Soluble Urokinase-Type Plasminogen Activator Receptor as a Prognostic Marker of Ugandan Children at Risk of Severe and Fatal Malaria
Bibliographic record
Abstract
BACKGROUND: Current malaria diagnostic tests do not reliably identify children at risk of severe and fatal infection. Host immune and endothelial activation contribute to malaria pathogenesis. Soluble urokinase-type plasminogen activator receptor (suPAR) is a marker of these pathways. We hypothesized that measuring suPAR at presentation could risk-stratify children with malaria. METHODS: Plasma suPAR levels were determined in consecutive febrile children with malaria at presentation to hospital in Jinja, Uganda. We evaluated the accuracy of suPAR in predicting in-hospital mortality, and whether suPAR could improve a validated clinical scoring system (Lambaréné Organ Dysfunction Score [LODS]). RESULTS: Of the 1226 children with malaria, 39 (3.2%) died. suPAR concentrations at presentation were significantly higher in children who went on to die than in those who survived (P < .0001). suPAR levels were associated with disease severity (LODS: 0 vs 1, P = .001; 1 vs 2, P < .001; 2 vs 3, 0 vs 2, 1 vs 3, and 0 vs 3, P < .0001). suPAR concentrations were excellent predictors of in-hospital mortality (area under the receiver operating characteristic curve [AUROC], 0.92 [95% confidence interval {CI}, .91-.94]). The prognostic accuracy of LODS (AUROC, 0.93 [95% CI, .91-.94]) was improved when suPAR was added (AUROC, 0.97 [95% CI, .96-.98]; P < .0001). CONCLUSIONS: Measuring suPAR at presentation can identify children at risk of severe and fatal malaria. Adding suPAR to clinical scores could improve the recognition and triage of children at risk of death. suPAR can be detected with a point-of-care test and can now be evaluated in prospective trials.
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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 | 0.002 | 0.007 |
| 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 | 0.001 |
| 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".