Prognostic value of admission H3.1 nucleosome levels in sepsis-associated acute kidney injury: a secondary analysis of the SISPCT randomised clinical trial
Bibliographic record
Abstract
Abstract Background NETosis is a key innate immune defence mechanism where neutrophils release extracellular traps (NETs). However, excessive NET formation may damage organs during sepsis. We investigated the association between NETs and sepsis outcomes, including mortality and acute kidney injury (AKI). Methods We analysed levels of H3.1 nucleosomes in 971 patients with severe sepsis and septic shock from the SISPCT trial (Effect of Sodium Selenite Administration and Procalcitonin-Guided Therapy on Mortality in Patients With Severe Sepsis or Septic Shock). We evaluated associations between H3.1 levels and mortality and the need for renal replacement therapy using multivariable Cox regression and receiver operating characteristic analyses. Results We analysed 971 critically ill patients with complete data including admission H3.1 levels. 443 patients (45.6%) presented with sepsis, 520 (53.6%) had septic shock, and eight patients had an unknown diagnosis as defined by Sepsis-3. Admission H3.1 levels were higher in patients with septic shock than with sepsis (median 921.84 vs 432.71 ng/mL; p<0.001). Admission H3.1 levels were higher in non-survivors, and in a univariate Cox analysis, each log-10 increase in H3.1 was associated with a hazard ratio of 1.86 (95% confidence interval 1.41–2.47, p<0.05). H3.1 was also higher in patients requiring renal replacement therapy with septic shock vs sepsis (1832 ng/mL vs 801.4 ng/mL, p=0.01) and demonstrated a dose-response relationship with the severity of AKI. Conclusion Elevated levels of H3.1 nucleosomes at admission are independently associated with mortality and severe kidney dysfunction requiring renal replacement therapy. Trial registration Clinicaltrials.gov Identifier, NCT00832039 .
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".