Pub Date : 2026-02-05DOI: 10.1093/eurheartj/ehaf784.2740
J D Gillmore, M Grogan, T Sutton, M Dupont, N M Fine, K Bhatt, D Delgado, C Chen, J F Tamby, S Siddhanti, J C Fox, M Fontana
Background The National Amyloidosis Centre (NAC) staging system for transthyretin amyloid cardiomyopathy (ATTR-CM) is used to classify patients into prognostic categories based on N-terminal pro-B-type natriuretic peptide (NT-proBNP) level and estimated glomerular filtration rate (eGFR). The NAC staging system has been shown to predict ongoing survival throughout the course of ATTR-CM, with survival progressively decreasing from stage I to stage III. Acoramidis, a highly selective, oral transthyretin stabilizer that achieves near-complete (≥90%) transthyretin stabilization, has recently been approved in Europe and the USA for the treatment of wild-type or variant ATTR-CM in adults. In the phase 3 ATTRibute-CM study, acoramidis treatment was well tolerated and led to a 42% relative risk reduction in the composite of all-cause mortality and recurrent cardiovascular hospitalizations over 30 months compared with placebo (p=0.0005). Purpose To evaluate the ability of acoramidis treatment, as compared with placebo, to stabilize or improve NAC stage after 30 months in participants with ATTR-CM from the ATTRibute-CM study. Methods Participants in ATTRibute-CM were randomized 2:1 to receive acoramidis or placebo for 30 months. Efficacy analyses were conducted in the modified intention-to-treat population, which consisted of all randomized participants who had received at least one dose of acoramidis or placebo, had at least one efficacy evaluation after baseline and had a baseline eGFR ≥30 mL/min/1.73 m2. NAC stage at baseline and Month 30 was assessed. Changes from baseline to Month 30 were categorized as "stable", "improved" or "worsened". The "stable" category comprised participants who stayed within the same NAC stage at baseline and Month 30. The "improved" category comprised participants who moved from a higher NAC stage at baseline to a lower stage at Month 30. The "worsened or missing" category comprised participants who moved from a lower NAC stage at baseline to a higher stage at Month 30 and participants whose Month 30 NAC stage was missing. The change in NAC stage was compared between treatment groups using a stratified Cochran-Mantel-Haenszel test with stratification factors of genotype, NT-proBNP level and eGFR as recorded in the interactive voice/web response system at randomization. Results Overall, 611 participants were analysed (acoramidis: n=409; placebo: n=202). Baseline characteristics were comparable between treatment groups. Most participants had NAC stage I at baseline (acoramidis: 58.9%; placebo: 59.4%; Table). At Month 30, NAC stage remained stable or improved in 52.1% of acoramidis participants compared with 43.1% of placebo participants (p=0.0351; Figure). Conclusions Acoramidis treatment resulted in a greater proportion of participants whose NAC stage improved or remained stable at Month 30 compared with placebo, indicating better stabilization of their disease.
{"title":"Acoramidis has a beneficial effect compared with placebo on change from baseline in NAC ATTR stage at month 30 in patients with ATTR-CM: results from the ATTRibute-CM study","authors":"J D Gillmore, M Grogan, T Sutton, M Dupont, N M Fine, K Bhatt, D Delgado, C Chen, J F Tamby, S Siddhanti, J C Fox, M Fontana","doi":"10.1093/eurheartj/ehaf784.2740","DOIUrl":"https://doi.org/10.1093/eurheartj/ehaf784.2740","url":null,"abstract":"Background The National Amyloidosis Centre (NAC) staging system for transthyretin amyloid cardiomyopathy (ATTR-CM) is used to classify patients into prognostic categories based on N-terminal pro-B-type natriuretic peptide (NT-proBNP) level and estimated glomerular filtration rate (eGFR). The NAC staging system has been shown to predict ongoing survival throughout the course of ATTR-CM, with survival progressively decreasing from stage I to stage III. Acoramidis, a highly selective, oral transthyretin stabilizer that achieves near-complete (≥90%) transthyretin stabilization, has recently been approved in Europe and the USA for the treatment of wild-type or variant ATTR-CM in adults. In the phase 3 ATTRibute-CM study, acoramidis treatment was well tolerated and led to a 42% relative risk reduction in the composite of all-cause mortality and recurrent cardiovascular hospitalizations over 30 months compared with placebo (p=0.0005). Purpose To evaluate the ability of acoramidis treatment, as compared with placebo, to stabilize or improve NAC stage after 30 months in participants with ATTR-CM from the ATTRibute-CM study. Methods Participants in ATTRibute-CM were randomized 2:1 to receive acoramidis or placebo for 30 months. Efficacy analyses were conducted in the modified intention-to-treat population, which consisted of all randomized participants who had received at least one dose of acoramidis or placebo, had at least one efficacy evaluation after baseline and had a baseline eGFR ≥30 mL/min/1.73 m2. NAC stage at baseline and Month 30 was assessed. Changes from baseline to Month 30 were categorized as \"stable\", \"improved\" or \"worsened\". The \"stable\" category comprised participants who stayed within the same NAC stage at baseline and Month 30. The \"improved\" category comprised participants who moved from a higher NAC stage at baseline to a lower stage at Month 30. The \"worsened or missing\" category comprised participants who moved from a lower NAC stage at baseline to a higher stage at Month 30 and participants whose Month 30 NAC stage was missing. The change in NAC stage was compared between treatment groups using a stratified Cochran-Mantel-Haenszel test with stratification factors of genotype, NT-proBNP level and eGFR as recorded in the interactive voice/web response system at randomization. Results Overall, 611 participants were analysed (acoramidis: n=409; placebo: n=202). Baseline characteristics were comparable between treatment groups. Most participants had NAC stage I at baseline (acoramidis: 58.9%; placebo: 59.4%; Table). At Month 30, NAC stage remained stable or improved in 52.1% of acoramidis participants compared with 43.1% of placebo participants (p=0.0351; Figure). Conclusions Acoramidis treatment resulted in a greater proportion of participants whose NAC stage improved or remained stable at Month 30 compared with placebo, indicating better stabilization of their disease.","PeriodicalId":11976,"journal":{"name":"European Heart Journal","volume":"2 1","pages":""},"PeriodicalIF":39.3,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146121966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-05DOI: 10.1093/eurheartj/ehaf784.2337
I Cohen, J G Malins, M Cohen-Shelly, Y Asaf, M Fiman, K Faierstein, K Sudri, E Zimlichman, E Schwammenthal, R Klempfner, E Maor
Background Mitral regurgitation (MR) and tricuspid regurgitation (TR) are prevalent valvular heart diseases associated with significant morbidity and mortality. Traditional echocardiography faces limitations in availability, cost, consistency, and reliability, leading to misdiagnosis and undertreatment. The application of artificial intelligence (AI) to echocardiographic scans has the potential to address these challenges. Methods This study evaluates the performance of an AI algorithm on an external population. The algorithm utilizes deep learning networks to analyze echocardiographic exams for diagnosing atrioventricular valve disorders. We tested the algorithm on transthoracic echocardiography data collected from a single center between 2013 and 2023. The model's performance was compared to ground truth values using two classification schemes: distinguishing between normal-mild and moderate-severe regurgitation, and categorizing results into four groups: normal, mild, moderate, and severe. Results The MR cohort included 280 patients, while the TR cohort comprised 298 patients. The model demonstrated a robust ability to identify clinically significant (moderate and above) atrioventricular valve regurgitation. The MR model achieved an area under the curve (AUC) of 0.98 (95% CI: 0.97–0.99), with 91% accuracy, 95% sensitivity, and 89% specificity. In comparison, the TR model exhibited an AUC of 0.96 (95% CI: 0.94–0.98), with 84% accuracy, 91% sensitivity, and 80% specificity. Conclusion The model demonstrated high diagnostic accuracy and reliability in assessing atrioventricular valve regurgitation severity, highlighting its potential as a valuable clinical tool. The findings underscore the role of AI in complementing expert evaluations and improving access to effective diagnostics, with future applications potentially including point-of-care diagnosis and monitoring of disease progression.
{"title":"Deep learning for atrioventricular regurgitation diagnosis: an external validation study","authors":"I Cohen, J G Malins, M Cohen-Shelly, Y Asaf, M Fiman, K Faierstein, K Sudri, E Zimlichman, E Schwammenthal, R Klempfner, E Maor","doi":"10.1093/eurheartj/ehaf784.2337","DOIUrl":"https://doi.org/10.1093/eurheartj/ehaf784.2337","url":null,"abstract":"Background Mitral regurgitation (MR) and tricuspid regurgitation (TR) are prevalent valvular heart diseases associated with significant morbidity and mortality. Traditional echocardiography faces limitations in availability, cost, consistency, and reliability, leading to misdiagnosis and undertreatment. The application of artificial intelligence (AI) to echocardiographic scans has the potential to address these challenges. Methods This study evaluates the performance of an AI algorithm on an external population. The algorithm utilizes deep learning networks to analyze echocardiographic exams for diagnosing atrioventricular valve disorders. We tested the algorithm on transthoracic echocardiography data collected from a single center between 2013 and 2023. The model's performance was compared to ground truth values using two classification schemes: distinguishing between normal-mild and moderate-severe regurgitation, and categorizing results into four groups: normal, mild, moderate, and severe. Results The MR cohort included 280 patients, while the TR cohort comprised 298 patients. The model demonstrated a robust ability to identify clinically significant (moderate and above) atrioventricular valve regurgitation. The MR model achieved an area under the curve (AUC) of 0.98 (95% CI: 0.97–0.99), with 91% accuracy, 95% sensitivity, and 89% specificity. In comparison, the TR model exhibited an AUC of 0.96 (95% CI: 0.94–0.98), with 84% accuracy, 91% sensitivity, and 80% specificity. Conclusion The model demonstrated high diagnostic accuracy and reliability in assessing atrioventricular valve regurgitation severity, highlighting its potential as a valuable clinical tool. The findings underscore the role of AI in complementing expert evaluations and improving access to effective diagnostics, with future applications potentially including point-of-care diagnosis and monitoring of disease progression.","PeriodicalId":11976,"journal":{"name":"European Heart Journal","volume":"9 1","pages":""},"PeriodicalIF":39.3,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146122017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-05DOI: 10.1093/eurheartj/ehaf784.2744
A Nojiri, E Fukuro, T Okuyama, I Anan, S Morimoto, M Kawai, K Hongo
Introduction Fabry disease is an X-linked hereditary disorder due to the lack or deficiency of the alpha-galactosidase A activity, which leads to the cardiac manifestation such as left ventricular hypertrophy (LVH). Pharmacological chaperone therapy (PCT) is a promising oral treatment to prevent various complications. However, the mid-term effect of PCT on LVH in Japanese Fabry disease patients has not been investigated. Purpose We investigated the mid-term effect of PCT on LVH in Japanese Fabry disease patients. Methods We analysed echocardiographic parameters of 15 Fabry disease patients (6 males and 9 females) followed at Jikei University hospital during the treatment with PCT (4.6 ± 1.2 years). To evaluate LVH, left ventricular mass (LVM) was calculated according to Devereux’s equation and was expressed as gram/height2.7 (g/ht2.7). Results At the start of PCT, all 6 male patients had LVH while only 2 female patients had LVH. LVM was almost stable during PCT treatment both in male patients and female patients (Figure 1). The slope of the changes in LVM was 1.59 ± 1.72 g/ht2.7/year in male patients and was -0.03 ± 1.42 g/ht2.7/year in female patients, which were significantly smaller than the values previously reported without treatment (4.07 ± 1.03 g/ht2.7/year in male patients and 2.31 ± 0.81 g/ht2.7/year in female patients, p<0.05) (Figure 2). Conclusions Mid-term PCT could effectively prevent LVH progression in Japanese Fabry disease patients. Especially in female patients, LVH progression was almost completely suppressed by PCT with or without prior LVH at the start of PCT.Figure 1 Figure 2
{"title":"Mid-term benefit of pharmacological chaperone therapy on cardiac manifestation in Japanese Fabry disease","authors":"A Nojiri, E Fukuro, T Okuyama, I Anan, S Morimoto, M Kawai, K Hongo","doi":"10.1093/eurheartj/ehaf784.2744","DOIUrl":"https://doi.org/10.1093/eurheartj/ehaf784.2744","url":null,"abstract":"Introduction Fabry disease is an X-linked hereditary disorder due to the lack or deficiency of the alpha-galactosidase A activity, which leads to the cardiac manifestation such as left ventricular hypertrophy (LVH). Pharmacological chaperone therapy (PCT) is a promising oral treatment to prevent various complications. However, the mid-term effect of PCT on LVH in Japanese Fabry disease patients has not been investigated. Purpose We investigated the mid-term effect of PCT on LVH in Japanese Fabry disease patients. Methods We analysed echocardiographic parameters of 15 Fabry disease patients (6 males and 9 females) followed at Jikei University hospital during the treatment with PCT (4.6 ± 1.2 years). To evaluate LVH, left ventricular mass (LVM) was calculated according to Devereux’s equation and was expressed as gram/height2.7 (g/ht2.7). Results At the start of PCT, all 6 male patients had LVH while only 2 female patients had LVH. LVM was almost stable during PCT treatment both in male patients and female patients (Figure 1). The slope of the changes in LVM was 1.59 ± 1.72 g/ht2.7/year in male patients and was -0.03 ± 1.42 g/ht2.7/year in female patients, which were significantly smaller than the values previously reported without treatment (4.07 ± 1.03 g/ht2.7/year in male patients and 2.31 ± 0.81 g/ht2.7/year in female patients, p&lt;0.05) (Figure 2). Conclusions Mid-term PCT could effectively prevent LVH progression in Japanese Fabry disease patients. Especially in female patients, LVH progression was almost completely suppressed by PCT with or without prior LVH at the start of PCT.Figure 1 Figure 2","PeriodicalId":11976,"journal":{"name":"European Heart Journal","volume":"79 1","pages":""},"PeriodicalIF":39.3,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146122067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-05DOI: 10.1093/eurheartj/ehaf784.4120
J L Cross, S Wahi, Y Im, J M Kwan
Background Cancer patients treated with immune checkpoint inhibitors (ICIs) have an increased risk of adverse cardiovascular events (ACE) [1]. Traditional cardiovascular risk scores may not adequately capture ICI-associated cardiovascular toxicities or the unique features that contribute to ACE risk in this population [1,2]. Recent studies have developed cardiovascular risk scores for cancer patients, which achieved area under the receiver operating characteristic curve (AUC) values in the 0.65 to 0.85 range [2]. Currently, there is no validated ACE risk algorithm designed specifically for ICI patients. Purpose Our study aims 1) to develop an interpretable, machine learning-based ACE risk score algorithm for cancer patients treated with ICI therapy, and 2) to integrate this algorithm into a clinically accessible online calculator interface. Methods We analyzed 5145 cancer patients treated with ICI therapy between 2013 and 2024 at a large academic centre. Patient variables included demographics, comorbidities, laboratory values, cancer type, ICI regimen (single vs dual), and key imaging findings from echocardiography (echo) and cardiac magnetic resonance (CMR) data. The composite ACE outcome comprised myocardial infarction, coronary artery disease (CAD), arrhythmias, heart failure (HF), valvular disease, atrioventricular block, and myocarditis. Data were partitioned into training (80%), test (10%), and holdout validation (10%) sets. An extreme gradient boosting (XGB) classifier was trained using 4-fold cross-validation on the training set, and performance was evaluated on the test set. Shapley Additive Explanation (SHAP) values were used to identify top predictive features. A multivariate logistic regression model was then fit using 15 selected features (based on SHAP ranking and clinical expertise) to form the final ACE risk score algorithm, which was subsequently validated on the holdout validation set. Results ACE occurred in 36.5% of patients in our cohort. The XGB model achieved an AUC of 0.73 on the test set (Figure 1B). The most influential SHAP features included age, body mass index, cancer type, creatinine, CAD, peripheral vascular disease, stroke, HF, hypertension, left ventricular ejection fraction, and global longitudinal strain (Figure 1A, 1D). These features, along with dual ICI status and left ventricular late gadolinium enhancement, were used to train the final ACE risk algorithm, which attained an AUC of 0.70 on the holdout validation set (Figure 1C). We integrate our ACE risk algorithm into a publicly accessible, user-friendly online calculator (Figure 2). Conclusion We present a novel, interpretable, and clinically usable ACE risk score algorithm tailored to cancer patients treated with ICI therapy, which may aid in improving risk stratification and cardiovascular monitoring in this high-risk population.Fig1.Top SHAP features & AUC curves Fig2.ACE risk calculator interface
背景：接受免疫检查点抑制剂（ICIs）治疗的癌症患者发生不良心血管事件（ACE）的风险增加。传统的心血管风险评分可能无法充分捕捉到ici相关的心血管毒性或导致该人群ACE风险的独特特征[1,2]。最近的研究开发了癌症患者的心血管风险评分，其接受者工作特征曲线下面积（AUC）值在0.65 ~ 0.85[2]之间。目前，还没有专门针对ICI患者设计的经过验证的ACE风险算法。本研究旨在1)为接受ICI治疗的癌症患者开发一种可解释的、基于机器学习的ACE风险评分算法；2)将该算法整合到临床可访问的在线计算器界面中。方法：我们分析了2013年至2024年在一家大型学术中心接受ICI治疗的5145例癌症患者。患者变量包括人口统计学、合并症、实验室值、癌症类型、ICI方案（单/双）以及超声心动图（echo）和心脏磁共振（CMR）数据的关键成像结果。ACE的复合结局包括心肌梗死、冠状动脉疾病（CAD）、心律失常、心力衰竭（HF）、瓣膜疾病、房室传导阻滞和心肌炎。数据被划分为训练集（80%）、测试集（10%）和坚持验证集（10%）。在训练集上使用4倍交叉验证训练极端梯度增强（XGB）分类器，并在测试集上评估性能。Shapley加性解释（SHAP）值用于识别顶级预测特征。然后使用15个选择的特征（基于SHAP排名和临床专业知识）拟合多元逻辑回归模型，形成最终的ACE风险评分算法，随后在拒绝验证集上进行验证。结果：ACE发生率为36.5%。XGB模型在测试集上的AUC为0.73（图1B）。最具影响的SHAP特征包括年龄、体重指数、癌症类型、肌酐、CAD、外周血管疾病、卒中、心衰、高血压、左心室射血分数和整体纵向应变（图1A， 1D）。这些特征，以及双ICI状态和左心室晚期钆增强，被用于训练最终的ACE风险算法，该算法在holdout验证集上获得了0.70的AUC（图1C）。我们将ACE风险算法集成到一个可公开访问的、用户友好的在线计算器中（图2）。我们提出了一种新的、可解释的、临床可用的ACE风险评分算法，该算法适用于接受ICI治疗的癌症患者，可能有助于改善这一高危人群的风险分层和心血管监测。图2. Top shape特征& AUC曲线ACE风险计算器界面
{"title":"A novel machine learning-based adverse cardiovascular events risk score for cancer patients treated with immune checkpoint inhibitors","authors":"J L Cross, S Wahi, Y Im, J M Kwan","doi":"10.1093/eurheartj/ehaf784.4120","DOIUrl":"https://doi.org/10.1093/eurheartj/ehaf784.4120","url":null,"abstract":"Background Cancer patients treated with immune checkpoint inhibitors (ICIs) have an increased risk of adverse cardiovascular events (ACE) [1]. Traditional cardiovascular risk scores may not adequately capture ICI-associated cardiovascular toxicities or the unique features that contribute to ACE risk in this population [1,2]. Recent studies have developed cardiovascular risk scores for cancer patients, which achieved area under the receiver operating characteristic curve (AUC) values in the 0.65 to 0.85 range [2]. Currently, there is no validated ACE risk algorithm designed specifically for ICI patients. Purpose Our study aims 1) to develop an interpretable, machine learning-based ACE risk score algorithm for cancer patients treated with ICI therapy, and 2) to integrate this algorithm into a clinically accessible online calculator interface. Methods We analyzed 5145 cancer patients treated with ICI therapy between 2013 and 2024 at a large academic centre. Patient variables included demographics, comorbidities, laboratory values, cancer type, ICI regimen (single vs dual), and key imaging findings from echocardiography (echo) and cardiac magnetic resonance (CMR) data. The composite ACE outcome comprised myocardial infarction, coronary artery disease (CAD), arrhythmias, heart failure (HF), valvular disease, atrioventricular block, and myocarditis. Data were partitioned into training (80%), test (10%), and holdout validation (10%) sets. An extreme gradient boosting (XGB) classifier was trained using 4-fold cross-validation on the training set, and performance was evaluated on the test set. Shapley Additive Explanation (SHAP) values were used to identify top predictive features. A multivariate logistic regression model was then fit using 15 selected features (based on SHAP ranking and clinical expertise) to form the final ACE risk score algorithm, which was subsequently validated on the holdout validation set. Results ACE occurred in 36.5% of patients in our cohort. The XGB model achieved an AUC of 0.73 on the test set (Figure 1B). The most influential SHAP features included age, body mass index, cancer type, creatinine, CAD, peripheral vascular disease, stroke, HF, hypertension, left ventricular ejection fraction, and global longitudinal strain (Figure 1A, 1D). These features, along with dual ICI status and left ventricular late gadolinium enhancement, were used to train the final ACE risk algorithm, which attained an AUC of 0.70 on the holdout validation set (Figure 1C). We integrate our ACE risk algorithm into a publicly accessible, user-friendly online calculator (Figure 2). Conclusion We present a novel, interpretable, and clinically usable ACE risk score algorithm tailored to cancer patients treated with ICI therapy, which may aid in improving risk stratification and cardiovascular monitoring in this high-risk population.Fig1.Top SHAP features & AUC curves Fig2.ACE risk calculator interface","PeriodicalId":11976,"journal":{"name":"European Heart Journal","volume":"301 1","pages":""},"PeriodicalIF":39.3,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146122068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-05DOI: 10.1093/eurheartj/ehaf784.4388
S Shibata, M Nishimori, Y Nishihara, M Shinohara
Background In clinical practice, physicians integrate multiple diagnostic tests to infer pathophysiology, but no AI models have been developed to replicate this complex decision-making process. A model capable of interpreting multimodal data within a unified space is required. For instance, in heart failure, chest X-rays and ECGs, while representing different modalities, both reflect the same underlying pathological condition. Purpose This study aims to develop a multimodal model leveraging the CLIP framework to classify heart failure pathophysiology by embedding data from distinct modalities into a shared representational space. Methods We analyzed data from cardiology patients at our University between January 2012 and December 2022, selecting only those who had concurrent ECG, chest X-rays, and echocardiography. The dataset comprised 9,632 patients, with 34,747 12-lead ECGs and 36,366 chest X-rays. CLIP was employed to pair ECGs and chest X-rays from the same patients. Using these vectors, the model was trained to predict LVEF and E/E' values from transthoracic echocardiograms. The model then predicted the classification of each criteria, based on the cut-off values of 50 for LVEF and 15 for E/E'. Results Dimensionality reduction of the vectors obtained from ECGs and chest X-rays demonstrated that both modalities were projected within the same representational space. Figure 1 shows the result of embedding ECG and chest X-ray data into a common feature space using CLIP, with dimensionality reduced to two dimensions using UMAP. Each point in the figure represents a single ECG or chest X-ray data sample, color-coded based on BNP values transformed into a log scale. Samples with high BNP values were distributed in the upper right, while those with low BNP values were located toward the lower left. This indicates that the pathophysiology of heart failure, as inferred from ECGs and chest X-rays, was successfully reproduced within this feature space. In the Figure 1, ECG and chest X-ray data were plotted with overlapping distributions, demonstrating that these modalities can be represented within the same feature space. The feature vectors obtained through CLIP were treated equivalently for ECG and chest X-rays, and the model was trained to predict E/E' and LVEF values. As a result, as shown in Figure 2, the model achieved an AUROC of 0.83 for predicting LVEF < 50 and an AUROC of 0.77 for predicting E/E' > 15. These results suggest that our model successfully classified the pathophysiology of heart failure. Conclusions This study demonstrates that the CLIP model can successfully classify heart failure pathophysiology by embedding multimodal data into a unified representational space.Figure1 Figure2
{"title":"A CLIP-based multimodal model for heart failure assessment","authors":"S Shibata, M Nishimori, Y Nishihara, M Shinohara","doi":"10.1093/eurheartj/ehaf784.4388","DOIUrl":"https://doi.org/10.1093/eurheartj/ehaf784.4388","url":null,"abstract":"Background In clinical practice, physicians integrate multiple diagnostic tests to infer pathophysiology, but no AI models have been developed to replicate this complex decision-making process. A model capable of interpreting multimodal data within a unified space is required. For instance, in heart failure, chest X-rays and ECGs, while representing different modalities, both reflect the same underlying pathological condition. Purpose This study aims to develop a multimodal model leveraging the CLIP framework to classify heart failure pathophysiology by embedding data from distinct modalities into a shared representational space. Methods We analyzed data from cardiology patients at our University between January 2012 and December 2022, selecting only those who had concurrent ECG, chest X-rays, and echocardiography. The dataset comprised 9,632 patients, with 34,747 12-lead ECGs and 36,366 chest X-rays. CLIP was employed to pair ECGs and chest X-rays from the same patients. Using these vectors, the model was trained to predict LVEF and E/E' values from transthoracic echocardiograms. The model then predicted the classification of each criteria, based on the cut-off values of 50 for LVEF and 15 for E/E'. Results Dimensionality reduction of the vectors obtained from ECGs and chest X-rays demonstrated that both modalities were projected within the same representational space. Figure 1 shows the result of embedding ECG and chest X-ray data into a common feature space using CLIP, with dimensionality reduced to two dimensions using UMAP. Each point in the figure represents a single ECG or chest X-ray data sample, color-coded based on BNP values transformed into a log scale. Samples with high BNP values were distributed in the upper right, while those with low BNP values were located toward the lower left. This indicates that the pathophysiology of heart failure, as inferred from ECGs and chest X-rays, was successfully reproduced within this feature space. In the Figure 1, ECG and chest X-ray data were plotted with overlapping distributions, demonstrating that these modalities can be represented within the same feature space. The feature vectors obtained through CLIP were treated equivalently for ECG and chest X-rays, and the model was trained to predict E/E' and LVEF values. As a result, as shown in Figure 2, the model achieved an AUROC of 0.83 for predicting LVEF &lt; 50 and an AUROC of 0.77 for predicting E/E' &gt; 15. These results suggest that our model successfully classified the pathophysiology of heart failure. Conclusions This study demonstrates that the CLIP model can successfully classify heart failure pathophysiology by embedding multimodal data into a unified representational space.Figure1 Figure2","PeriodicalId":11976,"journal":{"name":"European Heart Journal","volume":"28 1","pages":""},"PeriodicalIF":39.3,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146122139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-05DOI: 10.1093/eurheartj/ehaf784.156
J H Seo, H T Kim, J H Bae, T J Kim, Y M Kim, H S Jo, H J Chung, S H Lee, D S Han
Background Coronary atherosclerosis and calcific aortic valve disease (CAVD) have similar risk factors and disease mechanisms. However, the presence of coronary atherosclerosis does not warrant the development of CAVD. Therefore, proving the association between the degree of coronary artery calcium (CAC) and the progression of CAVD could improve follow-up and treatment strategies. Purpose To explore the assoication between the degree of CAC and the onset and progression of CAVD from a single-centre registry of coronary CT angiographic and serial echocardiographic examinations. Methods We retrospectively included 2,898 patients who underwent coronary CT angiography and serial echocardiographic examinations at intervals of every 6 months or more. The CAC was divided into 4 groups: 0, 1–99, 100–399 and ≥400. The progression of CAVD was defined in two ways: Progression 1 as at least one grade progression (e.g. from mild aortic stenosis (AS) to moderate or severe AS), Progression 2 as at least moderate AS at follow-up. We used multivariable logistic regression analyses to assess the factors associated with the progression of CAVD. Results Among the 2,898 patients, CAC 0, 1–99, 100–399 and ≥400 groups were 1,122, 786, 556 and 434, respectively. As CAC increased, those have more comorbidities and worsened echocardiographic parameters associated with left ventricular systolic and diastolic function. At the initial CAVD grade, patients with at least mild AS tended to increase with increasing CAC (P < 0.001). During a median follow-up of 3.2 years (IQR, 1.8–5.0 years), 101 patients (3.5%) experienced Progression 1 and 24 patients (0.8%) suffered Progression 2. There was a statistically significant increased risk of Progression 1 in CAC 100–399 and ≥400 groups than CAC 0 and 1–99 groups (P < 0.001). In Progression 2, the CAC ≥400 group was remarkably progressed than other groups (P < 0.001). The CAC had a fair ability to predict risk of Progression 2 (area under the curve = 0.737) and the cut-off value was 133. In multivariable logistic regression, age, CAC ≥400 (adjusted odds ratio [aOR], 2.55; 95% confidence interval [CI], 1.19–5.46; P = 0.016), body mass index and peak aortic jet velocity were associated with Progression 1. In Progression 2, CAC ≥400 (aOR, 44.5; 95% CI, 1.09–1810; P = 0.045) and peak aortic jet velocity were significant determinants. Conclusion Coronary artery calcium was significantly associated with the onset and progression of calcific aortic valve disease. Patients with CAC 100 or more need to consider screening and follow-up for AS, especially CAC 400 or more is crucial for the progression to significant AS.KM curve of Progression 1-free survival KM curve of Progression 2-free survival
{"title":"Coronary artery calcium is associated with the onset and progression of calcific aortic valve disease","authors":"J H Seo, H T Kim, J H Bae, T J Kim, Y M Kim, H S Jo, H J Chung, S H Lee, D S Han","doi":"10.1093/eurheartj/ehaf784.156","DOIUrl":"https://doi.org/10.1093/eurheartj/ehaf784.156","url":null,"abstract":"Background Coronary atherosclerosis and calcific aortic valve disease (CAVD) have similar risk factors and disease mechanisms. However, the presence of coronary atherosclerosis does not warrant the development of CAVD. Therefore, proving the association between the degree of coronary artery calcium (CAC) and the progression of CAVD could improve follow-up and treatment strategies. Purpose To explore the assoication between the degree of CAC and the onset and progression of CAVD from a single-centre registry of coronary CT angiographic and serial echocardiographic examinations. Methods We retrospectively included 2,898 patients who underwent coronary CT angiography and serial echocardiographic examinations at intervals of every 6 months or more. The CAC was divided into 4 groups: 0, 1–99, 100–399 and ≥400. The progression of CAVD was defined in two ways: Progression 1 as at least one grade progression (e.g. from mild aortic stenosis (AS) to moderate or severe AS), Progression 2 as at least moderate AS at follow-up. We used multivariable logistic regression analyses to assess the factors associated with the progression of CAVD. Results Among the 2,898 patients, CAC 0, 1–99, 100–399 and ≥400 groups were 1,122, 786, 556 and 434, respectively. As CAC increased, those have more comorbidities and worsened echocardiographic parameters associated with left ventricular systolic and diastolic function. At the initial CAVD grade, patients with at least mild AS tended to increase with increasing CAC (P &lt; 0.001). During a median follow-up of 3.2 years (IQR, 1.8–5.0 years), 101 patients (3.5%) experienced Progression 1 and 24 patients (0.8%) suffered Progression 2. There was a statistically significant increased risk of Progression 1 in CAC 100–399 and ≥400 groups than CAC 0 and 1–99 groups (P &lt; 0.001). In Progression 2, the CAC ≥400 group was remarkably progressed than other groups (P &lt; 0.001). The CAC had a fair ability to predict risk of Progression 2 (area under the curve = 0.737) and the cut-off value was 133. In multivariable logistic regression, age, CAC ≥400 (adjusted odds ratio [aOR], 2.55; 95% confidence interval [CI], 1.19–5.46; P = 0.016), body mass index and peak aortic jet velocity were associated with Progression 1. In Progression 2, CAC ≥400 (aOR, 44.5; 95% CI, 1.09–1810; P = 0.045) and peak aortic jet velocity were significant determinants. Conclusion Coronary artery calcium was significantly associated with the onset and progression of calcific aortic valve disease. Patients with CAC 100 or more need to consider screening and follow-up for AS, especially CAC 400 or more is crucial for the progression to significant AS.KM curve of Progression 1-free survival KM curve of Progression 2-free survival","PeriodicalId":11976,"journal":{"name":"European Heart Journal","volume":"23 1","pages":""},"PeriodicalIF":39.3,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146122196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-05DOI: 10.1093/eurheartj/ehaf784.1612
J Jannink, J Van Der Veen, J W Eikelboom, J H Cornel, T J S Opstal, C A Budgeon, S M Nidorf, F L J Visseren, F M A C Martens, A T L Fiolet, A Mosterd
Background Recent trials have demonstrated that low-dose colchicine can reduce the risk of cardiovascular events in patients with coronary artery disease.(1) However, discontinuation rates of trial medication ranged from 10.1% to 25.9%, potentially reducing its effectiveness.(2,3) The role of side effects in this remains unclear. Purpose To evaluate the incidence and type of side effects that led to premature discontinuation of trial medication in participants with chronic coronary syndromes in the Low-Dose Colchicine 2 (LoDoCo2) trial. Methods We evaluated the incidence of side effects leading to discontinuation of trial medication in the LoDoCo2 trial, which consisted of a 30-day run-in phase during which all participants received colchicine 0.5 mg once daily, followed by randomization to colchicine or placebo. Side effects of colchicine were evaluated at the end of the run-in phase (early side effects, within 30 days) and during the randomization phase (late side effects, median follow-up: 29.5 months). Cumulative incidences of side effects are presented using Kaplan-Meier curves and compared by treatment arm using Cox proportional hazards modelling. Results Trial medication was discontinued by 997 of 6519 (15.3%) participants during the run-in phase, of whom 618 (9.5%) participants reported early side effects. After randomization, 273 (10.0%) participants in the colchicine group and 281 (10.3%) participants in the placebo group discontinued trial medication. Among those who stopped trial medication, 114 participants in the colchicine group reported side effects (4.2%, 1.6 events per 100 person-years), compared to 120 participants in the placebo group (4.4%, 1.7 events per 100 person-years, hazard ratio [HR] 0.95, 95% confidence interval [CI] 0.73–1.22). Gastrointestinal upset and myalgia were the most common side effects (2.0% and 1.2% in the colchicine group and 1.7% and 1.6% in the placebo group, respectively), with no differences between treatment groups. Female sex was independently associated with an increased risk of both early- and late side effects, irrespective of treatment allocation. Statin use was independently associated with lower rates of late side effects, with no difference between colchicine and placebo. Conclusion After the open-label run-in phase, the incidence of late side effects in participants with chronic coronary syndromes did not differ between patients randomized to low-dose colchicine or placebo.
{"title":"Side effects of low-dose colchicine in chronic coronary syndromes, the LoDoCo2 trial","authors":"J Jannink, J Van Der Veen, J W Eikelboom, J H Cornel, T J S Opstal, C A Budgeon, S M Nidorf, F L J Visseren, F M A C Martens, A T L Fiolet, A Mosterd","doi":"10.1093/eurheartj/ehaf784.1612","DOIUrl":"https://doi.org/10.1093/eurheartj/ehaf784.1612","url":null,"abstract":"Background Recent trials have demonstrated that low-dose colchicine can reduce the risk of cardiovascular events in patients with coronary artery disease.(1) However, discontinuation rates of trial medication ranged from 10.1% to 25.9%, potentially reducing its effectiveness.(2,3) The role of side effects in this remains unclear. Purpose To evaluate the incidence and type of side effects that led to premature discontinuation of trial medication in participants with chronic coronary syndromes in the Low-Dose Colchicine 2 (LoDoCo2) trial. Methods We evaluated the incidence of side effects leading to discontinuation of trial medication in the LoDoCo2 trial, which consisted of a 30-day run-in phase during which all participants received colchicine 0.5 mg once daily, followed by randomization to colchicine or placebo. Side effects of colchicine were evaluated at the end of the run-in phase (early side effects, within 30 days) and during the randomization phase (late side effects, median follow-up: 29.5 months). Cumulative incidences of side effects are presented using Kaplan-Meier curves and compared by treatment arm using Cox proportional hazards modelling. Results Trial medication was discontinued by 997 of 6519 (15.3%) participants during the run-in phase, of whom 618 (9.5%) participants reported early side effects. After randomization, 273 (10.0%) participants in the colchicine group and 281 (10.3%) participants in the placebo group discontinued trial medication. Among those who stopped trial medication, 114 participants in the colchicine group reported side effects (4.2%, 1.6 events per 100 person-years), compared to 120 participants in the placebo group (4.4%, 1.7 events per 100 person-years, hazard ratio [HR] 0.95, 95% confidence interval [CI] 0.73–1.22). Gastrointestinal upset and myalgia were the most common side effects (2.0% and 1.2% in the colchicine group and 1.7% and 1.6% in the placebo group, respectively), with no differences between treatment groups. Female sex was independently associated with an increased risk of both early- and late side effects, irrespective of treatment allocation. Statin use was independently associated with lower rates of late side effects, with no difference between colchicine and placebo. Conclusion After the open-label run-in phase, the incidence of late side effects in participants with chronic coronary syndromes did not differ between patients randomized to low-dose colchicine or placebo.","PeriodicalId":11976,"journal":{"name":"European Heart Journal","volume":"56 1","pages":""},"PeriodicalIF":39.3,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146121964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-05DOI: 10.1093/eurheartj/ehaf784.1165
C D Yang, X Q Wang
Background Heart failure (HF) with improved ejection fraction (HFimpEF) has emerged as a distinct HF subtype, characterized by left ventricular reverse modeling and myocardial recovery. However, the underlying pathophysiological mechanisms remain unclear, and the specific diagnostic and predictive biomarkers are still lacking. Objectives This study analyzed the difference in serum proteomic profiles of HFimpEF and HF with reduced ejection fraction (HFrEF) patients, aiming to identify biomarkers indicating myocardial recovery potential and reveal novel molecular targets to improve cardiac function. Methods This study performed untargeted serum proteome profiling using data-independent acquisition-based label-free quantitative liquid chromatography–tandem mass spectrometry in 55 patients with HFimpEF, as compared with 33 HFrEF patients. Differentially expressed proteins (DEP) between the 2 groups were analyzed and were further classified using the Boruta algorithm. The proteins’ diagnostic performance was evaluated by area under the curve (AUC) and validated using the 10-fold cross-validation. Gene Ontology (GO) and pathway enrichment analysis were performed to identify significantly enriched biological functions and pathways associated with the DEP. Results Quantitative proteomic analysis identified 2,461 serum proteins across 88 patients with HFimpEF or HFrEF. Of these, 1,712 proteins showed high detection consistency (≥60% prevalence) and were retained for comparative analysis, resulting in a total of 35 DEP. Specifically, HFimpEF patients exhibited 33 up-regulated and 2 down-regulated proteins compared to those with HFrEF. The Boruta machine learning algorithm prioritized 9 signature proteins as key discriminators between the two phenotypes. The combination of immunoglobulin superfamily member 1, immunoglobulin heavy constant gamma 1, pentraxin-related protein PTX3, and 55 kDa erythrocyte membrane protein exhibited the best diagnostic precision (AUC: 0.90; 95% CI: 0.83-0.97) to distinguish patients with HFimpEF from HFrEF. Salient biologic themes related to thrombosis, immune regulation, cellular motility and membrane integrity were predominant in HFimpEF. Conclusions Characterized differences existed in serum proteomic profiles between HFrEF and HFimpEF patients. These newly identified proteins warrant further evaluation to establish their role in the restoration of myocardial function, and their diagnostic perspective to predict the incidence of HFimpEF.ROC Curve GO Analysis
{"title":"Proteome profiling by high-resolution mass spectrometry discriminates HFimpEF from HFrEF: a cohort-based biomarker discovery study","authors":"C D Yang, X Q Wang","doi":"10.1093/eurheartj/ehaf784.1165","DOIUrl":"https://doi.org/10.1093/eurheartj/ehaf784.1165","url":null,"abstract":"Background Heart failure (HF) with improved ejection fraction (HFimpEF) has emerged as a distinct HF subtype, characterized by left ventricular reverse modeling and myocardial recovery. However, the underlying pathophysiological mechanisms remain unclear, and the specific diagnostic and predictive biomarkers are still lacking. Objectives This study analyzed the difference in serum proteomic profiles of HFimpEF and HF with reduced ejection fraction (HFrEF) patients, aiming to identify biomarkers indicating myocardial recovery potential and reveal novel molecular targets to improve cardiac function. Methods This study performed untargeted serum proteome profiling using data-independent acquisition-based label-free quantitative liquid chromatography–tandem mass spectrometry in 55 patients with HFimpEF, as compared with 33 HFrEF patients. Differentially expressed proteins (DEP) between the 2 groups were analyzed and were further classified using the Boruta algorithm. The proteins’ diagnostic performance was evaluated by area under the curve (AUC) and validated using the 10-fold cross-validation. Gene Ontology (GO) and pathway enrichment analysis were performed to identify significantly enriched biological functions and pathways associated with the DEP. Results Quantitative proteomic analysis identified 2,461 serum proteins across 88 patients with HFimpEF or HFrEF. Of these, 1,712 proteins showed high detection consistency (≥60% prevalence) and were retained for comparative analysis, resulting in a total of 35 DEP. Specifically, HFimpEF patients exhibited 33 up-regulated and 2 down-regulated proteins compared to those with HFrEF. The Boruta machine learning algorithm prioritized 9 signature proteins as key discriminators between the two phenotypes. The combination of immunoglobulin superfamily member 1, immunoglobulin heavy constant gamma 1, pentraxin-related protein PTX3, and 55 kDa erythrocyte membrane protein exhibited the best diagnostic precision (AUC: 0.90; 95% CI: 0.83-0.97) to distinguish patients with HFimpEF from HFrEF. Salient biologic themes related to thrombosis, immune regulation, cellular motility and membrane integrity were predominant in HFimpEF. Conclusions Characterized differences existed in serum proteomic profiles between HFrEF and HFimpEF patients. These newly identified proteins warrant further evaluation to establish their role in the restoration of myocardial function, and their diagnostic perspective to predict the incidence of HFimpEF.ROC Curve GO Analysis","PeriodicalId":11976,"journal":{"name":"European Heart Journal","volume":"45 1","pages":""},"PeriodicalIF":39.3,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146121914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-05DOI: 10.1093/eurheartj/ehaf784.4228
O Garza Flores, F M Garcia Garcia, R L Polina Lugo, A Gonzalez Melendez, E C Garza Gonzalez, J R Azpiri Lopez, D A Galarza Delgado, I J Colunga Pedraza, J A Cardenas De La Garza, R I Arvizu Rivera, V M Fraga Enriquez
Background Psoriatic arthritis (PsA) is a systemic inflammatory disease linked to accelerated atherosclerosis and increased cardiovascular (CV) risk. Patients with moderate to severe skin involvement exhibit a heightened risk of CV events, likely due to systemic inflammation. The Psoriasis Area and Severity Index (PASI) quantifies skin involvement, which may drive immune activation, cytokine secretion, and potential alterations in ventricular geometry, diastolic, and systolic function. Objective To evaluate the association between active skin involvement, carotid plaque (CP), left ventricular geometry, and cardiac function in PsA patients. Methods We conducted a cross-sectional study including PsA patients (35–75 years) meeting the 2006 CASPAR criteria. Exclusion criteria were prior CV disease, pregnancy, and overlap syndrome. Patients were stratified by skin involvement: active (PASI >1) and inactive (PASI ≤1). Carotid ultrasound assessed CP (defined as cIMT ≥1.2 mm or focal thickness ≥0.5 mm) and carotid hyperplasia (cIMT ≥0.8 mm). Echocardiography evaluated cardiac geometry, diastolic, and systolic function per 2016 ASE/EACVI criteria. Normality was assessed via Kolmogorov-Smirnov test; comparisons used Student’s t-test, Chi-square, and Mann-Whitney U test. Statistical significance was set at p ≤ 0.05. Results A total of 78 patients were analyzed, classified based on skin involvement. Those with active skin involvement were younger (50.00 vs. 55.41 years, p = 0.050), with a similar female distribution (65.90% vs. 58.82%, p = 0.521). CV risk factors were comparable, except for diabetes mellitus (DM), which was more prevalent in the active group (25.00% vs. 5.88%, p = 0.025). Obesity, hypertension (HTN), dyslipidemia (DLP), and treatments showed no significant differences. Carotid ultrasound findings revealed no significant differences in CP prevalence (45.45% vs. 35.29%, p = 0.423) or carotid hyperplasia (12.82% vs. 9.67%, p = 0.604). Echocardiographic analysis showed a higher prevalence of eccentric hypertrophy in the active group (29.54% vs. 8.82%, p = 0.025) and a significantly higher left ventricular mass index (LVMI) (91.89 vs. 71.56 g/m², p = 0.002). No differences were observed in relative wall thickness (RWT) or systolic and diastolic function parameters, including left ventricular ejection fraction (LVEF), global longitudinal strain (GLS), and pulmonary systolic arterial pressure (PSAP). Conclusion PsA patients with active skin involvement exhibited higher LVMI and eccentric hypertrophy, suggesting early cardiac remodeling. However, no significant differences were found in CV risk factors, carotid ultrasound findings, or cardiac function parameters. These results highlight the importance of echocardiography in the CV assessment of PsA patients with ongoing cutaneous disease.
银屑病关节炎（PsA）是一种全身性炎症性疾病，与动脉粥样硬化加速和心血管（CV）风险增加有关。中度至重度皮肤受累的患者可能由于全身性炎症而表现出较高的心血管事件风险。银屑病面积和严重程度指数（PASI）量化皮肤受累程度，可能会驱动免疫激活、细胞因子分泌和心室几何形状、舒张和收缩功能的潜在改变。目的评价PsA患者皮肤受累、颈动脉斑块（CP）、左心室几何形状和心功能之间的关系。方法我们进行了一项横断面研究，纳入了符合2006年CASPAR标准的PsA患者（35-75岁）。排除标准为既往CV疾病、妊娠和重叠综合征。患者按皮肤受累程度分层：活动（PASI >1）和非活动（PASI≤1）。颈动脉超声评估CP（定义为cIMT≥1.2 mm或局灶厚度≥0.5 mm）和颈动脉增生（cIMT≥0.8 mm）。超声心动图根据2016年ASE/EACVI标准评估心脏几何形状、舒张和收缩功能。通过Kolmogorov-Smirnov检验评估正态性；比较采用学生t检验、卡方检验和Mann-Whitney U检验。p≤0.05为差异有统计学意义。结果共分析78例患者，根据受累程度进行分类。活跃皮肤受累者较年轻（50.00比55.41岁，p = 0.050），女性分布相似（65.90%比58.82%,p = 0.521）。CV危险因素具有可比性，但糖尿病（DM）在活动组更为普遍（25.00% vs. 5.88%, p = 0.025）。肥胖、高血压（HTN）、血脂异常（DLP）和治疗没有显著差异。颈动脉超声检查结果显示CP患病率（45.45%比35.29%,p = 0.423）和颈动脉增生（12.82%比9.67%,p = 0.604）差异无统计学意义。超声心动图分析显示，活动组偏心肥厚发生率较高（29.54% vs. 8.82%, p = 0.025），左室质量指数（LVMI）显著高于活动组（91.89 vs. 71.56 g/m²，p = 0.002）。在相对壁厚（RWT）或收缩和舒张功能参数，包括左室射血分数（LVEF）、整体纵向应变（GLS）和肺动脉收缩压（PSAP）方面没有观察到差异。结论皮肤受累的PsA患者LVMI升高，伴有偏心性肥厚，提示早期心脏重构。然而，在心血管危险因素、颈动脉超声检查结果或心功能参数方面没有发现显著差异。这些结果强调了超声心动图在持续皮肤疾病的PsA患者的CV评估中的重要性。
{"title":"Active cutaneous involvement is linked to left ventricular geometry changes in psoriatic arthritis patients","authors":"O Garza Flores, F M Garcia Garcia, R L Polina Lugo, A Gonzalez Melendez, E C Garza Gonzalez, J R Azpiri Lopez, D A Galarza Delgado, I J Colunga Pedraza, J A Cardenas De La Garza, R I Arvizu Rivera, V M Fraga Enriquez","doi":"10.1093/eurheartj/ehaf784.4228","DOIUrl":"https://doi.org/10.1093/eurheartj/ehaf784.4228","url":null,"abstract":"Background Psoriatic arthritis (PsA) is a systemic inflammatory disease linked to accelerated atherosclerosis and increased cardiovascular (CV) risk. Patients with moderate to severe skin involvement exhibit a heightened risk of CV events, likely due to systemic inflammation. The Psoriasis Area and Severity Index (PASI) quantifies skin involvement, which may drive immune activation, cytokine secretion, and potential alterations in ventricular geometry, diastolic, and systolic function. Objective To evaluate the association between active skin involvement, carotid plaque (CP), left ventricular geometry, and cardiac function in PsA patients. Methods We conducted a cross-sectional study including PsA patients (35–75 years) meeting the 2006 CASPAR criteria. Exclusion criteria were prior CV disease, pregnancy, and overlap syndrome. Patients were stratified by skin involvement: active (PASI &gt;1) and inactive (PASI ≤1). Carotid ultrasound assessed CP (defined as cIMT ≥1.2 mm or focal thickness ≥0.5 mm) and carotid hyperplasia (cIMT ≥0.8 mm). Echocardiography evaluated cardiac geometry, diastolic, and systolic function per 2016 ASE/EACVI criteria. Normality was assessed via Kolmogorov-Smirnov test; comparisons used Student’s t-test, Chi-square, and Mann-Whitney U test. Statistical significance was set at p ≤ 0.05. Results A total of 78 patients were analyzed, classified based on skin involvement. Those with active skin involvement were younger (50.00 vs. 55.41 years, p = 0.050), with a similar female distribution (65.90% vs. 58.82%, p = 0.521). CV risk factors were comparable, except for diabetes mellitus (DM), which was more prevalent in the active group (25.00% vs. 5.88%, p = 0.025). Obesity, hypertension (HTN), dyslipidemia (DLP), and treatments showed no significant differences. Carotid ultrasound findings revealed no significant differences in CP prevalence (45.45% vs. 35.29%, p = 0.423) or carotid hyperplasia (12.82% vs. 9.67%, p = 0.604). Echocardiographic analysis showed a higher prevalence of eccentric hypertrophy in the active group (29.54% vs. 8.82%, p = 0.025) and a significantly higher left ventricular mass index (LVMI) (91.89 vs. 71.56 g/m², p = 0.002). No differences were observed in relative wall thickness (RWT) or systolic and diastolic function parameters, including left ventricular ejection fraction (LVEF), global longitudinal strain (GLS), and pulmonary systolic arterial pressure (PSAP). Conclusion PsA patients with active skin involvement exhibited higher LVMI and eccentric hypertrophy, suggesting early cardiac remodeling. However, no significant differences were found in CV risk factors, carotid ultrasound findings, or cardiac function parameters. These results highlight the importance of echocardiography in the CV assessment of PsA patients with ongoing cutaneous disease.","PeriodicalId":11976,"journal":{"name":"European Heart Journal","volume":"59 1","pages":""},"PeriodicalIF":39.3,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146121965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-05DOI: 10.1093/eurheartj/ehaf784.215
P G Masci
Background and Purpose Cardiovascular (CV) diseases and dementia share common risk factors and mechanisms and often coexist in elderly. Understanding the cardiovascular-brain interaction is essential for tackling their interconnected disease burden. This study investigated the link between CV phenotypes, brain architecture and cognition. Methods In the UK-Biobank cohort, we analysed 15,519 participants free of neurodegenerative diseases and stroke (median age 64 years, 49% female). Confirmatory-factor-analysis aggregated 18 CV magnetic-resonance-imaging biomarkers into latent variables for left ventricular systolic (gSyst) function, diastolic (gDiast) function, and geometry (gGeom); arterial stiffness was measured by aortic distensibility (AoD). Multivariable-linear-regression models assessed associations between CV phenotypes and brain MRI measures, including gray matter volume, white-matter-hyperintensities, MRI-diffusion white matter microstructure, and hippocampal volume. Models were adjusted for age, body size, CV risk factors, and mean blood pressure. Mediation analysis was employed to explore indirect effects. Results gGeom, reflecting larger myocardial mass, wall thickness, and ventricular volumes, was the strongest predictor of larger hippocampal volume in females (β=0.082, P=1.12×10⁻⁸) and males (β=0.039, P=0.0109) and the only CV phenotype linked to better cognition, including higher fluid intelligence (β=0.042, P=0.004 females; β=0.060, P=2.14×10⁻⁵ males) and shorter reaction time (β=-0.034, P=0.017 females; β=-0.033, P=0.017 males). Hippocampal volume consistently mediated the positive relationship between gGeom and cognition across sexes. In contrast, gSyst, gDiast, and AoD showed weaker and inconsistent associations with brain structure and were unrelated to cognition. Conclusion Ventricular geometry emerges as the key determinant of brain architecture and cognition. Hippocampal integrity mediates the cognitive benefits associated with ventricular geometry.Figure-1.Study outline and main outcome Figure-2.Brain-Heart association
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