|
审核状态: Project audit state: |
通过审核 Successful |
|
注册号: Registration number: |
ChiCTR2600126239 |
|
最近更新日期: Date of Last Refreshed on: |
2026-06-05 11:27:58 |
|
注册时间: Date of Registration: |
2026-06-05 00:00:00 |
|
注册号状态: |
补注册 |
|
Registration Status: |
Retrospective registration |
|
注册题目: |
多模态数据驱动的脓毒症早期诊断及免疫分型智能模型开发与应用 |
|
Public title: |
Development and Application of Intelligent Models for Early Diagnosis of Sepsis and Immunological Subtyping Driven by Multimodal Data |
|
注册题目简写: |
|
|
English Acronym: |
|
|
研究课题的正式科学名称: |
多模态数据驱动的脓毒症早期诊断及免疫分型智能模型开发与应用 |
|
Scientific title: |
Development and Application of Intelligent Models for Early Diagnosis of Sepsis and Immunological Subtyping Driven by Multimodal Data |
|
研究课题代号(代码): Study subject ID: |
|
|
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: |
|
申请注册联系人: |
胡林辉 |
研究负责人: |
胡林辉 |
|
Applicant: |
Hu Linhui |
Study leader: |
Hu Linhui |
|
申请注册联系人电话: Applicant telephone: |
+86 13580013426 |
研究负责人电话:
Study leader's |
+86 668 2922131 |
|
申请注册联系人传真 : Applicant Fax: |
研究负责人传真: Study leader's fax: |
||
|
申请注册联系人电子邮件: Applicant E-mail: |
linchuangyjzx@163.com |
研究负责人电子邮件: Study leader's E-mail: |
hulihuihong@163.com |
|
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): |
||
|
申请注册联系人通讯地址: |
中国广东省茂名市茂南区为民路103号大院194号 |
研究负责人通讯地址: |
中国广东省茂名市茂南区为民路101号 |
|
Applicant address: |
103 Weimin Road, Compound 194, Maonan District, Maoming, Guangdong, China |
Study leader's address: |
101 Weimin Road, Maonan District, Maoming, Guangdong, China |
|
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: |
||
|
申请人所在单位: |
茂名市人民医院 |
||
|
Applicant's institution: |
Maoming People's Hospital |
||
|
研究负责人所在单位: |
茂名市人民医院 |
||
|
Affiliation of the Leader: |
Maoming People's Hospital |
||
|
是否获伦理委员会批准: |
是 |
||
|
Approved by ethic committee: |
Yes |
||
|
伦理委员会批件文号: Approved No. of ethic committee: |
PJ2025MI-K073-01 |
伦理委员会批件附件: Approved file of Ethical Committee: |
查看附件View |
|
批准本研究的伦理委员会名称: |
茂名市人民医院医学伦理委员会 |
||
|
Name of the ethic committee: |
Medical Ethics Committee of Maoming People's Hospital |
||
|
伦理委员会批准日期: Date of approved by ethic committee: |
2025-06-24 00:00:00 | ||
|
伦理委员会联系人: |
陈西燕 |
||
|
Contact Name of the ethic committee: |
Chen Xiyang |
||
|
伦理委员会联系地址: |
中国广东省茂名市茂南区为民路101号 |
||
|
Contact Address of the ethic committee: |
101 Weimin Road, Maonan District, Maoming, Guangdong, China |
||
|
伦理委员会联系人电话: Contact phone of the ethic committee: |
+86 668 2922871 |
伦理委员会联系人邮箱: Contact email of the ethic committee: |
13432351502@163.com |
|
研究实施负责(组长)单位: |
茂名市人民医院 |
||||||||||||||||||||||
|
Primary sponsor: |
Maoming People's Hospital |
||||||||||||||||||||||
|
研究实施负责(组长)单位地址: |
中国广东省茂名市茂南区为民路101号 |
||||||||||||||||||||||
|
Primary sponsor's address: |
101 Weimin Road, Maonan District, Maoming, Guangdong, China |
||||||||||||||||||||||
|
试验主办单位(项目批准或申办者): Secondary sponsor: |
|
||||||||||||||||||||||
|
经费或物资来源: |
茂名市科技计划 |
||||||||||||||||||||||
|
Source(s) of funding: |
Maoming Municipal Techological Programme |
||||||||||||||||||||||
|
研究疾病: |
脓毒症相关性急性肾损伤 |
||||||||||||||||||||||
|
Target disease: |
Sepsis-induced acute kidney injury |
||||||||||||||||||||||
|
研究疾病代码: |
|
||||||||||||||||||||||
|
Target disease code: |
|
||||||||||||||||||||||
|
研究类型: |
观察性研究 |
||||||||||||||||||||||
|
Study type: |
Observational study |
||||||||||||||||||||||
|
研究所处阶段: |
其它 | ||||||||||||||||||||||
|
Study phase: |
N/A |
||||||||||||||||||||||
|
研究设计: |
病例对照研究 |
||||||||||||||||||||||
|
Study design: |
Case-Control study |
||||||||||||||||||||||
|
研究目的: |
本研究旨在基于多模态数据和机器学习技术,构建脓毒症的精准免疫分型与早期预警系统,通过整合临床指标、基因组学和代谢组学等多源信息,开发高精度预测模型,实现脓毒症的早期诊断与个体化治疗,最终提升识别率、降低病死率,并优化医疗资源配置。 |
||||||||||||||||||||||
|
Objectives of Study: |
The aim of this study is to develop a precise immune typing and early warning system for sepsis based on multimodal data and machine learning technologies. By integrating multi-source information such as clinical indicators, genomics, and metabolomics, the project seeks to create high-precision predictive models for early diagnosis and personalized treatment of sepsis. The ultimate goals are to improve early detection rates, reduce mortality, and optimize the allocation of medical resources. |
||||||||||||||||||||||
|
药物成份或治疗方案详述: |
|
||||||||||||||||||||||
|
Description for medicine or protocol of treatment in detail: |
|
||||||||||||||||||||||
|
纳入标准: |
1. 年龄18岁及以上; 2. 入住ICU的患者; 3. 脓毒症风险:qSOFA(快速序贯器官衰竭评估)评分至少满足以下三个指标中的两个:呼吸频率 >= 22次/分钟;意识水平:GCS(格拉斯哥昏迷评分)< 15;收缩压 <= 100 mmHg。 |
||||||||||||||||||||||
|
Inclusion criteria |
1. Aged 18 years or older; 2. Patients admitted to the ICU; 3. Sepsis Risk: A qSOFA (Quick Sequential Organ Failure Assessment) score meeting at least two of the following three criteria: Respiratory Rate >= 22 breaths per minute; Level of Consciousness: GCS (Glasgow Coma Scale) score < 15; Systolic Blood Pressure <= 100 mmHg. |
||||||||||||||||||||||
|
排除标准: |
1. 特定生理阶段:处于妊娠期或哺乳期的女性患者。 |
||||||||||||||||||||||
|
Exclusion criteria: |
1. Specific Physiological Status: Female patients who are pregnant or lactating. |
||||||||||||||||||||||
|
研究实施时间: Study execute time: |
从 From 2025-08-01 00:00:00至 To 2027-07-31 00:00:00 |
征募观察对象时间: Recruiting time: |
从 From 2025-10-01 00:00:00 至 To 2025-12-31 00:00:00 |
|
干预措施: Interventions: |
|
|
研究实施地点: Countries of recruitment and research settings: |
|
||||||||||||||||||||||||||||
|
测量指标: Outcomes: |
|
|
采集人体标本:
Collecting sample(s)
|
|
|
征募研究对象情况: Recruiting status: |
结束 /Completed |
年龄范围: Participant age: |
|
||||||
|
性别: |
男女均可 |
Gender: |
Both |
||||||
|
随机方法(请说明由何人用什么方法产生随机序列): |
无 |
||||||||
|
Randomization Procedure (please state who generates the random number sequence and by what method): |
None |
||||||||
|
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 |
|
盲法: |
无 |
|
Blinding: |
None |
|
是否共享原始数据: IPD sharing |
是Yes |
|
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): |
研究结束后半年;国家生物信息中心(https://www.cncb.ac.cn/) |
|
The way of sharing IPD”(include metadata and protocol, If use web-based public database, please provide the url): |
Six months after the completion of the research; China National Center for Bioinformation (https://www.cncb.ac.cn/) |
|
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: |
1. 数据采集 (Data Collection): 数据来源: 建立多中心ICU临床数据库。数据来源于合作医院的ICU。 数据类型: 收集多模态数据,包括: 临床数据: 生命体征(心率、血压、呼吸频率等)、实验室指标(白细胞计数、乳酸水平等)、病历记录、影像学资料。 组学数据: 基因组学数据(通过血液样本基因组测序)、代谢组学数据(对血液、尿液等生物样本进行代谢组学分析)。 免疫分型数据: 通过流式细胞术分析免疫细胞亚群(T细胞、B细胞等);检测细胞因子水平(TNF-α, IL-6, IL-10);检测免疫检查点分子(PD-1, CTLA-4)。 采集标准: 制定并统一多中心数据采集标准,确保数据一致性。 2. 数据管理 (Data Management): 数据清洗与预处理: 对收集的数据进行清洗、去噪、标准化处理,确保数据的完整性和一致性。 数据整合: 统一多模态数据的格式和标准,进行数据融合,为分析与建模做准备。 隐私与安全: 数据安全与隐私保护将严格遵循医疗数据管理规范。采用技术手段(如匿名化)保护患者隐私。 数据库建设: 项目团队已成功建立了多中心临床数据库和重症患者专病库,收集了超过5000例ICU患者的临床数据。 |
|
Data collection and Management (A standard data collection and management system include a CRF and an electronic data capture: |
1. Data Collection: Data Source: Establishment of a multicenter ICU clinical database. Data is sourced from ICUs of partner hospitals. Data Types: Collection of multimodal data, including: Clinical Data: Vital signs (heart rate, blood pressure, respiratory rate, etc.), laboratory parameters (white blood cell count, lactate level, etc.), medical records, imaging data. Omics Data: Genomic data (via genomic sequencing of blood samples), metabolomic data (metabolomic analysis of biological samples such as blood and urine). Immune Phenotyping Data: Analysis of immune cell subsets (T cells, B cells, etc.) via flow cytometry; detection of cytokine levels (TNF-α, IL-6, IL-10); detection of immune checkpoint molecules (PD-1, CTLA-4). Collection Standards: Development and unification of multicenter data collection standards to ensure data consistency. 2. Data Management: Data Cleaning & Preprocessing: Collected data undergoes cleaning, denoising, and standardization to ensure completeness and consistency. Data Integration: Unification of data formats and standards for multimodal data, followed by data fusion to prepare for analysis and modeling. Privacy & Security: Data security and privacy protection will strictly comply with medical data management regulations. Technical measures (e.g., anonymization) are employed to protect patient privacy. Database Development: The project team has successfully established a multicenter clinical database and a specialized critical patient database, containing clinical data from over 5,000 ICU patients. |
|
数据与安全监察委员会: Data and Safety Monitoring Committee: |
有/Yes |