基于机器学习与多模态数据构建腰椎间盘突出症个体化疗效预测模型

注册号:

Registration number:

ChiCTR2600118363 

最近更新日期:

Date of Last Refreshed on:

2026-02-04 16:02:25 

注册时间:

Date of Registration:

2026-02-04 00:00:00 

注册号状态:

预注册

Registration Status:

Prospective registration

注册题目:

基于机器学习与多模态数据构建腰椎间盘突出症个体化疗效预测模型

Public title:

Development and validation of an individualized efficacy prediction model for lumbar disc herniation based on machine learning and multimodal data

注册题目简写:

English Acronym:

研究课题的正式科学名称:

基于多模态数据构建腰椎间盘突出症个体化疗效预测的机器学习融合模型研究

Scientific title:

Development and validation of an individualized efficacy prediction model for lumbar disc herniation based on machine learning and 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:

王志鹏 

研究负责人:

王志鹏 

Applicant:

Wang Zhipeng 

Study leader:

Wang Zhipeng 

申请注册联系人电话:

Applicant telephone:

+86 187 9479 2634

研究负责人电话:

Study leader's
telephone:

+86 187 9479 2634

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:

wzp0818@163.com

研究负责人电子邮件:

Study leader's E-mail:

wzp0818@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:

甘肃省兰州市城关区嘉峪关西路732号

研究负责人通讯地址:

甘肃省兰州市城关区嘉峪关西路732号

Applicant address:

No. 732, Jiayuguan West Road, Chengguan District, Lanzhou City, Gansu Province

Study leader's address:

No. 732, Jiayuguan West Road, Chengguan District, Lanzhou City, Gansu Province

申请注册联系人邮政编码:

Applicant postcode:

730020

研究负责人邮政编码:

Study leader's postcode:

730020

申请人所在单位:

甘肃中医药大学附属医院

Applicant's institution:

Affiliated Hospital of Gansu University of Chinese Medicine

研究负责人所在单位:

甘肃中医药大学附属医院

Affiliation of the Leader:

Affiliated Hospital of Gansu University of Chinese Medicine

是否获伦理委员会批准:

Approved by ethic committee:

Yes

伦理委员会批件文号:

Approved No. of ethic committee:

伦理[2025]014号

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:

甘肃中医药大学附属医院医学伦理委员会

Name of the ethic committee:

Medical Ethics Committee of the Affiliated Hospital of Gansu University of Chinese Medicine

伦理委员会批准日期:

Date of approved by ethic committee:

2025-03-04 00:00:00

伦理委员会联系人:

张璐娟

Contact Name of the ethic committee:

Zhang Lujuan

伦理委员会联系地址:

甘肃省兰州市城关区嘉峪关西路732号

Contact Address of the ethic committee:

No. 732, Jiayuguan West Road, Chengguan District, Lanzhou City, Gansu Province

伦理委员会联系人电话:

Contact phone of the ethic committee:

+86 136 5944 1668

伦理委员会联系人邮箱:

Contact email of the ethic committee:

研究实施负责(组长)单位:

甘肃中医药大学附属医院

Primary sponsor:

Affiliated Hospital of Gansu University of Chinese Medicine

研究实施负责(组长)单位地址:

甘肃省兰州市城关区嘉峪关西路732号

Primary sponsor's address:

No. 732, Jiayuguan West Road, Chengguan District, Lanzhou City, Gansu Province

试验主办单位(项目批准或申办者):

Secondary sponsor:

国家:

中国

省(直辖市):

甘肃

市(区县):

兰州

Country:

China

Province:

Gansu

City:

Lanzhou

单位(医院):

甘肃中医药大学附属医院

具体地址:

甘肃省兰州市城关区嘉峪关西路732号

Institution
hospital:

Affiliated Hospital of Gansu University of Chinese Medicine

Address:

No. 732, Jiayuguan West Road, Chengguan District, Lanzhou City, Gansu Province

经费或物资来源:

国家自然科学基金

Source(s) of funding:

National Natural Science Foundation of China

研究疾病:

腰椎间盘突出症  

Target disease:

Lumbar intervertebral disc

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:

队列研究 

Study design:

Cohort study 

研究目的:

通过人工智能的初步学习及分析,前期进行了初步研究,确定使用机器学习的方法进行验证。同时借助少量的、易收集的、检测成本低廉的预测因子来预测疾病的状态和预后。采用大量丰富的数据,复杂的模型和算法(机器学习、人工智能),针对多种预测因子对疾病影响的机制研究,构建中西医多模态特征融合预后预测模型,以更精准的结果指导医生及医疗决策者。  

Objectives of Study:

Through preliminary learning and analysis utilizing artificial intelligence, initial research was conducted to determine the application of machine learning methodologies for validation. Concurrently, a limited number of easily collectible and cost-effective predictive factors were employed to forecast disease status and prognosis. By leveraging extensive and diverse datasets, sophisticated models, and advanced algorithms (including machine learning and artificial intelligence), a comprehensive investigation into the mechanisms by which multiple predictive factors influence disease progression was undertaken. This research facilitated the development of a multimodal feature fusion prognostic prediction model integrating traditional Chinese and Western medicine, thereby providing more precise guidance for clinicians and healthcare decision-makers.

药物成份或治疗方案详述:

 

Description for medicine or protocol of treatment in detail:

 

纳入标准:

Inclusion criteria

排除标准:

( 1)意识不清楚,不能够准确表达主观不适症状;明确诊断为精神病患者; ( 2) 伴有腰椎骨折、腰椎滑脱、脊柱肿瘤、腰椎结核及布氏杆菌性脊柱炎等的患者; ( 3) 重度骨质疏松症及精神疾病患者,如抑郁症、焦虑症等;存在严重心肺功能不全疾病的患者;不能行核磁检查的患者。

Exclusion criteria:

(1) unclear consciousness, unable to accurately express subjective discomfort symptoms; Or a definite diagnosis of psychosis; (2) patients with lumbar fractures, lumbar spondylolisthesis, spinal tumors, lumbar tuberculosis and brucella spondylitis; (3) patients with severe osteoporosis and mental disorders, such as depression and anxiety; Patients with severe cardiopulmonary dysfunction; Patients who could not undergo MRI examination.

研究实施时间:

Study execute time:

From 2026-01-01 00:00:00 To 2028-12-31 00:00:00  

征募观察对象时间:

Recruiting time:

From 2026-03-01 00:00:00 To 2027-02-28 00:00:00

干预措施:

Interventions:

组别:

训练集

样本量:

245

Group:

Train set

Sample size:

干预措施:

干预措施代码:

Intervention:

None

Intervention code:

组别:

测试集

样本量:

105

Group:

Test set

Sample size:

干预措施:

干预措施代码:

Intervention:

None

Intervention code:

研究实施地点:

Countries of recruitment and research settings:

国家:

中国

省(直辖市):

甘肃 

市(区县):

兰州 

Country:

China

Province:

Gansu

City:

Lanzhou

单位(医院):

甘肃中医药大学附属医院 

单位级别:

三甲 

Institution
hospital:

Affiliated Hospital of Gansu University of Chinese Medicine

Level of the institution:

Tertiary A

测量指标:

Outcomes:

指标中文名:

VAS评分

指标类型:

主要指标

Outcome:

VAS score

Type:

Primary indicator

测量时间点:

测量方法:

Measure time point of outcome:

Measure method:

指标中文名:

JOA评分

指标类型:

主要指标

Outcome:

Japanese orthopeadic association score

Type:

Primary indicator

测量时间点:

测量方法:

Measure time point of outcome:

Measure method:

指标中文名:

ODI评分

指标类型:

主要指标

Outcome:

ODI score

Type:

Primary indicator

测量时间点:

测量方法:

Measure time point of outcome:

Measure method:

指标中文名:

年龄

指标类型:

次要指标

Outcome:

Age

Type:

Secondary indicator

测量时间点:

测量方法:

Measure time point of outcome:

Measure method:

指标中文名:

BMI值

指标类型:

次要指标

Outcome:

BMI cutoff points

Type:

Secondary indicator

测量时间点:

测量方法:

Measure time point of outcome:

Measure method:

指标中文名:

性别

指标类型:

次要指标

Outcome:

Gender

Type:

Secondary indicator

测量时间点:

测量方法:

Measure time point of outcome:

Measure method:

指标中文名:

吸烟史

指标类型:

次要指标

Outcome:

Smoking history

Type:

Secondary indicator

测量时间点:

测量方法:

Measure time point of outcome:

Measure method:

指标中文名:

饮酒史

指标类型:

次要指标

Outcome:

Drinking history

Type:

Secondary indicator

测量时间点:

测量方法:

Measure time point of outcome:

Measure method:

指标中文名:

高血压病史

指标类型:

次要指标

Outcome:

History of hypertension

Type:

Secondary indicator

测量时间点:

测量方法:

Measure time point of outcome:

Measure method:

指标中文名:

糖尿病史

指标类型:

次要指标

Outcome:

History of diabetes

Type:

Secondary indicator

测量时间点:

测量方法:

Measure time point of outcome:

Measure method:

指标中文名:

病程

指标类型:

次要指标

Outcome:

Disease duration

Type:

Secondary indicator

测量时间点:

测量方法:

Measure time point of outcome:

Measure method:

指标中文名:

重体力劳动

指标类型:

次要指标

Outcome:

Heavy manual workers

Type:

Secondary indicator

测量时间点:

测量方法:

Measure time point of outcome:

Measure method:

指标中文名:

实验室指标

指标类型:

次要指标

Outcome:

Laboratory indicators

Type:

Secondary indicator

测量时间点:

测量方法:

Measure time point of outcome:

Measure method:

指标中文名:

手术时间

指标类型:

次要指标

Outcome:

Surgical time

Type:

Secondary indicator

测量时间点:

测量方法:

Measure time point of outcome:

Measure method:

指标中文名:

手术入路

指标类型:

次要指标

Outcome:

Surgical approach

Type:

Secondary indicator

测量时间点:

测量方法:

Measure time point of outcome:

Measure method:

指标中文名:

出血量

指标类型:

次要指标

Outcome:

Bleeding volume

Type:

Secondary indicator

测量时间点:

测量方法:

Measure time point of outcome:

Measure method:

指标中文名:

止血材料

指标类型:

次要指标

Outcome:

Hemostatic materials

Type:

Secondary indicator

测量时间点:

测量方法:

Measure time point of outcome:

Measure method:

指标中文名:

血栓形成

指标类型:

次要指标

Outcome:

Thrombosis occurs

Type:

Secondary indicator

测量时间点:

测量方法:

Measure time point of outcome:

Measure method:

采集人体标本:

Collecting sample(s)
from participants:

标本中文名:

组织:

Sample Name:

None

Tissue:

人体标本去向

其它  

说明

Fate of sample:

0thers  

Note:

征募研究对象情况:

Recruiting status:

尚未开始

Not yet recruiting

年龄范围:

Participant age:

最小 Min age 18 years
最大 Max age years

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):

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:

公开/Public

盲法:

Blinding:

None

试验完成后的统计结果(上传文件):

Calculated Results after
the Study Completed(upload file):

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):

2029年3月采用在临床试验公共管理平台ResMan(www.medresman.org)共享原始数据

The way of sharing IPD”(include metadata and protocol, If use web-based public database, please provide the url):

In March 2029, the original data will be shared on the clinical trial public management platform ResMan (www.medresman.org)

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:

病历记录表

Data collection and Management (A standard data collection and management system include a CRF and an electronic data capture:

Case record form

数据与安全监察委员会:

Data and Safety Monitoring Committee:

暂未确定/Not yet

注册人:

Name of Registration:

 2026-02-04 16:02:17