O052 / #846: SEPSIS ASSOCIATED AKI PHENOTYPES ARE ASSOCIATED WITH UNIQUE PATIENT OUTCOMES
Bibliographic record
Abstract
Aims & Objectives: Although sepsis associated acute kidney injury (S-AKI) is generally associated with poor patient outcomes, the majority of epidemiology utilize an all-inclusive and binary classification for AKI. The clinical phenotype of AKI, however, varies considerably. We sought to delineate the associations of AKI phenotypes with outcomes in septic patients. Methods: Post-hoc analysis of a multi-national study of critically ill children. AKI was defined by KDIGO stage in the first 48 hours. Temporal phenotype was defined as transient (return to baseline by 48 hours) or persistent (injury beyond 48 hours). Severity phenotype was delineated as mild (stage 1) or severe (stage 2-3). The primary outcome was mortality censored at 28-days. ICU course complexity was assessed by mechanical ventilation, length of stay, and use of extracorporeal therapy. Results: Sepsis was present in 757 (15.4%), AKI in 1275 (26.1%), and S-AKI in 307 (6.3%) of 4898 patients (male, 54.8%). S-AKI was associated with increased mortality versus sepsis alone (12.1% vs. 4.7%, p-value <0.001). Mortality in S-AKI varied by AKI duration: persistent (13.9%), transient (12.1%) (p<0.001 vs. no AKI for both). Mortality also varied by AKI severity: severe (18.6%), mild (4.4%) (p<0.001 versus no AKI only for severe). Sub-phenotyping associated with unique mortality incidence: mild-transient (4.4%), mild-persistent (9.4%), severe-transient (21.6%), and severe-persistent (15.8%). Complex ICU outcome ranged from 15% to 55% between the S-AKI phenotypes versus 34.5% for “any” S-AKI. Conclusions: Our data indicate clinical S-AKI subphenotypes are associated with unique outcomes. Refinement in S-AKI classification may allow population enrichment facilitating biologic analysis, trial design, and targeted therapeutics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".