Risultati per: Cancro al seno: Screening e diagnosi

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 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 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 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 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.

Circulation, Volume 150, Issue Suppl_1, Page A4145119-A4145119, November 12, 2024. Background:The burden of cardiovascular disease (CVD) is increasing among people with HIV (PWH) in sub-Saharan Africa. Integrating CVD screening into routine HIV care represents an opportunity to diagnose CVD at an earlier stage in a potentially high-risk population.Research questionsIs integrating CVD risk factor screening feasible and sustainable in a public HIV clinic in Mwanza, Tanzania? What is the magnitude of CVD risk of the general adult PWH population? What is the unmet need for blood pressure (BP) and diabetes management?Methods:We adapted the AHA Life’s Essential 8 (LE8) into a rapid questionnaire that was administered to every PWH in a large public adult HIV clinic. Questions included demographics; LE8 risk factors (BMI, diet, physical activity, sleep, and smoking); and the hypertension and diabetes continuum of care. Every patient had their BP measured; BP was measured two additional times for those with an initial BP >140/90 mmHg. We administered random blood glucose screening to anyone with a high BP, obese BMI, current smoking, or history of diabetes. Implementation and effectiveness were evaluated using the RE-AIM framework.Results:In 3 months, 1072 PWH were screened at least once. Mean age was 50 years and 72% were female. On average, PWH had a nutritious diet and received adequate physical activity per AHA guidelines. The prevalence of hypertension was 34%; the continuum of care is shown in Figure 1. Of those screened, 21% had diabetes or pre-diabetes. Evaluation via the RE-AIM framework is shown in Table 1. Successes included the reach and effectiveness of screening in only 3 months. Adoption was the biggest challenge due to staffing and supply constraints. The intervention was feasible, implemented with fidelity, and is ongoing.Conclusions:Integrating CVD risk screening into routine HIV care in a busy Tanzanian clinic was feasible and demonstrated a high magnitude of undiagnosed and untreated hypertension among the general PWH population.

Circulation, Volume 150, Issue Suppl_1, Page A4147650-A4147650, November 12, 2024. Background:Invasive hemodynamics are the gold standard for diagnosis of heart failure with preserved ejection fraction (HFpEF). A novel, FDA-approved artificial intelligence (AI) technology that uses a single, 4-chamber transthoracic echocardiogram (TTE) image to screen patients for HFpEF shows promise as a non-invasive tool to assist in diagnosis. Development of right ventricular (RV) dysfunction is a sign of a more advanced HFpEF. Advanced RV hemodynamic parameters, beyond pulmonary arterial pressures (PAP), have not been well studied in HFpEF. We sought to correlate advanced RV hemodynamic parameters in patients screened for HFpEF with this AI screening tool.Method:We retrospectively evaluated two cohorts of patients with suspected HFpEF that underwent TTE and RHC at our institution. The most recent TTE for each patient was screened using the AI-based analysis tool and was reported as either “suggestive” or “non-suggestive” of HFpEF – labeled as “positive” or “negative,” respectively. Mean PAP, pulmonary vascular resistance (PVR), pulmonary artery pulsatility index (PAPI), RV cardiac power output (RV-CPO), RV myocardial performance score (RV-MPS), and right atrial pressure to pulmonary capillary wedge pressure ratio (RA:PCWP) were calculated using invasive hemodynamic parameters at rest, and exercise when available. RV-CPO was calculated as [(mean PAP-RAP) x cardiac output] /451, and RV-MPS was calculated as (RV-CPO x PAP)x1.5. Median values were calculated. AI positive and negative groups were compared using Student’s t-test.Results:A total of 47 patients (82% women, 79% Black, average EF 62%) were included, with 23 undergoing subsequent exercise RHC. There were 18 (38%) that screened positive for HFpEF, and 29 (62%) screened negative by TTE AI software. Positive patients had a significantly higher mean PAP (median 31 vs 23 mmHg, p=0.01), PVR (2.1 vs 1.3 WU, p=0.02), and RV-CPO (0.26 vs. 0.17, p=0.04) than patients who were screened negative. There were no significant differences in PAPI, RV-MPS, and RA:PCWP at rest. There were no significant differences in mean PAP, PVR, PAPI RV-CPO, RV-MPS, or RA:PCWP with exercise.Conclusion:Patients screened positive for HFpEF by a novel AI TTE software had significantly higher PAP and RV-CPO at rest, but no differences in PAPI, RV-MPS, or RA:PCWP ratio. This tool may help identify more advanced HFpEF.

Circulation, Volume 150, Issue Suppl_1, Page A4145962-A4145962, November 12, 2024. Introduction:Obstructive sleep apnea (OSA) affects nearly a billion adults worldwide, and is associated with an increased risk of coronary artery disease, heart attack, heart failure, and arrhythmias – notably atrial fibrillation (AF). Low cost, point of care mobile electrocardiograms (MobileECGs) record and detect heart rhythm abnormalities in 30 seconds. This study aims to assess the effectiveness of the KardiaMobile (AliveCor) MobileECG device as an AF screen in the OSA patient population.Methods:The MobileECG Sleep Study enrolled 500 adult University of Florida Health patients in an observational study between March 2021 and March 2024. After providing consent and completing a brief survey regarding pre-existing health conditions and overall sleep health, a trained research assistant performed the AF screening with the KardiaMobile ECG device. ECG readings were marked for previously undetected abnormalities (potential AF, tachycardia, bradycardia, etc.) and statistically analyzed to determine stroke risk using the CHA2DS2-VASc scoring system. CHA2DS2-VASc criteria includes congestive heart failure, hypertension, age ≥75 (doubled), diabetes, stroke (doubled), vascular disease, age 65 to 74 and sex category (female).Results:A total of 500 participants were enrolled over a 3 year period at University of Florida Health Sleep Center. Of which 276 (55.2%) were female and 224 (44.8%) were male, with a mean age of 56.34 (SD 15.74) and a mean weight of 222.50 (SD 63.25). Of those tested, 68 (13.6%) had irregular, previously undetected AF readings. Patients with irregular AF readings using the KardiaMobile ECG device had CHA2DS2-VASc scores of t(68) = 2.15, p = .042, d = 0.26 indicating an intermediate risk for stroke. Oral anticoagulation is recommended for a score of ≥ 2 if the patient has no contraindication. After prior 12-lead ECG data for patients is obtained the determinations will be compared to the KardiaMobile ECG readings using Cohen’s Kappa.Conclusion:MobileECGs offer a rapid, point of care screening tool for AF in an outpatient sleep clinic setting. Early detection of AF in the OSA patient population can result in improved outcomes and reduced instances of stroke events through anticoagulation therapy guided by CHA2DS2-VASc scores. Further research is necessary to understand the long term impact of surveillance AF screening in high risk patient populations on mortality and cost of healthcare.

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 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 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 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 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 A4137986-A4137986, November 12, 2024. Background:Clinical trial screening is labor-intensive, time-consuming, and error prone. We have developed RECTIFIER, an AI-based clinical trial screening tool, to enhance the efficiency and accuracy of patient recruitment. This study aims to evaluate RECTIFIER’s effectiveness compared to manual screening in a randomized implementation study.Methods:This study was designed as an implementation study as part of an active heart failure trial named COPILOT-HF (NCT05734690). Potential eligible patients were identified via a structured electronic medical record query and randomized to be screened for clinical trial eligibility either by RECTIFIER or manually by clinical staff. The outcome measures included the number of patients contacted, and the number of patients reached for clinical trial enrollment. Data was collected over a period of 3 months.Results:A total of 3834 patients were included in the study, with 1919 patients randomized to the RECTIFIER group and 1915 patients to the manual screening group (Figure). Study staff could manually screen only 1367 patients at the end of the 3-month period. RECTIFIER identified more eligible patients compared to manual screening (833[43.4%] vs. 284[14.8%], p

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 A4147292-A4147292, November 12, 2024. Background:Tissue hypoxia and chronic anemia associated with sickle cell disease (SCD) leads to structural and physiological alterations in the heart. Early detection of heart failure (HF) in patients with SCD can assist with timely interventions, but current methods (e.g., echocardiogram and heart MRI) are not easily accessible in resource-deprived settings. The integration of artificial intelligence (AI)-powered tools utilizing low-cost ECG data to increase the power to detect more patients eligible for early treatment, thus improving patient outcomes, and needs to be validated.Hypothesis:We hypothesize that ECG-AI models developed to detect incident HF in the general population can detect HF in SCD patients.Methods/Approach:We previously developed an ECG-AI model employing convolutional neural networks to classify patients with HF using a large ECG-repository at Wake Forest Baptist Health (WFBH). This model was developed using 1,078,198 digital ECGs from 165,243 patients, 73% White, 19% Black, and 52% female individuals, with a mean age (SD) of 58 (15) years. The hold-out AUC of this previous model in distinguishing ECGs of HF patients from controls was 0.87. In this study, we externally validated this ECG-AI model using SCD patients’ data from the University of Tennessee Health Science Center (UTHSC). Additionally, a logistic regression (LR) model was constructed in the UTHSC cohort by incorporating other simple demographic variables with the outcome of ECG-AI model.Results/Data:The UTHSC external validation cohort included data from 2,107 SCD patients (188 HF and 1,919 SCD patients with no HF), 98% were Black, 72% were female, with a mean age of 39 (14) years. Despite demographic differences between the validation (more Blacks) and derivation cohorts (lower age), our ECG-AI model accurately identified HF with an AUC of 0.80 (0.77-0.82) in the UTHSC SCD cohort. When incorporating ECG-AI outcome (an ECG-based risk value between 0 and 1), age, sex, and race in a LR model, the AUC significantly improved (DeLong Test, p