A prospective evaluation of urine microscopy in septic and non-septic acute kidney injury
Bibliographic record
Abstract
BACKGROUND: Sepsis is the most common trigger for acute kidney injury (AKI) in critically ill patients. We sought to determine whether there are unique patterns to urine sediment in septic compared with non-septic AKI. METHODS: Prospective two center cohort study of adult critically ill patients with septic and non-septic AKI, defined by the RIFLE criteria. Eligible patients had clinical, physiologic and laboratory data extracted. Blood and urine were sampled for urine biochemistry, microscopy and neutrophil gelatinase-associated lipocalin (NGAL). A urine microscopy score (UMS) was derived based on the observed quantification of renal tubular cells and casts in the sediment. The UMS was compared between septic and non-septic AKI and correlated with NGAL, worsening AKI, renal replacement therapy (RRT) and hospital mortality. RESULTS: Eighty-three patients were enrolled. Mean (SD) age was 64.3 (16.6) years, 60.2% were male, Charlson comorbidity score was 3.3 (2.8) and Acute Physiology and Chronic Health Evaluation II score was 21.4 (7.6). Septic AKI was present in 43 patients (51.8%). RIFLE class at enrollment was not different between groups (P = 0.43). Septic AKI was associated with higher UMS compared with non-septic AKI (P = 0.001). There was no correlation between UMS and fractional excretion of sodium (FeNa) or fractional excretion of urea (FeU). Elevated urine NGAL (uNGAL) correlated with higher UMS (P = 0.0003), while correlation with plasma NGAL was modest (P = 0.05). Worsening AKI occurred in 22.9% with no difference between septic and non-septic groups. A UMS score ≥ 3 was associated with increased odds of worsening AKI [adjusted odds ratio 8.0; 95% confidence intervals (CI), 1.03-62.5, P = 0.046]. For a UMS ≥3, sensitivity and specificity were 0.67 (95% CI, 0.39-0.86) and 0.95 (0.84-0.99) and positive and negative predictive values we re 0.80 (0.49-0.94) and 0.91 (0.78-0.96) for detecting worsening AKI, respectively. While there were no differences between septic and non-septic AKI, higher UMS correlated with need for RRT (15.7%, P = 0.02) and in-hospital death (30.1%, P = 0.01); however, this did not persist in multivariable analysis. CONCLUSIONS: Septic AKI is associated with greater urine microscopy evidence of kidney injury compared with non-septic AKI, despite similar severity of AKI. A UMS ≥ 3 correlated with higher uNGAL and was predictive of worsening AKI. Urine microscopy may have a complementary role for discerning septic from non-septic AKI, discriminating severity and predicting worsening AKI in critically ill patients.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".