Flow adhesion of whole blood to P‐selectin: a prognostic biomarker for vaso‐occlusive crisis in sickle cell disease
Bibliographic record
Abstract
Blood cell adhesion to P-selectin and vascular cell adhesion molecule-1 (VCAM-1) contributes to the pathophysiology of vaso-occlusion crisis (VOC) events in individuals with sickle cell disease (SCD). We evaluated the use of standardized flow adhesion biomarkers in a six-month, 35-subjects longitudinal study (ELIPSIS). Flow adhesion of whole blood on P-selectin (FA-WB-Psel) and VCAM1 (FA-WB-VCAM), and of isolated white blood cells on P-selectin (FA-WBC-Psel) and VCAM-1 (FA-WBC-VCAM) were elevated on VOC days compared with non-VOC days, but only FA-WB-Psel reached statistical significance (P = 0·015). Optimal cut-off values were established with Cox regression models for FA-WB-Psel [46 cells/mm²; hazard ratio (HR): 2·3; 95% confidence interval (CI):1·4-4·0; P = 0·01] and FA-WB-VCAM (408 cells/mm², HR:1·8; 95% CI: 0·9-3·45; P = 0·01). A combined (FA-WB-Psel and FA-WB-VCAM) multimarker risk score was also significantly (P = 0·0006) correlated with VOC risk that was two-fold higher for intermediate and 5·64-fold higher for high score. The concordance (C)-index for the multimarker score was 0·63 in the six-month period (95% CI: 0·56-0·70), indicating a better ability to distinguish patient risk of VOC, compared to individual biomarkers FA-WB-VCAM (C-index: 0·57; 95% CI: 0·49-0·65) or FA-WB-Psel (C-index: 0·58; 95% CI: 0·53-0·62). The presented multimarker score can be used to risk-stratify individuals with SCD during their steady state into low, intermediate, and high-risk strata for self-reported VOCs. Such risk stratification could help focus healthcare resources more efficiently to maintiain health, personalize treatment selection to each patient's individual needs, and potentially reduce healthcare costs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".