Sepsis as a cause and consequence of acute kidney injury: Program to Improve Care in Acute Renal Disease
Bibliographic record
Abstract
PURPOSE: Sepsis commonly contributes to acute kidney injury (AKI); however, the frequency with which sepsis develops as a complication of AKI and the clinical consequences of this sepsis are unknown. This study examined the incidence of, and outcomes associated with, sepsis developing after AKI. METHODS: We analyzed data from 618 critically ill patients enrolled in a multicenter observational study of AKI (PICARD). Patients were stratified according to their sepsis status and timing of incident sepsis relative to AKI diagnosis. RESULTS: We determined the associations among sepsis, clinical characteristics, provision of dialysis, in-hospital mortality, and length of stay (LOS), comparing outcomes among patients according to their sepsis status. Among the 611 patients with data on sepsis status, 174 (28%) had sepsis before AKI, 194 (32%) remained sepsis-free, and 243 (40%) developed sepsis a median of 5 days after AKI. Mortality rates for patients with sepsis developing after AKI were higher than in sepsis-free patients (44 vs. 21%; p < 0.0001) and similar to patients with sepsis preceding AKI (48 vs. 44%; p = 0.41). Compared with sepsis-free patients, those with sepsis developing after AKI were also more likely to be dialyzed (70 vs. 50%; p < 0.001) and had longer LOS (37 vs. 27 days; p < 0.001). Oliguria, higher fluid accumulation and severity of illness scores, non-surgical procedures after AKI, and provision of dialysis were predictors of sepsis after AKI. CONCLUSIONS: Sepsis frequently develops after AKI and portends a poor prognosis, with high mortality rates and relatively long LOS. Future studies should evaluate techniques to monitor for and manage this complication to improve overall prognosis.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".