Zhihui Yang1, Haiyan Xu2, Livia Sura3, Rawad Daniel Arja2, Robert Logan Patterson2, Candace Rossignol3, Mehmet Albayram4, Dhanashree Rajderkar4, Suman Ghosh3, Kevin Wang2,5, Michael D Weiss3. 1. Department of Emergency Medicine, University of Florida, 1149 Newell Drive, L3-166, Gainesville, FL, 32611, USA. zhihuiyang@ufl.edu. 2. Department of Emergency Medicine, University of Florida, 1149 Newell Drive, L3-166, Gainesville, FL, 32611, USA. 3. Department of Pediatrics, University of Florida, Gainesville, FL, 32610, USA. 4. Department of Radiology, University of Florida, Gainesville, FL, 32610, USA. 5. Brain Rehabilitation Research Center, Malcom Randall VA Medical Center, North Florida/South Georgia Veterans Health System, 1601 SW Archer Road, Gainesville, FL, 32608, USA.
Abstract
BACKGROUND: Neuroprognostication in neonates with neonatal encephalopathy (NE) may be enhanced by early serial measurement of a panel of four brain-specific biomarkers. METHODS: To evaluate serum biomarkers, 40 NE samples and 37 healthy neonates from a biorepository were analyzed. Blood samples were collected at 0-6, 12, 24, 48, and 96 h of life. MRI provided a short-term measure of injury. Long-term outcomes included death or a Bayley III score at 17-24 months of age. RESULTS: Glial fibrillary acidic protein (GFAP), ubiquitin c-terminal hydrolase-L1 (UCH-L1), and Tau peaked at 0-6 h of life, while neurofilament light chain (NFL) peaked at 96 h of life. These four marker concentrations at 96 h of life differentiated moderate/severe from none/mild brain injury by MRI, while GFAP and Tau showed early discrimination. For long-term outcomes, GFAP, NFL, Tau, and UCH-L1 could differentiate a poor outcome vs good outcome as early as 0-6 h of life, depending on the Bayley domain, and a combination of the four markers enhanced the sensitivity and specificity. Machine learning trajectory analyses identified upward trajectory patients with a high concordance to poor outcomes. CONCLUSION: GFAP, NFL, Tau, and UCH-L1 may be of neuroprognostic significance after NE. IMPACT: Serial measurements of GFAP, NFL, Tau, and UCH-L1 show promise in aiding the bedside clinician in making treatment decisions in neonatal encephalopathy. The panel of four neuroproteins increased the ability to predict neurodevelopmental outcomes. The study utilized a trajectory analysis that enabled predictive modeling. A panel approach provides the bedside clinician with objective data to individualize care. This study provides the foundation to develop a point of care device in the future.
BACKGROUND: Neuroprognostication in neonates with neonatal encephalopathy (NE) may be enhanced by early serial measurement of a panel of four brain-specific biomarkers. METHODS: To evaluate serum biomarkers, 40 NE samples and 37 healthy neonates from a biorepository were analyzed. Blood samples were collected at 0-6, 12, 24, 48, and 96 h of life. MRI provided a short-term measure of injury. Long-term outcomes included death or a Bayley III score at 17-24 months of age. RESULTS: Glial fibrillary acidic protein (GFAP), ubiquitin c-terminal hydrolase-L1 (UCH-L1), and Tau peaked at 0-6 h of life, while neurofilament light chain (NFL) peaked at 96 h of life. These four marker concentrations at 96 h of life differentiated moderate/severe from none/mild brain injury by MRI, while GFAP and Tau showed early discrimination. For long-term outcomes, GFAP, NFL, Tau, and UCH-L1 could differentiate a poor outcome vs good outcome as early as 0-6 h of life, depending on the Bayley domain, and a combination of the four markers enhanced the sensitivity and specificity. Machine learning trajectory analyses identified upward trajectory patients with a high concordance to poor outcomes. CONCLUSION: GFAP, NFL, Tau, and UCH-L1 may be of neuroprognostic significance after NE. IMPACT: Serial measurements of GFAP, NFL, Tau, and UCH-L1 show promise in aiding the bedside clinician in making treatment decisions in neonatal encephalopathy. The panel of four neuroproteins increased the ability to predict neurodevelopmental outcomes. The study utilized a trajectory analysis that enabled predictive modeling. A panel approach provides the bedside clinician with objective data to individualize care. This study provides the foundation to develop a point of care device in the future.
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