Risultati per: Screening del cancro del pancreas

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

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

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

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

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

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

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

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