Albuminuria Predicts a Rapid Decline in Kidney Function in 2 International, Longitudinal Cohorts of Adults With Sickle Cell Anemia
Bibliographic record
Abstract
ABSTRACT Chronic kidney disease (CKD) is common and a major contributor to increased morbidity and early mortality in people with sickle cell anemia (SCA). Urine albumin‐to‐creatinine ratio (uACR) is recommended to identify patients with SCA‐related CKD but its utility in predicting long‐term kidney dysfunction remains unclear in this patient population. In two independent, longitudinal cohorts of patients with hemoglobin SS or Sβ 0 ‐thalassemia (USA: n = 268, median follow‐up 6 years; France: n = 310, median follow‐up 8.2 years) we investigated the utility of uACR, as well as other clinical and modifiable risk factors, for predicting a decline in kidney function as determined by the rate of estimated glomerular filtration rate (eGFR) decline. Using linear mixed‐effects models, a higher baseline uACR independently predicted a faster rate of eGFR decline as well as a more rapid annual eGFR decline, defined as ≥ 3 mL/min/1.73m 2 ( p ≤ 0.009). Furthermore, baseline uACR of ≥ 100 mg/g creatinine was independently associated with the rate of eGFR decline and rapid annual eGFR decline in both cohorts. Tobacco smoking was also associated with a faster rate of eGFR decline and was congruous between the two cohorts. In conclusion, we demonstrate that uACR is an important clinical tool that predicts a more rapid decline in kidney function and should be routinely monitored in people with SCA. Our data also support preventative care to reduce tobacco smoking for mitigating the risk of CKD progression in this high‐risk population.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".