Developing an HbA1c-Based Equation to Estimate Blood Glucose in Maintenance Hemodialysis Patients
Bibliographic record
Abstract
OBJECTIVE: Hemoglobin A1c (HbA(1c)) has been widely used as a clinically important assessment tool for outcome analyses related to glycemic control. However, because of special conditions in dialysis patients, including the uremic milieu, there is no HbA(1c) blood glucose (BG) equation specific for patients on dialysis. In this study, we sought to develop HbA(1c)-BG equation models for hemodialysis patients. RESEARCH DESIGN AND METHODS: We examined associations between HbA(1c) and random serum BG over time in a contemporary cohort of diabetic patients with hemodialysis treated in DaVita dialysis clinics. We identified 11,986 patients (63 ± 12 years old and 49% male) with 69,764 paired measurements of HbA(1c) and BG over the course of 5 years (2001-2006). Bootstrapping method was used to estimate average BG and corresponding HbA(1c) levels. The association was adjusted by patient factors using linear regression. RESULTS: Linear regression analyses yielded the following three regression equations: BG = 59.2 + 29.4 × HbA(1c) - 20.8 × Alb (R(2) = 0.483); BG = 104.8 + 29.7 × HbA(1c) - 18.4 × Alb - 4.7 × Hb (R(2) = 0.486); and BG = 82.9 + 30.7 × HbA(1c) - 16.5 × Alb - 5.4 × Hb + 0.3 × age + race (R(2) = 0.491). All our models showed stronger association than previous equation models (R(2) = 0.468 in the Diabetes Control and Complications Trial and A1c-Derived Average Glucose equations). CONCLUSIONS: The association between HbA(1c) and BG in hemodialysis patients is different than that of patients with normal kidney function. Our analysis suggests that equations including serum albumin or hemoglobin are better for hemodialysis patients.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".