Pub Date : 2024-09-07DOI: 10.1101/2024.09.06.24313219
Luca Fazzini, Matteo Castrichini, Yan Li, Jose F de Melo, Marta Figueiral, Jenny J Cao, Eric Klee, Christian Cadeddu Dessalvi, Martha Grogan, Angela Dispenzieri, Naveen L. Pereira
BACKGROUND: Hereditary transthyretin amyloid cardiomyopathy (ATTRv-CM) is being increasingly diagnosed due to enhanced awareness and availability of newer therapeutics. Multiple TTR variants have been described worldwide, but with uncertain disease penetrance. The characteristics and outcomes of "previously undiagnosed" pathogenic-likely pathogenic (P/LP) TTR variant (genotype or G+; cardiac phenotype or P-) carriers are unknown which has important prognostic and therapeutic implications, especially for affected family members. This descriptive study aimed to delineate phenotype and cardiac penetrance in "previously undiagnosed" G+P- family members of ATTRv probands. METHODS: Demographic, electrocardiographic (ECG), genetic, and imaging (echocardiography, cardiac technetium-99m pyrophosphate (PYP) and magnetic resonance imaging) data were analyzed. The prediction effect of selected baseline characteristics for ATTRv-CM development was evaluated. Kaplan-Meier and Cox regression methods were used to describe risk and predictors of ATTRv-CM development in family members. RESULTS: There were 85 G+P- family members identified. Mean age was 48.5±11.7 years, 39% were male, 18% had a diagnosis of peripheral neuropathy, 15% with a history of carpal tunnel syndrome, and 4% had atrioventricular block at baseline. Of these, 55 patients had follow-up imaging studies. After a median 6.8-year follow-up, 22% developed ATTR-CM with a 10-year estimated risk of 29.5% (95% CI 7.9-46.0). Cardiac penetrance increased with increasing family member's age. Proband's diagnosis age (p=0.0096) and artificial intelligence (AI)-ECG prediction (p=0.0091) were promising baseline predictors of time to ATTRv-CM development. CONCLUSION: In previously undiagnosed G+P- ATTRv family members, the incidence of subsequent CM is high. Predictors for CM development such as proband's diagnosis age and AI-determined ECG probability of ATTR-CM require further investigation.
{"title":"Phenotypic presentation of family members of ATTRv probands and subsequent disease penetrance","authors":"Luca Fazzini, Matteo Castrichini, Yan Li, Jose F de Melo, Marta Figueiral, Jenny J Cao, Eric Klee, Christian Cadeddu Dessalvi, Martha Grogan, Angela Dispenzieri, Naveen L. Pereira","doi":"10.1101/2024.09.06.24313219","DOIUrl":"https://doi.org/10.1101/2024.09.06.24313219","url":null,"abstract":"BACKGROUND: Hereditary transthyretin amyloid cardiomyopathy (ATTRv-CM) is being increasingly diagnosed due to enhanced awareness and availability of newer therapeutics. Multiple TTR variants have been described worldwide, but with uncertain disease penetrance. The characteristics and outcomes of \"previously undiagnosed\" pathogenic-likely pathogenic (P/LP) TTR variant (genotype or G+; cardiac phenotype or P-) carriers are unknown which has important prognostic and therapeutic implications, especially for affected family members. This descriptive study aimed to delineate phenotype and cardiac penetrance in \"previously undiagnosed\" G+P- family members of ATTRv probands. METHODS: Demographic, electrocardiographic (ECG), genetic, and imaging (echocardiography, cardiac technetium-99m pyrophosphate (PYP) and magnetic resonance imaging) data were analyzed. The prediction effect of selected baseline characteristics for ATTRv-CM development was evaluated. Kaplan-Meier and Cox regression methods were used to describe risk and predictors of ATTRv-CM development in family members. RESULTS: There were 85 G+P- family members identified. Mean age was 48.5±11.7 years, 39% were male, 18% had a diagnosis of peripheral neuropathy, 15% with a history of carpal tunnel syndrome, and 4% had atrioventricular block at baseline. Of these, 55 patients had follow-up imaging studies. After a median 6.8-year follow-up, 22% developed ATTR-CM with a 10-year estimated risk of 29.5% (95% CI 7.9-46.0). Cardiac penetrance increased with increasing family member's age. Proband's diagnosis age (p=0.0096) and artificial intelligence (AI)-ECG prediction (p=0.0091) were promising baseline predictors of time to ATTRv-CM development.\u0000CONCLUSION: In previously undiagnosed G+P- ATTRv family members, the incidence of subsequent CM is high. Predictors for CM development such as proband's diagnosis age and AI-determined ECG probability of ATTR-CM require further investigation.","PeriodicalId":501297,"journal":{"name":"medRxiv - Cardiovascular Medicine","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142227462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-06DOI: 10.1101/2024.09.06.24313174
Sai Koundinya Upadhyayula
Coronary artery disease (CAD), primarily driven by atherosclerosis, poses significant health risks, contributing to a rising mortality rate globally. This study introduces a deep learning framework designed for the automated segmentation of coronary arteries and quantification of coronary artery calcium (CAC) from CT scans, facilitating improved risk stratification in patients. Leveraging data from the National Lung Screening Trial, we developed a three-step model that includes heart localization, coronary calcium segmentation, and calcium scoring. Various configurations of the UNet architecture were employed, with the Extended UNet utilizing an autoencoder achieving the highest validation performance, reflected by an Intersection over Union (IoU) score of 0.78 and an F1 score of 0.83. The model's efficacy was validated against manually segmented masks, showcasing its potential for accurate risk assessment based on CAC scores. This automated approach significantly reduces the time and expertise required for traditional calcium scoring, enabling rapid and reliable assessments in clinical settings. Our findings indicate that the deep learning system can effectively classify patients into risk categories, underscoring its potential utility in enhancing the management of CAD and improving patient outcomes. This research highlights the feasibility of integrating advanced computational techniques into routine clinical practice, paving the way for more efficient cardiovascular risk stratification.
{"title":"Deep Learning based CT-scan Coronary Artery Segmentation and Calcium Scoring","authors":"Sai Koundinya Upadhyayula","doi":"10.1101/2024.09.06.24313174","DOIUrl":"https://doi.org/10.1101/2024.09.06.24313174","url":null,"abstract":"Coronary artery disease (CAD), primarily driven by atherosclerosis, poses significant health risks, contributing to a rising mortality rate globally. This study introduces a deep learning framework designed for the automated segmentation of coronary arteries and quantification of coronary artery calcium (CAC) from CT scans, facilitating improved risk stratification in patients. Leveraging data from the National Lung Screening Trial, we developed a three-step model that includes heart localization, coronary calcium segmentation, and calcium scoring. Various configurations of the UNet architecture were employed, with the Extended UNet utilizing an autoencoder achieving the highest validation performance, reflected by an Intersection over Union (IoU) score of 0.78 and an F1 score of 0.83.\u0000The model's efficacy was validated against manually segmented masks, showcasing its potential for accurate risk assessment based on CAC scores. This automated approach significantly reduces the time and expertise required for traditional calcium scoring, enabling rapid and reliable assessments in clinical settings. Our findings indicate that the deep learning system can effectively classify patients into risk categories, underscoring its potential utility in enhancing the management of CAD and improving patient outcomes. This research highlights the feasibility of integrating advanced computational techniques into routine clinical practice, paving the way for more efficient cardiovascular risk stratification.","PeriodicalId":501297,"journal":{"name":"medRxiv - Cardiovascular Medicine","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142218930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-06DOI: 10.1101/2024.09.05.24313076
Haodong Tian, Yuxi Liu, Frederick Au, Guannning Lin
Fetal heart health is a critical part of diagnosis and treatment, and one of the methods is fetal cardiac ultrasound. A key aspect of the process is the detection of standard ultrasound slices, which is essential for accurate diagnosis. The effectiveness of diagnosis relies heavily on the clinical experience and expertise of the ultrasound physician. To improve detection efficiency and minimize misdiagnosis, we developed a single-stage detection model for fetal cardiac ultrasound standard planes (FCUM) that uses multi-task learning and hybrid attention mechanisms to support the ultrasound physician’s diagnostic work.
{"title":"Enhancing Fetal Cardiac Ultrasound Diagnosis: A Multi-Task Hybrid Attention Model for Accurate Standard Plane Detection","authors":"Haodong Tian, Yuxi Liu, Frederick Au, Guannning Lin","doi":"10.1101/2024.09.05.24313076","DOIUrl":"https://doi.org/10.1101/2024.09.05.24313076","url":null,"abstract":"Fetal heart health is a critical part of diagnosis and treatment, and one of the methods is fetal cardiac ultrasound. A key aspect of the process is the detection of standard ultrasound slices, which is essential for accurate diagnosis. The effectiveness of diagnosis relies heavily on the clinical experience and expertise of the ultrasound physician. To improve detection efficiency and minimize misdiagnosis, we developed a single-stage detection model for fetal cardiac ultrasound standard planes (FCUM) that uses multi-task learning and hybrid attention mechanisms to support the ultrasound physician’s diagnostic work.","PeriodicalId":501297,"journal":{"name":"medRxiv - Cardiovascular Medicine","volume":"72 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142218929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-06DOI: 10.1101/2024.09.06.24313111
Carlo Pappone, Adriana Tarantino, Dario Melgari, Marco Piccoli, Giuseppe Ciconte, Anthony Frosio, Emanuele Micaglio, Serena Calamaio, Chiara Vantellino, Federica Cirillo, Pasquale Creo, Valerio Castoldi, Rachele Prevostini, Alessandro De Toma, Antonio Boccellino, Gabriele Negro, Luigi Giannelli, Zarko Calovic, Letizia Leocani, Vincenzo Santinelli, Domenico De Toma, Ilaria Rivolta, Luigi Anastasia
Background and Aims Patients with metastatic breast cancer have an increased risk of sudden cardiac death (SCD) that cannot be fully explained by cardiotoxic treatments. Recent evidence shows that autoantibodies targeting the cardiac NaV1.5 sodium channel in Brugada syndrome (BrS) can trigger arrhythmias and elevate SCD risk. Similarly, autoantibodies against the neonatal NaV1.5 isoform have been found in metastatic breast cancer patients. Given the high homology between these NaV1.5 isoforms, we investigated whether these autoantibodies cross-react with the cardiac isoform, potentially contributing to SCD in this population. Methods Plasma from twenty metastatic breast cancer patients was analyzed for anti-NaV1.5 autoantibodies using HEK293A cells expressing the NaV1.5 protein, followed by Western blotting. The effects of these autoantibodies on sodium current density were assessed in cellular models and wild-type mice, with electrocardiographic monitoring after plasma infusion. Results Fifteen plasma samples from metastatic breast cancer patients tested positive for anti-NaV1.5 autoantibodies, significantly reducing sodium current density in vitro. Mice injected with these plasma samples developed severe arrhythmias and a Brugada syndrome-like ECG pattern. In contrast, plasma samples either without the autoantibodies or with IgG depletion showed no such effects, underscoring the role of IgG in sodium current reduction and confirming the pathogenicity of the autoantibodies. Conclusions This study demonstrates that anti-NaV1.5 autoantibodies in metastatic breast cancer patients can cross-react with the cardiac NaV1.5 isoform, potentially leading to fatal arrhythmias. These findings highlight a novel mechanism for the high SCD rate in this population and suggest that therapies involving sodium blockers should be used with caution to avoid exacerbating this risk. Reliable diagnostic tests and targeted therapies are urgently needed to mitigate SCD risk in affected patients.
{"title":"Cardiac Cross-Reactivity of NaV Autoantibodies in Metastatic Breast Cancer: A Possible Trigger for Sudden Cardiac Death","authors":"Carlo Pappone, Adriana Tarantino, Dario Melgari, Marco Piccoli, Giuseppe Ciconte, Anthony Frosio, Emanuele Micaglio, Serena Calamaio, Chiara Vantellino, Federica Cirillo, Pasquale Creo, Valerio Castoldi, Rachele Prevostini, Alessandro De Toma, Antonio Boccellino, Gabriele Negro, Luigi Giannelli, Zarko Calovic, Letizia Leocani, Vincenzo Santinelli, Domenico De Toma, Ilaria Rivolta, Luigi Anastasia","doi":"10.1101/2024.09.06.24313111","DOIUrl":"https://doi.org/10.1101/2024.09.06.24313111","url":null,"abstract":"Background and Aims\u0000Patients with metastatic breast cancer have an increased risk of sudden cardiac death (SCD) that cannot be fully explained by cardiotoxic treatments. Recent evidence shows that autoantibodies targeting the cardiac NaV1.5 sodium channel in Brugada syndrome (BrS) can trigger arrhythmias and elevate SCD risk. Similarly, autoantibodies against the neonatal NaV1.5 isoform have been found in metastatic breast cancer patients. Given the high homology between these NaV1.5 isoforms, we investigated whether these autoantibodies cross-react with the cardiac isoform, potentially contributing to SCD in this population. Methods\u0000Plasma from twenty metastatic breast cancer patients was analyzed for anti-NaV1.5 autoantibodies using HEK293A cells expressing the NaV1.5 protein, followed by Western blotting. The effects of these autoantibodies on sodium current density were assessed in cellular models and wild-type mice, with electrocardiographic monitoring after plasma infusion. Results\u0000Fifteen plasma samples from metastatic breast cancer patients tested positive for anti-NaV1.5 autoantibodies, significantly reducing sodium current density in vitro. Mice injected with these plasma samples developed severe arrhythmias and a Brugada syndrome-like ECG pattern. In contrast, plasma samples either without the autoantibodies or with IgG depletion showed no such effects, underscoring the role of IgG in sodium current reduction and confirming the pathogenicity of the autoantibodies. Conclusions\u0000This study demonstrates that anti-NaV1.5 autoantibodies in metastatic breast cancer patients can cross-react with the cardiac NaV1.5 isoform, potentially leading to fatal arrhythmias. These findings highlight a novel mechanism for the high SCD rate in this population and suggest that therapies involving sodium blockers should be used with caution to avoid exacerbating this risk. Reliable diagnostic tests and targeted therapies are urgently needed to mitigate SCD risk in affected patients.","PeriodicalId":501297,"journal":{"name":"medRxiv - Cardiovascular Medicine","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142227263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-05DOI: 10.1101/2024.09.04.24312980
Rajashekar Korutla, Anne Hicks, Marko Milosevic, Dipti Kulkarni, Felistas Mazhude, Mehdi Mortazawy, Yashar Seyed Vahedein, Tyler Kelting, Jaime B Rabib, Qingchu Jin, Robert Kramer, Douglas Sawyer, Raimond L Winslow, Saeed Amal
Accurate prediction of post-operative adverse events following cardiothoracic surgery is crucial for timely interventions, potentially improving patient outcomes and reducing healthcare costs. By leveraging advanced deep learning techniques, this study highlights the transformative potential of incorporating intraoperative variables into predictive analytics models to enhance postoperative care for cardiothoracic surgery patients in the ICU. We developed deep learning predictive models for anticipating adverse events in patients following cardiothoracic surgery using a dataset from the Society of Thoracic Surgeons’ database (4) and intraoperative data. Our models perform late fusion by integrating static patient data and intra-operative time-series data, utilizing Fully Connected Neural Networks (FCNN) and long short-term memory (LSTM) networks, respectively. The hybrid model was validated through five-fold cross-validation, demonstrating robust performance with a mean AUC of 0.93, Sensitivity of 0.83 and Specificity of 0.89. This work represents a significant step forward in the proactive management of cardio thoracic surgery patients in the ICU by effectively predicting potential adverse events associated with mortality in the post operative period.
{"title":"Deep Learning Model and Multi Modal Late Fusion For Predicting Adverse Events Following Cardiothoracic Surgery in the ICU Using STS Data and Time Series Intraoperative Data","authors":"Rajashekar Korutla, Anne Hicks, Marko Milosevic, Dipti Kulkarni, Felistas Mazhude, Mehdi Mortazawy, Yashar Seyed Vahedein, Tyler Kelting, Jaime B Rabib, Qingchu Jin, Robert Kramer, Douglas Sawyer, Raimond L Winslow, Saeed Amal","doi":"10.1101/2024.09.04.24312980","DOIUrl":"https://doi.org/10.1101/2024.09.04.24312980","url":null,"abstract":"Accurate prediction of post-operative adverse events following cardiothoracic surgery is crucial for timely interventions, potentially improving patient outcomes and reducing healthcare costs. By leveraging advanced deep learning techniques, this study highlights the transformative potential of incorporating intraoperative variables into predictive analytics models to enhance postoperative care for cardiothoracic surgery patients in the ICU. We developed deep learning predictive models for anticipating adverse events in patients following cardiothoracic surgery using a dataset from the Society of Thoracic Surgeons’ database (<sup>4</sup>) and intraoperative data. Our models perform late fusion by integrating static patient data and intra-operative time-series data, utilizing Fully Connected Neural Networks (FCNN) and long short-term memory (LSTM) networks, respectively. The hybrid model was validated through five-fold cross-validation, demonstrating robust performance with a mean AUC of 0.93, Sensitivity of 0.83 and Specificity of 0.89. This work represents a significant step forward in the proactive management of cardio thoracic surgery patients in the ICU by effectively predicting potential adverse events associated with mortality in the post operative period.","PeriodicalId":501297,"journal":{"name":"medRxiv - Cardiovascular Medicine","volume":"59 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142218957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-04DOI: 10.1101/2024.09.02.24312944
Joerg Reifart, Paul Anthony Iaizzo
Introduction Access to simulators for interventional cardiology is currently limited. High acuity, low occurrence procedures (HALO), such as coronary perforation or iatrogenic dissection, are not trained in currently available simulators. We developed a cost-effective coronary intervention simulator designed to enhance the training of both novice and experienced interventionalists.
{"title":"Enhancing Interventional Cardiology Training: A Porcine Heart-Based Coronary Intervention Simulator","authors":"Joerg Reifart, Paul Anthony Iaizzo","doi":"10.1101/2024.09.02.24312944","DOIUrl":"https://doi.org/10.1101/2024.09.02.24312944","url":null,"abstract":"<strong>Introduction</strong> Access to simulators for interventional cardiology is currently limited. High acuity, low occurrence procedures (HALO), such as coronary perforation or iatrogenic dissection, are not trained in currently available simulators. We developed a cost-effective coronary intervention simulator designed to enhance the training of both novice and experienced interventionalists.","PeriodicalId":501297,"journal":{"name":"medRxiv - Cardiovascular Medicine","volume":"59 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142218961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-04DOI: 10.1101/2024.09.03.24313045
Young Jun Park, Sungjoo Lee, Sungjun Hong, Kyunga Kim, Juwon Kim, Ju Youn Kim, Kyoung-Min Park, Young Keun On, Seung-Jung Park
Background Previous studies on pacemaker-associated heart failure (PaHF) have predominantly analyzed relatively small, single-center datasets, mainly focusing on incidence and predictors. However, the clinical implications of PaHF on mortality, particularly in relation to standard HF medications or upgrading to cardiac resynchronization therapy (CRT), has been underexplored.
{"title":"Risks and long-term prognosis of new-onset heart failure after de novo permanent pacemaker implantation: nationwide cohort study","authors":"Young Jun Park, Sungjoo Lee, Sungjun Hong, Kyunga Kim, Juwon Kim, Ju Youn Kim, Kyoung-Min Park, Young Keun On, Seung-Jung Park","doi":"10.1101/2024.09.03.24313045","DOIUrl":"https://doi.org/10.1101/2024.09.03.24313045","url":null,"abstract":"<strong>Background</strong> Previous studies on pacemaker-associated heart failure (PaHF) have predominantly analyzed relatively small, single-center datasets, mainly focusing on incidence and predictors. However, the clinical implications of PaHF on mortality, particularly in relation to standard HF medications or upgrading to cardiac resynchronization therapy (CRT), has been underexplored.","PeriodicalId":501297,"journal":{"name":"medRxiv - Cardiovascular Medicine","volume":"303 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142218956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-04DOI: 10.1101/2024.09.03.24312989
Huijun Edelyn Park, Leslie S. Cho, Natalia Fendrikova-Mahlay, Pulkit Chaudhury, Scott J. Cameron
Background Spontaneous coronary artery dissection (SCAD) is an understudied cause of acute coronary syndrome (ACS), particularly in women. Heart muscle damage may result from spontaneous dissection of coronary arteries. There is no clear consensus regarding the optimum antiplatelet medication regimen and treatment duration for SCAD despite current American Heart Association (AHA) consensus guidelines recommending 12-month regimen of dual antiplatelet therapy (DAPT) consisting of a P2Y12 inhibitor and aspirin for patients following myocardial infarction (MI). The objective of this study was to evaluate the safety and effectiveness of DAPT compared to using a single antiplatelet therapy (SAPT) as part of the medical armamentarium to treat SCAD.
{"title":"Antiplatelet Therapy Following Spontaneous Coronary Artery Dissection: Systemic Review","authors":"Huijun Edelyn Park, Leslie S. Cho, Natalia Fendrikova-Mahlay, Pulkit Chaudhury, Scott J. Cameron","doi":"10.1101/2024.09.03.24312989","DOIUrl":"https://doi.org/10.1101/2024.09.03.24312989","url":null,"abstract":"<strong>Background</strong> Spontaneous coronary artery dissection (SCAD) is an understudied cause of acute coronary syndrome (ACS), particularly in women. Heart muscle damage may result from spontaneous dissection of coronary arteries. There is no clear consensus regarding the optimum antiplatelet medication regimen and treatment duration for SCAD despite current American Heart Association (AHA) consensus guidelines recommending 12-month regimen of dual antiplatelet therapy (DAPT) consisting of a P2Y<sub>12</sub> inhibitor and aspirin for patients following myocardial infarction (MI). The objective of this study was to evaluate the safety and effectiveness of DAPT compared to using a single antiplatelet therapy (SAPT) as part of the medical armamentarium to treat SCAD.","PeriodicalId":501297,"journal":{"name":"medRxiv - Cardiovascular Medicine","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142218958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-04DOI: 10.1101/2024.09.03.24313046
Rebecca K Kelly, Katie Harris, Cheryl Carcel, Paul Muntner, Mark Woodward
Background Recent studies show that the risk of cardiovascular disease (CVD) increases from a lower nadir of systolic blood pressure (SBP) in women than men, and increases thereafter at a greater rate. This has led to a suggestion that sex-based SBP thresholds are required. We aimed to investigate sex differences in the associations of SBP and incident atherosclerotic CVD in a large prospective cohort.
{"title":"Should women have lower blood pressure targets than men? Sex differences in blood pressure and cardiovascular disease in the UK Biobank","authors":"Rebecca K Kelly, Katie Harris, Cheryl Carcel, Paul Muntner, Mark Woodward","doi":"10.1101/2024.09.03.24313046","DOIUrl":"https://doi.org/10.1101/2024.09.03.24313046","url":null,"abstract":"<strong>Background</strong> Recent studies show that the risk of cardiovascular disease (CVD) increases from a lower nadir of systolic blood pressure (SBP) in women than men, and increases thereafter at a greater rate. This has led to a suggestion that sex-based SBP thresholds are required. We aimed to investigate sex differences in the associations of SBP and incident atherosclerotic CVD in a large prospective cohort.","PeriodicalId":501297,"journal":{"name":"medRxiv - Cardiovascular Medicine","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142218931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-03DOI: 10.1101/2024.08.31.24312888
Paul-Adrian Călburean, Anda-Cristina Scurtu, Paul Grebenisan, Ioana-Andreea Nistor, Victor Vacariu, Reka-Katalin Drincal, Ioana Paula Sulea, Tiberiu Oltean, László Hadadi
Introduction Out-of-hospital mortality in coronary artery disease (CAD) is particularly high and established adverse event prediction tools are yet to be available. Our study aimed to investigate whether precision phenotyping can be performed using routine laboratory parameters for the prediction of out-of-hospital survival in a CAD population treated by percutaneous coronary intervention (PCI).
{"title":"Precision phenotyping from routine laboratory parameters for machine learning out-of-hospital survival prediction using 4D time-dependent SHAP plots in an all-comers prospective PCI registry","authors":"Paul-Adrian Călburean, Anda-Cristina Scurtu, Paul Grebenisan, Ioana-Andreea Nistor, Victor Vacariu, Reka-Katalin Drincal, Ioana Paula Sulea, Tiberiu Oltean, László Hadadi","doi":"10.1101/2024.08.31.24312888","DOIUrl":"https://doi.org/10.1101/2024.08.31.24312888","url":null,"abstract":"<strong>Introduction</strong> Out-of-hospital mortality in coronary artery disease (CAD) is particularly high and established adverse event prediction tools are yet to be available. Our study aimed to investigate whether precision phenotyping can be performed using routine laboratory parameters for the prediction of out-of-hospital survival in a CAD population treated by percutaneous coronary intervention (PCI).","PeriodicalId":501297,"journal":{"name":"medRxiv - Cardiovascular Medicine","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142218959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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