Inverse relationship of serum albumin to the risk of venous thromboembolism among acutely ill hospitalized patients: Analysis from the APEX trial
Bibliographic record
Abstract
Hypoalbuminemia is a common finding and independent predictor for unfavorable prognosis. The prognostic value of albumin measurement for short-term VTE prediction in hospitalized patients remains unclear. In the APEX trial (ClinicalTrials.gov identifier: NCT01583218), medical inpatients were randomized to receive either extended-duration betrixaban or shorter-duration enoxaparin and followed for 77 days. Baseline albumin concentrations were obtained in 7266 subjects with evaluable VTE endpoints. The association of baseline albumin to VTE was assessed, with adjustment for patient characteristics, thromboprophylaxis, and biomarkers for fibrinolysis and inflammation (ie, D-dimer and C-reactive protein [CRP]). VTE risk refinement was evaluated by incorporation of albumin to well-validated risk assessment models. A stepwise increase in the risk of VTE (P < .0001) was observed with lower levels of albumin. Patients at the bottom albumin quartile (<35 g/L) had a two-fold greater odds for developing VTE compared with the top quartile (≥42 g/L) (OR = 2.119 [95% CI, 1.592-2.820]; adjusted OR = 2.079 [1.485-2.911]). The odds for VTE increased by 1.368 (95% CI, 1.240-1.509) times per SD decrement of albumin (5.24 g/L). Compared with the propensity score-matched pairs of patients with albumin ≥35 g/L, patients with albumin <35 g/L had a greater risk of VTE (OR = 1.623 [1.260-2.090]; adjusted OR = 1.658 [1.209-2.272]). Albumin measurement also refined VTE risk discrimination and reclassification after inclusion in the risk assessment models. In conclusion, acutely ill hospitalized patients with low serum albumin had an increased VTE risk through 77 days. VTE risk assessment models for medical inpatients should consider incorporation of baseline albumin measurement.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".