Prevalence of and factors associated with undiagnosed stage 3 chronic kidney disease in patient with a history of heart failure: a report from REVEAL-CKD
Bibliographic record
Abstract
Abstract Background Chronic kidney disease (CKD) is a serious debilitating condition affecting 10% of the world's population, yet it remains largely under recognised even among patients with pre-existing comorbidities. Approximately 5% of patients with CKD have a history of heart failure (HF), however factors associated with undiagnosed CKD in patients with multimorbidity remain unclear. Purpose REVEAL-CKD is a multinational initiative to assess undiagnosed CKD. This analysis aims to assess prevalence and factors associated with undiagnosed stage 3 CKD in patients with heart failure. Methods From the US, we utilised TriNetX, a federated research network providing statistics on electronic health records. Adult patients, with two consecutive estimated glomerular filtration rate (eGFR) measurements ≥30 and <60 mL/min/1.732 at least 90 days apart were identified between 2015–2020. HF status was ascertained by ICD codes prior to the index date (date of the second eGFR measurement). Patients with no ICD code for CKD at any time before or up to 6 months after the index date were considered to have undiagnosed CKD. Results The study cohort included 31,263 patients with eGFR values indicating stage 3 CKD and pre-existing HF with mean age of 72 years (standard deviation: 11 years). The overall prevalence of undiagnosed CKD was 48.5% (n=15,159, 95% Confidence Interval: 47.9–49.0). Prevalence of undiagnosed CKD increased with age, was greater than 50% in female patients and was between 38% and 46% in patients with other pre-existing comorbidities (Table 1). Compared to patients with diagnosed CKD, the undiagnosed group had more females (41% versus 60%) and a had higher proportion of patients in the older age group (≥75 years: 43% versus 52%). Fewer undiagnosed CKD patients had pre-existing comorbidities than those with diagnosed CKD. Conclusion This study suggests that a large proportion of either older or female patients with baseline HF comorbidity have undiagnosed CKD. These results suggest that an opportunity exists for more proactive CKD diagnosis and monitoring of patients with comorbidities Funding Acknowledgement Type of funding sources: Private company. Main funding source(s): AstraZeneca
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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 | 0.002 |
| 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 | 0.001 | 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".