sST2 is a key outcome biomarker in COVID-19: insights from discovery randomized trial
Bibliographic record
Abstract
We investigated whether baseline levels of biomarkers related to endotheliopathy, thromboinflammation, and fibrosis were associated with clinical outcomes in hospitalized COVID-19 patients. We analyzed the associations between baseline levels of 21 biomarkers and time to hospital discharge and change in NEWS-2 score in patients from DisCoVeRy trial. We fitted multivariate models adjusted for baseline ISARIC 4C score, disease severity, D-dimer values, and treatment regimen. Between March 22 and June 29, 2020, 603 participants were randomized; 454 had a sample collected at baseline and analyzed. The backward selection of multivariate models showed that higher baseline levels of soluble suppressor of tumorigenicity 2 (sST2) and nucleosomes were statistically associated with a lower chance of hospital discharge before day 29 (sST2: aHR 0.24, 95% CI [0.15–0.38], p < 10 −9 ; nucleosomes: aHR 0.62, 95% CI [0.48–0.81], p < 10 −3 ). Likewise, higher levels of baseline sST2 were statistically associated with lower changes in the NEWS-2 score between baseline and day 15 (adjusted beta 4.47, 95% CI [2.65–6.28], p < 10 −5 ). Moreover, we evaluated sST2 involvement in a confirmation cohort (SARCODO study, 103 patients) and found that elevated baseline sST2 levels were significantly associated with lower rates of hospital discharge before day 29 and a higher model performance (AUC at day 29 of 92%) compared to models without sST2. sST2 emerged as an independent predictor of clinical outcomes in two large cohort of hospitalized COVID-19 patients, warranting further investigation to elucidate its role in disease progression and potential as a therapeutic target.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| 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".