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.
{"title":"Sex-specific anatomic differences in patients undergoing transcatheter aortic valve implantation: insights from the ST-TAVI registry.","authors":"Andrija Matetic, Ivica Kristić, Nikola Crnčević, Jakša Zanchi, Tea Domjanović Škopinić, Darija Baković Kramarić, Frane Runjić","doi":"10.1016/j.hjc.2025.01.002","DOIUrl":"10.1016/j.hjc.2025.01.002","url":null,"abstract":"<p><strong>Objective: </strong>The anatomic considerations of transcatheter aortic valve implantation (TAVI) have an important role for the procedure planning; however, sex-specific data are lacking.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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 mm<sup>2</sup>, p < 0.001), LVOT (area 373.3 vs. 481.8 mm<sup>2</sup>, 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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":55062,"journal":{"name":"Hellenic Journal of Cardiology","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143030419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-10DOI: 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.
{"title":"In-depth computational analysis reveals the significant dysregulation of key gap junction proteins (GJPs) driving thoracic aortic aneurysm development.","authors":"Dimitrios E Magouliotis, Serge Sicouri, Arian Arjomandi Rad, John Skoularigis, Grigorios Giamouzis, Andrew Xanthopoulos, Anna P Karamolegkou, Alessandro Viviano, Thanos Athanasiou, Basel Ramlawi","doi":"10.1016/j.hjc.2025.01.001","DOIUrl":"10.1016/j.hjc.2025.01.001","url":null,"abstract":"<p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>These outcomes support the important role of certain gap junction proteins in the pathogenesis of TAA.</p>","PeriodicalId":55062,"journal":{"name":"Hellenic Journal of Cardiology","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142973334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-03DOI: 10.1016/j.hjc.2024.12.008
Leizhi Ku, Shengpeng Guo, Xiaojing Ma
{"title":"Congenital left aortic sinus of valsalva to left ventricle tunnel.","authors":"Leizhi Ku, Shengpeng Guo, Xiaojing Ma","doi":"10.1016/j.hjc.2024.12.008","DOIUrl":"10.1016/j.hjc.2024.12.008","url":null,"abstract":"","PeriodicalId":55062,"journal":{"name":"Hellenic Journal of Cardiology","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142928863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-03DOI: 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.
{"title":"Obesity modifies the association between abnormal glucose metabolism and atrial fibrillation in older adults: a community-based longitudinal and prospective cohort study.","authors":"Xinyi Yu, Xin Wang, Siyi Dun, Hua Zhang, Yanli Yao, Zhendong Liu, Juan Wang, Weike Liu","doi":"10.1016/j.hjc.2024.12.007","DOIUrl":"10.1016/j.hjc.2024.12.007","url":null,"abstract":"<p><strong>Objective: </strong>To investigate the modifying role of obesity in the association between abnormal glucose metabolism and atrial fibrillation (AF) risk in older individuals.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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 P<sub>adj.</sub> < 0.001).</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":55062,"journal":{"name":"Hellenic Journal of Cardiology","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142933338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 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.
{"title":"Clinical phenotypes and outcomes of patients with left ventricular thrombus: an unsupervised cluster analysis","authors":"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","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> < 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}
Pub Date : 2025-01-01DOI: 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.
{"title":"Digital twins: reimagining the future of cardiovascular risk prediction and personalised care","authors":"Katarzyna Dziopa , Karim Lekadir , Pim van der Harst , Folkert W. Asselbergs","doi":"10.1016/j.hjc.2024.06.001","DOIUrl":"10.1016/j.hjc.2024.06.001","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":55062,"journal":{"name":"Hellenic Journal of Cardiology","volume":"81 ","pages":"Pages 4-8"},"PeriodicalIF":2.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141297319","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}
Pub Date : 2025-01-01DOI: 10.1016/j.hjc.2025.01.009
Panos E. Vardas , Charalambos Vlachopoulos
{"title":"From algorithms to clinical outcomes: how artificial intelligence shapes metaclinical medicine","authors":"Panos E. Vardas , Charalambos Vlachopoulos","doi":"10.1016/j.hjc.2025.01.009","DOIUrl":"10.1016/j.hjc.2025.01.009","url":null,"abstract":"","PeriodicalId":55062,"journal":{"name":"Hellenic Journal of Cardiology","volume":"81 ","pages":"Pages 1-3"},"PeriodicalIF":2.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420572","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}
Pub Date : 2025-01-01DOI: 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.
{"title":"Intelligent diagnosis of Kawasaki disease from real-world data using interpretable machine learning models","authors":"Yifan Duan , Ruiqi Wang , Zhilin Huang , Haoran Chen , Mingkun Tang , Jiayin Zhou , Zhengyong Hu , Wanfei Hu , Zhenli Chen , Qing Qian , Haolin Wang","doi":"10.1016/j.hjc.2024.08.003","DOIUrl":"10.1016/j.hjc.2024.08.003","url":null,"abstract":"<div><h3>Objective</h3><div>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.</div></div><div><h3>Methods</h3><div>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).</div></div><div><h3>Results</h3><div>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.</div></div><div><h3>Conclusion</h3><div>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.</div></div>","PeriodicalId":55062,"journal":{"name":"Hellenic Journal of Cardiology","volume":"81 ","pages":"Pages 38-48"},"PeriodicalIF":2.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141918164","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}
Pub Date : 2025-01-01DOI: 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.
{"title":"A complexity evaluation system for mitral valve repair based on preoperative echocardiographic and machine learning","authors":"Kun Zhu , Hang Xu , Shanshan Zheng , Shui Liu , Zhaoji Zhong , Haining Sun , Fujian Duan , Sheng Liu","doi":"10.1016/j.hjc.2024.04.003","DOIUrl":"10.1016/j.hjc.2024.04.003","url":null,"abstract":"<div><h3>Background</h3><div>To develop a novel complexity evaluation system for mitral valve repair based on preoperative echocardiographic data and multiple machine learning algorithms.</div></div><div><h3>Methods</h3><div>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.</div></div><div><h3>Results</h3><div>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.</div></div><div><h3>Conclusion</h3><div>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.</div></div>","PeriodicalId":55062,"journal":{"name":"Hellenic Journal of Cardiology","volume":"81 ","pages":"Pages 25-37"},"PeriodicalIF":2.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140830606","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}
Pub Date : 2025-01-01DOI: 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.
{"title":"Unraveling phenotypic heterogeneity in stanford type B aortic dissection patients through machine learning clustering analysis of cardiovascular CT imaging","authors":"Kun Liu , Deyin Zhao , Lvfan Feng , Zhaoxuan Zhang , Peng Qiu , Xiaoyu Wu , Ruihua Wang , Azad Hussain , Jamol Uzokov , Yanshuo Han","doi":"10.1016/j.hjc.2024.08.006","DOIUrl":"10.1016/j.hjc.2024.08.006","url":null,"abstract":"<div><h3>Objective</h3><div>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.</div></div><div><h3>Methods</h3><div>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.</div></div><div><h3>Results</h3><div>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.</div></div><div><h3>Conclusion</h3><div>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.</div></div>","PeriodicalId":55062,"journal":{"name":"Hellenic Journal of Cardiology","volume":"81 ","pages":"Pages 49-64"},"PeriodicalIF":2.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141918167","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}