tbuckley/cabot-search huggingface dataset:
Abstract 4138355: Machine Learning for Prediction of All-Cause Mortality in Acute Coronary Syndrome
Authors: Aashray Gupta, Ammar Zaka, Cecil Mustafiz, Daud Mutahar, Razeen Parvez, Brandon Stretton, Joshua Kovoor, Naim Mridha, Fabio Ramponi, Justin Chan, Paul Gould, Gopal Sivagangabalan, Sarah Zaman, Aravinda Thiagalingam, Clara Chow, Pramesh Kovoor, Stephen Bacchi, Jayme Bennetts, Guy Maddern
Journal: Circulation (2024)

Background: Acute coronary syndrome (ACS) remains one of the leading causes of death globally. Accurate and reliable mortality risk prediction of ACS patients is essential for developing targeted treatment strategies. Existing traditional models offer moderate discriminative value, and do not incorporate contemporary predictors of ACS prognosis. Machine learning (ML) models have emerged as an alternate method that may offer improved risk assessment. This study aims to compare machine learning models with traditional risk scores for predicting all-cause mortality in patients with ACS. Methods: PubMed, EMBASE, Web of Science and Cochrane databases were searched until 1 st November 2023 for studies comparing ML models with traditional statistical methods for event prediction of ACS patients. The primary outcome was comparative discrimination measured by C-statistics with 95% confidence intervals between ML models and traditional methods in estimating the risk of all-cause mortality. Results: Ten studies were included (239627 patients). The summary C-statistic of all ML models across all endpoints was 0.89 (95% CI, 0.86-0.92), compared to traditional methods 0.82 (95% CI, 0.79-0.85). The difference in C-statistic between all ML models and traditional methods was 0.07 (p&lt;0.05). Three models undertook external validation, and calibration was inconsistently reported. Conclusion: ML models demonstrated superior discrimination of all-cause mortality for ACS patients compared to traditional risk scores. Despite outperforming well-established prognostic tools such as the GRACE and TIMI scores, current clinical applications of ML approaches remain uncertain, particularly in view of the methodological limitations of existing studies and the need for greater model validation.
Abstract 4138407: Exome-wide association study of over a million individuals identified effector genes and variants for blood lipids
Authors: Satoshi Koyama, Zhi Yu, Seung Hoan Choi, Sean Jurgens, Margaret Selvaraj, Derek Klarin, Jennifer Huffman, Michael Trinh, Akshaya Ravi, Jacqueline Dron, Catherine Spinks, Ida Surakka, Whitney Hornsby, Scott Damrauer, Kyong-Mi Chang, Themistocles Assimes, Philip Tsao, Daniel Rader, Kelly Kim, Gina Peloso, Patrick Ellinor, Yan Sun, Peter Wilson, Pradeep Natarajan
Journal: Circulation (2024)

Background: Inherited hypercholesterolemia is an important Mendelian condition associated with early onset myocardial infarction. However, current genetic diagnostic criteria only partially explain severe clinical hypercholesterolemia. Furthermore, current pathogenicity categorizations do not consistently inform prognosis, but quantification is limited by scarcity of such rare variants. Aim: Exploiting populations scale genotype/phenotype data, to determine the architecture of the genetic variants associating with blood lipid levels with quantitative assessments. Methods: Here, we discovered and characterized rare coding alleles contributing to genetic dyslipidemia, a principal risk for coronary artery disease, among over a million individuals combining two large contemporary genetic datasets (Million Veteran Program N = 626,554 and UK Biobank, N = 431,026). Results: Testing 2,869,269 rare (minor allele frequency &lt; 1%), coding variants, we identified 793 exome-wide significant associations ( P &lt; 4.4 × 10 -11 ), including 8 previously undiscovered loci. Associated alleles are highly enriched in functional variant classes; high confidence predicted loss of function, deleterious missense, and cryptic splicing, showed significant and recessive associations, had similar effects across populations. Overall, rare variants contributed to additional &gt; 16% of genetic trait variances on top of common genetic variants. The effect estimates of associated signals confirmed and refined curated pathogenic database, highlighted by the identification of African or South Asian specific pathogenic alleles previously controversial in its pathogenicity. Conclusion: This study provides resources and insights for understanding causal mechanisms and quantifying the expressivity of rare coding alleles for blood lipids by large scale sequencing study. Furthermore, identifying and characterizing population enriched variants, our study underscored the importance of multi-population rare variant study.
Abstract 4138446: Ceramides Surpass LDL-Cholesterol in Predicting Cardiovascular Mortality in Diabetic and Non-Diabetic Patients
Authors: Andreas Leiherer, Axel Muendlein, Christoph H Saely, Thomas Plattner, Barbara Larcher, Arthur Mader, Alexander Vonbank, Reijo Laaksonen, Peter Fraunberger, Heinz Drexel
Journal: Circulation (2024)

The power of cardiovascular risk prediction is biased by comorbidities (including T2DM), medical treatment, and age, which impact cholesterol levels. Consequently, when applying LDL-C, the true cardiovascular risk of such confounded patients is often underestimated. However, it is not clear whether this constraint for risk prediction may likewise apply to other lipid risk markers in particular ceramides. In this observational cohort study, we recorded cardiovascular mortality for 16 years in 1195 patients. Their median age was 67 years, all of them had a high preexisting cardiovascular risk, 51% were taking statins, and 30% had T2DM. We found a U-shaped association between cardiovascular mortality and LDL-C. We thus stratified the population according to the vertex into high (≥150mg/dL) and lower LDL-C (&lt;150mg/dL) and also according to the T2DM status. A Cox regression analysis revealed that LDL-C failed to predict cardiovascular mortality in any subgroup. In contrast, no U-shaped but a linear association was found for ceramide-based markers. They were able to significantly predict cardiovascular mortality after stratification and even after multivariate adjustment. We thus propose that ceramide-based predictors rather than LDL-C should be applied for a more accurate cardiovascular risk prediction in diabetic and other high-risk patients.
Abstract 4138335: A drug target Mendelian randomization study of triglyceride lowering therapies for aortic stenosis
Authors: Jonathan Ciofani, Daniel Han, Dipender Gill, Karan Rao, Usaid Allahwala, Ravinay Bhindi
Journal: Circulation (2024)

Introduction: Large observational and Mendelian randomization (MR) studies have demonstrated a strong association between elevated triglycerides (TG) and risk of aortic stenosis (AS). However, at present there remains no evidence for the use of lipid-lowering therapies to prevent or treat AS. To address this gap, we used a drug target MR approach to investigate whether emerging TG lowering therapies may reduce the risk of AS. Methods: We collected summary statistics for TG levels and AS from genome-wide association studies including 1,253,277 and 653,867 European participants, respectively. We first performed conventional MR techniques to investigate a causal relationship between TG levels and AS risk. Next, we identified genetic proxies for the TG-lowering therapies fenofibrate, volanesorsen and evinacumab as single nucleotide polymorphisms (SNPs) within 200kb of the target genes ( PPARA, APOC3 and ANGPTL3 , respectively) which were also significantly associated with TG at p&lt;5x10 -8 and clumped using threshold r 2 &lt;0.1. Inverse-variance weighted (IVW) was the primary analysis method. We then performed sensitivity analyses including weighted-median and MR-Egger. Additional outlier-exclusion analyses were performed using MR-PRESSO and Cook’s distance. Finally, we completed sensitivity analyses using a more stringent clumping threshold of r 2 &lt;0.01. Results: Consistent with previous findings we found genetically predicted TG levels were significantly associated with increased risk of AS (IVW: OR 1.28 per standard deviation increase in genetically predicted log TG level, 95% CI 1.18-1.39, p&lt;0.0001). We further found that genetically proxied volanesorsen was significantly associated with reduced risk of AS (OR 0.54, 95% CI 0.39-0.75, p=0.0002), which was robust in all sensitivity analyses. We did not find evidence of a causal relationship between genetically proxied fenofibrate (IVW: OR 0.50, 95% CI 0.09-2.76, p=0.43) or evinacumab (IVW: OR 1.03, 95% CI 0.73-1.44, p=0.88) and risk of AS. Conclusions: Genetically proxied APOC3 inhibition, but not PPARA or ANGPTL3 , was robustly associated with reduced risk of AS. This suggests that APOC3 inhibition may be a therapeutic opportunity for AS management and warrants further research in the clinical trial setting.
Abstract 4138376: A Machine Learning Approach to Predict Percutaneous Coronary Intervention in Patients with Critical Illness and Signs of Myocardial Injury
Authors: Joshua Mueller, Daria Stepanova, Vignesh Chidambaram, Ukash Nakarmi, Subhi Al'Aref
Journal: Circulation (2024)

Background: Critically ill patients frequently experience multi-organ dysfunction and unstable vital signs, increasing their risk of myocardial injury and elevated serum troponin levels. Myocardial injury from oxygen supply-demand mismatch, whether due to systemic disease or obstructive coronary artery disease (CAD), often leads to patients undergoing an invasive coronary angiogram (ICA) and potentially percutaneous coronary intervention (PCI) if obstructive CAD is identified. However, this procedure carries significant risks for critically ill patients. To address this challenge, we aimed to develop a machine learning (ML) model to predict the need for PCI in critically ill patients with myocardial injury. Methods: The study cohort included critically ill patients with elevated serum troponin levels who underwent ICA during their hospitalization at a single institution from 2012 to 2022. We defined critical illness as a life-threatening condition requiring intensive support for vital sign instability. Patients with atherothrombosis identified during ICA were excluded from the study, focusing only on patients with type 2 MI. We utilized Matplotlib to analyze patient data, including demographics, lab results, vital signs, and comorbidities. We then used XGBoost, a supervised ML algorithm, for binary classification tasks. An ensemble of decision trees was employed, with gradient boosting to correct errors from previous trees. XGBoost iteratively adjusted predictions, creating a model to predict which patients required PCI. Results: Out of 765 patients, 173 (22.6%) underwent PCI. The cohort had a mean age of 60.5 ± 13.5 years, with 41.7% being female. The ML model accurately identified non-PCI candidates at 75% and PCI candidates at 64%, achieving an overall accuracy of 73%. The three most important features identified by the model were a history of diabetes, diastolic heart failure, and whether the patient received a blood transfusion during hospitalization. Conclusion: A ML approach using XGBoost demonstrated moderate accuracy in predicting the need for PCI in critically ill patients with signs of myocardial injury. This prediction holds clinical significance due to the heightened risks associated with ICA in these patients. Given that PCI occurred in only 22.6% of the cohort, future models should include a higher proportion of patients who underwent PCI to improve the robustness and true positive accuracy of the model.
Abstract 4138304: Macrophage ZIP8 Enhances Efferocytosis and Mitigates Atherosclerosis by Modulating cellular Manganese (Mn) Homeostasis
Authors: Ting Yi, Zhijie Huang, Ji Chen, Donglu Qin, Fengjiao Wang, Chenyu Li, Bilian Yu, Mi Wang
Journal: Circulation (2024)

Introduction: ZIP8, also known as SLC39A8, is a transmembrane transporter responsible for the movement of various divalent metal ions. Multiple genome-wide association studies (GWAS) have revealed a significant association between a mutation in ZIP8 (rs13107325; Ala391Thr) and cardiovascular diseases. While its physiologic role is incompletely understood, recent studies implicate that Zn2+ influx into monocytes regulated by ZIP8 is a novel factor determining their adhesion and recruitment to atherosclerotic lesions. The potential role of ZIP8 in modulating macrophage function and thereby influencing the development of atherosclerosis warrants further investigation. Methods and Results: Using an ApoE -/- ZIP8 A393T missense mutation mouse model, we quantified atherosclerotic plaque burden and lipid profiles following induction of atherosclerosis. The presence of the ZIP8 A393T missense mutation resulted in increased necrotic regions and plaque load in the atherosclerosis mouse model, while having no impact on total cholesterol (TC) or low-density lipoprotein cholesterol (LDL-C) levels. Confocal microscopy and flow cytometry to investigate how reduced ZIP8 expression affects macrophage efferocytosis. When siRNA was used to knock down ZIP8 in vitro, macrophage efferocytosis was reduced. Subsequently, Macrophage-specific ZIP8 knockout mice (ZIP8 LysM ) demonstrated a decrease in the efferocytosis index in primary macrophage. Additionally, in vivo thymic and peritoneal efferocytosis assays showed an impaired efferocytosis phenotype in ZIP8 LysM mice. Confocal microscopy showed ZIP8 LysM mice have no effect on macrophage binding to apoptotic cells. The lack of or incompleteness of the F-ACTIN ring formed by macrophages surrounding apoptosis in the ZIP8 LysM mice, and the prolonged duration of phagocytosis ring formation suggests that the internalization process is compromised. The role of ZIP8 ion transport in the efferocytosis impairment mechanism was also investigated.The regulation of macrophage efferocytosis via the metabolism of D-galactose, glutamine, glutamate, and Zn 2+ is unaffected by ZIP8. Mn 2+ was identified as a vital regulatory factor in the efferocytosis function of ZIP8 in macrophages. Conclusions: ZIP8 influences the efferocytotic capacity of macrophages and regulates the clearance of apoptotic cells through Mn 2+ , suggesting that ZIP8 and Mn 2+ hold promise as a key therapeutic targets for the treatment of atherosclerotic cardiovascular diseases.
Abstract 4138306: Impact of Early Menopause and Hormonal Replacement Therapy on Aortic Stenosis Progressio
Authors: Kathia Abdoun, Lionel Tastet, Elisabeth Bedard, Marie Arsenault, Philippe Pibarot, Marie-Annick Clavel
Journal: Circulation (2024)

Background: Early menopause is associated with an increased risk of cardiovascular disease and mortality in women. The present study aimed to examine the impact of early menopause and hormonal replacement therapy (HRT) on the progression of aortic stenosis (AS). Methods: Thirty-three postmenopausal women (mean age 65 ± 10 years) with mild or moderate AS prospectively recruited in the PROGRESSA study (NCT01679431) were included in this sub-analysis. All patients underwent multidetector computed tomography and Doppler-echocardiography at least twice during follow-up to assess both anatomic and hemodynamic AS severity, based on aortic valve calcification (AVC), mean pressure gradient (MG), and aortic valve area indexed to body surface area (AVA). Annualized changes in AVC, MG and AVAi were calculated between baseline and the last follow-up. Results: Over a median follow-up of 2 [1-4] years, early menopausal women had a faster progression rate of AVC, MG, and AVAi compared to other women: (100[58-130] vs. 23[2-71] AU/year, p=0.03); (2.37[0.82-3.61] vs. 0.31[0.01-1.78] mmHg/year, p=0.04); and (-0.12[-0.23-0.002] vs. -0.004[-0.07-0.08] cm 2 /m 2 /year, p=0.08) respectively. In multivariate analysis adjusted for age, AS severity at baseline, and comorbidities, early menopause remained significantly associated with faster AVC progression (p=0.003). Moreover, after comprehensive adjustment, women who received HRT (45%) had a slower progression rate of AVC (20[10-42] AU/year vs. 62[2-100], adjusted p=0.04). Conclusion: Early menopause is associated with faster progression of AS, both anatomically and hemodynamically. However, the use of HRT is associated with slower progression of AS. Integrating female-specific risk factors, particularly menopausal history and hormonal therapy status, into the clinical management of AS could enhance patient care.
Abstract 4138449: High Output Heart Failure from an Aortocaval Fistula
Authors: Laura Wubker Diaz, Shanice Spence-Miller, Rhea Sancassani, Arash Bornak, Rosario Colombo
Journal: Circulation (2024)

Introduction: High output heart failure (HOHF) due to a post-traumatic aortocaval fistula is a rare entity. This case seeks to explore the presentation as well as complexities of management of these patients. Case Presentation: A 50-year-old male with a prior right abdominal stab wound requiring exploratory laparotomy 20 years ago presented with worsening dyspnea on exertion and palpitations. Physical examination was remarkable for jugular venous distention, lower extremity edema, and a pulsatile mass in the right abdomen with an audible bruit. A 12-lead ECG showed rapid atrial flutter. A transthoracic echocardiogram (TTE) showed a reduced left ventricular ejection fraction (LVEF) of 30-35%, severely dilated atria and inferior vena cava (IVC) with high velocity flow. Abdominal computed tomography venogram showed dilation of the IVC up to 8cm and an aortocaval fistula at the level of the right renal vessels, resulting in an atrophic right kidney. Cardiac catheterization was notable for a cardiac output (CO) of 12L/min, cardiac index (CI) of 7L/min/m2 and reduced systemic vascular resistance (SVR). He was managed with beta blockers and diuretics, followed by TEE-guided cardioversion for atrial flutter to restore sinus rhythm. Vascular surgery performed a successful, first of its kind endovascular repair of the abdominal aorta with stenting of the right common iliac artery and placement of a 20mm Amplatzer Septal Occluder device in the right renal artery. He tolerated the procedure well, and post-operative TTE showed a recovered LVEF of 55-60% and CO of 6L/min. Discussion: HOHF is defined by a CO more than 8L/min or CI more than 4L/min/m2 along with clinical symptoms. Decreased SVR leads to sympathetic activation and stimulation of the renin-angiotensin-aldosterone system, leading to fluid retention, cardiac remodeling, and exacerbation of symptoms. Challenges present in the management of HOHF and the use of vasodilatory agents should be avoided, as they can exacerbate the problem. In our case, limitations were also faced in the use of many traditional HF therapies due to renal dysfunction as a result of the fistula. Therefore, repair was the only viable long-term solution. Conclusion: This case displays an uncommon etiology of HOHF, emphasizing the need for meticulous evaluation to ensure proper management. Early detection of aortocaval fistulas is crucial to prevent worsening cardiac dysfunction, with symptoms resolving rapidly post-repair.
Abstract 4138272: CMR Quantification of Myocardial Oxygenation Extraction Fraction Maps Using a Model-based Self-supervised Learning Network: Initial Experience in Patients with Cardiomyopathies
Authors: Qi Huang, Haoteng Tang, Keyang Wang, Ran Li, Marcus Zhan, Yang Yang, Pamela Woodard, Jie Zheng
Journal: Circulation (2024)

Background: Measurements of myocardial oxygenation extraction fraction (mOEF) maps with cardiovascular magnetic resonance (CMR) has been recently developed to assess myocardial oxygen metabolism in healthy. Recent studies focus on developing supervised machine learning methods to improve the quantification of mOEF. However, these supervised learning methods may be limited by the lack of ground-truth mOEF maps because MRI cannot directly measure mOEF with the same precision as PET. Hypothesis: Model-based self-supervised learning neural networks maybe feasible to generate mOEF maps accurately. Aims: To develop a physical model-based self-supervised learning neural network to accurately estimate mOEF for the patients with a variety of cardiomyopathies. Methods: An ECG triggered, two-dimensional asymmetric spin-echo (ASE) prepared sequence was employed to scan 71 patients with dilated, hypertrophic, or ischemic cardiomyopathies. At least 3 slices were scanned for each patient and each ASE scan was performed free-breathing with a spatial resolution of 1.7 × 1.7 × 8 mm 3 and scan time of 18 RR-interval. Acquisition of late gadolinium enhancement (LGE) was performed in each patient after the administration of 0.15 mmol/kg gadolinium agent for the visualization of myocardial infarction regions. A fully convolutional neural network with U-Net architecture was optimized with a physical model-based signal loss function (see Figure 1) on the ASE training dataset from 67 patients to generate mOEF maps. To evaluate if the method can accurately quantify abnormal mOEF, four patients with myocardial infarction were selected as the test set. Results: A previous publication reported normal mOEF levels at approximately 0.6-0.7 in healthy subjects. Figure 2 shows our proposed method generated mOEF maps from two representative patients and their LGE images for comparisons. In comparison with the remote region (mOEF = 0.70±0.10), the mOEF of myocardial regions with infarction was significantly lower (mOEF = 0.36±0.12, p &lt; 0.001). The infarction areas predicted by the network overlapped well with the LGE. Conclusion: Quantification of mOEF using the proposed model-based self-supervised U-Net is feasible to detect abnormalities in regional myocardial oxygenation. Current study is ongoing to evaluate the proposed technique in a large cardiac patient dataset.