Discrepancy Between eGFR Cystatin C and eGFR Creatinine in Recently Hospitalized Adults
Bibliographic record
Abstract
Background: Having a lower estimated glomerular filtration rate using cystatin C (eGFRcys) than creatinine (eGFRcr) is associated with a higher risk of cardiac disease and death in the outpatient setting. However, the distribution of this discrepancy and its prognostic values in recently hospitalized adults are not well described. Methods: In 1534 hospitalized adults enrolled in the Assessment, Serial Evaluation, and Subsequent Sequelae of Acute Kidney Injury (ASSESS-AKI) cohort, we characterized the difference between eGFRcys and eGFRcr at 3 months after discharge. We used survival analysis to determine the associations between differences in eGFRcys and eGFRcr and risk of end-stage kidney disease (ESKD), major adverse cardiac events (MACE), first heart failure hospitalization, and death after a median follow up of 4.7 years. Results: The mean age of study participants was 64.5 years, 37.3% are female, and 50% had AKI during hospitalization. At 3 months after hospitalization, eGFRcys was lower than eGFRcr by a large margin (Table). The difference between eGFRcys and eGFRcr at 3 months was 4.4% (2.4%- 6.5%) and 7.1% (3.8%- 10.5%) larger in those with AKI and sepsis during hospitalization, respectively. Having a lower in eGFRcys than eGFRcr was further associated with a higher risk of MACE, heart failure, ESKD, and death (Table), and these associations are consistent in participants with and without AKI (p for interaction with AKI all > 0.1). Conclusions: By systematically measuring eGFRcys and eGFRcr in a cohort of recently hospitalized adults, we found that having lower eGFRcys than eGFRcr is commonly observed and provides additional prognostication for adverse clinical events. Funding: NIDDK Support
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".