Correlates affecting survival in chronic hemodialysis patients: The combined impact of albumin and high hemoglobin levels on improving outcomes, local and national results
Bibliographic record
Abstract
While national mortality rates for end-stage renal disease (ESRD) patients remain high, for the past 4 years, lower than expected local mortality rates have been consistently seen in our facilities. Because of these progressive improvements in mortality rates, a study of 687 hemodialysis patients over a 4-year period, 2003 through 2006, was undertaken to analyze which factors may be contributing to the enhanced survival rates. We also examined the partially overlapping United States Renal Data System clinical performance measures national data sets of hemodialysis patients for 2001 to 2004. Proportional hazards and logistic regression models were used to determine significant predictors of short-term survival. Variables tested included hemoglobin (Hb), albumin, calcium, phosphorus, infections, hospitalizations, URR, Kt/V, erythropoietic stimulating agents (epoetin-alpha) use, and comorbid conditions. The local and national models identified albumin, Hgb, and hospitalization as statistically significant predictors of survival. Local models also found years of dialysis as a significant predictor. Locally, there was a 69-fold increase from 16.1 deaths/1000 patient years for albumin > or =4.0 with Hgb> or =14.0 to 1115.9 deaths/1000 patient-years for albumin <3.5 with Hgb<11.0. The increase nationally is a 4-fold increase from 96 deaths/1000 patient-years for albumin > or =4.0 with Hgb> or =14.0 to 406 deaths/1000 patient-years for albumin <3.5 with Hgb<11.0. There was no evidence that higher erythropoietic stimulating agents dose levels were associated with higher mortality rates, independent of the other significant factors. In conclusion, the findings indicate that individually higher Hgb and albumin levels are associated with increased survival, and when higher Hgb levels are in association with high albumin levels, the survival rates and hospitalizations are synergistically improved.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".