Calcium, Phosphate, and Parathyroid Hormone Levels in Combination and as a Function of Dialysis Duration Predict Mortality
Bibliographic record
Abstract
Current literature suggests associations between abnormal mineral metabolism (MM) to cardiovascular disease in dialysis populations, with conflicting results. MM physiology is complex; therefore, it was hypothesized that constellations of MM parameters, reflecting this complexity, would be predictive of mortality and that this effect would be modified by dialysis duration (DD). Prevalent dialysis patients in British Columbia, Canada, who had measurements of calcium (Ca), phosphate (Pi), and parathyroid hormone (iPTH) between January and March 2000 were followed prospectively until December 2002. Statistical analysis included Cox proportional hazard models with Ca, Pi, and iPTH alone and in combination as explanatory variables; analyses were stratified by DD. The 515 patients included in this analysis represent British Columbia and Canadian dialysis populations: 69% were on hemodialysis, mean age was 60 +/- 17 yr, 40% were female, and 34% had diabetes. Mean Ca and Pi values were 2.32 +/- 0.22 mmol/L and 1.68 +/- 0.59 mmol/L, respectively, and median iPTH was 15.8 pmol/L (25th to 75th percentile: 6.9 to 37.3 pmol/L). Serum Pi, after adjusting for demographic, dialysis type and adequacy, hemoglobin, and albumin, independently predicted mortality (risk ratio [RR], 1.56 per 1 mmol/L; 95% confidence interval [CI], 1.15 to 2.12; P = 0.004). When combinations of parameters were modeled (overall P = 0.003), the combinations of high serum Pi and Ca with high iPTH (RR, 3.71; 95% CI, 1.53 to 9.03; P = 0.004) and low iPTH (RR, 4.30; 95% CI, 2.01 to 9.22; P < 0.001) had highest risks for mortality as compared with the combination of high iPTH with normal serum Ca and Pi that had the lowest mortality and was used as index category. These effects varied across different strata of DD. This analysis demonstrates the importance of examining combinations of MM parameters as opposed to single variables alone and the effect of DD. In so doing, the complex interaction of time and MM can begin to be understand. Further exploration is required.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".