继续教育

儿科数字化研究网络现状与展望

  • 王博 ,
  • 周欣 ,
  • 孙晶 ,
  • 赵鎏丹 ,
  • 孙锟
展开
  • 上海交通大学医学院附属新华医院儿心脏中心(上海 200092)

收稿日期: 2023-08-29

  网络出版日期: 2024-02-02

Status and prospects of the children's digital research network

  • Bo WANG ,
  • Xin ZHOU ,
  • Jing SUN ,
  • Liudan ZHAO ,
  • Kun SUN
Expand
  • Department of Pediatric Heart Center, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China

Received date: 2023-08-29

  Online published: 2024-02-02

摘要

由于儿童的生长发育特点,儿童疾病的诊断治疗与成人存在相当差异,亟待建立一系列符合儿童疾病与人群特征的诊治指南。但大多数儿童疾病发病率较低,单中心临床研究无法形成有效的证据支持。数字化研究网络是由多家医学机构、专病研究网络以及数据合作平台共同组成的互联互享的研究平台,可以募集足够的样本量,高效地开展儿科临床研究,帮助更好地理解儿童疾病,促进诊疗方案的改进。本文主要从基于儿科研究网络的临床研究及关键技术进行综述,讨论现存的问题及后续的发展思路,以期为后续儿科研究网络的建设发展提供借鉴,进一步提升我国儿科的临床研究水平。

本文引用格式

王博 , 周欣 , 孙晶 , 赵鎏丹 , 孙锟 . 儿科数字化研究网络现状与展望[J]. 临床儿科杂志, 2024 , 42(2) : 171 -176 . DOI: 10.12372/jcp.2024.23e0824

Abstract

Due to the characteristics of children's growth and development, the diagnosis and treatment of pediatric diseases differ significantly from those of adults, and there is an urgent need to develop a series of diagnostic and treatment guidelines that are appropriate for pediatric diseases and populations. However, the prevalence of most childhood disorders is low, and single-center clinical investigations cannot provide sufficient empirical support. Big data research networks are interconnected research platforms made up of multiple medical institutions, disease-specific research networks, and data collaboration platforms. They can collect a large enough sample size to efficiently conduct pediatric clinical research, better understand pediatric diseases, and improve diagnostic and treatment protocols. This article reviews the clinical research and key technologies based on the pediatric research network, discusses the existing problems, and proposes future development ideas, in order to provide reference for the subsequent construction and development of the pediatric research network, and further enhance the level of pediatric clinical research in China.

参考文献

[1] van der Lee JH, Mokkink LB, Grootenhuis MA, et al. Definitions and measurement of chronic health conditions in childhood: a systematic review[J]. JAMA, 2007, 297(24): 2741-2751.
[2] 李作祥, 汪鹏, 陈香美, 等. 大数据科研分析平台在肾脏病研究的应用探讨[J]. 中国数字医学, 2019, 14(8): 29-31.
[3] Fleurence RL, Curtis LH, Califf RM, et al. Launching PCORnet, a national patient-centered clinical research network[J]. J Am Med Inform Assoc, 2014, 21(4): 578-582.
[4] Forrest CB, Margolis PA, Bailey LC, et al. PEDSnet: a national pediatric learning health system[J]. J Am Med Inform Assoc, 2014, 21(4): 602-606.
[5] McKenzie PL, Maltenfort M, Bruckner AL, et al. Evaluation of the prevalence and incidence of pediatric alopecia areata using electronic health record data[J]. JAMA Dermatol, 2022, 158(5): 547-551.
[6] Lang JE, Bunnell HT, Hossain MJ, et al. Being overweight or obese and the development of asthma[J]. Pediatrics, 2018, 142(6): e20182119.
[7] Bailey LC, Razzaghi H, Burrows EK, et al. Assessment of 135 794 pediatric patients tested for severe acute respiratory syndrome coronavirus 2 across the United States[J]. JAMA Pediatr, 2021, 175(2): 176-184.
[8] Mueller S, Jain P, Liang WS, et al. A pilot precision medicine trial for children with diffuse intrinsic pontine glioma-PNOC003: a report from the pacific pediatric neuro-oncology consortium[J]. Int J Cancer, 2019, 145(7): 1889-1901.
[9] Ellison JS, Lorenzo M, Beck H, et al. Comparative effectiveness of paediatric kidney stone surgery (the PKIDS trial): study protocol for a patient-centred pragmatic clinical trial[J]. BMJ Open, 2022, 12(4): e056789.
[10] Freedman DS, Goodwin Davies AJ, Phan TT, et al. Measuring BMI change among children and adolescents[J]. Pediatr Obes, 2022, 17(6): e12889.
[11] Block JP, Bailey LC, Gillman MW, et al. Early antibiotic exposure and weight outcomes in young children[J]. Pediatrics, 2018, 142(6): e20180290.
[12] Kamal Nor N, Ghozali AH, Ismail J. Prevalence of overweight and obesity among children and adolescents with autism spectrum disorder and associated risk factors[J]. Front Pediatr, 2019, 7: 38.
[13] Rammah M, Théveniau-Ruissy M, Sturny R, et al. PPARγ and NOTCH regulate regional identity in the murine cardiac outflow tract[J]. Circ Res, 2022, 131(10): 842-858.
[14] Hampl SE, Hassink SG, Skinner AC, et al. Clinical practice guideline for the evaluation and treatment of children and adolescents with obesity[J]. Pediatrics, 2023, 151(2): e2022060640.
[15] Tai V, Grey A, Bolland MJ. Results of observational studies: analysis of findings from the nurses' health study[J]. PLoS One, 2014, 9(10): e110403.
[16] Khare R, Utidjian L, Ruth BJ, et al. A longitudinal analysis of data quality in a large pediatric data research network[J]. J Am Med Inform Assoc, 2017, 24(6): 1072-1079.
[17] Pathak J, Kho AN, Denny JC. Electronic health records-driven phenotyping: challenges, recent advances, and perspectives[J]. J Am Med Inform Assoc, 2013, 20(e2): e206-e211.
[18] Denburg MR, Razzaghi H, Bailey LC, et al. Using electronic health record data to rapidly identify children with glomerular disease for clinical research[J]. J Am Soc Nephrol, 2019, 30(12): 2427-2435.
[19] Phillips CA, Razzaghi H, Aglio T, et al. Development and evaluation of a computable phenotype to identify pediatric patients with leukemia and lymphoma treated with chemotherapy using electronic health record data[J]. Pediatr Blood Cancer, 2019, 66(9):e27876.
[20] Khare R, Kappelman MD, Samson C, et al. Development and evaluation of an EHR-based computable phenotype for identification of pediatric Crohn's disease patients in a national pediatric learning health system[J]. Learn Health Syst, 2020, 4(4): e10243.
[21] Wenderfer SE, Chang JC, Goodwin Davies A, et al. Using a multi-institutional pediatric learning health system to identify systemic lupus erythematosus and lupus nephritis: development and validation of computable phenotypes[J]. Clin J Am Soc Nephrol, 2022, 17(1): 65-74.
[22] Huang W, Chen J, Weng W, et al. Development of cancer prognostic signature based on pan-cancer proteomics[J]. Bioengineered, 2020, 11(1): 1368-1381.
[23] Kline C, Jain P, Kilburn L, et al. Upfront biology-guided therapy in diffuse intrinsic pontine glioma: therapeutic, molecular, and biomarker outcomes from PNOC003[J]. Clin Cancer Res, 2022, 28(18): 3965-3978.
[24] Sundaram L, Gao H, Padigepati SR, et al. Predicting the clinical impact of human mutation with deep neural networks[J]. Nat Genet, 2018, 50(8): 1161-1170.
[25] Landrum MJ, Lee JM, Benson M, et al. ClinVar: improving access to variant interpretations and supporting evidence[J]. Nucleic Acids Res, 2018, 46(D1): D1062-D1067.
[26] Clark MM, Hildreth A, Batalov S, et al. Diagnosis of genetic diseases in seriously ill children by rapid whole-genome sequencing and automated phenotyping and interpretation[J]. Sci Transl Med, 2019, 11(489): eaat6177.
[27] Juhn Y, Liu H. Artificial intelligence approaches using natural language processing to advance EHR-based clinical research[J]. J Allergy Clin Immunol, 2020, 145(2): 463-469.
[28] Xiao G, Pfaff E, Prud'hommeaux E, et al. FHIR-Ontop-OMOP: building clinical knowledge graphs in FHIR RDF with the OMOP common data model[J]. J Biomed Inform, 2022, 134: 104201.
[29] Turki H, Taieb Ma H, Shafee T, et al. Representing COVID-19 information in collaborative knowledge graphs: the case of Wikidata[J]. Semantic Web, 2022, 13(2): 233-264.
[30] Kahn MG, Bailey LC, Forrest CB, et al. Building a common pediatric research terminology for accelerating child health research[J]. Pediatrics, 2014, 133(3): 516-525.
[31] Gipson DS, Kirkendall ES, Gumbs-Petty B, et al. Development of a pediatric adverse events terminology[J]. Pediatrics, 2017, 139(1): e20160985.
[32] Carter P, Laurie GT, Dixon-Woods M. The social licence for research: why care.data ran into trouble[J]. J Med Ethics, 2015, 41(5): 404-409.
文章导航

/