Soluble triggering receptor expressed on myeloid cells 1 (sTREM-1) predicts mortality in patients with febrile illness in southern Mozambique
Bibliographic record
Abstract
BACKGROUND: Fever is a leading reason for seeking healthcare globally. Early in the course of febrile illness, it is challenging to identify patients at risk of severe and fatal infections. Quantifying biomarkers of immune and endothelial activation may facilitate patient triage. METHODS: We prospectively enrolled children ≥2 months and adults with fever visiting two Mozambican hospitals from December 2018 to February 2021. Standard clinical and laboratory parameters, including lactate levels, were assessed at presentation. Plasma levels of Angpt-2, CHI3L1, CRP, IL-6, IL-8, PCT, sFlt-1, sTNFR1, sTREM-1, and suPAR at presentation were retrospectively quantified. Clinical outcomes were evaluated up to 28 days. We assessed the prognostic performance of biomarkers for 28-day mortality and explored their association with other adverse outcomes. RESULTS: This study includes 1955 participants, with 93 deaths occurring within 28 days. We show that all biomarker levels are elevated in inpatients compared to outpatients and are associated with 28-day mortality (all p < 0.001). sTREM-1 is the best biomarker predicting 28-day mortality with an AUROC of 0.82 (95% CI: 0.78-0.86), superior to that of PCT (p < 0.001), CRP (p < 0.001), and lactate (p = 0.0033). Its prognostic performance is consistent across age and sex, but is reduced in HIV-positive individuals (AUROC = 0.73, 95% CI: 0.66-0.80). Adding sTREM-1 improves the discrimination of clinical severity scores for 28-day mortality. Among discharged inpatients, sTREM-1 is positively correlated with duration of hospitalisation (p < 0.001). Among outpatients, sTREM-1 levels are higher in those seeking further care (p = 0.0022) or subsequently hospitalised (p = 0.012). CONCLUSIONS: sTREM-1 is a promising biomarker for risk stratification of all-age, all-cause febrile illnesses in resource-limited settings.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".