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Sex-specific anatomic differences in patients undergoing transcatheter aortic valve implantation: insights from the ST-TAVI registry.
IF 2.7 3区 医学 Q2 CARDIAC & CARDIOVASCULAR SYSTEMS Pub Date : 2025-01-21 DOI: 10.1016/j.hjc.2025.01.002
Andrija Matetic, Ivica Kristić, Nikola Crnčević, Jakša Zanchi, Tea Domjanović Škopinić, Darija Baković Kramarić, Frane Runjić

Objective: The anatomic considerations of transcatheter aortic valve implantation (TAVI) have an important role for the procedure planning; however, sex-specific data are lacking.

Methods: All eligible cases undergoing evaluation for TAVI procedure in the period from November 2019 to July 2023 at the University Hospital of Split were included. Cardiac computed tomography was analyzed to derive the measures of left ventricular outflow tract (LVOT), aortic root, ascending aorta, and ilio-femoral arteries. A sex-based comparison was conducted using the descriptive statistics.

Results: There were 140 female (43.8%) and 180 male patients (56.2%). Female patients had smaller dimensions of aortic annulus (area 391.9 vs. 491.5 mm2, p < 0.001), LVOT (area 373.3 vs. 481.8 mm2, p < 0.001), and ascending aorta (maximal diameter 32.7 vs. 34.5 mm, p < 0.001), as well as ilio-femoral arteries bilaterally (p < 0.001). There was no significant difference in the proportion of ilio-femoral unfeasibility for transfemoral TAVI procedure, as measured by diameter of ilio-femoral arteries <5.0 mm (9.0% in males vs. 6.1% in females, p = 0.441) and <5.5 mm (24.7% in males vs. 16.7% in females, p = 0.156). Female patients were more likely to receive the smallest valve across different valve platforms (p < 0.001). There were sex-specific differences in the availability of conventional valve sizes across different platforms (p < 0.001). Female patients had significantly higher periprocedural mortality (7.9% vs. 1.7%, p = 0.030), whereas there were no differences in other clinical outcomes and no association of periprocedural mortality with anatomic measures.

Conclusion: Female patients showed smaller absolute dimensions of LVOT, aortic root, and ilio-femoral arteries than male patients. There were no differences in the prevalence of ilio-femoral unfeasibility for the transfemoral TAVI procedure; however, there were sex-specific differences in the availability of conventional valve sizes across different platforms. Female patients exhibited a higher periprocedural mortality, with no difference in other clinical outcomes.

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引用次数: 0
In-depth computational analysis reveals the significant dysregulation of key gap junction proteins (GJPs) driving thoracic aortic aneurysm development. 深入的计算分析揭示了驱动胸主动脉瘤发展的关键间隙连接蛋白(GJPs)的显著失调。
IF 2.7 3区 医学 Q2 CARDIAC & CARDIOVASCULAR SYSTEMS Pub Date : 2025-01-10 DOI: 10.1016/j.hjc.2025.01.001
Dimitrios E Magouliotis, Serge Sicouri, Arian Arjomandi Rad, John Skoularigis, Grigorios Giamouzis, Andrew Xanthopoulos, Anna P Karamolegkou, Alessandro Viviano, Thanos Athanasiou, Basel Ramlawi

Objective: Thoracic aortic aneurysm (TAA) represents an aortic pathology that is caused by the deranged integrity of the three layers of the aortic wall and is related to severe morbidity and mortality. Consequently, it is crucial to identify the biomarkers implicated in the pathogenesis and biology of TAA. The aim of the current computational study was to assess the differential gene expression profile of the gap junction proteins (GJPs) in patients with TAA to identify novel potential biomarkers for the diagnosis and treatment of this disease.

Methods: We implemented bioinformatics methodology to construct the gene network of the GJPs family, evaluate their expression in pathologic aortic tissue excised from patients with TAA, and compare it with healthy controls. We also investigated the related biological functions and miRNA families.

Results: We extracted raw data related to the transcriptomic profile of selected genes from a microarray dataset, incorporating 43 TAA and 43 healthy control samples. A total of 17 GJPs were evaluated. Eight GJPs (47%) were downregulated in TAA (GJA3, GJA9, GJA10, GJB1 GJC2, GJD2, GJD3, and GJD4). We also demonstrated the important correlations among the differentially expressed genes (DEGs). Four GJPs (GJA3, GJA9, GJC2, and GJD3) were associated with fair discrimination and calibration traits in predicting TAA presentation. Finally, we performed gene set enrichment analysis (GSEA) and identified the major biological functions and miRNA families (hsa-miR-5001-3p, hsa-miR-942-5p, hsa-miR-7113-3p, hsa-miR-6867-3p, and hsa-miR-4685-3p) associated with the DEGs.

Conclusion: These outcomes support the important role of certain gap junction proteins in the pathogenesis of TAA.

目的:胸主动脉瘤(TAA)是一种由三层主动脉壁完整性紊乱引起的主动脉病理,与严重的发病率和死亡率有关。因此,鉴定与TAA发病机制和生物学相关的生物标志物是至关重要的。当前计算研究的目的是评估间隙连接蛋白(GJPs)在TAA患者中的差异基因表达谱,以确定诊断和治疗这种疾病的新的潜在生物标志物。方法:应用生物信息学方法构建GJPs家族基因网络,评价其在TAA患者病理性主动脉组织中的表达,并与健康对照进行比较。我们还研究了相关的生物学功能和miRNA家族。结果:我们从包含43个TAA和43个健康对照样本的微阵列数据集中提取了与选定基因转录组谱相关的原始数据。共评价17个gjp。8个gjp(47%)在TAA中下调(GJA3、GJA9、GJA10、GJB1、GJC2、GJD2、GJD3、GJD4)。我们还证明了差异表达基因(DEGs)之间的重要相关性。四种GJPs (GJA3、GJA9、GJC2、GJD3)与预测TAA表现的公平歧视和校准特性相关。最后,我们进行了基因集富集分析(GSEA),并鉴定了与deg相关的主要生物学功能和miRNA家族(hsa-miR-5001-3p、hsa-miR-942-5p、hsa-miR-7113-3p、hsa-miR-6867-3p和hsa-miR-4685-3p)。结论:这些结果支持某些间隙连接蛋白在TAA发病机制中的重要作用。
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引用次数: 0
Congenital left aortic sinus of valsalva to left ventricle tunnel. 先天性左主动脉窦至左心室隧道。
IF 2.7 3区 医学 Q2 CARDIAC & CARDIOVASCULAR SYSTEMS Pub Date : 2025-01-03 DOI: 10.1016/j.hjc.2024.12.008
Leizhi Ku, Shengpeng Guo, Xiaojing Ma
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引用次数: 0
Obesity modifies the association between abnormal glucose metabolism and atrial fibrillation in older adults: a community-based longitudinal and prospective cohort study. 肥胖改变了老年人异常糖代谢和房颤之间的关系:一项基于社区的纵向和前瞻性队列研究。
IF 2.7 3区 医学 Q2 CARDIAC & CARDIOVASCULAR SYSTEMS Pub Date : 2025-01-03 DOI: 10.1016/j.hjc.2024.12.007
Xinyi Yu, Xin Wang, Siyi Dun, Hua Zhang, Yanli Yao, Zhendong Liu, Juan Wang, Weike Liu

Objective: To investigate the modifying role of obesity in the association between abnormal glucose metabolism and atrial fibrillation (AF) risk in older individuals.

Methods: From April 2007 to November 2011, 11,663 participants aged ≥60 years were enrolled in the Shandong area. Glucose metabolic status was determined using fasting plasma glucose and hemoglobin A1c levels, and obesity was determined using body mass index (BMI), waist-to-hip ratio (WHR), and visceral fat area (VFA). Obesity-associated metabolic activities were assessed using the adiponectin-to-leptin ratio (ALR), galectin-3, and triglyceride-glucose index (TyG). New-onset AF was diagnosed by ICD-10.

Results: During an average of 11.1 years of follow-up, 1343 participants developed AF. AF risks were higher in those with prediabetes, uncontrolled diabetes, and well-controlled diabetes than with normoglycemia. The hazard ratios were decreased by 14.79%, 40.29%, and 25.23% in those with prediabetes; 31.44%, 53.56%, and 41.90% in those with uncontrolled diabetes; and 21.16%, 42.38%, and 27.59% in those with well-controlled diabetes after adjusting for BMI, WHR, and VFA, respectively. The population-attributable risk percentages of general obesity, central obesity, and high VFA for new-onset AF were 10.43%, 34.78%, and 31.30%, respectively. ALR, galectin-3, and TyG significantly mediated the association of BMI, WHR, and VFA with AF risk (all Padj. < 0.001).

Conclusion: Obesity mediates the association between abnormal glucose metabolism and AF risk in older individuals. WHR is a more effective modifier than BMI and VFA for moderating the association. ALR, TyG, and galectin-3 mediate the moderating effect of obesity on the association between abnormal glucose metabolism and AF risk.

目的:探讨肥胖在老年人糖代谢异常与房颤(AF)风险相关性中的调节作用。方法:2007年4月至2011年11月,在山东地区招募年龄≥60岁的11663名受试者。葡萄糖代谢状态通过空腹血糖和血红蛋白A1c水平来确定,肥胖通过体重指数(BMI)、腰臀比(WHR)和内脏脂肪面积(VFA)来确定。通过脂联素-瘦素比值(ALR)、半乳糖凝集素-3和甘油三酯-葡萄糖指数(TyG)评估肥胖相关代谢活动。采用ICD-10诊断新发房颤。结果:在平均11.1年的随访期间,1343名参与者发生房颤。糖尿病前期、未控制的糖尿病和控制良好的糖尿病患者的房颤风险高于血糖正常的患者。调整BMI、WHR和VFA后,糖尿病前期患者的危险比分别下降了14.79%、40.29%和25.23%,未控制糖尿病患者的危险比分别下降了31.44%、53.56%和41.90%,控制良好的糖尿病患者的危险比分别下降了21.16%、42.38%和27.59%。一般肥胖、中心性肥胖和高VFA对新发房颤的人群归因风险百分比分别为10.43%、34.78%和31.30%。ALR、半乳糖凝集素-3和TyG显著介导BMI、WHR和VFA与房颤风险的关联。结论:肥胖介导了老年人糖代谢异常与房颤风险之间的关联。WHR是比BMI和VFA更有效的调节因子。ALR、TyG和半乳糖凝集素-3介导肥胖对糖代谢异常与房颤风险关联的调节作用。
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引用次数: 0
Clinical phenotypes and outcomes of patients with left ventricular thrombus: an unsupervised cluster analysis 左心室血栓患者的临床表型和预后:无监督聚类分析
IF 2.7 3区 医学 Q2 CARDIAC & CARDIOVASCULAR SYSTEMS Pub Date : 2025-01-01 DOI: 10.1016/j.hjc.2024.08.010
Aloysius S.T. Leow , Fang Qin Goh , Benjamin Y.Q. Tan , Jamie S.Y. Ho , William K.F. Kong , Roger S.Y. Foo , Mark Y.Y. Chan , Leonard L.L. Yeo , Ping Chai , A. Geru , Tiong-Cheng Yeo , Siew Pang Chan , Xin Zhou , Gregory Y.H. Lip , Ching-Hui Sia

Background

Left ventricular thrombus (LVT) can develop in a diverse group of patients with various underlying causes, resulting in divergent natural histories and trajectories with treatment. Our aim was to use cluster analysis to identify unique clinical profiles among patients with LVT and then compare their clinical characteristics, treatment strategies, and outcomes.

Methods

We conducted a retrospective study involving 472 patients with LVT whose data were extracted from a tertiary center's echocardiography database, from March 2011 to January 2021. We used the TwoStep cluster analysis method, examining 19 variables.

Results

Our analysis of the 472 patients with LVT revealed two distinct patient clusters. Cluster 1, comprising 247 individuals (52.3%), was characterized by younger patients with a lower incidence of traditional cardiovascular risk factors and relatively fewer comorbidities compared with Cluster 2. Most patients had LVT attributed to an underlying ischemic condition, with a larger proportion being due to post-acute myocardial infarction in Cluster 1 (68.8%), and due to ischemic cardiomyopathy in Cluster 2 (57.8%). Notably, patients in Cluster 2 exhibited a reduced likelihood of LVT resolution (hazard ratio [HR] 0.58, 95% confidence interval [CI] 0.44–0.77, p < 0.001) and a higher risk of all-cause mortality (HR 2.27, 95% CI 1.43–3.60, p = 0.001). These associations persisted even after adjusting for variables such as anticoagulation treatment, the presence of left ventricular aneurysms, and specific LVT characteristics such as mobility, protrusion, and size.

Conclusion

Through TwoStep cluster analysis, we identified two distinct clinical phenotypes among patients with LVT, each distinguished by unique baseline clinical attributes and varying prognoses.
背景:左心室血栓(LVT)可发生在不同的患者群体中,其潜在病因各不相同,因此自然病史和治疗轨迹也各不相同。我们的目的是利用聚类分析确定 LVT 患者的独特临床特征,然后比较他们的临床特征、治疗策略和结果:我们进行了一项回顾性研究,涉及 472 名左心室颤动患者,研究数据来自一家三级中心的超声心动图数据库,时间跨度为 2011 年 3 月至 2021 年 1 月。我们采用了两步聚类分析法,研究了19个变量:我们对 472 名左心室颤动患者的分析显示出两个不同的患者集群。聚类 1 包括 247 人(52.3%),与聚类 2 相比,聚类 1 的特点是患者较年轻,传统心血管风险因素发生率较低,合并症相对较少。大多数患者的 LVT 都是由潜在的缺血性疾病引起的,群组 1 中因急性心肌梗死后引起 LVT 的比例较大(68.8%),群组 2 中因缺血性心肌病引起 LVT 的比例较大(57.8%)。值得注意的是,群组 2 患者的 LVT 解救可能性降低(HR 0.58,95% CI 0.44 - 0.77,p < 0.001),全因死亡风险升高(HR 2.27,95% CI 1.43 - 3.60,p = 0.001)。即使在调整了抗凝治疗、是否存在左心室动脉瘤以及左心室室间隔缺损的具体特征(如活动度、突出度和大小)等变量后,这些关联仍然存在:通过 TwoStep 聚类分析,我们在左心室畸形患者中发现了两种不同的临床表型,每种表型都有独特的基线临床属性和不同的预后。
{"title":"Clinical phenotypes and outcomes of patients with left ventricular thrombus: an unsupervised cluster analysis","authors":"Aloysius S.T. Leow ,&nbsp;Fang Qin Goh ,&nbsp;Benjamin Y.Q. Tan ,&nbsp;Jamie S.Y. Ho ,&nbsp;William K.F. Kong ,&nbsp;Roger S.Y. Foo ,&nbsp;Mark Y.Y. Chan ,&nbsp;Leonard L.L. Yeo ,&nbsp;Ping Chai ,&nbsp;A. Geru ,&nbsp;Tiong-Cheng Yeo ,&nbsp;Siew Pang Chan ,&nbsp;Xin Zhou ,&nbsp;Gregory Y.H. Lip ,&nbsp;Ching-Hui Sia","doi":"10.1016/j.hjc.2024.08.010","DOIUrl":"10.1016/j.hjc.2024.08.010","url":null,"abstract":"<div><h3>Background</h3><div>Left ventricular thrombus (LVT) can develop in a diverse group of patients with various underlying causes, resulting in divergent natural histories and trajectories with treatment. Our aim was to use cluster analysis to identify unique clinical profiles among patients with LVT and then compare their clinical characteristics, treatment strategies, and outcomes.</div></div><div><h3>Methods</h3><div>We conducted a retrospective study involving 472 patients with LVT whose data were extracted from a tertiary center's echocardiography database, from March 2011 to January 2021. We used the TwoStep cluster analysis method, examining 19 variables.</div></div><div><h3>Results</h3><div>Our analysis of the 472 patients with LVT revealed two distinct patient clusters. Cluster 1, comprising 247 individuals (52.3%), was characterized by younger patients with a lower incidence of traditional cardiovascular risk factors and relatively fewer comorbidities compared with Cluster 2. Most patients had LVT attributed to an underlying ischemic condition, with a larger proportion being due to post-acute myocardial infarction in Cluster 1 (68.8%), and due to ischemic cardiomyopathy in Cluster 2 (57.8%). Notably, patients in Cluster 2 exhibited a reduced likelihood of LVT resolution (hazard ratio [HR] 0.58, 95% confidence interval [CI] 0.44–0.77, <em>p</em> &lt; 0.001) and a higher risk of all-cause mortality (HR 2.27, 95% CI 1.43–3.60, <em>p</em> = 0.001). These associations persisted even after adjusting for variables such as anticoagulation treatment, the presence of left ventricular aneurysms, and specific LVT characteristics such as mobility, protrusion, and size.</div></div><div><h3>Conclusion</h3><div>Through TwoStep cluster analysis, we identified two distinct clinical phenotypes among patients with LVT, each distinguished by unique baseline clinical attributes and varying prognoses.</div></div>","PeriodicalId":55062,"journal":{"name":"Hellenic Journal of Cardiology","volume":"81 ","pages":"Pages 65-74"},"PeriodicalIF":2.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142114942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Digital twins: reimagining the future of cardiovascular risk prediction and personalised care 数字双胞胎:重塑心血管风险预测和个性化医疗的未来。
IF 2.7 3区 医学 Q2 CARDIAC & CARDIOVASCULAR SYSTEMS Pub Date : 2025-01-01 DOI: 10.1016/j.hjc.2024.06.001
Katarzyna Dziopa , Karim Lekadir , Pim van der Harst , Folkert W. Asselbergs
The rapid evolution of highly adaptable and reusable artificial intelligence models facilitates the implementation of digital twinning and has the potential to redefine cardiovascular risk prevention. Digital twinning combines vast amounts of data from diverse sources to construct virtual models of an individual. Emerging artificial intelligence models, called generalist AI, enable the processing of different types of data, including data from electronic health records, laboratory results, medical texts, imaging, genomics, or graphs. Among their unprecedented capabilities are an easy adaptation of a model to previously unseen medical tasks and the ability to reason and explain output using precise medical language derived from scientific literature, medical guidelines, or knowledge graphs. The proposed combination of a digital twinning approach with generalist AI is a path to accelerate the implementation of precision medicine and enhance early recognition and prevention of cardiovascular disease. This proposed strategy may extend to other domains to advance predictive, preventive, and precision medicine and also boost health research discoveries.
高度适应性和可重复使用的人工智能模型的快速发展促进了数字孪生的实施,并有可能重新定义心血管风险预防。数字孪生结合了来自不同来源的大量数据,以构建个人的虚拟模型。被称为通用人工智能的新兴人工智能模型能够处理不同类型的数据,包括来自电子健康记录、实验室结果、医学文本、成像、基因组学或图表的数据。其前所未有的能力包括:模型可轻松适应以前从未见过的医疗任务,并能使用从科学文献、医疗指南或知识图谱中提取的精确医疗语言推理和解释输出结果。将数字孪生方法与通用人工智能相结合的建议,是加快实施精准医疗、加强早期识别和预防心血管疾病的一条途径。这一建议的策略可以推广到其他领域,以推进预测、预防和精准医疗,同时促进健康研究的发现。
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引用次数: 0
From algorithms to clinical outcomes: how artificial intelligence shapes metaclinical medicine
IF 2.7 3区 医学 Q2 CARDIAC & CARDIOVASCULAR SYSTEMS Pub Date : 2025-01-01 DOI: 10.1016/j.hjc.2025.01.009
Panos E. Vardas , Charalambos Vlachopoulos
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引用次数: 0
Intelligent diagnosis of Kawasaki disease from real-world data using interpretable machine learning models 利用可解释的机器学习模型从真实世界数据中智能诊断川崎病
IF 2.7 3区 医学 Q2 CARDIAC & CARDIOVASCULAR SYSTEMS Pub Date : 2025-01-01 DOI: 10.1016/j.hjc.2024.08.003
Yifan Duan , Ruiqi Wang , Zhilin Huang , Haoran Chen , Mingkun Tang , Jiayin Zhou , Zhengyong Hu , Wanfei Hu , Zhenli Chen , Qing Qian , Haolin Wang

Objective

This study aimed to leverage real-world electronic medical record data to develop interpretable machine learning models for diagnosis of Kawasaki disease while also exploring and prioritizing the significant risk factors.

Methods

A comprehensive study was conducted on 4087 pediatric patients at the Children’s Hospital of Chongqing, China. The study collected demographic data, physical examination results, and laboratory findings. Statistical analyses were performed using IBM SPSS Statistics, Version 26.0. The optimal feature subset was used to develop intelligent diagnostic prediction models based on the Light Gradient Boosting Machine, Explainable Boosting Machine (EBM), Gradient Boosting Classifier (GBC), Fast Interpretable Greedy-Tree Sums, Decision Tree, AdaBoost Classifier, and Logistic Regression. Model performance was evaluated in three dimensions: discriminative ability via receiver operating characteristic curves, calibration accuracy using calibration curves, and interpretability through SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-Agnostic Explanations).

Results

In this study, Kawasaki disease was diagnosed in 2971 participants. Analysis was conducted on 31 indicators, including red blood cell distribution width and erythrocyte sedimentation rate. The EBM model demonstrated superior performance relative to other models, with an area under the curve of 0.97, second only to the GBC model. Furthermore, the EBM model exhibited the highest calibration accuracy and maintained its interpretability without relying on external analytical tools such as SHAP and LIME, thus reducing interpretation biases. Platelet distribution width, total protein, and erythrocyte sedimentation rate were identified by the model as significant predictors for the diagnosis of Kawasaki disease.

Conclusion

This study used diverse machine learning models for early diagnosis of Kawasaki disease. The findings demonstrated that interpretable models such as EBM outperformed traditional machine learning models in terms of both interpretability and performance. Ensuring consistency between predictive models and clinical evidence is crucial for the successful integration of artificial intelligence into real-world clinical practice.
目的:本研究旨在利用真实世界的电子病历(EMR)数据开发可解释的川崎病诊断机器学习模型:本研究旨在利用真实世界的电子病历(EMR)数据开发可解释的川崎病诊断机器学习模型,同时探索并优先考虑重要的风险因素:方法:对中国重庆市儿童医院的 4087 名儿科患者进行了一项综合研究。研究收集了人口统计学数据、体格检查结果和实验室检查结果。使用 SPSS 26.0 进行统计分析。利用最优特征子集开发了基于光梯度提升机(LGBM)、可解释提升机(EBM)、梯度提升分类器(GBC)、快速可解释贪婪树和(FIGS)、决策树(DT)、AdaBoost 分类器(AdaBoost)和逻辑回归(LR)的智能诊断预测模型。模型性能从三个方面进行评估:通过接收者操作特征曲线评估分辨能力,通过校准曲线评估校准准确性,以及通过夏普利加法解释(SHAP)和本地可解释模型-诊断解释(LIME)评估可解释性:在这项研究中,2971 名参与者被诊断为川崎病。对31项指标进行了分析,包括红细胞分布宽度和红细胞沉降率。与其他模型相比,EBM 模型表现出更优越的性能,其曲线下面积(AUC)为 0.97,仅次于 GBC 模型。此外,EBM 模型的校准精度最高,无需依赖 SHAP 和 LIME 等外部分析工具即可保持其可解释性,从而减少了解释偏差。该模型将血小板分布宽度、总蛋白和红细胞沉降率确定为诊断川崎病的重要预测指标:本研究采用了多种机器学习模型来进行川崎病的早期诊断。研究结果表明,EBM 等可解释模型在可解释性和性能方面均优于传统的机器学习模型。确保预测模型与临床证据之间的一致性是人工智能成功融入现实世界临床实践的关键。
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引用次数: 0
A complexity evaluation system for mitral valve repair based on preoperative echocardiographic and machine learning 基于术前超声心动图和机器学习的二尖瓣修复术复杂性评估系统
IF 2.7 3区 医学 Q2 CARDIAC & CARDIOVASCULAR SYSTEMS Pub Date : 2025-01-01 DOI: 10.1016/j.hjc.2024.04.003
Kun Zhu , Hang Xu , Shanshan Zheng , Shui Liu , Zhaoji Zhong , Haining Sun , Fujian Duan , Sheng Liu

Background

To develop a novel complexity evaluation system for mitral valve repair based on preoperative echocardiographic data and multiple machine learning algorithms.

Methods

From March 2021 to March 2023, 231 consecutive patients underwent mitral valve repair. Clinical and echocardiographic data were included in the analysis. The end points included immediate mitral valve repair failure (mitral replacement secondary to mitral repair failure) and recurrence regurgitation (moderate or greater mitral regurgitation [MR] before discharge). Various machine learning algorithms were used to establish the complexity evaluation system.

Results

A total of 231 patients were included in this study; the median ejection fraction was 66% (63–70%), and 159 (68.8%) patients were men. Mitral repair was successful in 90.9% (210 of 231) of patients. The linear support vector classification model has the best prediction results in training and test cohorts and the variables of age, A2 lesions, leaflet height, MR grades, and so on were risk factors for failure of mitral valve repair.

Conclusion

The linear support vector classification prediction model may allow the evaluation of the complexity of mitral valve repair. Age, A2 lesions, leaflet height, MR grades, and so on may be associated with mitral repair failure.
基于术前超声心动图数据和多种机器学习算法,开发一种新型二尖瓣修复术复杂性评估系统。从 2021 年 3 月到 2023 年 3 月,231 名连续患者接受了二尖瓣修复术。临床和超声心动图数据均纳入分析。终点包括二尖瓣修复即刻失败(继发于二尖瓣修复失败的二尖瓣置换术)和二尖瓣反流复发(出院前中度或更严重的二尖瓣反流 [MR])。复杂性评估系统采用了多种机器学习算法。本研究共纳入231名患者,其中射血分数中位数为66%(63-70%),159名(68.8%)患者为男性。90.9%的患者(231 例中的 210 例)成功进行了二尖瓣修复。线性支持向量分类模型在训练队列和测试队列中的预测结果最佳,年龄、A2病变、瓣叶高度、MR分级等变量是二尖瓣修复失败的风险因素。线性支持向量分类预测模型可用于评估二尖瓣修复术的复杂性。年龄、A2病变、瓣叶高度、MR分级等可能与二尖瓣修复失败有关。
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引用次数: 0
Unraveling phenotypic heterogeneity in stanford type B aortic dissection patients through machine learning clustering analysis of cardiovascular CT imaging 通过心血管 CT 成像的机器学习聚类分析揭示斯坦福 B 型主动脉夹层患者的表型异质性
IF 2.7 3区 医学 Q2 CARDIAC & CARDIOVASCULAR SYSTEMS Pub Date : 2025-01-01 DOI: 10.1016/j.hjc.2024.08.006
Kun Liu , Deyin Zhao , Lvfan Feng , Zhaoxuan Zhang , Peng Qiu , Xiaoyu Wu , Ruihua Wang , Azad Hussain , Jamol Uzokov , Yanshuo Han

Objective

Aortic dissection remains a life-threatening condition necessitating accurate diagnosis and timely intervention. This study aimed to investigate phenotypic heterogeneity in patients with Stanford type B aortic dissection (TBAD) through machine learning clustering analysis of cardiovascular computed tomography (CT) imaging.

Methods

Electronic medical records were collected to extract demographic and clinical features of patients with TBAD. Exclusion criteria ensured homogeneity and clinical relevance of the TBAD cohort. Controls were selected on the basis of age, comorbidity status, and imaging availability. Aortic morphological parameters were extracted from CT angiography and subjected to K-means clustering analysis to identify distinct phenotypes.

Results

Clustering analysis revealed three phenotypes of patients with TBAD with significant correlations with population characteristics and dissection rates. This pioneering study used CT-based three-dimensional reconstruction to classify high-risk individuals, demonstrating the potential of machine learning in enhancing diagnostic accuracy and personalized treatment strategies. Recent advancements in machine learning have garnered attention in cardiovascular imaging, particularly in aortic dissection research. These studies leverage various imaging modalities to extract valuable features and information from cardiovascular images, paving the way for more personalized interventions.

Conclusion

This study provides insights into the phenotypic heterogeneity of patients with TBAD using machine learning clustering analysis of cardiovascular CT imaging. The identified phenotypes exhibit correlations with population characteristics and dissection rates, highlighting the potential of machine learning in risk stratification and personalized management of aortic dissection. Further research in this field holds promise for improving diagnostic accuracy and treatment outcomes in patients with aortic dissection.
背景:主动脉夹层仍然是一种威胁生命的疾病,需要准确诊断和及时干预。本研究旨在通过对心血管 CT 成像进行机器学习聚类分析,揭示斯坦福 B 型主动脉夹层(TBAD)患者的表型异质性:收集电子病历,提取 TBAD 患者的人口统计学和临床特征。排除标准确保了 TBAD 队列的同质性和临床相关性。根据年龄、合并症状况和成像可用性选择对照组。从CT血管造影(CTA)中提取主动脉形态学参数,并进行k均值聚类分析,以确定不同的表型:结果:聚类分析显示 TBAD 患者有三种表型,与人群特征和夹层率有显著相关性。这项开创性的研究利用基于CT的三维重建技术对高危人群进行分类,展示了机器学习在提高诊断准确性和个性化治疗策略方面的潜力。机器学习的最新进展在心血管成像领域,尤其是主动脉夹层研究中备受关注。这些研究利用各种成像模式从心血管图像中提取有价值的特征和信息,为更个性化的干预措施铺平了道路:本研究通过对心血管 CT 成像进行机器学习聚类分析,深入了解了 TBAD 患者的表型异质性。确定的表型与人群特征和夹层发生率存在相关性,凸显了机器学习在主动脉夹层风险分层和个性化管理方面的潜力。该领域的进一步研究有望提高主动脉夹层患者的诊断准确性和治疗效果。
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Hellenic Journal of Cardiology
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