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  • Title: Brain-Specific Serum Biomarkers Predict Neurological Morbidity in Diagnostically Diverse Pediatric Intensive Care Unit Patients.
    Author: Au AK, Bell MJ, Fink EL, Aneja RK, Kochanek PM, Clark RSB.
    Journal: Neurocrit Care; 2018 Feb; 28(1):26-34. PubMed ID: 28612133.
    Abstract:
    BACKGROUND: Unexpected neurological morbidity in Pediatric Intensive Care Units (PICUs) remains high and is difficult to detect proactively. Brain-specific biomarkers represent a novel approach for early detection of neurological injury. We sought to determine whether serum concentrations of neuron-specific enolase (NSE), myelin basic protein (MBP), and S100B, specific for neurons, oligodendrocytes, and glia, respectively, were predictive of neurological morbidity in critically ill children. METHODS: Serum was prospectively collected on days 1-7 from diagnostically diverse PICU patients (n = 103). Unfavorable neurological outcome at hospital discharge was defined as Pediatric Cerebral Performance Category (PCPC) score of 3-6 with a deterioration from baseline. NSE, MBP, and S100B concentrations were measured by enzyme-linked immunosorbent assay. RESULTS: Peak biomarker levels were greater in patients with unfavorable versus favorable neurological outcome [NSE 39.4 ± 44.1 vs. 12.2 ± 22.9 ng/ml (P = 0.005), MBP 9.1 ± 11.5 vs. 0.6 ± 1.3 ng/ml (P = 0.003), S100B 130 ± 232 vs. 34 ± 70 pg/ml (P = 0.04), respectively; mean ± SD]. Peak levels were each independently associated with unfavorable neurological outcome when controlling for presence of primary neurologic admission diagnosis and poor baseline PCPC using logistic regression analysis (NSE, P = 0.04; MBP, P = 0.004; S100B, P = 0.04), and had the following receiver operating characteristics: NSE 0.75 (0.58, 0.92), MBP 0.81 (0.66, 0.94), and S100B 0.80 (0.67, 0.93) (area under the curve [95% confidence intervals]). CONCLUSIONS: Prospectively collected brain-specific serum biomarkers predict unfavorable neurological outcome in critically ill children. Serum biomarkers used in conjunction with clinical data could be used to generate models predicting early detection of neurological injury, allowing for more timely diagnostic and therapeutic interventions, potentially reducing neurological morbidity in the PICU.
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