Time-Varying Association of Individual BP Components with eGFR in Late-Stage CKD
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The association of individual BP components with changes in eGFR in patients with late-stage CKD is unknown. The objectives of our study were to examine the associations of systolic BP, diastolic BP, and pulse pressure with continuous temporal changes in eGFR and an eGFR decline ≥30% in late-stage CKD. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We performed a retrospective cohort study (2010-2015) of patients with CKD in a multidisciplinary CKD clinic with an eGFR≤30. The associations of repeat measures of BP (systolic BP, diastolic BP, and pulse pressure) with eGFR were examined using general linear mixed models. The associations of BP components and eGFR decline ≥30% were examined with time-varying Cox models. RESULTS: <0.001), whereas pulse pressure was not. Patients with extremes of systolic BP (<105 or >170) and high diastolic BP (>90) measures were at a higher risk of GFR decline ≥30% (systolic BP <105: hazard ratio, 1.51; 95% confidence interval, 0.98 to 2.34; systolic BP >170: hazard ratio, 1.62; 95% confidence interval, 1.05 to 2.49; referent systolic BP =121-130; diastolic BP =81-90: hazard ratio, 1.40; 95% confidence interval, 0.99 to 1.86; diastolic BP >90: hazard ratio, 1.83; 95% confidence interval, 1.21 to 2.77; referent diastolic BP =61-70). The findings were consistent after multiple sensitivity analyses. Pulse pressure was not significantly associated with risk of eGFR decline. CONCLUSIONS: In patients referred to a multidisciplinary care clinic with late-stage CKD, only extremes of systolic BP and elevations of diastolic BP were associated with eGFR decline.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".