CHOP PEDIATRIC CENTER OF EXCELLENCE IN NEPHROLOGY ABSTRACT We propose to establish a Pediatric Center of Excellence in Nephrology (PCEN) at the Children?s Hospital of Philadelphia (CHOP). Our theme is to address barriers to clinical trials implementation in children with kidney disease. As kidney disease in children is uncommon, identification of adequate numbers of children with specific disorders and recruitment for trials is difficult. Additionally, methods to clinically phenotype children with kidney disease in terms of growth, development, nutritional issues, cardiovascular disease risk factors, bone and mineral disorders often vary from study to study, and quality control is variable. Expertise in study design and analysis is needed to achieve appropriate inferences from observational data, and to design clinical trials, however, frequently pediatric centers in nephrology lack this expertise. We will address these challenges through a regional and national collaboration of clinical and translational researchers at CHOP, Johns Hopkins, and children?s hospitals participating in PEDSnet. PEDSnet is a consortium of eight academic pediatric health centers that collectively provide care for >4.5 million children. PEDSnet has established a common institutional review board and has harmonized the diverse EHR systems of its participating centers to create a standardized multi-institutional data network in order to facilitate the efficient conduct of observational research, quality improvement and clinical trials. The CHOP-PCEN will have three biomedical research cores; a Design and Analysis core, a Clinical Phenotyping Core focused on CVD risk factors and Nutrition/Bone health, a Learning Health System (LHS) Core, as well as a pilot and feasibility program and an enrichment core. We include 2 research project proposals utilizing the cores: ?Bone Quality and Vascular Health in Adolescents with Urinary Stone Disease?, ?Derivation and Validation of Imaging Biomarkers for CKD Progression? and a third, integrated into the LHS core, ?Skeletal Outcomes in Children and Young Adults with Glomerular Disease?. The research base is comprised of 39 investigators, who are PI?s, co-investigators or collaborators on over 150 funded projects totaling over $35 million in annual direct costs, over $12 million of this funding is in research projects relevant to pediatric nephrology led by likely users of the core services. The PCEN will build upon the strong foundation of clinical research at CHOP and Hopkins, as well as the University of Penn Adult Nephrology Division and Center for Clinical Epidemiology and Biostatistics to catalyze the design and implementation of clinical trials in children with kidney disease.

Public Health Relevance

CHOP PEDIATRIC CENTER OF EXCELLENCE IN NEPHROLOGY PROJECT NARRATIVE We propose to establish a Pediatric Center of Excellence in Nephrology at the Children?s Hospital of Philadelphia (CHOP) to address barriers to clinical trials implementation in children with kidney disease. Kidney disease is a major cause of illness and death in children and adolescents, however, there is a scant evidence base for therapies as few clinical trials have been performed in this population. Our center will build on the remarkable research base at CHOP, Johns Hopkins and the University of Pennsylvania to accelerate the design and implementation of clinical trials in pediatric nephrology. Using the remarkable infrastructure of PEDsnet, a clinical data research network of 8 leading children?s hospitals with the ability to collect and use information from multiple data sources such as electronic health records (EHRs), we will create a learning health system in pediatric nephrology in order to establish a strong evidence base and the logistical framework for implementation of clinical trials in childhood kidney disease.

Agency
National Institute of Health (NIH)
Institute
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Type
Specialized Center (P50)
Project #
5P50DK114786-04
Application #
10003836
Study Section
Special Emphasis Panel (ZDK1)
Program Officer
Kirkali, Ziya
Project Start
2017-09-18
Project End
2022-08-31
Budget Start
2020-09-01
Budget End
2021-08-31
Support Year
4
Fiscal Year
2020
Total Cost
Indirect Cost
Name
Children's Hospital of Philadelphia
Department
Type
DUNS #
073757627
City
Philadelphia
State
PA
Country
United States
Zip Code
19146
Li, Hongming; Satterthwaite, Theodore D; Fan, Yong (2018) BRAIN AGE PREDICTION BASED ON RESTING-STATE FUNCTIONAL CONNECTIVITY PATTERNS USING CONVOLUTIONAL NEURAL NETWORKS. Proc IEEE Int Symp Biomed Imaging 2018:101-104
Zhu, Xiaofeng; Zhang, Weihong; Fan, Yong et al. (2018) A Robust Reduced Rank Graph Regression Method for Neuroimaging Genetic Analysis. Neuroinformatics 16:351-361
Zheng, Qiang; Tasian, Gregory; Fan, Yong (2018) TRANSFER LEARNING FOR DIAGNOSIS OF CONGENITAL ABNORMALITIES OF THE KIDNEY AND URINARY TRACT IN CHILDREN BASED ON ULTRASOUND IMAGING DATA. Proc IEEE Int Symp Biomed Imaging 2018:1487-1490
Zheng, Qiang; Warner, Steven; Tasian, Gregory et al. (2018) A Dynamic Graph Cuts Method with Integrated Multiple Feature Maps for Segmenting Kidneys in 2D Ultrasound Images. Acad Radiol 25:1136-1145
Li, Hongming; Galperin-Aizenberg, Maya; Pryma, Daniel et al. (2018) Unsupervised machine learning of radiomic features for predicting treatment response and overall survival of early stage non-small cell lung cancer patients treated with stereotactic body radiation therapy. Radiother Oncol 129:218-226
Li, Hongming; Zhu, Xiaofeng; Fan, Yong (2018) Identification of Multi-scale Hierarchical Brain Functional Networks Using Deep Matrix Factorization. Med Image Comput Comput Assist Interv 11072:223-231
Li, Hongming; Fan, Yong (2018) Brain Decoding from Functional MRI Using Long Short-Term Memory Recurrent Neural Networks. Med Image Comput Comput Assist Interv 11072:320-328
Li, Hongming; Fan, Yong (2018) Identification of Temporal Transition of Functional States Using Recurrent Neural Networks from Functional MRI. Med Image Comput Comput Assist Interv 11072:232-239
Li, Hongming; Fan, Yong (2018) NON-RIGID IMAGE REGISTRATION USING SELF-SUPERVISED FULLY CONVOLUTIONAL NETWORKS WITHOUT TRAINING DATA. Proc IEEE Int Symp Biomed Imaging 2018:1075-1078