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.

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.

Urinary neutrophil gelatinase-associated lipocalin rules out nephrotoxic acute kidney injury in children.

BACKGROUND: Nephrotoxic medication exposure is a common cause of acute kidney injury (AKI) in hospitalized children. A key component of the NINJA quality improvement initiative is systematic daily serum creatinine assessment in non-critically ill children exposed to ≥ 3 nephrotoxic medications on 1 day, or intravenous aminoglycoside or vancomycin for ≥ 3 days. Daily venipuncture is invasive and associated with disposable and personnel healthcare costs. Urine neutrophil gelatinase-associated lipocalin (uNGAL) is a marker of renal tubular injury associated with certain nephrotoxic medications. We investigated whether uNGAL is a reliable screening tool for AKI in NINJA and could decrease the need for daily venipuncture. METHODS: This two-center prospective study enrolled 113 children who met NINJA criteria from May 2018 through March 2019. Daily urine samples were obtained for up to the first 7 days of qualifying exposure and 2 days after exposure ended. Our primary outcome was severe AKI (KDIGO stage 2 or 3 AKI). Maximum uNGAL was highest concentration on the day of, or 3 days prior to, severe AKI. The highest uNGAL level from all assessment days was used for patients who did not develop AKI or severe AKI. RESULTS: Urine NGAL thresholds of 150 and 300 ng/ml demonstrated excellent specificity (92.4 and 97.1% respectively) and negative predictive values (93.3 and 92.8% respectively) for ruling out severe AKI. CONCLUSIONS: We suggest that uNGAL could be used to supplant some of the daily serum creatinine venipunctures in NINJA. The most optimal combination of serum creatinine and uNGAL assessment requires further study.

Early Sequential Risk Stratification Assessment to Optimize Fluid Dosing, CRRT Initiation and Discontinuation in Critically Ill Children with Acute Kidney Injury: Taking Focus 2 Process Article.

BACKGROUND: Acute Kidney Injury (AKI) is common in critically ill children and is associated with increased morbidity and mortality. Recognition and management of AKI is often delayed, predisposing patients to risk of clinically significant fluid accumulation (Fluid Overload (FO)). Early recognition and intervention in high risk patients could decrease fluid associated morbidity. We aim to assess an AKI Clinical Decision Algorithm (CDA) using a sequential risk stratification strategy integrating the Renal Angina Index (RAI), urine Neutrophil Gelatinase-Associated Lipocalin (NGAL) and the Furosemide Stress Test (FST) to optimize AKI and FO prediction and management in critically ill children. METHODS/DESIGN: This single center prospective observational cohort study evaluates the AKI CDA in a Pediatric Intensive Care Unit (PICU). Every patient ≥ 3 months old has the risk score RAI calculated automatically at 12 hours of admission. Patients with a RAI ≥ 8 (fulfilling renal angina) have risk further stratified with a urine NGAL and, if positive (NGAL ≥ 150ng/mL), subsequently by their response to a standardized dose of furosemide (namely FST). RAI negative or NGAL negative patients are treated per usual care. FST-responders are managed conservatively, while non-responders receive fluid restrictive strategy and/or continuous renal replacement therapy (CRRT) at 10%-15% of FO. 2100 patients over 3 years will be evaluated to capture 210 patients with severe AKI (KDIGO Stage 2 or 3 AKI), 100 patients with >10% FO, and 50 requiring CRRT. Primary analyses: Standardizing a pediatric FST and assessing prediction accuracy of CDA for severe AKI, FO>10% and CRRT requirement in children. Secondary analyses in patients with AKI: Renal function return to baseline, RRT and mortality within 28 days. DISCUSSION: This will be the first prospective evaluation of feasibility of AKI CDA, integrating individual prediction tools in one cohesive and comprehensive approach, and its prediction of FO>10% and AKI, as well as the first to standardize the FST in the pediatric population. This will increase knowledge on current AKI prediction tools and provide actionable insight for early interventions in critically ill children based on their level of risk.