Early prediction of pediatric acute kidney injury from the emergency department: A pilot study.

BACKGROUND: Identifying acute kidney injury (AKI) early can inform medical decisions key to mitigation of injury. An AKI risk stratification tool, the renal angina index (RAI), has proven better than creatinine changes alone at predicting AKI in critically ill children. OBJECTIVE: To derive and test performance of an "acute" RAI (aRAI) in the Emergency Department (ED) for prediction of inpatient AKI and to evaluate the added yield of urinary AKI biomarkers. METHODS: Study of pediatric ED patients with sepsis admitted and followed for 72 h. The primary outcome was inpatient AKI defined by a creatinine >1.5× baseline, 24-72 h after admission. Patients were denoted renal angina positive (RA+) for an aRAI score above a population derived cut-off. Test characteristics evaluated predictive performance of the aRAI compared to changes in creatinine and incorporation of 4 urinary biomarkers in the context of renal angina were assessed. RESULTS: 118 eligible subjects were enrolled. Mean age was 7.8 ± 6.4 years, 16% required intensive care admission. In the ED, 27% had a +RAI (22% had a >50% creatinine increase). The aRAI had an AUC of 0.92 (0.86-0.98) for prediction of inpatient AKI. For AKI prediction, RA+ demonstrated a sensitivity of 94% (69-99) and a negative predictive value of 99% (92-100) (versus sensitivity 59% (33-82) and NPV 93% (89-96) for creatinine ≥2× baseline). Biomarker analysis revealed a higher AUC for aRAI alone than any individual biomarker. CONCLUSIONS: This pilot study finds the aRAI to be a sensitive ED-based tool for ruling out the development of in-hospital AKI.

Utilizing Predictive Inflammatory Markers for Guiding the Use of Biologicals in Severe Asthma.

Asthma is a chronic respiratory disease characterized by chronic airway inflammation and airflow obstruction. Up to ten percent of asthmatics have severe asthma, and many remain uncontrolled despite optimal medical management. With our increased understanding of the heterogeneity of asthma and its complex pathophysiology, several biomarkers have been developed and in the recent past, several biologic therapies for severe asthma have been developed and are now in widespread use. Although these biological agents have shown great benefit in treating severe asthma, not all patients respond equally well, and some do not derive any benefit. As much of the current literature of these medications have not assessed biomarkers or have used different cutoffs, it is often challenging to decide the best medication for an individual patient. Here, we review common asthma subtypes, current available biologic therapies for asthma, the clinical application of currently available type 2 biomarkers, as well as summarizing the evidence on how patient characteristics and biomarkers can help with choosing the optimal biologic for a patient that has the highest likelihood of success.

Diagnostic Accuracy of Midline Pediatric Neck Masses.

OBJECTIVE: To assess clinical evaluation, ultrasound, and previously published predictive score at preoperatively diagnosing midline neck masses and demographic or clinical associations that aid in differentiation of thyroglossal duct and dermoid cysts. STUDY DESIGN: Retrospective chart review. SETTING: Tertiary care children's hospital. SUBJECTS: Patients <18 years undergoing primary midline neck mass surgery with histopathologic diagnosis of thyroglossal duct or dermoid cyst who had preoperative ultrasound performed were included. METHODS: An electronic medical record query generated 142 patients whose histopathologic diagnosis was thyroglossal duct cysts (TGDCs) or dermoid cysts (DCs). Charts were reviewed for demographic and clinical features. A radiologist blindly reviewed patients' ultrasounds for SIST (septae + irregular walls + solid components = thyroglossal) score components. Each patient received 3 preoperative diagnoses: clinical, ultrasound, and SIST. Statistical analyses were conducted to determine association of demographic, clinical, or radiographic variables with diagnoses. Specificity, sensitivity, and predictive values were evaluated for each candidate diagnosis. RESULTS: There were 83 TGDCs and 59 DCs. Tenderness, infection history, depth relative to strap muscles, and SIST components were more common among TGDCs. Sensitivity and positive and negative predictive values surpassed 63% for each diagnostic modality. SIST score outperformed other diagnostic modalities with sensitivity, positive predictive value, and negative predictive value of 84%, 91%, and 81%, respectively. Clinical and ultrasound assessments were largely inconclusive for dermoid cysts, but SIST correctly identified 89% of DCs. CONCLUSION: SIST score was the most accurate predictor of pediatric midline neck masses. Clinical and radiographic findings may help guide preoperative diagnosis, although further evaluation is required to develop more efficacious diagnostic tools.