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审核状态: Project audit state: |
通过审核 Successful |
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注册号: Registration number: |
ChiCTR2500111082 |
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最近更新日期: Date of Last Refreshed on: |
2025-10-24 16:17:11 |
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注册时间: Date of Registration: |
2025-10-24 00:00:00 |
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注册号状态: |
预注册 |
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Registration Status: |
Prospective registration |
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注册题目: |
人工智能驱动HFpEF精准分型与预后风险模型的构建与优化研究 |
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Public title: |
AI-Based Precision Phenotyping and Prognostic Modeling in HFpEF |
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注册题目简写: |
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English Acronym: |
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研究课题的正式科学名称: |
人工智能驱动HFpEF精准分型与预后风险模型的构建与优化研究 |
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Scientific title: |
AI-Based Precision Phenotyping and Prognostic Modeling in HFpEF |
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研究课题代号(代码): Study subject ID: |
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在二级注册机构或其它机构的注册号: The registration number of the Partner Registry or other register: |
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申请注册联系人: |
花欣炜 |
研究负责人: |
花欣炜 |
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Applicant: |
Xinwei Hua |
Study leader: |
Xinwei Hua |
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申请注册联系人电话: Applicant telephone: |
+86 132 6428 1026 |
研究负责人电话: Study leader's telephone: |
+86 10 8226 2626 |
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申请注册联系人传真 : Applicant Fax: |
研究负责人传真: Study leader's fax: |
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申请注册联系人电子邮件: Applicant E-mail: |
xhua@bjmu.edu.cn |
研究负责人电子邮件: Study leader's E-mail: |
xhua@pku.edu.cn |
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申请单位网址(自愿提供): Applicant website(voluntary supply): |
研究负责人网址(自愿提供): Study leader's website(voluntary supply): |
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申请注册联系人通讯地址: |
北京市海淀区花园北路49号 |
研究负责人通讯地址: |
北京市海淀区花园北路49号 |
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Applicant address: |
49 Huayuan N Rd, Beijing |
Study leader's address: |
49 North Garden Rd.,Haidian District Beijing ,P.R.China |
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申请注册联系人邮政编码: Applicant postcode: |
研究负责人邮政编码: Study leader's postcode: |
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申请人所在单位: |
北京大学第三医院 |
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Applicant's institution: |
Peking University Third Hospital |
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研究负责人所在单位: |
北京大学第三医院 |
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Affiliation of the Leader: |
Peking University Third Hospital |
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是否获伦理委员会批准: |
是/Yes |
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Approved by ethic committee: |
Yes |
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伦理委员会批件文号: Approved No. of ethic committee: |
(2025)医伦审第(710-02)号 |
伦理委员会批件附件: Approved file of Ethical Committee: |
查看附件View |
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批准本研究的伦理委员会名称: |
北京大学第三医院医学科学研究伦理委员会一组 |
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Name of the ethic committee: |
Peking University Third Hospital Medical Science Research Ethics Committee |
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伦理委员会批准日期: Date of approved by ethic committee: |
2025-09-30 00:00:00 |
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伦理委员会联系人: |
梁力均 |
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Contact Name of the ethic committee: |
Liang LiJun |
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伦理委员会联系地址: |
北京市海淀区花园北路49号 |
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Contact Address of the ethic committee: |
49 North Garden Rd.,Haidian District Beijing ,P.R.China |
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伦理委员会联系人电话: Contact phone of the ethic committee: |
+86 10 8226 6872 |
伦理委员会联系人邮箱: Contact email of the ethic committee: |
timon_peng@sina.com |
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研究实施负责(组长)单位: |
北京大学第三医院 |
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Primary sponsor: |
Peking University Third Hospital |
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研究实施负责(组长)单位地址: |
北京市海淀区花园北路49号 |
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Primary sponsor's address: |
49 North Garden Rd.,Haidian District Beijing ,P.R.China |
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试验主办单位(项目批准或申办者): Secondary sponsor: |
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经费或物资来源: |
癌症、心脑血管、呼吸和代谢性疾病防治研究国家科技重大专项2025年度项目 |
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Source(s) of funding: |
Noncommunicable Chronic Diseases-National Science and Technology Major Project |
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Target disease: |
Heart failure |
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Target disease code: |
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研究类型: |
观察性研究 |
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Study type: |
Observational study |
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研究所处阶段: |
其它 | ||||||||||||||||||||||
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Study phase: |
N/A |
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研究设计: |
队列研究 |
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Study design: |
Cohort study |
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研究目的: |
(1) 建立一个全面的心力衰竭患者前瞻性队列,包含心力衰竭患者的人口学及临床数据、影像学及生物样本(包括血液、尿液、粪便和唾液样本)等信息,旨在开发新型HFpEF分型工具及探索HFpEF潜在病理生理学机制,最终指导HFpEF治疗策略的改进。 (2) 分型:基于人工智能无监督学习方法,整合多模态数据识别HFpEF核心表型亚群;利用可解释性AI方法揭示分型模型的关键驱动因素,优化分型标准。 (3) 治疗:探索HFpEF治疗反应与预后的影响因素,系统评估宿主遗传特征、肠道微生态、环境暴露等因素对HFpEF患者药物治疗应答、疾病进展及长期预后的调控作用,制定HFpEF精准诊疗策略循证依据。 (4) 预后:通过深度学习算法构建多模态危险分层模型,结合时序性临床数据预测患者短期和长期预后。 |
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Objectives of Study: |
(1) Establish a comprehensive prospective cohort of patients with heart failure, encompassing demographic and clinical information, multimodal imaging data, and biological specimens (including blood, urine, stool, and saliva). The cohort aims to develop novel phenotyping tools for HFpEF and to elucidate its underlying pathophysiological mechanisms, ultimately providing evidence to inform and optimize therapeutic strategies for HFpEF. (2) Phenotyping: Employ artificial intelligence–based unsupervised learning approaches to integrate multimodal data for the identification of core phenotypic subgroups in HFpEF. Utilize explainable AI techniques to uncover key driving factors within the model and to refine and standardize the phenotyping criteria. (3) Therapeutics: Investigate the determinants of treatment response and prognosis in HFpEF. Systematically evaluate the regulatory roles of host genetic characteristics, gut microbiota composition, and environmental exposures on pharmacologic responses, disease progression, and long-term outcomes, thereby establishing an evidence base for precision diagnosis and therapy in HFpEF. (4) Prognosis: Develop a multimodal risk stratification model using deep learning algorithms, incorporating longitudinal and time-series clinical data to predict both short-term and long-term outcomes in patients with HFpEF. |
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药物成份或治疗方案详述: |
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Description for medicine or protocol of treatment in detail: |
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纳入标准: |
1.患者的纳入标准是取得书面知情同意的成年患者(≥18岁),具有心衰症状或体征且临床诊断为心力衰竭患者。 |
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Inclusion criteria |
1.Adult patients (>= 18 years) who have provided written informed consent and present with symptoms or signs of heart failure, with a clinical diagnosis of heart failure. |
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排除标准: |
1.排除标准为患者后续撤回知情同意。 |
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Exclusion criteria: |
1.Patients who subsequently withdraw their informed consent. |
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研究实施时间: Study execute time: |
从 From 2025-08-01 00:00:00至 To 2029-07-31 00:00:00 |
征募观察对象时间: Recruiting time: |
从From 2025-11-01 00:00:00 至 To 2027-11-30 00:00:00 |
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干预措施: Interventions: |
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研究实施地点: Countries of recruitment and research settings: |
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测量指标: Outcomes: |
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采集人体标本:
Collecting sample(s)
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征募研究对象情况: Recruiting status: |
尚未开始 Not yet recruiting |
年龄范围: Participant age: |
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性别: |
男女均可 |
Gender: |
Both |
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随机方法(请说明由何人用什么方法产生随机序列): |
无 |
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Randomization Procedure (please state who generates the random number sequence and by what method): |
None |
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是否公开试验完成后的统计结果: Calculated Results after the Study Completed public access: |
不公开/Private |
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盲法: |
无 |
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Blinding: |
None |
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是否共享原始数据: IPD sharing |
No |
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共享原始数据的方式(说明:请填入公开原始数据日期和方式,如采用网络平台,需填该网络平台名称和网址): |
研究数据不共享 |
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The way of sharing IPD”(include metadata and protocol, If use web-based public database, please provide the url): |
The study data are not publicly available. |
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数据采集和管理(说明:数据采集和管理由两部分组成,一为病例记录表(Case Record Form, CRF),二为电子采集和管理系统(Electronic Data Capture, EDC),如ResMan即为一种基于互联网的EDC: |
根据研究方案及临床研究病例报告表 (CRF)模板,由研究者负责起草研究CRF,并交由数据管理员、统计师进行CRF审阅和修订,经讨论无异议,最终形成CRF终版。 采用REDCap数据管理系统构建基于互联网的登记数据库,内容包括但不限于字段、表单、访视。单一患者的全部数据按照不同数据集分为多个表单进行储存,例如人口基线表单、住院信息表单、超声心动图表单等,不同表单中包括身份标识和时间标识变量,用于关联同一患者数据。表单储存于互联网可访问的服务器中,服务器存放于北医三院(如果后续基于此进行扩展登记注册的临床机构,各自医院的原始数据均储存于各自医院的服务器)。 研究团队将依据数据核查计划,在REDCap中编写数据核查程序,以便数据进入系统后即时进行数据核验,并在REDCap系统中设置角色权限配置、进度报表配置等。 |
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Data collection and Management (A standard data collection and management system include a CRF and an electronic data capture: |
According to the study protocol and the template of the Case Report Form (CRF) for clinical research, the CRF will be initially drafted by the investigators and subsequently reviewed and revised by the data manager and biostatistician. After discussion and consensus, the final version of the CRF will be established. A web-based registry database will be constructed using the REDCap data management system, including but not limited to data fields, forms, and visit records. All data pertaining to an individual participant will be stored across multiple forms corresponding to different datasets, such as the demographic baseline form, hospitalization information form, and echocardiography form. Each form will contain unique identifiers and time-stamped variables to ensure accurate linkage of all records belonging to the same participant. The database will be hosted on an internet-accessible server located at Peking University Third Hospital (PUTH). If additional clinical sites are incorporated in future registry expansions, each participating institution will store its original data on servers located within its own hospital. The research team will develop and implement data validation procedures within the REDCap platform according to a predefined data verification plan, enabling real-time data quality checks upon entry. Role-based access control, progress reports, and permission configurations will also be established within the REDCap system to ensure data integrity, security, and standardized management. |
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数据与安全监察委员会: Data and Safety Monitoring Committee: |
有/Yes |