Risultati per: Screening del cancro del polmone

Circulation, Volume 150, Issue Suppl_1, Page A4144947-A4144947, November 12, 2024. Background:Right heart catheterization (RHC) is the gold standard for diagnosing heart failure with preserved ejection fraction (HFpEF). An FDA-approved artificial intelligence (AI) technology uses a four-chamber transthoracic echocardiogram (TTE) image to screen patients for HFpEF.Methods:We compared invasive hemodynamic data between patients screened for HFpEF by this TTE AI algorithm. We retrospectively collected data from two cohorts of patients with an ejection fraction (EF) ≥ 50% undergoing RHC for the evaluation of HFpEF. The most recent TTE was screened using the AI tool and reported as either suggestive or non-suggestive for HFpEF – labeled as “positive” or “negative,” respectively. Invasive hemodynamic parameters at rest and during exercise were collected. Positive and negative groups were compared using Student’s t-test and Mann-Whitney U test.Results:A total of 47 patients (82% women, 79% Black, average EF 62%) had a previous RHC, with 23 undergoing subsequent exercise RHC. There were 18 patients (38%) with a positive AI result and 29 (62%) negative. Positive patients had significantly higher rates of atrial fibrillation (38% vs 11%, p=.03), NT-proBNP levels (median 451 vs 117 ug/mL, p=.001), and H2FPEF (median 6 vs 4, p 15 mmHg, consistent with HFpEF, compared to only 14 of 28 (50%) negative patients. With exercise 6 of 7 (86%) positive patients had PCWP ≥ 25 mmHg, consistent with HFpEF, compared to 11 of 20 (55%) negative patients. At rest, positive patients had significantly higher PCWP, mean pulmonary arterial pressure (mPAP), and pulmonary vascular resistance (PVR). After exercise, there were no significant differences in PCWP or mPAP between the two groups, but thermodilution cardiac output was significantly lower in the positive patients.Conclusion:Patients identified as HFpEF positive by a validated TTE-guided AI tool were more likely to have HFpEF confirmed invasively, indicating its potential for risk stratification. However, the negative predictive value for HFpEF confirmed by invasive hemodynamics was low in this population.

Circulation, Volume 150, Issue Suppl_1, Page A4146283-A4146283, November 12, 2024. Objective:The incidence of cognitive decline following coronary artery bypass grafting (CABG) is well-documented, significantly impacting patient morbidity, mortality, and quality of life. We conducted a systematic review that examines cognitive outcomes in CABG randomized controlled trials (RCTs) to identify which cognitive assessments were used, their administration frequency, attrition rates, and their effectiveness in detecting perioperative cognitive changes in control groups.Methods:We conducted a search of MEDLINE, EMBASE, Cochrane Library, and PsycINFO for CABG RCTs that included cognitive assessments, from January 2005 to December 2023. Descriptive statistics were used to summarize the frequency, domains, and attrition rates of each cognitive task. For tasks assessed both pre- and post-operatively in at least three RCTs, control group scores and standard deviations were reported.Results:Out of 3337 screened studies, 2163 were CABG RCTs, and only 69 (3.2%) included cognitive evaluations (Figure 1). These trials involved 15,839 subjects (79% male, mean age 64.4, median follow-up time 90 days) and used 145 unique cognitive tasks. The Trailmaking Test Part B (40/69; 58.0%) and Part A (38/69; 55.0%) were the most frequently used. Only 7 tasks had means and standard deviations reported before and after surgery in more than three RCTs, and none detected significant pre- to post-operative changes. Attrition rates averaged 19.3%, with a wide range from 0% to 62%. Figure 2 demonstrates the decline in cognitive assessments in CABG trials over the years, with a sharp decline after 2014. Trials that assessed cogntion after 2014 tended to favor screening tasks (MMSE/MoCA) alone.Conclusion:Cognitive assessments are infrequent in CABG trials, and existing tests fail to consistently detect cognitive changes. To effectively evaluate and address cognitive impact after CABG, new assessment strategies that are resilient to attrition and practical for use in diverse trial settings are needed.

Circulation, Volume 150, Issue Suppl_1, Page A4143150-A4143150, November 12, 2024. Background:The FACTOR-64 study was a randomized controlled trial designed to assess whether routine screening for CAD by coronary computed tomography angiography (CCTA) in high-risk patients with diabetes followed by CCTA-directed therapy would reduce the risk of death and nonfatal coronary outcomes. Results at four years showed a lower revascularization rate (3.1% (14) vs. 8.9% (40), p

Circulation, Volume 150, Issue Suppl_1, Page A4112869-A4112869, November 12, 2024. Introduction:Sitosterolemia, a hereditary disorder marked by elevated plant sterol levels, presents diagnostic challenges due to its similarity to other lipid disorders. The development of the Sitosterolemia Risk Prediction Scale (SRPS) aims to address this by synthesising genetic, clinical, and dietary data into a coherent risk assessment model.Research Question:We propose that a structured risk scale, integrating diverse factors known to affect sitosterolemia, can significantly improve the accuracy of predicting the disorder. The SRPS is hypothesised to facilitate early detection and inform targeted interventions.Aim:The primary aim is to conceptualise and outline the SRPS, which categorises individuals into risk categories based on a point system reflecting genetic predispositions, clinical symptoms, dietary habits, and response to treatments. This scale seeks to enhance the clinical identification of sitosterolemia, promoting timely and personalised management strategies.Methods:A detailed table was generated to present the SRPS, categorising risk factors into genetic, clinical, dietary, and response to treatment. This innovative method allowed for the efficient synthesis and visualisation of complex data.Results:The SRPS table methodically organizes risk factors into low (0-2 points), moderate (3-5 points), and high (6+ points) categories. This stratification guides further diagnostic actions, ranging from exploring alternative causes of hyperlipidemia to necessitating comprehensive genetic and lipid analyses.Conclusion:The SRPS represents an innovative framework for assessing sitosterolemia risk, highlighting the potential benefits of integrating genetic, clinical, and dietary information. It further underscores the importance of a multifactorial approach in the early detection and management of sitosterolemia.

Circulation, Volume 150, Issue Suppl_1, Page A4141112-A4141112, November 12, 2024. Background:It is estimated that Peripheral Artery Disease (PAD) affects between 8.5 and 12 million Americans and its prevalence among adults over 40 years of age is increasing. PAD disproportionately affects Black Americans who, at any age, are twice as likely to experience PAD as their white counterparts but are less likely to be screened and benefit from early diagnosis and treatment.Research Questions/Hypothesis:Despite the high prevalence of PAD and the importance of early intervention, screening for PAD remains limited and/or underutilized particularly in primary care settings where most cases of PAD can be identified. This study sought to understand provider knowledge of PAD, associated risk factors, treatment, understanding of disparities in PAD and barriers and facilitators of PAD screening. It was hypothesized that limited resources, lack of awareness on the part of providers and patients, limitations of training in vascular medicine, and other issues are contributing to PAD morbidity and mortality, particularly among Black and Hispanic populations.Methods:Because no current PAD survey was found in the literature, a survey for providers to determine their knowledge, attitude, and beliefs about PAD and the importance and process of PAD screening for patients at risk was developed. The survey was administered to CommonSpirit Health providers in Sacramento, CA between December 2023- January 2024. Specialties engaged in the survey (N=145) included primary care, endocrine, nephrology, cardiology and podiatry providers.Results:Response rate was 21%. Of those responding, primary care was the specialty most represented(69%). A total of 65% of respondents identified medical treatment of risk factors as the primary way to treat PAD, 32% rated their knowledge of risk reduction therapies in PAD as below average, and 88% of respondents were either somewhat or not familiar with racial disparities in PAD. 24% of respondents identified the ‘lack of knowledge of PAD management guidelines’ as the most important barrier to their patients with PAD not receiving risk reduction therapies.Conclusions:Initial survey of providers identifies lack of knowledge as a key indicator of PAD screening practices, including knowledge on racial disparities in PAD. These identified gaps can inform targeted interventions to improve screening, early detection and treatment of PAD.

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 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 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 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 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 A4144083-A4144083, November 12, 2024. Background:The AI-CVD initiative aims to extract all useful opportunistic screening information from coronary artery calcium (CAC) scans and combines them with traditional risk factors to create a stronger predictor of cardiovascular diseases (CVD). These measurements include cardiac chambers volumes (left atrium (LA), left ventricle (LV), right atrium (RA), right ventricle (RV), and left ventricular mass (LVM)), aortic wall and valvular calcification, aorta and pulmonary artery volumes, torso visceral fat, emphysema score, thoracic bone mineral density, and fatty liver score. We have previously reported that the automated cardiac chambers volumetry component of AI-CVD predicts incident atrial fibrillation (AF), heart failure (HF), and stroke in the Multi-Ethnic Study of Atherosclerosis (MESA). In this report, we examine the contribution of other AI-CVD components for all coronary heart disease (CHD), AF, HF, stroke plus transient ischemic attack (TIA), all-CVD, and all-cause mortality.Methods:We applied AI-CVD to CAC scans of 5830 individuals (52.2% women, age 61.7±10.2 years) without known CVD that were previously obtained for CAC scoring at MESA baseline examination. We used 10-year outcomes data and assessed hazard ratios for AI-CVD components plus CAC score and known CVD risk factors (age, sex, diabetes, smoking, LDL-C, HDL-C, systolic and diastolic blood pressure, hypertension medication). AI-CVD predictors were modeled per standard deviation (SD) increase using Cox proportional hazards regression.Results:Over 10 years of follow-up, 1058 CVD (550 AF, 198 HF, 163 stroke, 389 CHD) and 628 all-cause mortality events accrued with some cases having multiple events. Among AI-CVD components, CAC score and chamber volumes were the strongest predictors of different outcomes. Expectedly, age was the strongest predictor for all outcomes except HF where LV volume and LV mass were stronger predictors than age. Figure 1 shows contribution of each predictor for various outcomes.Conclusion:AI-enabled opportunistic screening of useful information in CAC scans contributes substantially to CVD and total mortality prediction independently of CAC score and CVD risk factors. Further studies are warranted to evaluate the clinical utility of AI-CVD.

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.

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 A4137945-A4137945, November 12, 2024. Background:Juvenile sudden cardiac death (SCD) has high impact on the family and society of the victim. While SCD screening programmes are effective in athletes, most (70-80%) young non-athletes individuals are not routinely screened.Research question:We hypothesized that a low-cost screening program may early identify subjects at risk of juvenile SCD, even in non-athletes.Goals:To evaluate the prevalence of SCD-related abnormal findings and, ultimately, to test the effectiveness of a screening programme in high schools.Methods:Between April 2023 and June 2024, high school individuals were enrolled in a screening programme in Tuscany (Pisa, Lucca and Livorno), based on a questionnaire investigating family history of juvenile SCD or diseases predisposing to SCD and symptoms (syncope, palpitations, chest pain), and digitally recorded electrocardiograms (ECGs). In case of abnormal findings, second-line investigations locally (echocardiography, Holter ECG monitoring and/or exercise testing) or third-line investigations at Fondazione Monasterio, Pisa, Italy (cardiac MRI, genetics or electrophysiological testing) were planned. Only preliminary results of the first-line screening are hereby reported.Results:We have currently enrolled 872 individuals (age 17.1±1.8 years, 481 [55%] males, 288 [33%] smokers, 102 [11.7%] recreational drugs users, and 645 [74%] non-competitive athletes). At questionnaires, 56 individuals (6.4%) had a family history of SCD, 32 (3.7%) a first-degree relative with cardiomyopathy, and 13 (1.5%) with channelopathy. As for symptoms, 21 participants (2.4%) reported chest pain or 26 (3%) syncope during exertion, while 90 (10.3%) paroxysmal palpitations. At ECG, we found 2 cases (0.2%) with a type-2 Brugada pattern, 1 female case (0.1%) with prolonged QTc interval (QTc 480 ms), 20 cases (2.3%) with V1-V3 T wave inversion (age > 16 years), 18 cases (2%) of left ventricular hypertrophy (non-athletes), and 4 cases (0.5%) with atypical ventricular ectopy. After the first-line screening, 61 (7%) and 10 (1.2%) individuals were referred to second and third-line investigations, which are currently ongoing.Conclusions:We hereby propose a screening model in high schools that includes specific health questionnaires and digitally recorded ECGs. From preliminary analyses, this approach seems sensitive enough to be tested as a model to favour the early diagnosis of diseased conditions associated with juvenile SCD in the general population.

Circulation, Volume 150, Issue Suppl_1, Page A4144283-A4144283, November 12, 2024. Introduction:Pulmonary arterial hypertension (PAH) remains an underrecognized, fatal disease. Limited awareness, non-specific symptoms, and late referral to accredited PH centers all contribute to an overall poor prognosis. The previously validated Virtual Echocardiography Screening Tool (VEST) uses 3 routine transthoracic echocardiogram (TTE) parameters (left atrial size, transmitral E:e’ and systolic interventricular septal flattening) to recognize a high PAH likelihood. A positive VEST score has been shown to have 80% sensitivity and 76% specificity for PAH hemodynamics, while a VEST score of +3 has 92.7% specificity for PAH hemodynamics with a positive predictive value of 88.0%.Aim:We aimed to implement a novel algorithm via our electronic medical record (EMR) as an automated VEST calculator to identify patients with a high likelihood of PAH.Methods:An automated EMR VEST calculator was applied retrospectively to 4,952 patients who underwent TTE with TR velocity >/= 2.9 m/s at an accredited PH center from 12/2021-8/2023. Automated EMR VEST scores were validated by comparison to 60 manually scored echocardiograms. Those with VEST score of +3 (highest risk for PAH) underwent chart review to identify whether they were seen by a PH specialist.Results:There was 100% correlation between the automated EMR VEST scores and the manual results.Of the 4,952 patients, 1,655 had a positive automated EMR VEST score, and 355 had a score of +3, predicting the highest likelihood of PAH and warranting urgent referral to an accredited PH center. Of those patients with a +3 score, 103 (29.0%) were never seen by a PH specialist (Fig 1).Conclusion:VEST is a validated, noninvasive and accessible screening tool for identification of patients with a high likelihood of PAH likely to benefit from early referral to a PH center. We present a novel, accurate, and automated EMR algorithm for determination of the VEST score to prompt urgent referral for PH expert evaluation and timely initiation of complex medical therapies. These findings highlight the potential of future artificial intelligence and machine-learning applications for improved recognition of life-threatening PAH.