:Between May 2023 and April 2024, 3020 community screenings (ages 5-70 years) were conducted by 19 certified nurses. Among 113 positive screens, 61 (53.9%) were confirmed to have RHD. Among 430 negative screens, 14 (3.3%) had RHD. Screening sensitivity was 82.4% (95% CI 72.2-89.4%) and specificity 89.1% (85.9-91.6%). Positive and negative predictive values were 54.5% (45.2-63.4%) and 97.0% (94.9-98.2%). Likelihood ratios were 7.55(+) and 0.19(-). Accuracy was 88.3% (85.2 – 91.4%) and kappa was 0.59 (0.49-0.68).Conclusions:ADUNU’s novel approach to RHD active case finding achieved acceptable diagnostic performance. Nurse-led RHD screening programs that are integrated into routine clinical care shows potential for use in a comprehensive public health program. Very few RHD cases were missed, and under two referrals were generated for every positive case, an acceptable false positive rate. Further economic evaluation is underway to understand the budgetary impact and cost-effectiveness of this program.

Circulation, Volume 150, Issue Suppl_1, Page A4143538-A4143538, November 12, 2024. Introduction:Transthyretin amyloid cardiomyopathy (ATTR-CM) is an underdiagnosed disease that may result in heart failure (HF), arrhythmias, and valvular disease. Our aim was to develop (1) screening criteria to identify high-risk patients for ATTR-CM and (2) our own predictive tool of ATTR-CM.Methods:This was a prospective observational registry at 2 academic sites in Canada. We designed screening criteria to identify high-risk patients in HF, atrial fibrillation, transcatheter valve clinics, and in cardiologist’s offices from January 2019-December 2022. Patients >60 years were included if one of several screening criteria was met and they were referred for pyrophosphate scan by the cardiologist. Univariate and multivariate logistic regression were used to identify predictive clinical, imaging, and biochemical characteristics.Results:In total, 2500 patients were screened, and 200 patients were enrolled with a follow-up duration of 3 years. The mean age was 78 years and 65% were male. Forty-six (23%) had a diagnosis of ATTR-CM and 7 (4%) were diagnosed with AL-amyloidosis. ATTR-CM patients were older (83±7 vs. 77±8; p

Circulation, Volume 150, Issue Suppl_1, Page A4124675-A4124675, November 12, 2024. Introduction:The prevalence of hypertrophic cardiomyopathy (HCM) in the UK Biobank based on ICD-10 codes (.07%) is lower than global estimates of disease prevalence (0.2 – 0.5%). Prior studies using this data have remarked on the limitations of findings given likely underdiagnosis. The availability of cardiac MRI scans on a fraction of the participants offers an opportunity to identify missed diagnoses.Aims:This study seeks to utilize a generalizable deep learning model to detect likely cases of undiagnosed hypertrophic cardiomyopathy from cardiac MRIs in the UK Biobank.Methods:The foundational model was trained on a multi-institutional dataset of 14,073 cardiac MRIs via a self-supervised contrastive learning approach that sought to minimize the divergence between scans and their associated radiology reports. The pre-trained model was fine-tuned to diagnose hypertrophic cardiomyopathy on a distinct cohort of 4,870 MRIs with 368 cases of HCM, achieving an AUC of 0.94. The fine-tuned model was applied to the UK Biobank cardiac MRI dataset to ascertain predicted probabilities of HCM. Cases exceeding a threshold of 95% – correlating to the top 0.5% of cases (expected specificity of 97% and sensitivity of 60%) – were screened in for manual reading. In a blinded fashion, a board-certified radiologist was tasked with diagnosing HCM on a sample of cases composed of high and low predicted probabilities.Results:Of the 43,017 patients with cardiac MRIs, only 9 (.02%) had an ICD diagnosis of HCM. 266 cardiac MRIs were manually reviewed: 216 had greater than 95% predicted probability of HCM; 50 negative controls were randomly selected amongst cases with predicted probability less than 10%. The radiologist concurred with an HCM diagnosis for 115 cases (sensitivity 53%, specificity 98%), 112 of which were previously undiagnosed. The prevalence of hypertension and aortic stenosis did not significantly differ between the cohort of true positives (69.2%) and false positives (76.6%). The corrected prevalence of HCM in the UK BioBank MRI cohort is estimated at 0.28%.Conclusions:The findings of this study illustrate the remarkable ability of a generalizable deep learning model to detect undiagnosed cases of a rare disease process from cardiac MRIs. This is an important milestone that may allow for widespread screening of hypertrophic cardiomyopathy while minimizing demand for radiologist labor, and thereby allow patients to reap the substantial benefits of earlier treatment.

Circulation, Volume 150, Issue Suppl_1, Page A4145524-A4145524, November 12, 2024. Introduction:White-coat hypertension (WCH) and masked hypertension (MH) complicate accurate blood pressure (BP) monitoring. While ambulatory BP monitoring (ABPM) is effective, its high cost and limited availability are significant barriers.Hypothesis:We hypothesized that a machine learning (ML) model using clinical data from a single outpatient visit could accurately predict WCH and MH.Aims:This study aimed to develop and validate ML-based prediction models for WCH and MH using accessible clinical data to improve diagnostic efficiency and accessibility.Methods:We enrolled patients from two hypertension cohorts, after excluding those with incomplete data. Patients were classified by office BP and ABPM readings per American Heart Association guidelines. ML models, including Multi-layer Perceptron （MLP）, Support Vector Machine (SVM), and Tabular Prior-Data Fitted Network (Tab-PFN), were developed. Input parameters included demographic data (age, gender, height, weight, smoker), and office BP (OBP) and heart rate measurements. Principal Component Analysis (PCA), kernel PCA (kPCA), or t-distributed stochastic neighbor embedding (t-SNE) were used to improve class separability.Results:The study population comprised 1481 participants with a mean age of 47.6 years (SD 13.6), 65% of whom were male and 20.1% were smokers. OBP measurements showed a mean systolic BP (SBP) of 128.7 mmHg (SD 15.4) and a mean diastolic BP (DBP) of 84.2 mmHg (SD 11.6). ABPM showed a mean 24-hour systolic BP of 122.5 mmHg (SD 11.8) and diastolic BP of 79.3 mmHg (SD 10.1). The inclusion of demographic and OBP data, along with advanced resampling and dimensionality reduction techniques, significantly improved the model’s predictive ability. The final TabPFN model achieved the best performance with recall, precision, F1 score, and accuracy of 0.747, 0.931, 0.829, and 0.807 for WCH, and 0.713, 0.954, 0.816, and 0.907 for MH.Conclusion:Our ML-based model effectively predicts WCH and MH using accessible clinical data, offering a cost-effective alternative before applying ABPM.

Circulation, Volume 150, Issue Suppl_1, Page A4143847-A4143847, November 12, 2024. Introduction:Our previous studies have shown that sustained activation of the DNA damage response (DDR) in cardiomyocytes leads to p53/p21 activation and cardiac dysfunction. Although the DDR generally involves molecules in DNA replication and repair pathways, the non-proliferative nature of cardiomyocytes suggests a cardio-specific DDR mechanism. However, our understanding of DDR in cardiomyocytes remains limited. Here, we aim to use CRISPR interference (CRISPRi) knockdown screens to identify genes critically involved in DDR regulation in human cardiomyocytes. We hypothesize that identifying these gene clusters may allow us to develop methods to prevent cardiac dysfunction by suppressing DDR in cardiomyocytes.Methods and Results:We established a human iPS cell line stably expressing dCas9-KRAB, which allows CRISPRi-mediated gene knockdown, and differentiated the cells into cardiomyocytes. The resulting human iPS cell-derived cardiomyocytes (hiPSCMs) showed the achievement of approximately 80% knockdown efficiency after gRNA transfection. We stimulated the hiPSCMs with H2O2and quantitatively evaluated the expression levels of the DDR markers γH2AX and p21 by immunostaining using the Operetta®high content imaging system. The DDR markers showed a significant concentration-dependent increase in response to H2O2administration. For arrayed CRISPRi screening, we constructed a gRNA library targeting 437 DDR-related genes. Using this library, we knocked down each DDR-related gene in hiPSCMs followed by H2O2stimulation. We quantified the expression levels of DDR markers by calculating the fluorescence intensity ratios relative to control after gene knockdown, and standardized them to calculate Z scores for all 437 genes. The screening successfully revealed the differential impact of each gene knockdown on γH2AX and p21 expression. We identified 71 genes that significantly affected their expression (Z-score < -1 or > 1). Mapping these genes to DDR pathways highlighted the differential impact of gene knockdown within the same pathway, and stratified their importance in cardiomyocytes.Conclusions:Arrayed CRISPR screening using hiPSCMs revealed differential functional significance of DDR-related genes in cardiomyocytes, identifying 71 genes of particularly significant importance. These findings provide a critical understanding of the cardio-specific DDR pathway and important clues for establishing an appropriate method to suppress DDR in the failing heart.

Circulation, Volume 150, Issue Suppl_1, Page A4142502-A4142502, November 12, 2024. Background:AI-ECG is a cost-effective tool for left ventricular dysfunction (LVD) screening. However, its cost-effectiveness for other forms of structural heart disease (SHD) is unknown. While AI-ECG is inexpensive, a drawback is low positive predictive value (PPV), which leads to high costs from unnecessary follow-up tests. Therefore, strategies to improve the yield of AI-ECG-based screening are needed.Aim:To evaluate the cost savings of a stepwise approach to SHD screening with AI-ECG followed by POCUS compared to AI-ECG alone.Methods:286 adult outpatients undergoing AI-ECG were selected at random. Participants received same-day POCUS and had a recent TTE (our gold standard for SHD). We evaluated four SHDs: aortic stenosis (AS), cardiac amyloidosis (CA), HCM, and LVD. The costs of AI-ECG ($75) and TTE ($1,305) were obtained from Healthcare Bluebook. The cost of POCUS ($100) was estimated independently. Cost savings were analyzed for simultaneous screening for all forms of SHD and screening for individual SHDs.Results:AI-ECG identified potential SHD in 125 patients, but only 39 were true positives by TTE (31% PPV). In AI-ECG positive patients, POCUS demonstrated findings of SHD in 52/125. Compared to TTE, this stepwise approach yielded 32 true positives and 20 false positives (62% PPV). The cost per patient diagnosed with SHD was $4,733 with AI-ECG alone but decreased to $3,182 with stepwise screening (33% cost savings). Screening for individual SHDs resulted in cost reduction from $18,724 to $6,315 (66% savings) for AS, $21,023 to $12,230 (42% savings) for CA, $9,883 to $6,175 (38% savings) for HCM, and $4,019 to $3,582 (11% savings) for LVD.Conclusions:Stepwise screening for SHD with AI-ECG followed by POCUS significantly reduces costs compared to AI-ECG alone. We also suggest a model for parallel screening for multiple SHDs, which is likely more cost-effective than screening for individual SHDs.

Circulation, Volume 150, Issue Suppl_1, Page A4140494-A4140494, November 12, 2024. Background:Primary care after pregnancy is recommended, especially for individuals with recent adverse pregnancy outcomes (APOs, such as preeclampsia or gestational diabetes), who are at increased risk for future heart disease. Health-related social needs (HRSNs) are recognized barriers to care, yet their pregnancy-related prevalence and associations with care are unknown. We sought to (1) describe the pregnancy-related prevalence of HRSNs, and (2) assess associations between pregnancy-related HRSNs and subsequent linkage to primary care.Methods:We analyzed electronic health record data for individuals with prenatal care and delivery (2018-2021) at our urban safety-net hospital. HRSNs were assessed via a routine screener, and we summarized individual responses during pregnancy through 6 weeks post partum as: any positive, all negative, or never screened. Postpartum linkage to primary care was defined as a completed primary care visit after 6 weeks through 1 year post partum. We analyzed the prevalence of HRSNs and their associations with linkage to primary care, using adjusted log-linked binomial regression models. In stratified models we assessed for effect modification by APO history and other variables.Results:Of 4941 individuals in our sample, 53% identified as Black non-Hispanic and 21% as Hispanic, 68% were publicly insured, and 93% completed ≥1 HRSN screening. Nearly 1 in 4 screened positive for any HRSN, most often food insecurity (14%) or housing instability (12%), and 53% linked to primary care. Compared with those who screened negative for all HRSNs (n=3491), linkage to primary care was similar among those who screened positive for any HRSNs (n=1079; adjusted risk ratio, aRR 1.04, 95% confidence interval, CI: 0.98-1.10) and lower among those never screened (n=371; aRR 0.77, 95% CI: 0.68-0.86). We found no evidence of effect modification by APO history, race/ethnicity, insurance, language, or Covid-19 pandemic exposure.Conclusions:In this diverse postpartum sample, we identified a 24% prevalence of pregnancy-related HRSNs and 53% subsequent linkage to primary care. Linkage to primary care was not associated with HRSN screening result (positive versus negative) but was significantly negatively associated with being missed by HRSN screening. Further research is needed to better understand HRSN screening practices and who is missed by screening, and to identify modifiable barriers to postpartum primary care especially after APOs.

Circulation, Volume 150, Issue Suppl_1, Page A4141975-A4141975, November 12, 2024. Background:Refugee populations often experience high rates of cardiovascular disease (CVD). Factors such as significant physiological stress, trauma, limited access to healthcare, substance abuse, and poor lifestyle choices contribute to disease progression and an increased incidence of cardiovascular events. We sought to evaluate the feasibility of using wearables to obtain high-fidelity ECG signals for CVD screening in refugees in Jordan.Methods:This observational cross-sectional study involved outpatients at one of four regional United Nations’ primary care clinics for Palestinian refugee in Jordan. Research assistants collected health histories from consented patients and recorded a 30-second, 6-lead ECG using a handheld, Bluetooth-enabled, wearable device (KardiaMobile 6L, AliveCor Inc., Mountain View, CA, USA). The digital ECG signals were stored on the Bluetooth-synced mobile device and then exported to a cloud server for offline analysis. The raw ECG recordings were preprocessed, and a single median beat was calculated per lead. Waveforms were segmented, and duration and amplitude measures were determined using a previously validated custom algorithm (University of Pittsburgh, PA, USA). All ECG recordings were reviewed by an independent physician.Result:The sample included 31 patients (age 52±13, 64% Females). Risk factors were prevalent in this group, including hypertension (74%), high cholesterol (65%), diabetes (64%), in-camp living (33%), and smoking (30%). Figure 1 shows the population-averaged median beat with 99% CI distribution of this sample. Mean QRS duration was 95±23 ms (range 53−150) and QTc interval was 403±53 (range 267−513). Most patients were in normal sinus rhythm (84%), and remaining patients were in atrial fibrillation or flutter (16%). Other clinically significant abnormalities included non-specific ST-T changes (9.7%), left bundle branch block (1.6%), and LVH with left ventricular strain (1.6%).Conclusion:This pilot study demonstrated that it is feasible to obtain high fidelity ECG signals using wearables to screen for CVD in refugees. Such affordable, noninvasive, point-of-care screening tools could enable early diagnosis and treatment in these patients.

Circulation, Volume 150, Issue Suppl_1, Page A4131622-A4131622, November 12, 2024. Introduction:Chronic liver disease affects more than 1.5 billion adults worldwide, but the majority of cases are asymptomatic and undiagnosed. Echocardiography is broadly performed and visualizes the liver; however, this information is not diagnostically leveraged.Hypothesis and Aims:We hypothesized that a deep-learning algorithm can detect chronic liver diseases using subcostal echocardiography images that contains hepatic tissue. To develop and evaluate a deep learning algorithm on subcostal echocardiography videos to enable opportunistic screening for chronic liver disease.Methods:We identified adult patients who received echocardiography and abdominal imaging (either abdominal ultrasound or abdominal magnetic resonance imaging) with ≤30 days between tests. A convolutional neural network pipeline was developed to predict the presence of cirrhosis or steatotic liver disease (SLD) using echocardiogram images. The model performance was evaluated in a held-out test dataset, dataset in which diagnosis was made by magnetic resonance imaging, and external dataset.Results:A total of 2,083,932 echocardiography videos (51,608 studies) 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 presence of cirrhosis or SLD. In a total of 11,419 quality-controlled subcostal videos from 4,849 patients, a chronic liver disease detection model 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 0.726 (0.659-0.790) compared to MR elastography and SLD was detected with an AUC of 0.704 (0.689-0.718). In the external test cohort of 66 patients (n = 130 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:Deep learning assessment of clinically indicated 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 hepatic disease.

Circulation, Volume 150, Issue Suppl_1, Page A4135476-A4135476, November 12, 2024. Purpose:When the heart is exposed to stresses such as myocardial infarction or hypertension, it undergoes compensatory hypertrophy in response. However, continuation of the stress causes this compensatory mechanism to fail, and eventually systolic dysfunction or decompensated heart failure occur. As the hypertrophy of individual cardiomyocytes has been observed in this process, controlling cardiomyocyte hypertrophy is a potential target the prevention and treatment of heart failure. In this study, we constructed a high throughput screening (HTS) assay using cardiomyocyte hypertrophy as an index parameter. Compounds that inhibit cardiomyocyte hypertrophy were selected from our low molecular compound library.Methods and Results:In the primary screening, cultured rat primary cardiomyocytes were treated with each compound at a final concentration of 1 µM and then stimulated with 30 µM phenylephrine (PE) for 48 hours. These cells were subjected to fluorescent immunostaining with α-actinin, and cardiomyocyte area was measured using an ArrayScan™ system. The hypertrophy inhibition rate (%) of each compound was calculated as [(PE(+) – compound) / (PE(+) – PE(-))] × 100. The compounds with a hypertrophy inhibition rate greater than 50% and less than 150% were selected as hit compounds. In the secondary screening, these hit compounds were evaluated based on the dose-dependency of cardiomyocyte hypertrophy inhibition and the inhibition of the mRNA levels of the cardiac hypertrophy response genes ANF and BNP using real-time PCR. From the 269 low molecular-weight compounds in the original compound library, eight were selected through the primary and secondary screenings. Among them, we focused on Reference Number 409 (RFN-409). Western blotting indicated that RFN-409 inhibited PE-induced p38 activation. Next, we investigated the effect of RFN-409 on heart failure. Eight-week-old male C57 BL/6J mice were subjected to transverse aortic constriction (TAC) surgery and then randomly assigned to intraperitoneal treatment with RFN-409 (3, 10 mg/kg) or vehicle for eight weeks. RFN-409 at 10 mg/kg significantly prevented TAC-induced increase in left ventricular posterior wall thickness and decrease in left ventricular fractional shortening.Discussion:RFN-409 suppressed TAC-induced development of heart failure, at least partially by inhibiting p38 activity. These findings suggest that RFN-409 may be an effective agent for heart failure therapy.

Circulation, Volume 150, Issue Suppl_1, Page A4137770-A4137770, November 12, 2024. Background:Metabolic syndrome is a cluster of conditions that increase the risk of heart disease and diabetes. Early identification and management are crucial, particularly in economically challenged regions where access to healthcare may be limited.Research Questions/Hypothesis:User-friendly self-report data accurately predict metabolic outcomes.Aims:To develop and validate nomograms for individualized estimation of metabolic syndrome risk.Methods:Data from 521 college students (60.1% aged 17-20 years; 68.7% female; 28.0% white) were collected in 2022/2023 from two Brazilian cities. These cities are located in the country’s poorest states, with Gini indices of 0.56 and 0.43. The potential predictors include demographic and economic variables, school-related factors, behaviors, and body weight. Based on predictors for abdominal obesity identified through multilevel logistic regression, we created a nomogram model. We performed the Hosmer-Lemeshow test to assess model calibration and used a bootstrapping approach (B = 150) for internal validation. To evaluate external validity, we assessed metabolic syndrome in a subset of 375 students. The area under the receiver operating characteristic curve (AUROC), with a threshold of 0.70, was used to evaluate the model’s discrimination accuracy.Results:We identified 114 (23.0%) college students who were abdominally obese. We found ten variables associated with the primary outcome: age, biological sex, physical education facilities, enrollment in sports competition (during elementary school); grade retention, preferred subject, physical education classes per week; enrollment in sports training (during secondary school); adherence of 24-hour movement behaviors and body weight. The proposed nomogram showed acceptable performance in the AUROC (0.94 [95% CI: 0.92-0.96). The calibration assessment indicated reasonable consistency of our model (p > 0.05). In the internal validation, we observed a decreased predictive capability (AUROC = 0.86).Conclusion:The 24h-MESYN risk score offers an effective self-screening tool for college students from diverse racial and ethnic backgrounds in economically challenged regions to assess their risk of developing metabolic syndrome.

Circulation, Volume 150, Issue Suppl_1, Page A4141994-A4141994, November 12, 2024. Background:Screening trials for atrial fibrillation (AF) have produced mixed results; however, it is unclear if there is a subset of individuals for whom screening would be effective. Identifying such a subgroup would support targeted screening.Methods:We conducted a secondary analysis of VITAL-AF (NCT03515057), a randomized trial of one-time, single-lead ECG screening during primary care visits. We tested two approaches to identify a subgroup that would benefit from screening (i.e., heterogenous screening effects). First, we use a potential outcomes framework to develop an effect-based model. Specifically, we predicted the likelihood of AF diagnosis under both screening and usual care conditions using LASSO, a penalized regression method. The difference between these probabilities was the predicted screening effect. Second, we used the CHARGE-AF score, a validated AF risk model. We used interaction testing to determine if the observed diagnosis rates in the screening and control arms were statistically different when stratified by decile of the predicted screening effect and predicted AF risk.Results:Baseline characteristics were similar between the screening (n=15187) and usual care (n=15078) groups (mean age 74 years, 59% female). On average, screening did not significantly increase the AF diagnosis rate (2.55 vs. 2.30 per 100 person-years, rate difference 0.24, 95%CI -0.18 to 0.67). Patients in the highest decile of predicted screening efficacy (n=3026, 10%) experienced a large and statistically significant increase in AF diagnosis rates due to screening (6.5 vs. 3.06 per 100 person-years, rate difference 3.45, 95%CI 1.62 to 5.28; interaction p-value 0.038) (Figure 1). In this group, the mean age was 84 years and 68% were female. Participants in the highest decile of AF risk using the CHARGE-AF score did not have a statistically significant increase in AF diagnosis rates due to screening (Figure 2). Predicted screening effectiveness and predicted AF risk were poorly correlated (Spearman coefficient 0.13).Conclusions:One-time screening may increase AF diagnoses in a subgroup of older adults with the largest predicted screening effect. In contrast, predicted AF risk was a poor proxy for predicted screening efficacy. These data caution against the assumption that high AF risk is necessarily correlated with high screening efficacy. Prospective studies are needed to validate whether AF screening is effective in the subgroup identified in this study.