Administered paricalcitol dose and survival in hemodialysis patients: A marginal structural model analysis
Bibliographic record
Abstract
PURPOSE: Several observational studies have indicated that vitamin D receptor activators (VDRA), including paricalcitol, are associated with greater survival in maintenance hemodialysis (MHD) patients. However, patients with higher serum parathyroid hormone, a surrogate of higher death risk, are usually given higher VDRA doses, which can lead to confounding by indication and attenuate the expected survival advantage of high VDRA doses. METHODS: We examined mortality-predictability of low (>1 but <10 µg/week) versus high (≥10 µg/week) dose of administered paricalcitol over time in a contemporary cohort of 15 442 MHD patients (age 64 ± 15 years, 55% men, 44% diabetes, 35% African-Americans) from all DaVita dialysis clinics across the USA (7/2001-6/2006 with survival follow-ups until 6/2007) using conventional Cox regression, propensity score (PS) matching, and marginal structural model (MSM) analyses. RESULTS: In our conventional Cox models and PS matching models, low dose of paricalcitol was not associated with mortality either in baseline (hazard ratio (HR): 1.03, 95% confidence interval (CI): (0.97-1.09)) and (HR: 0.99, 95%CI:(0.86-1.14)) or time-dependent (HR: 1.04, 95%CI: (0.98-1.10)) and (HR: 1.12, 95%CI: (0.98-1.28)) models, respectively. In contrast, compared to high dose of paricalcitol, low dose was associated with a 26% higher risk of mortality (HR: 1.26, 95%CI: (1.19-1.35)) in MSM. The association between dose of paricalcitol and mortality was robust in almost all subgroups of patients using MSMs. CONCLUSIONS: Higher dose of paricalcitol appears causally associated with greater survival in MHD patients. Randomized controlled trials need to verify the survival effect of paricalcitol dose in MHD patients are indicated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".