Fluid overload is associated with use of a higher number of antihypertensive drugs in hemodialysis patients
Bibliographic record
Abstract
ABSTRACT Introduction Hypertension is multifactorial, highly prevalent in the hemodialysis (HD) population and its adequate control requires, in addition to adequate volume management, often the use of multiple antihypertensive drugs. We aimed to describe the use of antihypertensive agents in a group of HD patients and to evaluate the factors associated with the use of multiple classes (≥3) of antihypertensives. Methods We analyzed the baseline data from the HDFit study. Clinically stable patients with HD vintage between 3 and 24 months without any severe mobility limitation were recruited from sites throughout southern Brazil. Fluid status was measured pre‐dialysis with the Body Composition Monitor (BCM; Fresenius, Germany). Fluid overload (FO) was considered when the overhydration index (OH) was greater than 7% of extracellular water (OH/ECW > 7%) and overweight was defined as a body mass index (BMI) greater than 25 kg/m2. Prescriptions of antihypertensive drugs were obtained from participants' reports and medical records. Logistic regression was employed to determine factors associated with excessive use of antihypertensive medication (≥3 classes). Findings Of 195 studied patients, 171 with complete data were included (70% male, 53 ± 15 years old, 57% of them with FO). Pre‐dialysis systolic blood pressure (SBP) was 150 ± 24 mmHg and patients used a median of 2 (1–3) antihypertensive drugs. Vasodilators (20%) were of lowest prevalence, use of other classes varied from 40% to 53%. Sixty‐two (36%) subjects used ≥3 classes and presented a higher prevalence of diabetes and FO, lower prevalence of overweight, and higher SBP. In a logistic regression model age, BMI <25 kg/m2, and OH/ECW > 7% were associated with excessive drug use. Discussion More than one‐third of participants used ≥3 classes of antihypertensive drugs, and it was associated with older age, BMI <25 kg/m2 and FO. Strategies that better manage FO may aid better blood pressure control and avoid the use of multiple antihypertensive medications.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".