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Wang J, Zhu J, Li H, Wu S, Li S, Yao Z, Zhu T, Tang B, Tang S, Liu J. Multimodal Visualization and Explainable Machine Learning-Driven Markers Enable Early Identification and Prognosis Prediction for Symptomatic Aortic Stenosis and Heart Failure With Preserved Ejection Fraction After Transcatheter Aortic Valve Replacement: Multicenter Cohort Study. J Med Internet Res 2025; 27:e70587. [PMID: 40310672 DOI: 10.2196/70587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2025] [Revised: 04/09/2025] [Accepted: 04/09/2025] [Indexed: 05/02/2025] Open
Abstract
BACKGROUND Currently, there is a paucity of literature addressing personalized risk stratification using multimodal data in patients with symptomatic aortic stenosis and heart failure with preserved ejection fraction (HFpEF) following transcatheter aortic valve replacement (TAVR). OBJECTIVE This study aimed to enhance the performance of risk assessment models in this patient population by developing a predictive model for adverse outcomes using various machine learning (ML) techniques. METHODS This multicenter cohort study included 326 patients diagnosed with severe AS and HFpEF who underwent TAVR between January 2017 and December 2023. Patients were allocated to training (n=195) and independent validation (n=131) sets based on hospital affiliation. A dual-phase feature selection process, combining least absolute shrinkage and selection operator logistic regression and the Boruta algorithm, was used to identify relevant variables from the multimodal dataset. A total of 5 ML model-decision trees, K-nearest neighbors, random forest, support vector machine, and extreme gradient boosting were used to construct a visualization and explainable predictive framework to elucidate model decision-making processes. RESULTS The primary features identified included age, N-terminal pro-brain natriuretic peptide, fasting blood glucose, triglyceride/high-density lipoprotein cholesterol ratio, triglyceride glucose index, triglyceride glucose-BMI index, atherogenic index of plasma index, and Apolipoprotein B. Among the 5 models, the support vector machine demonstrated the best predictive performance for major adverse cardiovascular and cerebrovascular events in patients with severe AS and HFpEF following TAVR, achieving an area under the curve of 0.756 (95% CI 0.631-0.881) in the independent validation set. The model exhibited good calibration and robust predictive power in both training and validation sets and demonstrated the highest net benefit in decision curve analysis compared to other models. To extract significant variables influencing the algorithm and ensure model appropriateness, we interpreted cohort and personalized model predictions using Shapley Additive Explanations values. CONCLUSIONS Our ML-based multimodal model, incorporating 8 readily accessible predictors, demonstrated robust predictive capability for 12 months of major adverse cardiovascular and cerebrovascular events risk. This model can be used to identify high-risk individuals with AS and HFpEF following TAVR, potentially aiding in risk stratification and personalized treatment strategies.
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Affiliation(s)
- Jun Wang
- Department of Cardiology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, China
- Joint Research Center for Regional Diseases of IHM, Bengbu Medical University, Bengbu, China
- Joint Research Center for Regional Diseases of IHM, The First Affiliated Hospital of Bengbu Medical University, Bengbu, China
| | - Jiajun Zhu
- Department of Cardiology, First Affiliated Hospital of Xinjiang Medical University, Urumchi, China
| | - Hui Li
- Department of Cardiology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, China
| | - Shili Wu
- Department of Cardiology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, China
- Department of Cardiology, The People's Hospital of Bozhou, Bozhou, China
| | - Siyang Li
- Department of Cardiology, Xiangyang Central Hospital, Xiangyang, China
| | - Zhuoya Yao
- Department of Cardiology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, China
| | - Tongjian Zhu
- Department of Cardiology, Xiangyang Central Hospital, Xiangyang, China
| | - Bi Tang
- Department of Cardiology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, China
- Joint Research Center for Regional Diseases of IHM, Bengbu Medical University, Bengbu, China
- Joint Research Center for Regional Diseases of IHM, The First Affiliated Hospital of Bengbu Medical University, Bengbu, China
| | - Shengxing Tang
- Department of Cardiology, First Affiliated Hospital of Wannan Medical College, Wuhu, China
| | - Jinjun Liu
- Department of Cardiology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, China
- Joint Research Center for Regional Diseases of IHM, Bengbu Medical University, Bengbu, China
- Joint Research Center for Regional Diseases of IHM, The First Affiliated Hospital of Bengbu Medical University, Bengbu, China
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Wang Y, Zhu T, Zhou T, Wu B, Tan W, Ma K, Yao Z, Wang J, Li S, Qin F, Xu Y, Tan L, Liu J, Wang J. Hyper-DREAM, a Multimodal Digital Transformation Hypertension Management Platform Integrating Large Language Model and Digital Phenotyping: Multicenter Development and Initial Validation Study. J Med Syst 2025; 49:42. [PMID: 40172683 DOI: 10.1007/s10916-025-02176-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2025] [Accepted: 03/22/2025] [Indexed: 04/04/2025]
Abstract
Within the mHealth framework, systematic research that collects and analyzes patient data to establish comprehensive digital health archives for hypertensive patients, and leverages large language models (LLMs) to assist clinicians in health management and Blood Pressure (BP) control remains limited. In this study, our aims to describe the design, development and usability evaluation process of a management platform (Hyper-DREAM) for hypertension. Our multidisciplinary team employed an iterative design approach over the course of a year to develop the Hyper-DREAM platform. This platform's primary functionalities encompass multimodal data collection (personal hypertensive digital phenotype archive), multimodal interventions (BP measurement, medication assistance, behavior modification, and hypertension education) and multimodal interactions (clinician-patient engagement and BP Coach component). In August 2024, the mHealth App Usability Questionnaire (MAUQ) was conducted involving 51 hypertensive patients recruited from three distinct centers. In parallel, six clinicians engaged in management activities and contributed feedback via the Doctor's Software Satisfaction Questionnaire (DSSQ). Concurrently, a real-world comparative experiment was conducted to evaluate the usability of the BP Coach, ChatGPT-4o Mini, ChatGPT-4o and clinicians. The comparative experiment demonstrated that the BP Coach achieved significantly higher scores in utility (mean scores 4.05, SD 0.87) and completeness (mean scores 4.12, SD 0.78) when compared to ChatGPT-4o Mini, ChatGPT-4o, and clinicians. In terms of clarity, the BP Coach was slightly lower than clinicians (mean scores 4.03, SD 0.88). In addition, the BP Coach exhibited lower performance in conciseness (mean scores 3.00, SD 0.96). Clinicians reported a marked improvement in work efficiency (2.67 vs. 4.17, P < .001) and experienced faster and more effective patient interactions (3.0 vs. 4.17, P = .004). Furthermore, the Hyper-DREAM platform significantly decreased work intensity (2.5 vs. 3.5, P = .01) and minimized disruptions to daily routines (2.33 vs. 3.55, P = .004). The Hyper-DREAM platform demonstrated significantly greater overall satisfaction compared to the WeChat-based standard management (3.33 vs. 4.17, P = .01). Additionally, clinicians exhibited a markedly higher willingness to integrate the Hyper-DREAM platform into clinical practice (2.67 vs. 4.17, P < .001). Furthermore, patient management time decreased from 11.5 min (SD 1.87) with Wechat-based standard management to 7.5 min (SD 1.84, P = .01) with Hyper-DREAM. Hypertensive patients reported high satisfaction with the Hyper-DREAM platform, including ease of use (mean scores 1.60, SD 0.69), system information arrangement (mean scores 1.69, SD 0.71), and usefulness (mean scores 1.57, SD 0.58). In conclusion, our study presents Hyper-DREAM, a novel artificial intelligence-driven platform for hypertension management, designed to alleviate clinician workload and exhibiting significant promise for clinical application. The Hyper-DREAM platform is distinguished by its user-friendliness, high satisfaction rates, utility, and effective organization of information. Furthermore, the BP Coach component underscores the potential of LLMs in advancing mHealth approaches to hypertension management.
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Affiliation(s)
- Yijun Wang
- Department of Cardiology, The First Affiliated Hospital of Bengbu Medical Universtiy, 287 Changhuai Road, Longzihu District, Bengbu City, Anhui Province, 430060, P.R. China
- West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Tongjian Zhu
- Department of Cardiology, Institute of Cardiovascular Diseases, Xiangyang Central Hospital, Affliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
| | - Tong Zhou
- Department of Cardiology, The First Affiliated Hospital of Bengbu Medical Universtiy, 287 Changhuai Road, Longzihu District, Bengbu City, Anhui Province, 430060, P.R. China
| | - Bing Wu
- Institute of Clinical Medicine and Department of Cardiology, Renmin Hospital, Hubei University of Medicine, Shiyan, 442000, Hubei, China
| | - Wuping Tan
- Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Kezhong Ma
- Department of Cardiology, Institute of Cardiovascular Diseases, Xiangyang Central Hospital, Affliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
| | - Zhuoya Yao
- Department of Cardiology, The First Affiliated Hospital of Bengbu Medical Universtiy, 287 Changhuai Road, Longzihu District, Bengbu City, Anhui Province, 430060, P.R. China
| | - Jian Wang
- Department of Cardiology, The First Affiliated Hospital of Bengbu Medical Universtiy, 287 Changhuai Road, Longzihu District, Bengbu City, Anhui Province, 430060, P.R. China
| | - Siyang Li
- Department of Cardiology, Institute of Cardiovascular Diseases, Xiangyang Central Hospital, Affliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
| | - Fanglin Qin
- Mental Health Center and Psychiatric Laboratory, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Yannan Xu
- Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Bengbu Medical Universtiy, Bengbu, Anhui, China
| | - Liguo Tan
- Institute of Clinical Medicine and Department of Cardiology, Renmin Hospital, Hubei University of Medicine, Shiyan, 442000, Hubei, China.
| | - Jinjun Liu
- Department of Cardiology, The First Affiliated Hospital of Bengbu Medical Universtiy, 287 Changhuai Road, Longzihu District, Bengbu City, Anhui Province, 430060, P.R. China.
| | - Jun Wang
- Department of Cardiology, The First Affiliated Hospital of Bengbu Medical Universtiy, 287 Changhuai Road, Longzihu District, Bengbu City, Anhui Province, 430060, P.R. China.
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Duan S, Li X, Wang J, Wang Y, Xu T, Guo F, Wang Y, Song L, Li Z, Yang X, Shi X, Liu H, Zhou L, Wang Y, Jiang H, Yu L. Multimodal data-based longitudinal prognostic model for predicting atrial fibrillation recurrence after catheter ablation in patients with patent foramen ovale and paroxysmal atrial fibrillation. Eur J Med Res 2025; 30:39. [PMID: 39833853 PMCID: PMC11748319 DOI: 10.1186/s40001-025-02286-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Accepted: 01/10/2025] [Indexed: 01/22/2025] Open
Abstract
BACKGROUND Clinical studies on atrial fibrillation (AF) recurrence after catheter ablation in patients diagnosed with patent foramen ovale (PFO) and paroxysmal AF (PAF) are scarce. Here, we aimed to develop a nomogram model utilizing multimodal data for the risk stratification of AF recurrence following catheter ablation in individuals diagnosed with PFO and new-onset PAF. METHODS Patients with PFO and PAF who underwent catheter ablation at the Renmin Hospital of Wuhan University from January 2018 to June 2020 were consecutively enrolled. The identification of potential risk factors was conducted using the regression method known as least absolute shrinkage and selection operator. Subsequently, multivariate COX regression analysis was conducted to determine the independent risk factors, after which a nomogram scoring system was developed. The nomogram's performance was assessed via various statistical measures, including receiver operating characteristic curve analysis, calibration curve, and decision curve analysis (DCA). RESULTS The dataset was partitioned into the development cohort (n = 102) and the validation cohort (n = 43) using a 7:3 ratio. The constructed nomogram included four clinical variables: age, diabetes mellitus, lipoprotein (a), and right ventricular diameter. The area under the curve values of the development and validation cohorts at 1, 2, and 3 years post-catheter ablation were 0.911, 0.812, and 0.786 and 0.842, 0.761, and 0.785, respectively. Additionally, the nomogram demonstrated a significant correlation between the predicted and actual outcomes in the development and validation cohorts, indicating its excellent calibration. Lastly, the DCA findings suggested that the model had notable clinical applicability in predicting the likelihood of AF recurrence within 1, 2, and 3 years after catheter ablation. CONCLUSION The incorporation of multimodal data in a nomogram visualization tool facilitates the concise representation of multimodal data, thereby enhancing the comprehension of the clinical status of patients with PFO and PAF following catheter ablation and providing accurate risk stratification at 1, 2, and 3 years post-treatment. TRIAL REGISTRATION This trial was registered in the Chinese Clinical Trial Registry. (ChiCTR2300072320).
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Affiliation(s)
- Shoupeng Duan
- Department of Cardiology, Renmin Hospital of Wuhan University; Institute of Molecular Medicine, Renmin Hospital of Wuhan University; Hubei Key Laboratory of Autonomic Nervous System Modulation; Taikang Center for Life and Medical Sciences, Wuhan University; Cardiac Autonomic Nervous System Research Center of Wuhan University; Hubei Key Laboratory of Cardiology; Cardiovascular Research Institute, Wuhan University, No.238 Jiefang Road, Wuhan, Hubei, 430060, People's Republic of China
| | - Xujun Li
- Department of Cardiology, Renmin Hospital of Wuhan University; Institute of Molecular Medicine, Renmin Hospital of Wuhan University; Hubei Key Laboratory of Autonomic Nervous System Modulation; Taikang Center for Life and Medical Sciences, Wuhan University; Cardiac Autonomic Nervous System Research Center of Wuhan University; Hubei Key Laboratory of Cardiology; Cardiovascular Research Institute, Wuhan University, No.238 Jiefang Road, Wuhan, Hubei, 430060, People's Republic of China
| | - Jun Wang
- Department of Cardiology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui, China
| | - Yuhong Wang
- Department of Cardiology, Renmin Hospital of Wuhan University; Institute of Molecular Medicine, Renmin Hospital of Wuhan University; Hubei Key Laboratory of Autonomic Nervous System Modulation; Taikang Center for Life and Medical Sciences, Wuhan University; Cardiac Autonomic Nervous System Research Center of Wuhan University; Hubei Key Laboratory of Cardiology; Cardiovascular Research Institute, Wuhan University, No.238 Jiefang Road, Wuhan, Hubei, 430060, People's Republic of China
| | - Tianyou Xu
- Department of Cardiology, Renmin Hospital of Wuhan University; Institute of Molecular Medicine, Renmin Hospital of Wuhan University; Hubei Key Laboratory of Autonomic Nervous System Modulation; Taikang Center for Life and Medical Sciences, Wuhan University; Cardiac Autonomic Nervous System Research Center of Wuhan University; Hubei Key Laboratory of Cardiology; Cardiovascular Research Institute, Wuhan University, No.238 Jiefang Road, Wuhan, Hubei, 430060, People's Republic of China
| | - Fuding Guo
- Department of Cardiology, Renmin Hospital of Wuhan University; Institute of Molecular Medicine, Renmin Hospital of Wuhan University; Hubei Key Laboratory of Autonomic Nervous System Modulation; Taikang Center for Life and Medical Sciences, Wuhan University; Cardiac Autonomic Nervous System Research Center of Wuhan University; Hubei Key Laboratory of Cardiology; Cardiovascular Research Institute, Wuhan University, No.238 Jiefang Road, Wuhan, Hubei, 430060, People's Republic of China
| | - Yijun Wang
- Department of Cardiology, Renmin Hospital of Wuhan University; Institute of Molecular Medicine, Renmin Hospital of Wuhan University; Hubei Key Laboratory of Autonomic Nervous System Modulation; Taikang Center for Life and Medical Sciences, Wuhan University; Cardiac Autonomic Nervous System Research Center of Wuhan University; Hubei Key Laboratory of Cardiology; Cardiovascular Research Institute, Wuhan University, No.238 Jiefang Road, Wuhan, Hubei, 430060, People's Republic of China
| | - Lingpeng Song
- Department of Cardiology, Renmin Hospital of Wuhan University; Institute of Molecular Medicine, Renmin Hospital of Wuhan University; Hubei Key Laboratory of Autonomic Nervous System Modulation; Taikang Center for Life and Medical Sciences, Wuhan University; Cardiac Autonomic Nervous System Research Center of Wuhan University; Hubei Key Laboratory of Cardiology; Cardiovascular Research Institute, Wuhan University, No.238 Jiefang Road, Wuhan, Hubei, 430060, People's Republic of China
| | - Zeyan Li
- Department of Cardiology, Renmin Hospital of Wuhan University; Institute of Molecular Medicine, Renmin Hospital of Wuhan University; Hubei Key Laboratory of Autonomic Nervous System Modulation; Taikang Center for Life and Medical Sciences, Wuhan University; Cardiac Autonomic Nervous System Research Center of Wuhan University; Hubei Key Laboratory of Cardiology; Cardiovascular Research Institute, Wuhan University, No.238 Jiefang Road, Wuhan, Hubei, 430060, People's Republic of China
| | - Xiaomeng Yang
- Department of Cardiology, Renmin Hospital of Wuhan University; Institute of Molecular Medicine, Renmin Hospital of Wuhan University; Hubei Key Laboratory of Autonomic Nervous System Modulation; Taikang Center for Life and Medical Sciences, Wuhan University; Cardiac Autonomic Nervous System Research Center of Wuhan University; Hubei Key Laboratory of Cardiology; Cardiovascular Research Institute, Wuhan University, No.238 Jiefang Road, Wuhan, Hubei, 430060, People's Republic of China
| | - Xiaoyu Shi
- Department of Cardiology, Renmin Hospital of Wuhan University; Institute of Molecular Medicine, Renmin Hospital of Wuhan University; Hubei Key Laboratory of Autonomic Nervous System Modulation; Taikang Center for Life and Medical Sciences, Wuhan University; Cardiac Autonomic Nervous System Research Center of Wuhan University; Hubei Key Laboratory of Cardiology; Cardiovascular Research Institute, Wuhan University, No.238 Jiefang Road, Wuhan, Hubei, 430060, People's Republic of China
| | - Hengyang Liu
- Department of Cardiology, Renmin Hospital of Wuhan University; Institute of Molecular Medicine, Renmin Hospital of Wuhan University; Hubei Key Laboratory of Autonomic Nervous System Modulation; Taikang Center for Life and Medical Sciences, Wuhan University; Cardiac Autonomic Nervous System Research Center of Wuhan University; Hubei Key Laboratory of Cardiology; Cardiovascular Research Institute, Wuhan University, No.238 Jiefang Road, Wuhan, Hubei, 430060, People's Republic of China
| | - Liping Zhou
- Department of Cardiology, Renmin Hospital of Wuhan University; Institute of Molecular Medicine, Renmin Hospital of Wuhan University; Hubei Key Laboratory of Autonomic Nervous System Modulation; Taikang Center for Life and Medical Sciences, Wuhan University; Cardiac Autonomic Nervous System Research Center of Wuhan University; Hubei Key Laboratory of Cardiology; Cardiovascular Research Institute, Wuhan University, No.238 Jiefang Road, Wuhan, Hubei, 430060, People's Republic of China
| | - Yueyi Wang
- Department of Cardiology, Renmin Hospital of Wuhan University; Institute of Molecular Medicine, Renmin Hospital of Wuhan University; Hubei Key Laboratory of Autonomic Nervous System Modulation; Taikang Center for Life and Medical Sciences, Wuhan University; Cardiac Autonomic Nervous System Research Center of Wuhan University; Hubei Key Laboratory of Cardiology; Cardiovascular Research Institute, Wuhan University, No.238 Jiefang Road, Wuhan, Hubei, 430060, People's Republic of China
| | - Hong Jiang
- Department of Cardiology, Renmin Hospital of Wuhan University; Institute of Molecular Medicine, Renmin Hospital of Wuhan University; Hubei Key Laboratory of Autonomic Nervous System Modulation; Taikang Center for Life and Medical Sciences, Wuhan University; Cardiac Autonomic Nervous System Research Center of Wuhan University; Hubei Key Laboratory of Cardiology; Cardiovascular Research Institute, Wuhan University, No.238 Jiefang Road, Wuhan, Hubei, 430060, People's Republic of China.
| | - Lilei Yu
- Department of Cardiology, Renmin Hospital of Wuhan University; Institute of Molecular Medicine, Renmin Hospital of Wuhan University; Hubei Key Laboratory of Autonomic Nervous System Modulation; Taikang Center for Life and Medical Sciences, Wuhan University; Cardiac Autonomic Nervous System Research Center of Wuhan University; Hubei Key Laboratory of Cardiology; Cardiovascular Research Institute, Wuhan University, No.238 Jiefang Road, Wuhan, Hubei, 430060, People's Republic of China.
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Zhou B, Tan W, Duan S, Wang Y, Bian F, Zhao P, Wang J, Yao Z, Li H, Hu X, Wang J, Liu J. Inflammation Biomarker-Driven Vertical Visualization Model for Predicting Long-Term Prognosis in Unstable Angina Pectoris Patients with Angiographically Intermediate Coronary Lesions. J Inflamm Res 2024; 17:10571-10584. [PMID: 39659751 PMCID: PMC11629667 DOI: 10.2147/jir.s497546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2024] [Accepted: 11/28/2024] [Indexed: 12/12/2024] Open
Abstract
Objective Angina, a prevalent manifestation of coronary artery disease, is primarily associated with inflammation, an established contributor to the pathogenesis of atherosclerosis and acute coronary syndromes (ACS). Various inflammatory markers are employed in clinical practice to predict patient prognosis and optimize clinical decision-making in the management of ACS. This study investigated the prognostic significance of integrating commonly used, easily repeatable inflammatory biomarkers within a multimodal preoperative prediction model in patients presenting with unstable Angina Pectoris (UAP) and intermediate coronary lesions. Methods This retrospective analysis included patients diagnosed with UAP and intermediate coronary lesions (50%-70% stenosis) who underwent coronary angiography at our hospital between January 2019 and June 2021. The assessed outcome was the occurrence of major adverse cardiac and cerebrovascular events (MACCEs). The Boruta algorithm was applied to identify potential risk factors and develop a prognostic multimodal model. Results A total of 773 patients were enrolled and divided into a training cohort (n=463) and validation cohort (n=310). A nomogram was constructed to predict the probability of MACCE-free survival based on five clinical features: diabetes mellitus, current smoking, history of myocardial infarction, neutrophil-to-lymphocyte ratio, and fasting blood glucose. In the training cohort, the area under the curve values for the nomogram at 24, 32, and 40 months were 0.669, 0.707, and 0.718, respectively, while those in the validation cohort were 0.613, 0.612 and 0.630, respectively. The model demonstrated good calibration in both cohorts with predicted outcomes aligning well with actual results at all time points up to 40 months. Furthermore, decision curve analysis showed significant clinical utility of the model across the specified time intervals. Conclusion The developed preoperative prognostic model visually illustrates the association among inflammation, blood glucose level, established risk factors, and long-term MACCEs in UAP patients with intermediate coronary lesions.
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Affiliation(s)
- Bowen Zhou
- Graduate School, Bengbu Medical University, Bengbu, Anhui, People’s Republic of China
- Department of Cardiology; The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui, People’s Republic of China
- Department of Cardiology, Suzhou First People’s Hospital, Suzhou, Anhui, People’s Republic of China
| | - Wuping Tan
- Department of Cardiology, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, Guangzhou, People’s Republic of China
- Guangdong Province Key Laboratory of Arrhythmia and Electrophysiology, Guangzhou, People’s Republic of China
| | - Shoupeng Duan
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, Hubei, People’s Republic of China
| | - Yijun Wang
- National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, People’s Republic of China
| | - Fenlan Bian
- Graduate School, Bengbu Medical University, Bengbu, Anhui, People’s Republic of China
- Department of Cardiology; The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui, People’s Republic of China
| | - Peng Zhao
- Graduate School, Bengbu Medical University, Bengbu, Anhui, People’s Republic of China
- Department of Cardiology; The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui, People’s Republic of China
| | - Jian Wang
- Department of Cardiology; The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui, People’s Republic of China
| | - Zhuoya Yao
- Department of Cardiology; The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui, People’s Republic of China
| | - Hui Li
- Department of Cardiology; The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui, People’s Republic of China
| | - Xuemin Hu
- Department of Cardiology, Suzhou First People’s Hospital, Suzhou, Anhui, People’s Republic of China
| | - Jun Wang
- Graduate School, Bengbu Medical University, Bengbu, Anhui, People’s Republic of China
- Department of Cardiology; The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui, People’s Republic of China
| | - Jinjun Liu
- Graduate School, Bengbu Medical University, Bengbu, Anhui, People’s Republic of China
- Department of Cardiology; The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui, People’s Republic of China
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Wang J, Wang Y, Duan S, Xu L, Xu Y, Yin W, Yang Y, Wu B, Liu J. Multimodal Data-Driven Prognostic Model for Predicting Long-Term Prognosis in Patients With Ischemic Cardiomyopathy and Heart Failure With Preserved Ejection Fraction After Coronary Artery Bypass Grafting: A Multicenter Cohort Study. J Am Heart Assoc 2024; 13:e036970. [PMID: 39604036 DOI: 10.1161/jaha.124.036970] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/04/2024] [Accepted: 09/26/2024] [Indexed: 11/29/2024]
Abstract
BACKGROUND Limited data from the literature are available to assess the efficacy of coronary artery bypass grafting in patients with ischemic cardiomyopathy and heart failure with preserved ejection fraction. Therefore, our objective was to use machine learning techniques integrating clinical features, biomarker data, and echocardiography data to enhance comprehension and risk stratification in patients diagnosed with ischemic cardiomyopathy and heart failure with preserved ejection fraction who have undergone coronary artery bypass grafting surgery. METHODS AND RESULTS For this study, 294 patients with ischemic cardiomyopathy and heart failure with preserved ejection fraction who underwent coronary artery bypass grafting surgery were assigned to the development cohort (n=176) and the independent validation cohort (n=118). A total of 52 clinical variables were extracted for each patient. The principal clinical end point was the incidence of major adverse cardiovascular events, encompassing cardiac mortality, acute myocardial infarction, acute heart failure, and graft failure. From least absolute shrinkage and selection operator regression, 4 predictors were selected for the final prediction nomogram: diabetes, hypertension, the systemic immune-inflammation index, and NT-proBNP (N-terminal pro-B-type natriuretic peptide). The prediction nomogram achieved satisfactory prediction performance in both the development cohort (C index, 0.768 [95% CI, 0.701-0.835]) and independent validation cohort (C index, 0.633 [95% CI, 0.521-0.745]). Adequate calibration was noted for the likelihood of major adverse cardiovascular events in both the development and independent validation cohorts. Decision curve analysis confirmed the clinical usefulness of the established prediction nomogram. CONCLUSIONS A clinically feasible prognostic model, based on preoperative multimodal data, was developed for risk stratification of patients with ischemic heart and heart failure with preserved ejection fraction who receive coronary artery bypass grafting surgery. REGISTRATION https://www.chictr.org.cn; Unique identifier: ChiCTR2300074439.
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Affiliation(s)
- Jun Wang
- Department of Cardiology The First Affiliated Hospital of Bengbu Medical University Bengbu Anhui China
| | - Yijun Wang
- Center of Gerontology and Geriatrics, National Clinical Research Center for Geriatrics West China Hospital, Sichuan University Chengdu China
| | - Shoupeng Duan
- Department of Cardiology Renmin Hospital of Wuhan University Wuhan China
| | - Li Xu
- Department of Rheumatology and Immunology General Hospital of Central Theater Command Wuhan China
| | - Yanan Xu
- Pulmonary and Critical Care Medicine The First Affiliated Hospital of Bengbu Medical University Bengbu Anhui China
| | - Wenyuan Yin
- People's Hospital of Xinjiang Uygur Autonomous Region; Electrocardiology Department Urumqi China
| | - Yi Yang
- Xinjiang Medical University Urumqi China
- Department of Cardiology Fourth Ward The Xinjiang Medical University Affiliated Hospital of Traditional Chinese Medicine Urumqi China
| | - Bing Wu
- Institute of Clinical Medicine and Department of Cardiology Renmin Hospital, Hubei University of Medicine Shiyan Hubei China
| | - Jinjun Liu
- Department of Cardiology The First Affiliated Hospital of Bengbu Medical University Bengbu Anhui China
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Yao Z, Ding B, Wang J, Qian S, Song X, Li Y, Ding S, Wang H, Li M. Incorporating Inflammation Biomarker-Driven Multivariate Predictive Model for Coronary Microcirculatory Dysfunction in Acute Myocardial Infarction Following Emergency Percutaneous Coronary Intervention. Clin Cardiol 2024; 47:e70032. [PMID: 39429096 PMCID: PMC11491760 DOI: 10.1002/clc.70032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2024] [Revised: 09/20/2024] [Accepted: 10/01/2024] [Indexed: 10/22/2024] Open
Abstract
BACKGROUND Despite patients with successful revascularization as evidenced by angiographic findings, inadequate clinical management of coronary microcirculatory dysfunction (CMD) may result in preventable adverse outcomes. Therefore, it is imperative to use a multimodal data‑driven predictive model for the occurrence of CMD in patients with acute myocardial infarction (AMI) following emergency percutaneous coronary intervention (PCI). METHODS A prospective case-control analysis was conducted on a cohort of 77 patients with AMI who underwent PCI. The most informative predictors were selected for the predictive model through the application of LASSO analysis and multi-factor logistic regression. The diagnosis of CMD is based on findings from cardiac magnetic resonance (CMR). RESULTS Based on the findings from LASSO analysis and multi-factor logistic regression, variables including sex, neutrophil-to-lymphocyte ratio (NLR), Gensini score, and diabetes mellitus were identified as independent predictors for the development of CMD in AMI patients who underwent emergency PCI. The predictive model was evaluated using bootstrap self-sampling 500 times. The resulting predictive model demonstrated an AUC value of 0.897 (95% CI: 0.827-0.958). The calibration curves exhibited good concordance between the predictions generated by the model and the CMR analysis. Furthermore, decision curve analysis revealed that the predictive model provided valuable clinical benefit in predicting CMD. CONCLUSIONS The multivariate predictive model, constructed using readily available clinical variables in patients with AMI who underwent PCI, demonstrates satisfactory predictability for identifying comorbid CMD, thereby facilitating the identification of high-risk patients.
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Affiliation(s)
- Zhuoya Yao
- Department of Cardiovascular DiseaseThe First Affiliated Hospital of Bengbu Medical UniversityBengbuChina
| | - Bin Ding
- Department of Cardiovascular DiseaseThe First Affiliated Hospital of Bengbu Medical UniversityBengbuChina
| | - Jun Wang
- Department of Cardiovascular DiseaseThe First Affiliated Hospital of Bengbu Medical UniversityBengbuChina
| | - Shaohuan Qian
- Department of Cardiovascular DiseaseThe First Affiliated Hospital of Bengbu Medical UniversityBengbuChina
| | - Xilong Song
- Department of Cardiovascular DiseaseThe First Affiliated Hospital of Bengbu Medical UniversityBengbuChina
| | - Yao Li
- Department of Cardiovascular DiseaseThe First Affiliated Hospital of Bengbu Medical UniversityBengbuChina
| | - Siyu Ding
- Department of Cardiovascular DiseaseThe First Affiliated Hospital of Bengbu Medical UniversityBengbuChina
| | - Hongju Wang
- Department of Cardiovascular DiseaseThe First Affiliated Hospital of Bengbu Medical UniversityBengbuChina
| | - Miaonan Li
- Department of Cardiovascular DiseaseThe First Affiliated Hospital of Bengbu Medical UniversityBengbuChina
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