Quantifying Time to Diagnosis of CKD in the United States
Bibliographic record
Abstract
KEY POINTS: Many people with CKD are unaware of the condition. We estimated the time to CKD documentation after two eGFR measurements taken at least 90 days apart. Among many persons with early stage CKD, there are considerable delays in documenting the diagnosis in electronic health record-linked claims data. BACKGROUND: An estimated 14% of US adults have CKD with over 90% unaware of their condition. Health care professionals diagnose CKD after two abnormal laboratory test results taken at least 3 months apart. Although there is evidence that delays from CKD onset to documentation are common, these delays remain unquantified. The objective of this study was to quantify the time from laboratory-based evidence of CKD to the documentation of CKD using International Classification of Diseases codes. METHODS: A retrospective longitudinal cohort study was conducted using 2009-2020 Optum Market Clarity data. Adults aged 18 years and older were followed from the date of the second of two eGFRs <60 ml/min per 1.73 m 2 , 3-12 months apart until the first International Classification of Diseases Ninth or Tenth revision diagnosis of CKD, or censoring. Survival analysis was used to compare time to documentation among Kidney Disease Improving Global Outcomes (KDIGO) categories considering while analyzing deaths as competing risks. RESULTS: A total of 1.39 million adults with laboratory evidence of CKD and a mean age of 71 years (SD, 10; 63% women; 87% White) were included. Over 94% were in KDIGO stage G3, 5% in stage G4, and 1% in stage G5. The median time to CKD documentation was 3.6 years (interquartile range, 1.0-8.4), ranging from 4.8 years for those in KDIGO G3a, 2 years in KDIGO G3b, and <1 year in KDIGO G4 and G5. Patient characteristics associated with longer time to CKD diagnosis included absence of diabetes or heart failure, less severe CKD, older age, and female sex. CONCLUSIONS: There was a substantial delay between laboratory evidence of CKD and the diagnosis being documented via coding. Reducing this delay offers a target for earlier recognition and management of CKD.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".