Biomarkers of endothelial activation/dysfunction distinguish subgroups of Ugandan patients with sepsis and differing mortality risks
Bibliographic record
Abstract
BACKGROUND: Sepsis is a complex clinical syndrome with substantial heterogeneity. We sought to identify patterns of serum biomarkers of endothelial activation and dysfunction in individuals with sepsis and evaluate subgroup-specific differences in mortality. METHODS: Adult patients with sepsis (n=426) were consecutively recruited from two hospitals in Uganda. Clinical information was collected and serum concentrations of eleven biomarkers involved in the endothelial response to infection were measured in samples from 315 patients. Latent variable models were fit to evaluate whether the endothelial response to sepsis consists of one unified biological process or multiple processes and to identify subgroups of patients with distinct host-response profiles. Differences in survival at day 28 were evaluated using Kaplan-Meier survival curves. RESULTS: We identified three patient subgroups characterized by unique host endothelial response profiles. Patients fitting Profile 2 had significantly worse survival (log-rank p<0.001). Four latent factors (Factor 1-4) were identified, each potentially representing distinct biological processes for the endothelial response to sepsis: Factor 1 (CHI3L1, sTREM1, sFLT1); Factor 2 (ANGPT1, PF4, VEGF); Factor 3 (CXCL10, VWF, sICAM1); and Factor 4 (ANGPT2, sTEK). CONCLUSION: Patient profiles based on patterns of circulating biomarkers of endothelial responses may provide a clinically meaningful way to categorize patients into homogeneous subgroups and may identify patients with a high risk of mortality. Profile 2 may represent dysfunction of the endothelial response to infection. FUNDING: Primary funding: Investigator-Initiated Award provided by Pfizer, Inc (WMS, STJ). Additional support: Canadian Institutes of Health Research (CIHR) Foundation grant (KCK; FDN-148439) and the Canada Research Chair program (KCK).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".