ArticleNPJ digital medicine2021
Medical records-based chronic kidney disease phenotype for clinical care and "big data" observational and genetic studies.
Article in NPJ digital medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
42 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Predict, diagnose, and treat chronic kidney disease with machine learning: a systematic literature review.Journal of nephrology · 2023Pooled it
- Genetics in chronic kidney disease: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference.Kidney international · 2022Guideline
- Cardiovascular Disease in CKD: Ethnic and Regional Differences in Risk and Phenotype.Clinical journal of the American Society of Nephrology : CJASN · 2026Review
- Leveraging time-series electronic health records with large language models for chronic kidney disease diagnosis in primary care.BMJ health & care informatics · 2026Article
- Electronic Phenotype for Detection, Staging, and Subtyping of Acute Kidney Injury.American journal of kidney diseases : the official journal of the National Kidney Foundation · 2026Article
- Uromodulin T62P variant causes kidney tubular stress and injury modulated by age and polygenic risk.medRxiv : the preprint server for health sciences · 2026Article
- A Microsimulation-Based Approach for Mitigating Societal Bias in Chronic Kidney Disease Data.Medical decision making : an international journal of the Society for Medical Decision Making · 2026Article
- Artificial intelligence in nephrology: predicting CKD progression and personalizing treatment.International urology and nephrology · 2026Review
- Evaluation of the genome-informed risk assessment (GIRA) approach from eMERGE in an independent health system.medRxiv : the preprint server for health sciences · 2026Article
- Precision Diagnosis in APOL1 Kidney Disease With the p.N264K M1 Protective Variant.JAMA network open · 2026Article
- Genetic Prediction of Kidney Function: Why Measured GFR Matters.Kidney international reports · 2026Article
- Joint effects of arterial stiffness and polygenic risk on kidney function: the Dongfeng-Tongji cohort.Frontiers in cardiovascular medicine · 2026Article
- A genetic and clinical risk factor algorithm to aid in identifying new cases of chronic kidney disease from the general population.Frontiers in genetics · 2026Article
- A microsimulation-based framework for mitigating societal bias in primary care data.medRxiv : the preprint server for health sciences · 2025Article
- Data-driven cluster analysis identifies three clinical phenotypes in hemodialysis patients.Renal failure · 2025Article
- Liability threshold model-based disease risk prediction based on electronic health record phenotypes.Nature genetics · 2025Article
- A multi-ethnic polygenic risk score for chronic kidney disease is associated with increased risk of hypertension in African American individuals.BMC nephrology · 2025Article
- Identifying and Characterising a Chronic Kidney Disease Electronic-Phenotype Using Electronic Health Record-Derived Data: A Narrative Review of Strategies and Applications.Nephrology (Carlton, Vic.) · 2025Review
- Validation of an Algorithm to Identify End-Stage Kidney Disease in Electronic Health Records Data.American journal of kidney diseases : the official journal of the National Kidney Foundation · 2025Article
- The polygenic architecture of hidradenitis suppurativa reveals signaling mechanisms that implicate epithelial remodeling.medRxiv : the preprint server for health sciences · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
33 authors.
Funding
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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.