Clinical Usefulness of the Cardio-Ankle Vascular Index as a Predictor of Primary Cardiovascular Events in Patients With Chronic Kidney Disease
Bibliographic record
Abstract
BACKGROUND: The cardio-ankle vascular index (CAVI) is a physiologic marker reflecting arterial function. There have been no prospective studies investigating the relationship between CAVI and cardiovascular events in patients with chronic kidney disease (CKD). The aim of this prospective study was to assess the clinical usefulness of CAVI as a predictor of primary cardiovascular events in patients with CKD. METHODS: The study enrolled 460 outpatients with CKD but no history of cardiovascular disease (152 men and 308 women; mean ± standard deviation age, 74 ± 12 years). Patients were assigned to one of three groups: low (L, CAVI < 9; n = 100), medium (M, CAVI 9 - 10; n = 199), or high (H, CAVI > 10; n = 161). The utility of the CAVI as a predictor of primary cardiovascular events was evaluated. RESULTS: During the follow-up period (median 60.1 months), major adverse cardiovascular events (MACE) occurred in 91 cases (L, 8 (8.0%); M, 31 (15.6%); H, 52 (32.3%); P < 0.001, log-rank test). On multivariate Cox regression analysis, the risk for a MACE was significantly higher in group H than in non-group H (hazard ratio, 2.04; 95% confidence interval, 1.31 - 3.02; P < 0.01). A CAVI cut-off of 9.7 yielded the largest area under the curve, 0.701 (95% confidence interval: 0.657 - 0.743, P < 0.001), indicating a sensitivity of 74.0% and a specificity of 59.6% for discriminating between those who did and did not experience a MACE during follow-up. CONCLUSIONS: The results of this study showed that a high CAVI is a predictor of primary cardiovascular events in patients with CKD.
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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 | 0.004 |
| 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".