Urine Complement Factor Ba Is an AKI Biomarker in Critically Ill Children
Bibliographic record
Abstract
Background: Critically ill children with acute kidney injury (AKI) suffer from high morbidity and mortality and lack treatment options. Complement activation is implicated in AKI pathogenesis, which could potentially be treated with complement-targeted therapeutics. We assessed the association between urine Ba, an activated fragment of the alternative complement pathway, and AKI in a heterogeneous cohort of critically ill children. Methods: A biorepository of critically ill children was leveraged and identified children with pRIFLE criteria AKI (stage 1 eGFR 25% decreased; stage 2 eGFR 50% decreased; stage 3 eGFR 75% decreased). ELISAs quantified urine Ba values. The log value of Ba was used in ANOVA with pairwise comparison by the Tukey method. Logistic regression tested the association between urine Ba and AKI. Results: 73 patients from the original study had urine specimens available. 17 with no AKI, 26 with stage 1, 16 with stage 2, and 14 with stage 3 AKI. Ba was higher in patients with stage 3 AKI compared to all other stages, and higher in patients with stage 2 AKI versus no AKI (Figure 1; p<0.05). Multivariate analysis showed the association between urine Ba and AKI (OR 1.40, 95% CI 1.08-1.82, p = 0.002) after adjusting for PRISM (an estimate of illness severity). Conclusions: Urine factor Ba levels are increased in patients with AKI compared to patients without AKI. In patients with similar illness severity on admission, a doubling of urine Ba level was associated with a 40% increase in AKI diagnosis. Further studies are needed to investigate the role of complement activation in critically ill children at risk of AKI, to help stratify patients to study complement therapeutics in. Funding: NIDDK Support, Other NIH Support - National Institutes of Health Grants Eunice Kennedy Shriver Institute of Child Health & Human Development K12 HD 047349 Multivariate logistic regression
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".