Lipoprotein(a) Does Not Predict Thrombotic Events and In-Hospital Outcomes in Patients with COVID-19
Bibliographic record
Abstract
The prothrombotic and proinflammatory properties of lipoprotein(a) (Lp(a)) have been hypothesized to play a role in the pathogenesis of severe COVID-19; however, the prognostic impact of Lp(a) on the clinical course of COVID-19 remains controversial. This study aimed to investigate whether Lp(a) may be associated with biomarkers of thrombo-inflammation and the occurrence of thrombotic events or adverse clinical outcomes in patients hospitalized for COVID-19. We consecutively enrolled a cohort of patients hospitalized for COVID-19 and collected blood samples for Lp(a) assessment at hospital admission. A prothrombotic state was evaluated through D-dimer levels, whereas a proinflammatory state was evaluated through C-reactive protein (CRP), procalcitonin, and white blood cell (WBC) levels. Thrombotic events were marked by the diagnosis of deep or superficial vein thrombosis (DVT or SVT), pulmonary embolism (PE), stroke, transient ischemic attack (TIA), acute coronary syndrome (ACS), and critical limb ischemia (CLI). The composite clinical end point of intensive care unit (ICU) admission/in-hospital death was used to evaluate adverse clinical outcomes. Among 564 patients (290 (51%) men, mean age of 74 ± 17 years) the median Lp(a) value at hospital admission was 13 (10–27) mg/dL. During hospitalization, 64 (11%) patients were diagnosed with at least one thrombotic event and 83 (15%) patients met the composite clinical end point. Lp(a), as either a continuous or categorical variable, was not associated with D-dimer, CRP, procalcitonin, and WBC levels (p > 0.05 for all correlation analyses). In addition, Lp(a) was not associated with a risk of thrombotic events (p > 0.05 for multi-adjusted odds ratios) nor with a risk of adverse clinical outcomes (p > 0.05 for multi-adjusted hazard ratios). In conclusion, Lp(a) does not influence biomarkers of plasma thrombotic activity and systemic inflammation nor has any impact on thrombotic events and adverse clinical outcomes in patients hospitalized for COVID-19.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".