Assessing ICD-10-CM Algorithms for Repeated eGFR-Defined Chronic Kidney Disease in the TriNetX Network
-
By
-
Chung-An Wang
-
Wei-Jie Wang
-
Jui-Yi Chen
-
Chung-Yi Cheng
-
Vin-Cent Wu
-
October 13, 2026
Clinical Report: Assessing ICD-10-CM Algorithms for Repeated eGFR-Defined CKD
Overview
In 8,239,691 adults in the TriNetX US Collaborative Network with at least two eGFR measurements, 1,328,952 met laboratory criteria for CKD stage ≥3. ICD-10-CM algorithms showed high specificity and negative predictive value but moderate sensitivity; requiring repeated codes or restricting code ascertainment around the index eGFR increased PPV while reducing sensitivity.
Background
CKD ascertainment in electronic health records supports population research and longitudinal evaluation, but diagnosis codes may not capture all patients with laboratory-defined disease. The source notes prior evidence of under-recognition, including a TriNetX REVEAL-CKD finding that 64.3% of US patients with stage 3 CKD had no recorded CKD diagnosis. This study evaluated ICD-10-CM algorithms against repeated eGFR-defined CKD in the TriNetX network. KDIGO guidance defines CKD by kidney structure or function abnormalities present for at least 3 months and cautions against inferring chronicity from a single abnormal result.
Data Highlights
| Population or algorithm | Sensitivity | Specificity | PPV | NPV |
|---|---|---|---|---|
| Adults with ≥2 eGFR measurements, 2021–2025 | 8,239,691 | |||
| Laboratory-defined CKD stage ≥3 | 1,328,952 | |||
| Stage ≥3; ≥1 ICD-10-CM code | 64.1% | 95.6% | 73.8% | 93.3% |
| Stage ≥4; ≥1 ICD-10-CM code | 79.2% | 98.0% | 57.3% | 99.3% |
| Temporal restriction around index eGFR (±3 to ±12 months) | Declined | Not reported | Approximately 96% for stage ≥3; 93% for stage ≥4 | Not reported |
Key Findings
- The analysis included 8,239,691 adults with at least two eGFR measurements from 2021 through 2025; 1,328,952 met laboratory-defined criteria for CKD stage ≥3.
- For stage ≥3, requiring at least one ICD-10-CM code yielded 64.1% sensitivity, 95.6% specificity, 73.8% PPV, and 93.3% NPV.
- For stage ≥4, at least one code yielded 79.2% sensitivity, 98.0% specificity, 57.3% PPV, and 99.3% NPV.
- Requiring at least two ICD-10-CM codes decreased sensitivity and increased PPV.
- Restricting code ascertainment to ±3, ±6, or ±12 months of the index eGFR raised PPV to approximately 96% for stage ≥3 and 93% for stage ≥4, while sensitivity declined.
- The source reports that 64.3% of US patients with stage 3 CKD in the TriNetX REVEAL-CKD study had no recorded CKD diagnosis.
Clinical Implications
For research using TriNetX, the choice of ICD-10-CM phenotype should reflect whether the application prioritizes diagnostic certainty or case ascertainment, because repeated coding and temporal restrictions increased PPV while reducing sensitivity. The study concludes that no single ICD-based phenotype or temporal strategy is universally optimal and notes that combining diagnosis codes with longitudinal laboratory data may improve identification.
Conclusion
In the TriNetX network, ICD-10-CM algorithms identified repeated eGFR-defined CKD with high specificity but incompletely captured cases. Their performance varied with code-count and timing requirements, producing a trade-off between PPV and sensitivity.
Related Resources & Content
- KDIGO, Executive Summary, 2024 — Executive summary of the KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease: known knowns and known unknowns
- REVEAL-CKD study, BMJ Open — Prevalence of undiagnosed stage 3 chronic kidney disease in France, Germany, Italy, Japan and the USA: results from the multinational observational REVEAL-CKD study
- Frontiers in Cardiovascular Medicine — Evaluation of Various eGFR Formulas in Predicting 1-Year Major Adverse Cardiovascular Events Following Percutaneous Coronary Intervention in Acute Coronary Syndrome Patients
- ASCO Publications — Accuracy of fully automated, AI-generated models compared with validated clinical model to predict post-operative glomerular filtration rate after renal surgery.
- ASCO Publications — Heterogeneity in methods for estimating renal function in anticancer drug development.
- ASCO Publications — Accuracy of fully automated, AI-generated models compared with validated clinical model to predict post-operative glomerular filtration rate after renal surgery.
- Executive summary of the KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease
- Prevalence of undiagnosed stage 3 chronic kidney disease in France, Germany, Italy, Japan and the USA: results from the multinational observational REVEAL-CKD study
- TriNetX | Penn Medicine Clinical Research | Perelman School of Medicine at the University of Pennsylvania
- ICD-10-CM Guidelines FY25 October 1 2024
- A retrospective multi-site examination of chronic kidney disease using longitudinal laboratory results and metadata to identify clinical and financial risk - PubMed
- Diagnostic Validity of Chronic Kidney Disease in Health Claims Data Over Time: Results from a Cohort of Community-Dwelling Older Adults in Germany.
- Concordance of electronic health record data with ICD classification and evidence-based treatment guidelines: A systematic review and meta-analysis - ScienceDirect
- Prevalence of undiagnosed stage 3 chronic kidney disease in France, Germany, Italy, Japan and the USA: results from the multinational observational REVEAL-CKD study | BMJ Open
- Canagliflozin and Renal Outcomes in Type 2 Diabetes and Nephropathy | New England Journal of Medicine
- Finerenone in Persons with Chronic Kidney Disease without Diabetes | New England Journal of Medicine
- Evaluation of a Chronic Kidney Disease e-Phenotype: Identification and Characterisation by Electronic Health Record Data - PubMed
- Identifying and Characterising a Chronic Kidney Disease Electronic‐Phenotype Using Electronic Health Record‐Derived Data: A Narrative Review of Strategies and Applications - PMC
Based on findings from:
Validation of ICD-10-CM algorithms for repeated eGFR-defined chronic kidney disease in the TriNetX network
Chung-An Wang, Wei-Jie Wang, Jui-Yi Chen, Chung-Yi Cheng, Vin-Cent Wu. Bmj Health & Care Informatics, 2026.
https://informatics.bmj.com/content/33/1/e102413
This content is an AI-generated, fully rewritten summary based on a published scholarly article. It does not reproduce the original text and is not a substitute for the original publication. Readers are encouraged to consult the source for full context, data, and methodology.