Variations in Serum Albumin Levels Over Time in Patients Treated With Conventional Hemodialysis or Expanded Hemodialysis: A Cohort Study
Bibliographic record
Abstract
INTRODUCTION: Hypoalbuminemia is a well-established risk factor for mortality in chronic hemodialysis (HD) patients. To evaluate the association of time-varying serum albumin with the type of dialyzer, we analyzed serum albumin over time in two cohorts of HD patients, one receiving HDx therapy enabled by the Theranova dialyzer and the other conventional HD with high-flux dialyzer (HF-HD). METHODS: In this cohort study, 1092 prevalent adult HD patients (mean age 61 years; 62% men; 42% had diabetes; 19% had cardiovascular disease) at Renal Care Services Colombia undergoing either HDx therapy enabled by Theranova dialyzer (n = 559) or HF-HD (n = 533) were enrolled between September 1, 2017, and November 30, 2017, and then underwent repeated measurements of serum albumin for up to 48 months. Sociodemographic and clinical, and laboratory characteristics at baseline were recorded, and a repeated-measures analysis of variance (ANOVA) was conducted to examine differences in means of serum albumin at different time points. To evaluate the association between dialysis membrane and albumin levels during the follow-up, a linear panel regression analysis was performed, allowing control for imbalances in the cohorts of baseline clinical and demographic variables, as well as the time-dependent variables. RESULTS: Mean albumin concentration remained above 3.8 g/dL and did not differ over time between HDx and HF-HD (p = 0.789). No association (p = 0.208) between serum albumin levels varying over time and the use of the Theranova dialyzer was found in the linear panel regression model. However, serum albumin was linked to both inflammatory and nutritional markers, including C-reactive protein, ratio of platelets to lymphocytes, and protein-energy wasting. CONCLUSION: Variations in serum albumin levels over time were associated with protein-energy wasting, inflammation, high age, vascular access, and hospitalizations, but not with the type of dialyzer.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".