Quantifying the Delay from Laboratory-Based Detection to Diagnosis of CKD in the United States
Bibliographic record
Abstract
Background: While delays from chronic kidney disease (CKD) onset to diagnosis are common, these delays have yet to be quantified. The study objective was to quantify the delay from laboratory-based physiologic detection (using eGFR) to diagnosis of CKD (using ICD 9/10 code). Methods: A retrospective cohort study was conducted using 2009 -2020 Optum® Market Clarity data. Individuals aged 18 years and older, who had two laboratory records showing eGFR < 60 mL/min/1.73 m2, 3-12 months apart, were included. The second eGFR < 60 was defined as the index date. Patients with pre-existing ICD code of CKD during the 12-month pre-index period (baseline), or before the first eGFR <60 were excluded. Individuals were followed from the index date until a diagnosis of CKD or censoring (date of death, end of follow-up, or disenrollment). Cohorts were stratified by the presence or absence of comorbid diabetes and heart failure (HF) at baseline. Survival analysis was performed to evaluate the factors associated with delays in diagnosis, for overall and stratified cohorts. Results: A total of 1.39 million adults with laboratory evidence of CKD (mean (±SD) age 71 (±10); 63% women; 87% Caucasian) were included. Over 90% of them were in KDIGO stage 3, 5% in stage 4 and 1% in stage 5. Overall, 62% of individuals had neither diabetes nor HF, 24% had diabetes, 8% had HF, and 6% had both. The mean follow-up was 2.7 (SD 2.4) years. Overall, the median time to CKD diagnosis was 469 days (interquartile range [IQR]: 120-1062); time to CKD diagnosis was 537 days (IQR: 152-1163) among those without diabetes or HF, 449 days (IQR: 114-1007) among those only with diabetes, 319 days (IQR: 62-793) among those with only HF, and 270 days (IQR: 54-694) among those with both diabetes and HF. Factors associated with longer time to CKD diagnosis included: absence of diabetes or HF; less severe CKD; younger age; female sex; and Caucasian race. Conclusions: In a large cohort of individuals with laboratory-based evidence of CKD, the time to diagnosis was delayed, on average, by over a year. In light of newer therapies that can slow kidney disease progression and reduce cardiovascular complications, these findings reinforce the need for early recognition of CKD that would inform optimal guideline-based treatment and improve outcomes in these patients. Funding: Commercial Support - Boehringer Ingelheim
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".