Diagnostic Validity of Claims Data Diagnoses for CKD With Data From the Berlin Initiative Study (BIS)
Bibliographic record
Abstract
Background: Information on diagnostic validity of kidney disease within claims data is scarce. Thus, we aimed to estimate the validity of diagnostic codes of chronic kidney disease (CKD) in German claims data using estimated glomerular filtration rate (eGFR) as the gold standard. Methods: We used data of the Berlin Initiative Study (BIS), a community-dwelling cohort of 2,069 persons aged 70 or older, for whom primary clinical data linked with claims data are available. After baseline assessment (2010-2011), 4 follow-up visits were conducted biennially over a total observation period of 8 years. Correspondence of overall and stage-specific CKD ICD-10 diagnoses in claims data with CKD stages assessed by the creatinine-based CKD-EPI equation (eGFRCKD-EPI-Crea) was evaluated over time (crosssectionally and longitudinally) and for each stage, respectively. Results: The sensitivity of claims data diagnoses increases with CKD stages (Figure). For stages 3 and 4, stage-specific sensitivity increases from baseline to followup 4 (0.29/0.42 vs. 0.55/0.68 for stage 3/4) as well as longitudinally through ±1 year (Δ% = +3.6 - +12.5 / +3.6 - +17.4 for stage 3/4). Still, false-negative rates are high, as many patients do not receive diagnoses corresponding with the respective CKD stage (e.g., 1-sensitivitytotal = 0.91/0.83 for stage 1/2). Over all measures and stages, sensitivity is low (0.38 for overall and 0.22 for stage-specific diagnoses). Conclusions: Correspondence of claims data diagnoses with eGFR measurements can be interpreted as acceptable for CKD stages 5 and 4, limited for stage 3, and low for stages 2 and 1. Stage-specific sensitivity is lower than overall. Over time (cross-sectionally as well as longitudinally), correspondence seems to increase. Over all measures and stages, diagnostic validity of CKD diagnoses in claims data should be interpreted as restricted.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".