Risultati per: Screening per il cancro al seno

Background
Hepatocellular carcinoma (HCC) is plagued by failures across the cancer care continuum, leading to frequent late-stage diagnoses and high mortality. We evaluated the effectiveness of mailed outreach invitations plus patient navigation to promote HCC screening process completion in patients with cirrhosis.

Methods
Between April 2018 and September 2021, we conducted a multicentre pragmatic randomised clinical trial comparing mailed outreach plus patient navigation for HCC screening (n=1436) versus usual care with visit-based screening (n=1436) among patients with cirrhosis at three US health systems. Our primary outcome was screening process completion over a 36-month period, and our secondary outcome was the proportion of time covered (PTC) by screening. All patients were included in intention-to-screen analyses.

Results
All 2872 participants (median age 61.3 years; 32.3% women) were included in intention-to-screen analyses. Screening process completion was observed in 6.6% (95% CI: 5.3% to 7.9%) of patients randomised to outreach and 3.3% (95% CI: 2.4% to 4.3%) of those randomised to usual care (OR 2.05, 95% CI: 1.44 to 2.92). The intervention increased HCC screening process completion across most subgroups including age, sex, race and ethnicity, Child-Turcotte-Pugh class and health system. PTC was also significantly higher in the outreach arm than usual care (mean 37.5% vs 28.2%; RR 1.33, 95% CI: 1.31 to 1.35). Despite screening underuse, most HCC in both arms were detected at an early stage.

Conclusion
Mailed outreach plus navigation significantly increased HCC screening process completion versus usual care in patients with cirrhosis, with a consistent effect across most examined subgroups. However, screening completion remained suboptimal in both arms, underscoring a need for more intensive interventions.

Trial registration number
NCT02582918.

Circulation, Volume 150, Issue Suppl_1, Page A4144566-A4144566, November 12, 2024. Introduction:Sudden cardiac arrest (SCA) remains a leading cause of death among young individuals, often occurring without prior symptoms. Public confidence in recognizing SCA warning signs, understanding risk factors, and using cardiopulmonary resuscitation (CPR) and Automated External Defibrillators (AEDs) is generally low. The Eric Paredes Save A Life Foundation addresses this gap by offering free cardiac screenings and educational sessions. This abstract evaluates the foundation’s impact on participants’ confidence in these critical areas.Goals:The primary objectives were to enhance participants’ confidence in identifying SCA warning signs and risk factors, performing CPR, using AEDs during cardiac emergencies, and communicating youth heart health concerns to healthcare providers.Methods:Volunteer-led prevention screening sessions were conducted, incorporating educational components on SCA warning signs, risk factors, and hands-only CPR and AED training. Post-session surveys were administered to 1,123 participants to assess their confidence levels in these areas.Results:The screening sessions significantly increased participants’ confidence. Specifically, 96% of participants reported heightened confidence in recognizing SCA warning signs and understanding risk factors. Additionally, 94% felt more assured in discussing youth heart health with providers. Confidence in performing CPR during a cardiac emergency rose to 92%, while confidence in using an AED reached 89%. These results demonstrate the effectiveness of the sessions in empowering individuals with essential life-saving skills.Conclusions:The Eric Paredes Save A Life Foundation’s prevention screening sessions are highly effective in improving participants’ confidence in recognizing SCA warning signs, understanding risk factors, and using CPR and AEDs. The substantial increase in confidence levels underscores the importance of community-based education and training programs in reducing SCA-related fatalities among youth. These findings support the need for ongoing and expanded initiatives to further enhance public health outcomes and preparedness for cardiac emergencies.

Circulation, Volume 150, Issue Suppl_1, Page A4144583-A4144583, November 12, 2024. Donor-derived cell-free DNA (dd-cfDNA) has been increasingly used to detect acute rejection (AR). We aimed to compare our institutional dd-cfDNA results to previously published adult and pediatric dd-cfDNA AR cutoffs. We also hypothesized that in the absence of AR, elevated dd-cfDNA would be associated with CAV and positive DSA.Patients (pt) < 18 years at transplant with >1 dd-cfDNA between 2021-2023 were included. Using dd-cfDNA levels from this cohort, sensitivity, specificity, NPV, and PPV were calculated. False positives and false negatives (FN) were determined using published dd-cfDNA thresholds. AR was defined as decision-to-treat with increased immunosuppression, which was independent of dd-cfDNA in our cohort. In pt without AR,t-test was used to compare the means of dd-cfDNA levels in pt with and without DSA. χ2testing was then performed to evaluate the association between dd-cfDNA levels above and below 0.2% and the presence/absence of DSA and CAV. DSA was defined as allele-specific DSA identified by single antigen bead with mean fluorescence intensity >1000, and CAV as any disease by angiography.There were 379 samples among 163 pt, a median of 2 samples per pt, and 32 samples obtained at time of AR. Performance of dd-cfDNA in our cohort vs published dd-cfDNA thresholds is shown in Table 1. The FN rate ranged from 16 to 37% as the dd-cfDNA threshold increased. Mean dd-cfDNA was higher in patients with positive DSA versus those without (0.83% vs 0.19%, p0.2% were associated with a higher prevalence of positive DSA (n=66) (48% vs 13%, p

Circulation, Volume 150, Issue Suppl_1, Page A4145826-A4145826, November 12, 2024. Background:Previous studies have shown that AF screening in at-risk populations can reduce stroke incidence. However, non-targeted screening approaches often result in high false positive rates, placing an unnecessary burden on the healthcare system. In contrast, artificial intelligence-guided screening has been demonstrated to increase diagnostic yield in large prospective clinical trials. This approach, however, requires recording an ECG and a large-scale dataset for model training. Heart rate variability (HRV) analysis has proven effective in deciphering key heart dynamics. By analyzing HRV as interrelated dynamic systems, it may be possible to facilitate targeted AF screening using wearable devices that measure heart rate.The Koopman operator, used for data-driven modeling of interrelated dynamic systems, has been shown to accurately predict complex phenomena in chaotic systems such as climate forecasting and drug adverse reaction prediction. This is achieved by utilizing common characteristics of the systems for most model parameters, with only a small fraction of the parameters being specific to a certain system.Methods:Long ( >10 hour) records from 361 individuals (AFDB, LTAFDB) and healthy individuals’ datasets from PhysioNet and THEW were analyzed for inter-beat intervals. The unified dataset was then split into 94 training, 17 validation, and 250 test set patients. Recordings from the training set were used to train both the common and specific parts of the interrelated dynamic systems model for each patient, along with a shared small neural network classifying patients into low and high risk for AF based on the unique (not shared between patients) singular values of the dynamic system model. Patient-specific dynamic system models were then fitted for the validation and test sets to calculate the patient dynamic singular values, which were used to classify patients into low and high risk for AF groups.Results:Atrial fibrillation occurred in 48 of 202 (23%) patients classified as low risk and 35 of 48 (72.9%) patients classified as high risk (odds ratio 8.63, 95% CI 4.23-17.64), yielding 72.9% sensitivity with 76.2% specificity.Conclusion:In this retrospective analysis, classification of the dynamic system model singular values identified patients at high risk for atrial fibrillation from sinus rhythm period.

Circulation, Volume 150, Issue Suppl_1, Page A4139194-A4139194, November 12, 2024. Background:In the U.S., about 8% of adults never received cholesterol screening. Although machine learning (ML) has been used to develop decision tools for Atherosclerotic Cardiovascular Disease (ASCVD) risk prediction, its application in behavioral forecasting has not yet been explored in the context of cholesterol screening behaviors. This study aimed to examine the performance and accuracy of ML algorithms in forecasting cholesterol screening behaviors in adults after age 50.Methods:This analysis used deidentified data from the Health and Retirement Study (HRS) 2004-2018. HRS is a longitudinal survey among 23,000 households in the U.S. Participants were excluded from the current analysis if they passed away by 2019, ever had ASCVD or stroke, were under age 50 at baseline, or had missing data in self-reported cholesterol screening. In total, 7176 participants (mean age [SD]=62 [8]) met the inclusion criteria; participants were randomly split into a training set (80%) and a testing set (20%). The synthetic minority oversampling technique was used to solve the imbalance distribution of the rare event. Five ML algorithms were used: random forest, gradient boosting machine (GBM), XGBoost, Support Vector Machine (SVM), and logistic regression. Accuracy, AUROC, and positive predictive value (PPV) were used to compare model performance. The average gain was evaluated for feature importance in the demographic and health domains.Results:In total, 232 (3.2%) respondents did not receive any cholesterol screening from 2008 to 2018. Experiments with five ML algorithms suggested that XGBoost with deeper trees and learning rate performed better in classifying those who did not screen for cholesterol levels over 10 years. Adding prior cholesterol screening history (2004-2006) into the model significantly improved model performance. Hypertension, self-rated health, and smoking were the major health features, while insurance, poverty, and work status were the major demographic features in the predictive model (accuracy=0.97; AUROC=0.88; PPV=0.42).Conclusion:Findings underscore the potential utility of ML models in predicting cholesterol screening behaviors after age 50. This could be the basis for developing decision tools for clinicians to identify those with a lower chance of cholesterol screening or make reminders accordingly. The low-cost predictive model might improve the uptake of preventive screening behaviors in middle-aged and older adults.

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 A4144973-A4144973, November 12, 2024. Background:Cardiac calcium, which includes coronary and extra-coronary calcification, is often incidentally found in chest CT scans performed for various reasons. Despite its prognostic value, manual quantification of cardiac calcium in non-gated chest CT images is labor-intensive.Goals:This retrospective study aims to perform automatic quantification and scoring of cardiac calcium in non-contrast-enhanced chest CTs. The objective is to determine associations between automatic calcium scoring and outcomes such as all-cause mortality, non-fatal myocardial infarction (MI), and non-fatal stroke.Methods:We conducted a nationwide cohort study using the Taiwan National Health Insurance Research Database (NHIRD) from 2016 to 2022. Patients under 20 years old, with a diagnosis of malignancy, or with outcome events before the CT acquisition were excluded. HeaortaNet 1.0, a validated AI model, was used for cardiac calcium scoring. Comorbidities were determined using ICD diagnostic codes for ≥2 consecutive outpatient visits within the year before the index date. Outcomes were censored at the first occurrence of mortality or relevant ICD codes for MI or stroke.Results:The retrospective cohort included 279,415 patients (56.37% male, mean age 60.31±16.54). All-cause mortality occurred in 12.82% of patients within a 3-year follow-up. The 3-year incidence rates of non-fatal MI and non-fatal stroke were 0.86% and 2.07%, respectively. Multivariate-adjusted Cox hazard ratios (95% confidence intervals) for any composite outcome were 1.51 (1.46-1.57), 2.09 (2.01-2.17), 2.63 (2.53-2.74), and 3.37 (3.24-3.50) for cardiac calcium scores of 1-100, 101-400, 401-1000, and >1000, compared to a score of 0. Adjusted Cox hazard ratios for all-cause mortality were 1.62 (1.56-1.69), 2.29 (2.19-2.39), 2.91 (2.78-3.04), and 3.80 (3.64-3.96) for scores of 1-100, 101-400, 401-1000, and >1000, compared to a score of 0.Conclusion:AI-enabled opportunistic screening of non-contrast chest CT for cardiac calcium scoring is associated with all-cause mortality and cardiovascular events. This is the first large-scale cohort study to use an AI model for comprehensive cardiac calcium screening.

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 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 A4138955-A4138955, November 12, 2024. Introduction:Rheumatic heart disease (RHD) is the most common acquired heart disorder in children and adolescents worldwide. We developed and validated an automated artificial intelligence (AI)-guided RHD screening algorithm adapted for point-of-care ultrasonography (POCUS) in school-aged children.Methods:We employed a cross-domain transfer learning approach, in which a 3D convolutional neural network (CNN) was first trained to detect structural RHD deformation of the mitral or aortic valves in 244,523 videos, representing all views from 5,614 adult transthoracic echocardiograms (1:5 age and sex-matched cases and controls; median age 69 [58-80] years, 76.4% female) in a large US health system. The model was fine-tuned for stage ≥B (“definite”) RHD in 21,472 POCUS videos (2D parasternal and apical acquisitions) from 5,525 studies (75% training, 25% validation) in a pediatric screening program (median age 11 [IQR 10-13] years, 54.6% female) in Brazilian low-income schools. Testing was performed in a held-out set of 1,966 parasternal long-axis (PLAX) videos from 1,138 studies in Brazil (14 [1.2%] with stage ≥B RHD) as well as in an external pediatric screening set in Uganda consisting of 249 videos from 96 studies (34 [35.4%] with stage ≥B RHD) (Fig. 1).Results:Our model (Fig. 2) achieved a study-level AUROC (area under the receiver operating characteristic curve) of 0.88 across the held-out/external testing sets for identifying stage ≥B RHD from cardiac POCUS (Fig. 3A). On a video-level the model learned a continuous spectrum of phenotypes on PLAX acquisitions spanning stage ≥B (“definite”) and stage A (“borderline”) cases, ranging from a median video-level AI probability of 0.13 [0.01-0.73] for stage ≥B to 0.00 [0.00-0.01] for non-RHD POCUS (Fig. 3B). At the threshold that maximized Youden’s J in the held-out Brazil set, our algorithm’s performance in the set from Uganda showed 97% recall (sensitivity), a positive predictive value (precision) of 46%, and a negative predictive value of 95%.Conclusions:A transfer learning approach that employs multi-view learning achieves excellent performance for RHD on single-view two-dimensional cardiac POCUS without Doppler. Our study suggests a scalable approach to AI-enabled RHD detection with images that can be acquired by individuals with modest training.

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 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 A4138273-A4138273, November 12, 2024. Background:Rheumatic heart disease (RHD) causes 305,000 premature annual deaths, and education is one of the strategies to diminish disease burden. International RHD foundations aim do provide preventive and control efforts for RHD. We aimed to assess the acceptability and gain of knowledge of a series of education flipcharts presented during screening programs in high-burden areas of Brazil.Methods:Four flipcharts (“Introduction to rheumatic fever (RF) and RHD”, “RHD and pregnancy”, “RHD and surgery” and “RHD community awareness”) were developed over 3 years and taught during 36 months to patients, community, health and education professionals in Minas Gerais state. Training included in-person interactions and virtual workshops. Pre and post-training questionnaires were applied through an online and printed surveys in 2021 and 2022, and post-education evaluations were conducted from January 2023 to April, 2024.Results:Flipchart training was successfully delivered to 112 education professionals, 574 health providers and 598 community members (N=1284): 899 (70%) were enrolled in primary care, and 1109 (86%) responded the surveys. Among respondents of the survey for health and education professionals (N=589), 240 (41%) had been educated about RHD in the previous year. 569 (96%) learned any new information; the content was all new for 21 (4%). Nearly all professionals reported that flipcharts could improve patients’ lives (571, 97%) and felt confident to use the tool with someone with no knowledge about RHD (533, 91%); 86% of the teachers said they would use flipcharts as educational tools. In the survey for community / schoolchildren (N=520) only 128 (25%) respondents had previous education on RHD, 510 (98%) reported that learned new information, and content was completely new for 242 (47%). A total of 430 (83%) individuals reported that they will discuss RHD with families and community. All qualitative written reports were positive. In 2021/2022, 218/485 (45%) health and education professionals responded the pre/post questionnaire. Knowledge about RHD increased after training: RF as the cause of RHD (56% vs 86%), use of Benzathine Penicillin G (50% vs 97%), frequency of antibiotic prophylaxis (32% vs 90%) and overall moderate or expert understanding of RF or RHD (30% vs 82%).Conclusion:Flipchart educational sessions about RHD had a very positive acceptability in high-risk Brazilian populations, with remarkable gain of knowledge for health professionals.

Circulation, Volume 150, Issue Suppl_1, Page A4115235-A4115235, November 12, 2024. Intro:The AHA endorses screening youth athletes to identify risk for sudden cardiac arrest (SCA). Rates of SCA can be predicted by social determinants of health (SDOH) such as education level and proportion of Black residents in ZIP Code. The Child Opportunity Index (COI) quantifies neighborhood factors that influence health and development. The link between COI and youth cardiac screening findings and outcomes remains unclear.Hypothesis:Cardiac screening data will differ significantly by COI.Aims:To identify differences in cardiac screening data in children of varying COI.Methods:The HeartBytes Database, including sports exams, self-reported physical activity (PA), and zip codes from Simon’s Heart screenings was augmented with COI index zip code data. Chi-squared and logistic regression were used to analyze demographics, cardiac risk factors, and screening results.Data:Screening data of 11,431 youth athletes (median age 14.3 (IQR = 3), BMI 20.6 (4.8), 53.7% male, 70.6% White) was analyzed. The majority of children had very high overall COI (Figure 1). Hypertension, hyperlipidemia, Kawasaki disease, and heart infection were similar across COI levels (p > 0.05). Levels of physical activity varied significantly across levels of overall COI, with the highest levels reported in the lowest COI group (50.4% with >10 hours PA/week) (Chi-Squared; p = 0.007). Positive screening rates varied significantly by level of COI (p = 0.013) (Figure 2). The overall level of education, health environment, and socioeconomic COI did not predict positive screening outcomes in logistic regression analysis (all p >0.05).Conclusion:Prevalence of cardiac risk factors did not vary significantly across COI levels, however, positive screening rates were highest in moderate and very low COI levels. Simon’s Heart engaged communities across the COI spectrum; however, a majority of children had high or very high COI. Further efforts are needed to expand access to underserved populations of lower COI.

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 A4138647-A4138647, November 12, 2024. Introduction/Background:We previously demonstrated that an open-source deep learning model (CXR-CVD Risk) can predict 10-year major adverse cardiovascular events (myocardial infarction&stroke), based on a chest radiograph image (CXR). As deep learning models are black boxes, establishing the biological processes the model captures to predict risk may help build understanding and trust in the model.Research Questions/Hypothesis:To test associations between deep-learning derived CXR-CVD Risk and markers of cardiovascular disease including coronary artery calcium (CAC) and stenosis ≥50% on CT, systolic blood pressure (SBP), ankle brachial index (ABI), and prevalent myocardial infarction and stroke.Methods/Approach:We conducted external validation of CXR-CVD-Risk in two cohorts: 1) 2,097 volunteers in the Project Baseline Health Study (PBHS) and 2) 1,644 Mass General Brigham Biobank (MGBB) patients. The CXR-CVD-Risk model estimated 10-year cardiovascular event risk (probability between 0 and 1) from a CXR image. We calculated linear associations with SBP, ABI, and the logarithm of coronary artery calcium and odds ratios for prevalent hypertension, myocardial infarction, stroke, and, in the MGBB, coronary artery stenosis ≥50%. Analyses were adjusted for age, BMI, sex, smoking status, and enrolling site.Results/Data:CXR-CVD-Risk was associated with CAC in both populations (PBHS: 1.11-fold increase, 95% CI: [1.07-1.16]; MGBB: 1.03-fold increase [1.01-1.05] in CAC per 1% increase in CXR-CV-Risk). CXR-CVD-Risk was also associated with SBP (0.59 mmHg increase [0.24-0.93] in SBP per 1% increase in CXR-CV-Risk), history of hypertension, history of myocardial infarction, and stroke. There was an inverse association with ABI (0.010 decrease [0.005-0.014] in ABI) in the PBHS. In the MGBB, CXR-CVD-Risk was associated with coronary artery stenosis ≥50% (OR = 1.004 [1.002-1.007]). All estimates are after covariate adjustment.Conclusion:This deep learning CXR risk score was associated with coronary artery disease (calcium score and stenosis ≥50%), CVD risk factors, and prevalent CVD. Opportunistic screening using CXRs in the electronic record can identify patients at high risk of CVD who may benefit from prevention.