Journal
NPJ DIGITAL MEDICINE
Volume 4, Issue 1, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41746-021-00428-1
Keywords
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Funding
- National Human Genome Research Institute (NHGRI) [U01HG8680, U01HG8672, U01HG8657, U01HG8685, U01HG8666, U01HG6379, U01HG8679, U01HG8684, U01HG8673, MD007593, U01HG8676, U01HG8664]
- National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), Kidney Precision Medicine Project (KPMP) [UH3DK114926]
- National Library of Medicine [R01LM013061]
- Precision Medicine Pilot from the Irving Institute/Columbia CTSA [UL1TR001873]
- [R01DK105124]
- [RC2DK116690]
- [R01LM006910]
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The study implemented a portable and scalable electronic CKD phenotype to aid in early disease recognition and large-scale observational and genetic studies. Through manual validation and case-control validation, the algorithm showed high accuracy and detected a significant number of undetected CKD cases.
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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