ChiCTR2100050700 版本V1.0 版本创建时间2021/09/02 23:37:47 中国临床试验注册中心

审核状态：

Project audit state：

通过审核

Successful

注册号：

Registration number：

ChiCTR2100050700 

最近更新日期：

Date of Last Refreshed on：

2021-09-02 23:37:40 

注册时间：

Date of Registration：

2021-09-02 00:00:00 

注册号状态：

预注册

Registration Status：

Prospective registration

注册题目：

基于可视化的新生儿罕见病快速鉴别诊断方法研究

Public title：

A visualization-based method for rapid differential diagnosis of rare diseases in neonates

注册题目简写：

English Acronym：

研究课题的正式科学名称：

基于可视化的新生儿罕见病快速鉴别诊断方法研究

Scientific title：

A visualization-based method for rapid differential diagnosis of rare diseases in neonates

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：

李昊旻 

研究负责人：

李昊旻 

Applicant：

Haomin Li 

Study leader：

Haomin Li 

申请注册联系人电话：

Applicant telephone：

13867445504

研究负责人电话：

Study leader's telephone：

13867445504

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：

hmli@zju.edu.cn

研究负责人电子邮件：

Study leader's E-mail：

hmli@zju.edu.cn

申请单位网址(自愿提供)：

Applicant website(voluntary supply)：

http://www.zjuch.cn/

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：

浙江杭州滨江区滨盛路3333号

研究负责人通讯地址：

浙江杭州滨江区滨盛路3333号

Applicant address：

3333 Binsheng Road, Hangzhou, Zhejiang, PRC

Study leader's address：

3333 Binsheng Road, Hangzhou, Zhejiang, PRC

申请注册联系人邮政编码：

Applicant postcode：

310052

研究负责人邮政编码：

Study leader's postcode：

310052

申请人所在单位：

浙江大学医学院附属儿童医院

Applicant's institution：

The Children's Hospital, Zhejiang University School of Medicine

研究负责人所在单位：

浙江大学医学院附属儿童医院

Affiliation of the Leader：

The Children's Hospital, Zhejiang University School of Medicine

是否获伦理委员会批准：

是/Yes

Approved by ethic committee：

Yes

伦理委员会批件文号：

Approved No. of ethic committee：

2021-IRB-165

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：

浙江大学医学院附属儿童医院医学伦理委员会

Name of the ethic committee：

IRB&EC, The Children's Hospital, Zhejiang University

伦理委员会批准日期：

Date of approved by ethic committee：

2021-07-08 00:00:00

伦理委员会联系人：

马爱眉

Contact Name of the ethic committee：

Aimei Ma

伦理委员会联系地址：

浙江杭州滨江区滨盛路3333号

Contact Address of the ethic committee：

3333 Binsheng Road, Hangzhou, Zhejiang, PRC

伦理委员会联系人电话：

Contact phone of the ethic committee：

0571-86670076

伦理委员会联系人邮箱：

Contact email of the ethic committee：

zuchiec@163.com

研究实施负责（组长）单位：

浙江大学医学院附属儿童医院

Primary sponsor：

The Children's Hospital, Zhejiang University School of Medicine

研究实施负责（组长）单位地址：

浙江杭州滨江区滨盛路3333号

Primary sponsor's address：

3333 Binsheng Road, Hangzhou, Zhejiang, PRC

试验主办单位(项目批准或申办者)：

Secondary sponsor：

国家：

中国

省(直辖市)：

浙江

市(区县)：

杭州

Country：

China

Province：

Zhejiang

City：

Hangzhou

单位(医院)：

浙江大学医学院附属儿童医院

具体地址：

滨盛路3333号

Institution
hospital：

The Children's Hospital, Zhejiang University School of Medicine

Address：

3333 Binsheng Road

经费或物资来源：

国家自然科学基金及科研项目配套资金

Source(s) of funding：

NSFC and matched research funding

Target disease：

Rare disease

Target disease code：

研究类型：

诊断试验

Study type：

Diagnostic test

研究所处阶段：

 诊断试验新技术临床试验 

Study phase：

Diagnostic New Technique Clincal Study

研究设计：

诊断试验诊断准确性 

Study design：

Diagnostic test for accuracy 

研究目的：

随着新生儿相关医疗技术的发展，感染、营养、早产等疾病的诊治已有了长足的进步，但是新生儿遗传性疾病所致死亡或预后不良的发生比例依旧凸显。遗传性疾病中的单基因疾病目前估计6500余种，且每年以200种的速度增加[1,2]。据相关研究报道北美NICU、PICU和儿童CICU中因遗传性疾病导致的死亡高达15%[3-6]。一方面由于此类疾病病情恶化迅速，未及时诊治常致预后不良，快速进行病因诊断并针对性给予干预措施，可为患儿提供最佳的临床管理决策，促进经验治疗向精准治疗的转型，实现诊断率的提升和死亡率的下降；另一方面对于预后差的一些遗传疾病的快速诊断有助于做出临终照顾的决定从而减轻痛苦减少医疗资源的浪费；结合目前已知病因的单基因疾病中有大部分病种在生后28天内即可出现临床表现[7]，基因诊断在新生儿科的开展具有迫切性、高需求性和适合性。 然而现有的基因诊断报告生成周期需要数周甚至数月，有研究显示大概三分之一的NICU患儿在拿到实验室确认的基因诊断报告之前死亡[8]，因此往往满足不了很多急危重临床场景。针对该问题，研究一种快速的基于表型的鉴别诊断方法，提高临床对于罕见病鉴别诊断的能力具有重要临床意义，特别是针对新生儿领域，尽快完成诊断有可能可以改变治疗策略达到救人的目的。我们在前期工作中开发了一种基于表型距离计算的罕见病推荐工具RDmap[9]，该工具能够基于提供的一组比较标准的表型描述在众多罕见病中推荐可能的疾病列表。但是这个过程取决于所采集的表型的准确性、特异性程度，为此我们进一步开发了一个表型推荐和鉴别诊断的过程，该试验的目的是希望验证这个过程是否能够提高临床基于表型进行遗传罕见病诊断的能力。 参考文献： 1. Online Mendelian Inheritance in Man: McKusick-Nathans Institute of Genetic Medicine. Baltimore, MD, Johns Hopkins University. Available at: https://www.omim.org/statistics/entry. Accessed June 21, 2018 2. Chong JX, Buckingham KJ, Jhangiani SN, et al; Centers for Mendelian Genomics: The genetic basis of Mendelian phenotypes: Discoveries, challenges, and opportunities. Am J Hum Genet 2015;97:199–215。 3. Khokha MK, Mitchell LE, Wallingford JB. White paper on the study of birth defects. Birth Defects Res. 2017;109(2):180-185. 4. Murphy SL, Xu J, Kochanek KD, et al. Mortality in the United States, 2017. NCHS Data Brief. 2018;(328):1-8. 5. Yoon PW, Olney RS, Khoury MJ, et al. Contribution of birth defects and genetic diseases to pediatric hospitalizations. A population-based study. Arch Pediatr Adolesc Med. 1997;151(11):1096-1103. 6. Berry MA, Shah PS, Brouillette RT, et al. Predictors of mortality and length of stay for neonates admitted to children's hospital neonatal intensive care units. J Perinatol. 2008;28(4):297-302. 7. Bainbridge MN, Wiszniewski W, Murdock DR, et al: Whole-genome sequencing for optimized patient management. Sci Transl Med 2011; 3:87re3 8. Wojcik MH, Schwartz TS, Yamin I, et al: Genetic disorders and mortality in infancy and early childhood: Delayed diagnoses and missed opportunities. Genet Med 2018; 20:1396–1404 9. Jian Yang, Cong Dong, Huilong Duan, Qiang Shu, Haomin Li. RDmap: A Map for Exploring Rare Diseases. Orphanet Journal of Rare Diseases. 2021, 16(101).  

Objectives of Study：

With the development of neonatal-related medical technologies, there have been significant advances in the diagnosis and treatment of infections, nutrition, and prematurity, but the incidence of death or poor prognosis due to neonatal genetic disorders continues to be accentuated. The number of single gene disorders among genetic disorders is currently estimated to be more than 6500 and increasing at a rate of 200 per year [1,2]. Studies have reported up to 15% of deaths due to genetic disorders in NICUs, PICUs and pediatric CICUs in North America [3-6]. On the one hand, because of the rapid deterioration of these diseases, untimely diagnosis and treatment often lead to poor prognosis, rapid etiological diagnosis and targeted interventions can provide the best clinical management decisions for children, promote the transition from empirical treatment to precise treatment, and achieve an increase in diagnosis and decrease in mortality; on the other hand, rapid diagnosis of some genetic diseases with poor prognosis can help make end-of-life care decisions and thus On the other hand, rapid diagnosis of some genetic diseases with poor prognosis can help to make end-of-life care decisions and thus reduce pain and waste of medical resources; combined with the fact that most of the single-gene diseases with known etiology can show clinical manifestations within 28 days after birth [7], the development of genetic diagnosis in neonatology is urgent, in high demand and suitable. However, existing genetic diagnostic reports take weeks or even months to generate, and some studies have shown that approximately one-third of NICU children die before laboratory-confirmed genetic diagnostic reports are available [8], thus often failing to meet many acute and critical clinical scenarios. To address this problem, it is clinically important to study a rapid phenotype-based differential diagnosis method to improve the clinical ability for differential diagnosis of rare diseases, especially in the neonatal field, where the diagnosis can be completed as soon as possible to potentially change the treatment strategy to save lives. In our previous work, we developed a rare disease recommendation tool based on phenotypic distance calculation, RDmap [9], which is able to recommend a list of possible diseases among many rare diseases based on a set of relatively standard phenotypic descriptions provided. However, this process depends on the accuracy, degree of specificity of the collected phenotypes, and for this reason we further developed a process for phenotypic recommendation and differential diagnosis, and the aim of this trial was to verify whether this process could improve the ability of clinical diagnosis of genetic rare diseases based on phenotypes. Reference: 1. Online Mendelian Inheritance in Man: McKusick-Nathans Institute of Genetic Medicine. Baltimore, MD, Johns Hopkins University. Available at: https://www.omim.org/statistics/entry. Accessed June 21, 2018 2. Chong JX, Buckingham KJ, Jhangiani SN, et al; Centers for Mendelian Genomics: The genetic basis of Mendelian phenotypes: Discoveries, challenges, and opportunities. Am J Hum Genet 2015;97:199–215 3. Khokha MK, Mitchell LE, Wallingford JB. White paper on the study of birth defects. Birth Defects Res. 2017;109(2):180-185. 4. Murphy SL, Xu J, Kochanek KD, et al. Mortality in the United States, 2017. NCHS Data Brief. 2018;(328):1-8. 5. Yoon PW, Olney RS, Khoury MJ, et al. Contribution of birth defects and genetic diseases to pediatric hospitalizations. A population-based study. Arch Pediatr Adolesc Med. 1997;151(11):1096-1103. 6. Berry MA, Shah PS, Brouillette RT, et al. Predictors of mortality and length of stay for neonates admitted to children's hospital neonatal intensive care units. J Perinatol. 2008;28(4):297-302. 7. Bainbridge MN, Wiszniewski W, Murdock DR, et al: Whole-genome sequencing for optimized patient management. Sci Transl Med 2011; 3:87re3 8. Wojcik MH, Schwartz TS, Yamin I, et al: Genetic disorders and mortality in infancy and early childhood: Delayed diagnoses and missed opportunities. Genet Med 2018; 20:1396–1404 9. Jian Yang, Cong Dong, Huilong Duan, Qiang Shu, Haomin Li. RDmap: A Map for Exploring Rare Diseases. Orphanet Journal of Rare Diseases. 2021, 16(101).

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) 2021年9月1日至2022年9月1日住院收入到新生儿科
(2) 基于现有临床诊疗需求拟开展遗传基因诊断

Inclusion criteria

(1) Inpatient admission to the neonatal unit from September 1, 2021 to September 1, 2022
(2) Proposed genetic diagnosis based on existing clinical needs

排除标准：

(1) 患儿家属明确表示其相关临床和遗传检查数据不能用于临床研究的

Exclusion criteria：

(1) The child's family clearly indicates that the relevant clinical and genetic test data cannot be used for clinical research

研究实施时间：

Study execute time：

从 From 2021-09-01 00:00:00至 To 2022-12-31 00:00:00  

征募观察对象时间：

Recruiting time：

从From 2021-09-01 00:00:00 至 To 2022-09-01 00:00:00  

诊断试验：

Diagnostic Tests：

金标准或参考标准（即可准确诊断某疾病的单项方法或多项联合方法，在本研究中用于诊断是否有该病的临床参考标准）：

采用目前临床接受的各类遗传疾病诊断方法，包括不限于从串联质谱、尿有机酸分析和染色体核型分析，到DNA序列分析涉及的染色体微阵列分析(CMA)、多重连接探针扩增技术(MLPA)、Sanger测序及新一代高通量测序技术(基因panel，全外显子测序、全基因组测序、线粒体基因测序)等，最终可以明确诊断遗传疾病的方式。

Gold Standard or Reference Standard (The clinical reference standards required to establish the presence or absence of the target condition in the tested population in present study):

The use of various types of currently clinically accepted methods for the diagnosis of genetic diseases, including but not limited to tandem mass spectrometry, urinary organic acid analysis and karyotype analysis, to DNA sequence analysis involving chromosomal microarray analysis (CMA), multiplexed probe amplification (MLPA),e

指标试验（即本研究的待评估诊断试验，无论为方法、生物标志物或设备，均请列出名称）：

仅基于临床表型，采用软件形式的疾病表型可视化分析，作出临床诊断推荐

Index test:

Visual analysis of disease phenotypes in software form based on clinical phenotypes only, to make clinical diagnostic recommendations

目标人群（可以是某种疾病患者或正常人群，详细描述其疾病特征，注意应纳入符合分布特点的全序列病例，具有良好的代表性）

新生儿科收治的具有潜在遗传疾病可能的患儿

例数:

Sample size:

100

Target condition (The target condition is a particular disease or disease stage that the index test will be intended to identify. Please specify the characteristics in detail; the population should has a complete spectrum and good representative):

neonates with potential genetic disorders admitted to the neonatal unit

容易混淆的疾病人群（即与目标疾病不易区分的一种或多种不同疾病，应避免采用正常人群对照的病例-对照设计）：

例数:

Sample size:

0

Population with condition difficult to distinguish from the target condition, the normal population in a case-control study design should be avoid:

研究实施地点：

Countries of recruitment and research settings：

国家：

 中国

省(直辖市)：

 浙江 

市(区县)：

 杭州 

Country：

China 

Province：

Zhejiang 

City：

Hangzhou 

单位(医院)：

浙江大学医学院附属儿童医院 

单位级别：

三级甲等 

Institution
hospital：

The Children's Hospital, Zhejiang University School of Medicine

Level of the institution：

Tertiary A

测量指标：

Outcomes：

指标中文名：

临床表型

指标类型：

主要指标

Outcome：

Clinical Phenotype

Type：

Primary indicator

测量时间点：

测量方法：

Measure time point of outcome：

Measure method：

指标中文名：

SEN, SPE, ACC, AUC of ROC

指标类型：

主要指标

Outcome：

SEN, SPE, ACC, AUC of ROC

Type：

Primary 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：

无

组织：

Sample Name：

N/A

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 0 岁 years
最大 Max age 0 岁 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）：

不采用随机方法

Randomization Procedure (please state who generates the random number sequence and by what method)：

No randomization procedure in this study

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：

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

No

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）：

发表文章

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

Article Publication

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：

采用电子表单通过网络来采集相关实验数据。

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

Electronic forms are used to collect relevant experimental data via the web.

数据与安全监察委员会：

Data and Safety Monitoring Committee：

有/Yes

注册人：

Name of Registration：

  2021-09-02 23:37:40