ESTIMATED VERSUS MEASURED AORTIC STIFFNESS IN DIALYSIS POPULATION: IMPLICATIONS OF SEX, AGE AND DIABETES
Bibliographic record
Abstract
Objective: Aortic stiffness is measured through determination of carotid-femoral pulse wave velocity(cfPWV). More recently, authors have proposed to estimate PWV (ePWV) with age and brachial mean blood pressure (MBP) using an equation for the normal population and another for subjects with cardiovascular risk factors. However, patients with end-stage kidney disease are at extremely high risk of cardiovascular disease and have an altered circulatory system due to no perfusion of the kidneys. Therefore, the aim of this study was 1)examine the reliability of the ePWV as compared to measured cfPWV, and 2)examine if sex, age and diabetes have an impact on the relationship between ePWV and cfPWV in this population. Design and method: This is a cross-sectional study of 317 adults treated by chronic hemodialysis(n = 252) or peritoneal dialysis(n = 65). cfPWV was measured using second derivative algorithm through simultaneous recording of carotid and femoral arteries in triplicates(CompliorSP) using 80% of the direct distance between the carotid and femoral arteries. ePWV was calculated using the formula for subjects with cardiovascular risk factors from the Reference Values for Arterial Stiffness Collaboration. Results: In 317 patients(68%men), with a mean age of 65 ± 15 years, 43% had diabetes, 91% had hypertension and 52% had cardiovascular disease. MBP, cfPWV and the difference between ePWV and cfPWV(↗PWV) were respectively 92 ± 17mmHg, 10.8 ± 3.2m/s and 0.36(-5.87 – 11.84) in the overall group. Male sex, first and second age tertiles, and diabetes were associated with a negative ↗PWV. ePWV underestimated cfPWV in men (-0.80, 95%CI: -1.17 – -0.43, p < 0.001), in patients from the first (18-60 years)(-0.64, 95%CI: -0.94 – -0.32), p < 0.001) and second (60 - 73 years)(-0.50, 95%CI: -1.01 – 0.01,p = 0.053) tertiles of age, and in patients with diabetes(-1.08, 95%CI: -1.55 – -0.60, p < 0.001). ePWV was relatively accurate to estimated cfPWV in women (0.28, 95%CI: -0.09 – 0.66, p = 0.132) and in patients from the last age tertile(73 - 89 years)(0.05, 95%CI: -0.51 – 0.66,p = 0.866). Conclusions: These findings suggest that sex, age tertiles and diabetes impact the relationship between ePWV and cfPWV in our cohort of patients on dialysis; therefore the formula used to estimate PWV may benefit from better integrating these determinants for a more accurate ePWV.
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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 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".