Risk of venous thromboembolism and arterial events in patients with hypoalbuminemia: a comprehensive meta-analysis of more than 2 million patients
Bibliographic record
Abstract
BACKGROUND: Albumin has antiplatelet and anticoagulant functions. Hypoalbuminemia, as defined by serum values of <3.5 g/dL, is associated with arterial thrombosis; its impact on venous thromboembolism (VTE) is unclear. OBJECTIVES: The objective of this meta-analysis is to assess the VTE risk in patients with hypoalbuminemia. METHODS: MEDLINE and EMBASE were searched up to January 2024 for observational studies and randomized trials reporting data of interest. Primary outcome was the risk of VTE, while secondary outcomes were myocardial infarction and stroke risk in patients with hypoalbuminemia versus those without hypoalbuminemia. The risk of bias was evaluated using Newcastle-Ottawa scale and Cochrane tool. Risk ratios (RRs) with 95% confidence intervals (CIs) were calculated in a random-effects model. RESULTS: Forty-three studies for a total of 2 531 091 patients (39 738 medical and 2 491 353 surgical) were included in primary analysis; 79.1% of the studies used 3.5 g/dL cut-off value for hypoalbuminemia definition. Follow-up duration was 30 days in 60.5% of studies. Patients with hypoalbuminemia had a higher risk of VTE (RR, 1.88; 95% CI, 1.66-2.13). RRs were similar in both medical (RR, 1.87; 95% CI, 1.53-2.27) and surgical patients (RR, 1.87; 95% CI, 1.61-2.16) and in patients with (RR, 1.86; 95% CI, 1.66-2.10) and without cancer (RR, 1.89; 95% CI, 1.47-2.44). Risk of myocardial infarction (RR, 1.88; 95% CI, 1.54-2.31) and stroke (RR, 1.77; 95% CI, 1.26-2.48) was higher in patients with hypoalbuminemia. CONCLUSION: Hypoalbuminemia is a risk factor for VTE in both medical and surgical patients irrespective of cancer coexistence. Serum albumin analysis may represent a simple and cheap tool to identify patients at VTE risk.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.054 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".