Abstract FR428: Relationship Between Medication Class and Ambulatory Blood Pressure Profile in the Chronic Renal Insufficiency Cohort
Bibliographic record
Abstract
Introduction: Patients with chronic kidney disease (CKD) are at heightened risk for masked and nocturnal hypertension, conditions best identified through ambulatory blood pressure monitoring (ABPM). Our goal was to evaluate whether specific antihypertensive drug classes are associated with particular ABPM phenotypes. Methods: We conducted a cross-sectional analysis of participants from the Chronic Renal Insufficiency Cohort (CRIC), enrolled in 2003–2008, with ABPM data from 2008–2012. Antihypertensive medications were categorized into four classes: renin-angiotensin-aldosterone system inhibitors (RASis), beta blockers, calcium channel blockers (CCBs), and thiazide/loop diuretics. We used nominal logistic regression to evaluate the association between medication class and ABPM phenotype: controlled hypertension, white coat effect, sustained hypertension, and masked uncontrolled hypertension (MUCH). Secondary outcomes included nocturnal hypertension, dipping status, and blood pressure (BP) variability, assessed using adjusted logistic and linear regression models. All analyses were adjusted for key sociodemographic and clinical factors. Results: Among 1,499 participants, 66% used RASis, 52% beta blockers, 43% CCBs, and 50% thiazide/loop diuretics. RASi use was inversely associated with sustained hypertension (OR 0.60, 95% CI 0.42 to 0.84), while beta blocker use was positively associated with MUCH (OR 1.48, 95% CI: 1.12 to 1.96). In secondary analyses, RASis were linked to lower odds of nocturnal hypertension (OR 0.71, 95% CI: 0.55 to 0.91), whereas CCBs were associated with higher odds of nocturnal hypertension (OR 1.36, 95% CI 1.07 to 1.73). CCB use was also inversely associated with BP variability. Conclusion: In patients with CKD, beta blockers are associated with MUCH and CCBs with nocturnal hypertension. Patients on these antihypertensive classes may benefit from undergoing ABPM, even when office BP appears controlled. Further research to understand the reproducibility and mechanisms of these findings is warranted.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".