Prognostic Accuracy of Soluble Triggering Receptor Expressed on Myeloid Cells (sTREM-1)-based Algorithms in Febrile Adults Presenting to Tanzanian Outpatient Clinics
Bibliographic record
Abstract
BACKGROUND: The inability to identify individuals with acute fever at risk of death is a barrier to effective triage and management of severe infections, especially in low-resource settings. Since endothelial and immune activation contribute to the pathogenesis of various distinct life-threatening infections, we hypothesized that measuring mediators of these pathways at clinical presentation would identify febrile adults at risk of death. METHODS: Plasma concentrations of markers of endothelial (angiopoetin-1/2, soluble fms-like tyrosine kinase-1, soluble vascular cell adhesion molecule-1, soluble intercellular adhesion molecule-1) and immune (soluble triggering receptor expressed on myeloid cells [sTREM-1], interleukin-6, interleukin-8, chitinase-3-like protein-1, soluble tumor necrosis factor receptor-1, procalcitonin [PCT], C-reactive protein [CRP]) activation pathways were determined in consecutive adults with acute fever (≥38°C) at presentation to outpatient clinics in Dar es Salaam, Tanzania. We evaluated the accuracy of these mediators in predicting all-cause mortality and examined whether markers could improve the prognostic accuracy of clinical scoring systems, including the quick sequential organ failure assessment (qSOFA) and Glasgow coma scale (GCS). RESULTS: Of 507 febrile adults, 32 died (6.3%) within 28 days of presentation. We found that sTREM-1 was the best prognostic marker for 28-day mortality (area under the receiver operating characteristic [AUROC] 0.87, 95% confidence interval [CI] 0.81-0.92) and was significantly better than CRP (P < .0001) and PCT (P = .0001). The prognostic accuracy of qSOFA and the GCS were significantly enhanced when sTREM-1 was added (0.80 [95% CI 0.76-0.83] to 0.91 [95% CI 0.88-0.94; P < .05] and 0.72 [95% CI 0.63-0.80] to 0.94 [95% CI 0.91-0.97; P < .05], respectively). CONCLUSIONS: Measuring sTREM-1 at clinical presentation can identify febrile individuals at risk of all-cause febrile mortality. Adding severity markers such as sTREM-1 to simple clinical scores could improve the recognition and triage of patients with life-threatening infections 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".