Integration of the Renal Angina Index and Urine Neutrophil Gelatinase-Associated Lipocalin Improves Severe Acute Kidney Injury Prediction in Critically Ill Children and Young Adults
Bibliographic record
Abstract
Introduction: Acute kidney injury (AKI) occurs in one-fourth of children and young adults admitted to pediatric intensive care unit (PICU). Severe AKI (sAKI; Kidney Disease: Improving Global Outcomes stage 2 or 3) is associated with morbidity and mortality. An AKI risk stratification system, the Renal Angina Index (RAI) calculated at 12 hours of admission, exhibits excellent performance to rule out sAKI at 72 hours of admission. We found that integration of urine neutrophil gelatinase-associated lipocalin (NGAL) with RAI improves prediction of sAKI. We now report the first-year results after implementation of our prospective automated RAI-NGAL clinical decision support (CDS) program. Methods: Patients 3 months to 25 years of age were eligible. Admission order sets have a conditional order for urine NGAL released when a 12-hour RAI ≥8. The primary outcome was sAKI any time at days 2 to 4 of admission. We assessed performance of the RAI and RAI+/NGAL to predict the primary outcome. Results: A total of 1427 unique patients accounted for 1575 admissions. In 147 admissions, RAI was ≥8. RAI <8 had negative predictive value (NPV) of 0.98 (95% CI 0.97-0.99); RAI ≥ 8 had positive predictive value (PPV) of 0.37 (95% CI 0.30-0.46) to predict days 2 to 4 sAKI (area under the receiver operating characteristic curve [AUC-ROC] 0.88 [95% CI 0.84-0.92]). Of 147 RAI+ patients, 89 had NGAL available. RAI/NGAL combination improved PPV (0.64, 95% CI 0.50-0.79) without decrement in NPV (0.98, 95% CI 0.97-0.98). Conclusion: AKI biomarker assessment directed by risk stratification improves prediction of sAKI in critically ill children and young adults. This CDS process has potential to enrich the population for interventional study, although improvement to adherence to CDS is needed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".