Risultati per: Screening della retinopatia diabetica

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

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

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

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

Circulation, Volume 150, Issue Suppl_1, Page 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 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 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 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.