Age-Specific Approach to Arterial Stiffness Prediction in Apparently Healthy Patients
Bibliographic record
Abstract
Background: The high prevalence of traditional cardiovascular risk factors among the patients without cardiovascular disease (CVD) allows us to predict an increase in cardiovascular morbidity rate in the future. Arterial stiffness is one of the most important predictors and pathogenetic mechanisms of CVD development. The aim of our study was to evaluate the predictive differences of age-related and age-independent (universal) cardio-ankle vascular index (CAVI) reference values for detecting increased arterial stiffness in individuals without CVD. Methods: The study included 600 patients (43% men and 57% women, mean age 36.0 ± 18.3 years). All the patients underwent anthropometric measurements with obesity markers evaluation, assessment of arterial stiffness by sphygmomanometry. To create predictive models, we used universal and age-related CAVI thresholds: ≥ 9.0 (CAVI≥ 9) and CAVIAge according to the “Consensus of Russian experts on the evaluation of arterial stiffness in clinical practice". Results: In the < 50 years group, both the CAVIAge and CAVI≥ 9 models were significant (CAVIAge: b = 4.8, standard error b (st.err.b) = 0.27, P < 0.001; CAVI≥ 9: b = 3.2, st.err.b = 1.6, P < 0.001). The CAVIAge model demonstrated high sensitivity and specificity (> 70%) compared to the CAVI≥ 9 model (sensitivity 62%, specificity 58%). In the receiver operating characteristic (ROC) curve analysis, the CAVIAge model had a significantly higher area under the ROC curve (AUC) = 0.802 than the CAVI≥ 9 model: AUC = 0.674. In the ≥ 50 years group, both models were significant: CAVIAge (b = 2.6, st.err.b = 1.13, P < 0.001) and CAVI≥ 9 (b = 5.3, st.err.b = 0.94, P < 0.001). Both models demonstrated high sensitivity and specificity (> 70%). When ROC curves were analyzed for the CAVIAge model, the AUC value of 0.675 was significantly lower when compared to the CAVI≥ 9 model (AUC = 0.787, P = 0.031). Conclusions: In the < 50 years group, the model based on age-specific CAVI thresholds has the higher predictive value, sensitivity, and specificity for identifying individuals with increased arterial stiffness. In contrast, in the ≥ 50 years group, a predictive model using a universal threshold value of CAVI≥ 9 has advantages.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".