Medical records-based chronic kidney disease phenotype for clinical care and 'big data' observational and genetic studies
In: NPJ Digital Medicine npj Digital Medicine, Jg. 4 (2021), Heft 1, S. 1-13
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Zugriff:
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.
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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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Autor/in / Beteiligte Person: | Larson, Eric B. ; Khan, Atlas ; Carrell, David ; Brilliant, Murray H. ; Hripcsak, George ; Peissig, Peggy L. ; Ionita-Laza, Iuliana ; Verma, Shefali S. ; Gainer, Vivian S. ; Crosslin, David R. ; Jarvik, Gail P. ; Denny, Joshua C. ; Dart, Richard A. ; Tatonetti, Nicholas P. ; Mehl, Karla ; Carrol, Robert J. ; Mohan, Sumit ; Shang, Ning ; Polubriaginof, Fernanda ; Zanoni, Francesca ; Weng, Chunhua ; Ritchie, Marylyn D. ; Stanaway, Ian B. ; Hathcock, Matthew A. ; Kiryluk, Krzysztof ; Karlson, Elizabeth W. ; Pendergrass, Sarah A. ; Fasel, David ; Gharavi, Ali G. ; Drawz, Paul E. ; Arruda-Olson, Adelaide M. ; Gordon, Adam S. ; Benoit, Barbara |
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Zeitschrift: | NPJ Digital Medicine npj Digital Medicine, Jg. 4 (2021), Heft 1, S. 1-13 |
Veröffentlichung: | Nature Publishing Group UK, 2021 |
Medientyp: | unknown |
ISSN: | 2398-6352 (print) |
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