Medical records-based chronic kidney disease phenotype for clinical care and “big data” observational and genetic studies

Abstract Chronic Kidney Disease (CKD) represents a slowly progressive disorder that is typically silent until late stages, but early intervention can significantly delay its progression. We designed a portable and scalable electronic CKD phenotype to facilitate early disease recognition and empower...

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Autores principales:	Ning Shang, Atlas Khan, Fernanda Polubriaginof, Francesca Zanoni, Karla Mehl, David Fasel, Paul E. Drawz, Robert J. Carrol, Joshua C. Denny, Matthew A. Hathcock, Adelaide M. Arruda-Olson, Peggy L. Peissig, Richard A. Dart, Murray H. Brilliant, Eric B. Larson, David S. Carrell, Sarah Pendergrass, Shefali Setia Verma, Marylyn D. Ritchie, Barbara Benoit, Vivian S. Gainer, Elizabeth W. Karlson, Adam S. Gordon, Gail P. Jarvik, Ian B. Stanaway, David R. Crosslin, Sumit Mohan, Iuliana Ionita-Laza, Nicholas P. Tatonetti, Ali G. Gharavi, George Hripcsak, Chunhua Weng, Krzysztof Kiryluk
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/24b3c498eb59459db6ee0b4a55ddf3e3
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spelling	oai:doaj.org-article:24b3c498eb59459db6ee0b4a55ddf3e32021-12-02T14:26:26ZMedical records-based chronic kidney disease phenotype for clinical care and “big data” observational and genetic studies10.1038/s41746-021-00428-12398-6352https://doaj.org/article/24b3c498eb59459db6ee0b4a55ddf3e32021-04-01T00:00:00Zhttps://doi.org/10.1038/s41746-021-00428-1https://doaj.org/toc/2398-6352Abstract Chronic Kidney Disease (CKD) represents a slowly progressive disorder that is typically silent until late stages, but early intervention can significantly delay its progression. We designed a portable and scalable electronic CKD phenotype to facilitate early disease recognition and empower large-scale observational and genetic studies of kidney traits. The algorithm uses a combination of rule-based and machine-learning methods to automatically place patients on the staging grid of albuminuria by glomerular filtration rate (“A-by-G” grid). We manually validated the algorithm by 451 chart reviews across three medical systems, demonstrating overall positive predictive value of 95% for CKD cases and 97% for healthy controls. Independent case-control validation using 2350 patient records demonstrated diagnostic specificity of 97% and sensitivity of 87%. Application of the phenotype to 1.3 million patients demonstrated that over 80% of CKD cases are undetected using ICD codes alone. We also demonstrated several large-scale applications of the phenotype, including identifying stage-specific kidney disease comorbidities, in silico estimation of kidney trait heritability in thousands of pedigrees reconstructed from medical records, and biobank-based multicenter genome-wide and phenome-wide association studies.Ning ShangAtlas KhanFernanda PolubriaginofFrancesca ZanoniKarla MehlDavid FaselPaul E. DrawzRobert J. CarrolJoshua C. DennyMatthew A. HathcockAdelaide M. Arruda-OlsonPeggy L. PeissigRichard A. DartMurray H. BrilliantEric B. LarsonDavid S. CarrellSarah PendergrassShefali Setia VermaMarylyn D. RitchieBarbara BenoitVivian S. GainerElizabeth W. KarlsonAdam S. GordonGail P. JarvikIan B. StanawayDavid R. CrosslinSumit MohanIuliana Ionita-LazaNicholas P. TatonettiAli G. GharaviGeorge HripcsakChunhua WengKrzysztof KirylukNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 4, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Ning Shang Atlas Khan Fernanda Polubriaginof Francesca Zanoni Karla Mehl David Fasel Paul E. Drawz Robert J. Carrol Joshua C. Denny Matthew A. Hathcock Adelaide M. Arruda-Olson Peggy L. Peissig Richard A. Dart Murray H. Brilliant Eric B. Larson David S. Carrell Sarah Pendergrass Shefali Setia Verma Marylyn D. Ritchie Barbara Benoit Vivian S. Gainer Elizabeth W. Karlson Adam S. Gordon Gail P. Jarvik Ian B. Stanaway David R. Crosslin Sumit Mohan Iuliana Ionita-Laza Nicholas P. Tatonetti Ali G. Gharavi George Hripcsak Chunhua Weng Krzysztof Kiryluk Medical records-based chronic kidney disease phenotype for clinical care and “big data” observational and genetic studies
description	Abstract Chronic Kidney Disease (CKD) represents a slowly progressive disorder that is typically silent until late stages, but early intervention can significantly delay its progression. We designed a portable and scalable electronic CKD phenotype to facilitate early disease recognition and empower large-scale observational and genetic studies of kidney traits. The algorithm uses a combination of rule-based and machine-learning methods to automatically place patients on the staging grid of albuminuria by glomerular filtration rate (“A-by-G” grid). We manually validated the algorithm by 451 chart reviews across three medical systems, demonstrating overall positive predictive value of 95% for CKD cases and 97% for healthy controls. Independent case-control validation using 2350 patient records demonstrated diagnostic specificity of 97% and sensitivity of 87%. Application of the phenotype to 1.3 million patients demonstrated that over 80% of CKD cases are undetected using ICD codes alone. We also demonstrated several large-scale applications of the phenotype, including identifying stage-specific kidney disease comorbidities, in silico estimation of kidney trait heritability in thousands of pedigrees reconstructed from medical records, and biobank-based multicenter genome-wide and phenome-wide association studies.
format	article
author	Ning Shang Atlas Khan Fernanda Polubriaginof Francesca Zanoni Karla Mehl David Fasel Paul E. Drawz Robert J. Carrol Joshua C. Denny Matthew A. Hathcock Adelaide M. Arruda-Olson Peggy L. Peissig Richard A. Dart Murray H. Brilliant Eric B. Larson David S. Carrell Sarah Pendergrass Shefali Setia Verma Marylyn D. Ritchie Barbara Benoit Vivian S. Gainer Elizabeth W. Karlson Adam S. Gordon Gail P. Jarvik Ian B. Stanaway David R. Crosslin Sumit Mohan Iuliana Ionita-Laza Nicholas P. Tatonetti Ali G. Gharavi George Hripcsak Chunhua Weng Krzysztof Kiryluk
author_facet	Ning Shang Atlas Khan Fernanda Polubriaginof Francesca Zanoni Karla Mehl David Fasel Paul E. Drawz Robert J. Carrol Joshua C. Denny Matthew A. Hathcock Adelaide M. Arruda-Olson Peggy L. Peissig Richard A. Dart Murray H. Brilliant Eric B. Larson David S. Carrell Sarah Pendergrass Shefali Setia Verma Marylyn D. Ritchie Barbara Benoit Vivian S. Gainer Elizabeth W. Karlson Adam S. Gordon Gail P. Jarvik Ian B. Stanaway David R. Crosslin Sumit Mohan Iuliana Ionita-Laza Nicholas P. Tatonetti Ali G. Gharavi George Hripcsak Chunhua Weng Krzysztof Kiryluk
author_sort	Ning Shang
title	Medical records-based chronic kidney disease phenotype for clinical care and “big data” observational and genetic studies
title_short	Medical records-based chronic kidney disease phenotype for clinical care and “big data” observational and genetic studies
title_full	Medical records-based chronic kidney disease phenotype for clinical care and “big data” observational and genetic studies
title_fullStr	Medical records-based chronic kidney disease phenotype for clinical care and “big data” observational and genetic studies
title_full_unstemmed	Medical records-based chronic kidney disease phenotype for clinical care and “big data” observational and genetic studies
title_sort	medical records-based chronic kidney disease phenotype for clinical care and “big data” observational and genetic studies
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/24b3c498eb59459db6ee0b4a55ddf3e3
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Medical records-based chronic kidney disease phenotype for clinical care and “big data” observational and genetic studies

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