Usefulness of the Whole Blood Passage Time as a Predictor of Primary Cardiovascular Events in Patients With Traditional Cardiovascular Risk Factors
Bibliographic record
Abstract
BACKGROUND: Recent clinical studies have reported that impaired hemorheology is a significant cardiovascular risk factor, but there has been no prospective study of its relationship with cardiovascular events. The aim of this prospective study was to assess the efficacy of whole blood passage time (WBPT), measured by a microchannel array flow analyzer (MC-FAN), as a predictor of primary cardiovascular events in patients with traditional cardiovascular risk factors. METHODS: The study enrolled 1,134 outpatients with traditional cardiovascular risk factors but no history of cardiovascular events (438 men and 696 women; mean ± standard deviation age, 67 ± 11 years). Based on the value of WBPT, the patients were assigned to one of three groups: L (low, WBPT < 50 s; n = 499), M (medium, WBPT 50 - 70 s; n = 295), or H (high, WBPT > 70 s; n = 340). The utility of the WBPT as a predictor of primary cardiovascular events was evaluated. RESULTS: During the follow-up period (median 81.9 months), major adverse cardiovascular events (MACE) occurred in 95 cases (L, 21 cases (4.2%); M, 24 cases (8.1%); H, 50 cases (14.7%); P < 0.001, log-rank test). In multivariate Cox regression analyses, the risk for MACE was significantly higher in group H than in group L (hazard ratio, 2.32; 95% confidence interval, 1.31 - 3.20; P < 0.01). A WBPT cut-off of 72.4 s yielded the largest area under the curve of 0.705 (95% confidence interval: 0.678 - 0.732), with a sensitivity of 51.7% and specificity of 85.4% for discriminating between those who did and did not experience MACE during the follow-up period. CONCLUSION: This study showed that WBPT evaluated by a MC-FAN was a predictor of primary cardiovascular events in patients with traditional cardiovascular risk factors.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".