Undiagnosed Early CKD in Patients with Hypertension and Cardiovascular Disease in the United States
Bibliographic record
Abstract
Background: Chronic kidney disease (CKD) is a growing public health issue and widely under-recognized in the United States. Hypertension (HTN) and cardiovascular diseases (CVD) are well-known risk factors for CKD; and KDIGO recommends screening in both high-risk groups. Early diagnosis of CKD and active management can slow disease progression and improve outcomes, but the prevalence of undiagnosed earlystage CKD in patients with comorbidities other than type 2 diabetes (T2D) has not been reported. This analysis assessed the prevalence of undiagnosed stage 3 CKD in patients with HTN and CVD in the absence of T2D. Methods: Data were extracted from the US TriNetX database. Patients were aged ≥18 years with 2 consecutive estimated glomerular filtration rate (eGFR) results ≥30 and <60mL/min/1.73m2 recorded 91-730 days apart between 2015 and 2020. Undiagnosed CKD was defined as the absence of a CKD diagnosis code any time before and up to 6 months after the second eGFR (index date). The analysis cohorts included patients with the following at or before index: 1) HTN ICD 9/10 diagnosis code but not for T2D; 2) HTN or atherosclerotic cardiovascular disease (ASCVD) ICD 9/10 diagnosis code but not for T2D; 3) HTN or heart failure (HF) ICD 9/10 diagnosis code but not for T2D; and 4) ICD 9/10 diagnosis code for T2D. Results: In the absence of T2D, the proportion of undiagnosed stage 3 CKD in patients with HTN was 68.4% (95%CI: 68.2%, 68.7%). Similar proportions were observed in patients with either HTN or ASCVD (68.7%, 95%CI: 68.4%, 69.0%), and with HTN or HF (68.6%, 95%CI: 68.3%, 68.8%). These proportions were greater than those with undiagnosed stage 3 CKD and T2D (51.7%, 95%CI: 51.3%, 52.0%) (Figure). Conclusions: A high prevalence of undiagnosed CKD in patients with existing HTN and CVD in the absence of T2D was observed in a large contemporary US database. These results highlight an opportunity to increase early identification of CKD in people with high-risk comorbidities other than T2D in order to implement targeted evidencebased therapies to slow progression of CKD and improve patient outcomes. Funding: Commercial Support - AstraZenecaFigure:: Prevalence of undiagnosed stage 3 chronic kidney disease in patients with comorbidities
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".