Diagnostic Validity of Chronic Kidney Disease in Health Claims Data Over Time: Results from a Cohort of Community-Dwelling Older Adults in Germany
Bibliographic record
Abstract
Purpose: The validity of ICD-10 diagnostic codes for chronic kidney disease (CKD) in health claims data has not been sufficiently studied in the general population and over time. Patients and Methods: We used data from the Berlin Initiative Study (BIS), a prospective longitudinal cohort of community-dwelling individuals aged ≥70 years in Berlin, Germany. With estimated glomerular filtration rate (eGFR) as reference, we assessed the diagnostic validity (sensitivity, specificity, positive [PPV], and negative predictive values [NPV]) of different claims-based ICD-10 codes for CKD stages G3-5 (eGFR <60mL/min/1.73m²: ICD-10 N18.x-N19), G3 (eGFR 30-<60mL/min/1.73m²: N18.3), and G4-5 (eGFR <30mL/min/1.73m²: N18.4-5). We analysed trends over five study visits (2009-2019). Results: We included data of 2068 participants at baseline (2009-2011) and 870 at follow-up 4 (2018-2019), of whom 784 (38.9%) and 440 (50.6%) had CKD G3-5, respectively. At baseline, sensitivity for CKD in claims data ranged from 0.25 (95%-confidence interval [CI] 0.22-0.28) to 0.51 (95%-CI 0.48-0.55) for G3-5, depending on the included ICD-10 codes, 0.20 (95%-CI 0.18-0.24) for G3, and 0.36 (95%-CI 0.25-0.49) for G4-5. Over the course of 10 years, sensitivity increased by 0.17 to 0.29 in all groups. Specificity, PPVs, and NPVs remained mostly stable over time and ranged from 0.82-0.99, 0.47-0.89, and 0.66-0.98 across all study visits, respectively. Conclusion: German claims data showed overall agreeable performance in identifying older adults with CKD, while differentiation between stages was limited. Our results suggest increasing sensitivity over time possibly attributable to improved CKD diagnosis and awareness.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".