Neurologic Monitoring of Neonates in the Neonatal Intensive Care Unit

The American Clinical Neurophysiology Society has identified neonates at high risk for neurologic sequelae. These clinical conditions include: hypoxic ischemic encephalopathy, neonatal seizures, significant intraventricular hemorrhage, neonates placed on extracorporeal membrane oxygenation, central nervous system abnormalities, neuromuscular abnormalities, metabolic or neuromuscular disorders, and any change in dose or type of seizure medication. Neonates with these conditions often develop seizures and benefit from neurologic monitoring through electroencephalography (EEG) technology. Appropriate monitoring can lead to identification of prognostic EEG patterns and accurate diagnosis of seizures and non-seizure events. Neonatal seizures are typically subclinical thus making accurate diagnosis a significant challenge. Timely and accurate diagnosis of seizures leads to improved neurologic and developmental outcomes. This project was to develop an algorithm to guide clinician ordering of neurologic monitoring at sites where a lack of consistency was problematic. The objectives for this doctoral project included: 1) Development of a Neurologic Monitoring Algorithm; 2) Introduction of the Algorithm to clinicians at the implementation sites; 3) Assessment of EEG testing and neurology consult ordered at admission after implementation; and 4) Dissemination of results to the Neuro Newborn Intensive Care Unit steering committee and submission of an abstract for poster presentation at the 2017 Academy of Neonatal Nursing Las Vegas Conference, National Neonatal Conference. A literature review was conducted to determine the prevalence of abnormal brain activity in infants, conditions that increase the risk of seizures, the consequences of untreated seizures and the value of neurologic monitoring. The search confirmed that specific guidelines for neurologic monitoring could be used to guide diagnosis of seizures, response to treatment, and correlation with infant exam. Prospective studies concluded that monitoring led to earlier identification of seizures and acute neurologic deterioration plus the ability to guide treatment. A Neurologic Monitoring Algorithm for infants with high-risk clinical conditions was developed and implemented. A chart audit of neurologic testing ordered and neurology consult at admission were analyzed. The results of this audit found improved testing based on the algorithm with appropriate timeframes, improved neurology consultation in a timely manner, and monitoring was used to diagnose sub-clinical seizures in 30% of infants and assisted in prognostic data and treatment decision making for 30% of infants. The algorithms guided clinicians in appropriate neurologic monitoring. The evidence-based algorithms improved seizure identification and decreased time to treatment leading to better long-term and developmental outcomes. Recommendations include continued used of algorithms with yearly training to clinicians and regular chart audits.

Running Head: NEUROLOGIC MONITORING OF NEONATES Neurologic Monitoring of Neonates in the Neonatal Intensive Care Unit Jennifer R. Messick University of Utah in partial fulfillment of the requirements for the Doctor of Nursing Practice April 2112, 2017 1 NEUROLOGIC MONITORING OF NEONATES 2 Executive Summary The American Clinical Neurophysiology Society has identified neonates at high risk for neurologic sequelae. These clinical conditions include: hypoxic ischemic encephalopathy, neonatal seizures, significant intraventricular hemorrhage, neonates placed on extracorporeal membrane oxygenation, central nervous system abnormalities, neuromuscular abnormalities, metabolic or neuromuscular disorders, and any change in dose or type of seizure medication. Neonates with these conditions often develop seizures and benefit from neurologic monitoring through electroencephalography (EEG) technology. Appropriate monitoring can lead to identification of prognostic EEG patterns and accurate diagnosis of seizures and non-seizure events. Neonatal seizures are typically subclinical thusin making accurate diagnosis a significant challenge. Timely and accurate diagnosis of seizures leads to improved neurologic and developmental outcomes. This project was to develop an algorithm to guide clinician ordering of neurologic monitoring at sites where a lack of consistency was problematic. The objectives for this doctoral project included: 1) Development of a Neurologic Monitoring Algorithm; 2) Introduction of the Algorithm to clinicians at the implementation sites; 3) Assessment of EEG testing and neurology consult ordered at admission after implementation; and 4) Dissemination of results to the Neuro Newborn Intensive Care Unit steering committee and submission of an abstract for poster presentation at the 2017 Academy of Neonatal Nursing Las Vegas Conference, National Neonatal Conference. A literature review was conducted to determine the prevalence of abnormal brain activity in infants, conditions that increase the risk of seizures, the consequences of untreated seizures and the value of neurologic monitoring. The search confirmed that specific guidelines for neurologic monitoring could be used to guide diagnosis of seizures, response to treatment, and correlation with infant exam. Prospective studies concluded that monitoring led to earlier identification of seizures and acute neurologic deterioration plus the ability to guide treatment. A 50% decrease in subsequent epilepsy was seen in infants monitored. A Neurologic Monitoring Algorithm for infants with high-risk clinical conditions was developed and implemented. A chart audit of neurologic testing ordered and neurology consult at admission were analyzed. The results of this audit found improved testing based on the algorithm with appropriate timeframes, improved neurology consultation in a timely manner, and monitoring was used to diagnose sub-clinical seizures in 30% of infants and assisted in prognostic data and treatment decision making for 30% of infants. The algorithm guided clinicians in appropriate neurologic monitoring. The evidencebased algorithm improved seizure identification and decreased time to treatment leading to betterwhich has been shown in prior studies to improve long-term neurologic and developmental outcomes. Recommendations include continued use of the algorithm with yearly training to clinicians and regular chart audits of all clinical conditions of all conditions. Committee members included Gillian Tufts, DNP, APRN, CFNP Project Chair, Kim Friddle, PhD, APRN, NNP-BC Neonatal Nurse Practitioner Track Director, Pamela Hardin, PhD, RN, CNE Assistant Dean for MS and DNP Programs. Content expert was Betsy Ostrander, M.D. Director of the Fetal and Neonatal Neurology Program, Assistant Professor of Formatted: Indent: First line: 0.5" NEUROLOGIC MONITORING OF NEONATES Pediatrics, University of Utah. TABLE OF CONTENTS Executive Summary ............................................................................................................ 2 Acknowledgements ............................................................................................................. 5 Problem Statement .............................................................................................................. 6 Clinical Significance ........................................................................................................... 6 Project Purpose and Objectives .......................................................................................... 8 Literature Review................................................................................................................ 8 Background ...................................................................................................................... 9 Neurologic Monitoring .................................................................................................. 10 Neonatal Seizures .......................................................................................................... 11 Neonatal Seizure Rates .................................................................................................. 11 Risks for Seizure Development .................................................................................. 12 Consequences of Seizure Development ..................................................................... 13 Seizure Detection ....................................................................................................... 13 Hypoxic Ischemic Encephalopathy ............................................................................... 14 Intraventricular Hemorrhage ......................................................................................... 15 Extracorporeal Membrane Oxygenation ..................................................................... 176 Metabolic Disorders ...................................................................................................... 18 Need for Consistent Monitoring ....................................................................................... 19 Current Practice ......................................................................................................... 2019 Theoretical Framework ..................................................................................................... 20 Implementation & Evaluation Table................................................................................. 21 Implementation ............................................................................................................... 221 Evaluation ......................................................................................................................... 24 Results ............................................................................................................................... 24 Recommendations ............................................................................................................. 27 The Doctorate of Nursing Practice Essentials .................................................................. 28 Conclusion ........................................................................................................................ 29 References ......................................................................................................................... 31 Appendices ........................................................................................................................ 35 Appendix A: Proposal PowerPoint ................................................................................ 35 3 NEUROLOGIC MONITORING OF NEONATES Appendix B: IRB Exemption ........................................................................................ 40 Appendix C: Neurologic Monitoring Algorithm ........................................................... 41 Appendix D: Clinician Engagement .............................................................................. 43 Appendix E: DNP Poster Presentation .......................................................................... 50 Appendix F: Dissemination PowerPoint for Neuro-NICU Steering Committee .......... 51 4 NEUROLOGIC MONITORING OF NEONATES 5 Acknowledgments This project is the result of support and guidance from my project chair and content expert. I would like to express my gratitude to Dr. Gillian Tufts for her consistent feedback and encouragement. This project would not have been possible without the vision and expert advice from Dr. Betsy Ostrander; I am so appreciative of your time and the wisdom you shared. To my family, your unwavering confidence and support made completion of this project possible. To my parents for filling in as surrogate parents at times during this journey and always encouraging me to reach for the stars. To my children (Braden, Camille, Brock, Brianna, and Braxton) for their understanding and patience through this process. To my husband, Matt, you are my biggest cheerleader and rock; with you anything is possible. Thank you for believing in me and always lending a listening ear. Formatted: Indent: First line: 0.5" NEUROLOGIC MONITORING OF NEONATES 6 Problem Statement The American Clinical Neurophysiology Society (ACNS) has identified neonates at Formatted: Indent: First line: 0.5" high risk for neurologic sequelae. These infants benefit from long-term neurologic monitoring through electroencephalography (EEG) technology (Shellhaas et al., 2011). Neonates with these conditions often develop encephalopathy and seizures. Appropriate monitoring can lead to identification of prognostic EEG patterns and accurate diagnosis of seizures and non-seizure events; neonatal seizures are typically subclinical in nature making accurate diagnosis a significant challenge (Shellhaas, 2015). Two local Neonatal Intensive Care Units (NICUs), level III and level IV centers, were selected for implementation of the evidence-based neurologic monitoring algorithm. Critically ill infants, infants identified as those who benefit from EEG monitoring by Shellhaas (2015), are treated by these centers in significant numbers without an evidence-based algorithm to direct appropriate testing and monitoring. The specialists and technology to support timely and accurate diagnosis were also accessible in the centers making them the ideal setting for this project. Clinical Significance Neonates at risk for the development of encephalopathy and seizures represent a significant percentage of the infants cared for in the NICU. This population subset includes: neonates who have experienced hypoxic ischemic encephalopathy (HIE), neonatal seizures, clinical conditions requiring extracorporeal membrane oxygenation, infants with a grade III and Formatted: Indent: First line: 0.5" NEUROLOGIC MONITORING OF NEONATES 7 IV intraventricular hemorrhage (IVH), post hemorrhagic hydrocephalus, central nervous system abnormalities and infection, inborn errors of metabolism, and neuromuscular disorders (Shellhaas, 2015). Encephalopathy and seizure activity have been proven detrimental to neurologic Formatted: Left, Indent: First line: 0.5", Tab stops: Not at 0.15" + 0.5" development and cognitive function (Shellhaas, 2015). Early diagnosis with appropriate medication and interventional therapy has been shown to improve neonatal outcomes (Azzopardi, 2015). The World Health Organization (2011), reported the prevalence of seizure activity of infants in the high-risk groups ranging from 5-40% depending on the etiology and when both preterm and term infants were included. The utility of appropriate EEG monitoring for infants with encephalopathy will benefit clinicians in treatment as a prognostic aid to better predict survival and/or long term disability (Shellhaas et al., 2011). Formatted: Indent: First line: 0.5" Data obtained from neurologic monitoring can be used to predict outcomes, guide seizure management, and neurologic care for at risk neonates (Azzopardi, 2015). Neonates at risk for developing neonatal seizures cover a broad spectrum of diagnoses seen in the NICU. The cost of seizure management and subsequent impacts to neurologic and cognitive development encompasses the life-span. Therefore, it is important to arrive at an early diagnosis with an appropriate treatment regimen to decrease seizures that leads to optimal cognitive and neurodevelopmental outcomes (Azzopardi, 2015). Conversely, neonates with events that may be interpreted as a seizure may be treated with medications bearing significant side effects that can be avoided when proper monitoring and accurate diagnosis takes place through appropriate neurologic testing (Shellhaas, 2015). Development of an evidence-based neurologic monitoring algorithm will provide for appropriate testing based on the infant's clinical condition, prescribe Formatted: Left, Indent: First line: 0.5", Tab stops: Not at 0.15" + 0.5" NEUROLOGIC MONITORING OF NEONATES 8 testing length, and initiate pediatric neurology involvement in treatment and care. Formatted: Left, Tab stops: Not at 0.15" + 0.5" Formatted: Normal (Web), Left, Widow/Orphan control, Adjust space between Latin and Asian text, Adjust space between Asian text and numbers, Tab stops: Not at 0.15" + 0.5" Formatted: Left, Tab stops: Not at 0.15" + 0.5" Project Purpose The purpose of this project was to develop a clinical practice algorithm for appropriate Formatted: Indent: First line: 0.5" neurologic monitoring of critically ill neonates. Objectives 1) Develop a Neurologic Monitoring algorithm for neonates with high-risk clinical conditions. 2) Implement Neurologic Monitoring algorithm and introduce it to the clinicians at the implementation sites. 3) Assess appropriate neurologic testing, length of monitoring, and neurology consult ordered at admission after algorithm was implemented. 4) Dissemination of results to the Neuro NICU steering committee and submit an abstract for poster presentation at the 2017 Academy of Neonatal Nursing (ANN) Las Vegas Conference, National Neonatal Conference. Literature Review A literature search was conducted through PubMed, Medline Plus, CINAHL, and Cochrane Library using the search terms "neurologic monitoring AND neonate OR infant", Formatted: Indent: First line: 0.5" NEUROLOGIC MONITORING OF NEONATES 9 "EEG AND neonate", "hypoxic ischemic encephalopathy AND seizure AND EEG", "seizure AND EEG AND neonate", "intraventricular hemorrhage AND seizure AND EEG", "ECMO AND seizure AND EEG", "inborn errors of metabolism AND seizure AND EEG", "neuromuscular disorder AND seizure AND neonate AND EEG." Results included in the literature review were limited to peer-reviewed journals from 2009-2016. Other methods used included recommendations from and personal communication with the content expert. Background The purpose of this literature review was to establish the evidence for neurologic monitoring in seizure identification, the incidence of neonatal seizures, risk and consequences of seizure activity, and seizure detection. Additionally, the evidence for neurologic monitoring of each high risk clinical condition identified by the ACNS will be discussed. The value of neurologic monitoring is enhanced by an algorithm that provides triggers for conditions that may benefit from neurologic monitoring technology. The neurologic monitoring algorithm should also guide clinicians to the appropriate test(s) for each condition continuous EEG (cEEG) vs. amplitude integrated EEG (aEEG) or a combination approach (Azzopardi, 2015). Recommendations for diagnosis based initiation of the appropriate EEG test, length of monitoring, and specialty consult from neurology should be contained in the algorithm (Azzopardi, 2015). According to the Utah Department of Health (2015), there were 51,164 live births in the state during 2014. Estimates of neonatal seizures in the United States range from 1.8-5 for every 1,000 live births (Jensen, 2009). These estimates include the entire birth population; whereas the infants admitted to the implementation sites fall into one of the high-risk categories more frequently than the average infant. The risk for seizures at these implementation sites is Formatted: Indent: First line: 0.5" NEUROLOGIC MONITORING OF NEONATES 10 significantly higher than the usual estimates. This factor made development and implementation of the neurologic monitoring algorithm vital. Formatted: Indent: First line: 0.5" Neurologic Monitoring to enhance seizure identification Pediatric physicians in Canada studied critically ill children to identify seizure activity through cEEG monitoring and correlated the findings with short-term neurological impacts (Payne, et al., 2014). This observational study included neonates through children of 18 years of life with a critical illness categorized by diagnosis with risk for seizure development (Payne, et al., 2014). Neurologic status was documented in children who developed seizures and the information was correlated with EEG data (Payne et al., 2014). The study found that as seizure burden increased neurologic deterioration was independently associated. The findings were determined to support the hypothesis that seizures contribute to brain injury in critically ill children (Payne, et al., 2014). Neonatal seizures are often subclinical with the majority originating in the temporal or central region therefore aEEG technology is beneficial for this population (Azzopardi, 2015). Prognosis and progression of encephalopathy can be assessed with the combined use of aEEG and cEEG monitoring (Azzopardi, 2015). Specific guidelines for neurologic monitoring with Formatted: Indent: First line: 0.5" NEUROLOGIC MONITORING OF NEONATES 11 aEEG and cEEG can be used to guide assessment, progression of seizures, response to treatment, and correlation with infant exam (Azzopardi, 2015). A prospective study to evaluate the value of aEEG for prediction of short-term outcomes in at-risk infants reported how aEEG allowed for improved categorization of severity and identification of subclinical seizures, and was useful as an assessment tool of treatment response (Tosso, et al., 2014). More than half of the infants in the study were found to have altered aEEG recordings (Tosso, et al., 2014). The majority of these infants developed, subclinical seizures identified by aEEG monitoring (Tosso, et al., 2014). Infants with severe respiratory distress syndrome (RDS) were found to have neurologic alterations captured by aEEG monitoring that were not discovered by clinical exam (Tosso, et al., 2014). The authors concluded that monitoring with aEEG resulted in earlier identification of seizures and acute neurologic deterioration plus the ability to guide treatment (Tosso, et al., 2014). One study identified nearly a a 50% decrease in epilepsy in neonates with seizures who were monitored with aEEG technology and received earlier treatment (Tooset,so Groenendaal, Osredkar, Huffelen, & Vries,, et al., 20142005). Recommendations also exist for evaluation of high-risk infants prone to seizures with cEEG monitoring for 24 hours; while others for at least 60 minutes (Shellhaas, 2015). Evaluation of infants with paroxysmal events should be conducted with cEEG to diagnose seizures or rule-out the presence of this clinical condition to avoid medication (Shellhaas, 2015). This population sub-set should be monitored with cEEG if feasible; because this technology is cumbersome and time-consuming it should be used wisely (Shellhaas, 2015). Monitoring with NEUROLOGIC MONITORING OF NEONATES 12 cEEG can definitively diagnose seizures, quantify seizure activity, and guide treatment while sparing unnecessary medication administration to those whose paroxysmal movements are not seizure activity (Shellhaas, 2015). Neonatal Seizures Neonatal seizure rates. Incidence rates of seizure activity based only on clinical findings is reported as 0.5% of all term infants and 20% of preterm infants (Hill, 2009). Seizure risk and activity increases for each high-risk clinical condition recommended for neurologic monitoring. Seizure rates for infants with HIE increase to ~50% (Glass, et al., 2009). Seizure activity in infants with a metabolic disorder is one of the leading characteristics that leads to diagnosis of inborn errors of metabolism, therefore accurate diagnosis of the seizure activity and root cause is imperative (Rahman, et al., 2012). Infants diagnosed with IVH particularly those graded as 3 or 4 or with post-hemorrhagic hydrocephalus have an increased risk of seizure development reported as 510% (McCrea & Ment, 2008). When infants who have received pharmacological paralyzation are monitored with EEG technology the incidence of seizures detected increases to 40% in many high-risk populations (Hill, 2009). Risks for seizure development. The ACNS has identified multiple clinical conditions that increase risk of seizure activity, subclinical seizures are common in these patient populations (Shellhaas, et al., 2011). These clinical conditions include: HIE, neonatal seizures (including strokes and infantile epilepsy), IVH in premature infants including post-hemorrhagic hydrocephaly, central nervous Formatted: Indent: First line: 0.5" NEUROLOGIC MONITORING OF NEONATES 13 system abnormalities and infections, metabolic disorders, neuromuscular disorders, and infants requiring ECMO (Shellhaas, et al., 2011). Infants who have suffer from HIE or a stroke are at higher risk for subclinical seizure activity during the acute phase of treatment whereas those with a central nervous system abnormality, certain genetic syndromes, and neonatal epilepsy have high risk for recurrent seizures (Shellhaas, et al., 2011). Once seizures are identified and treated a phenomenon referred to as electric uncoupling can occur and seizure are only able to be detected by cEEG monitoring (Abend & Wusthoff, 2012). Glass and associates (2016) reported that the burden of seizures among infants with HIE, stroke, and IVH have the highest burden of seizures; with more than 40% of these populations suffering from frequent recurrent seizures or status epilepticus. Consequences of seizure activity. Seizure activity is associated with detrimental developmental outcomes both short and long term (Glass, et al., 2016). Infants with seizure activity suffer significant developmental disabilities including cerebral palsy, post-neonatal epilepsy, and intellectual disabilities (Glass, et al., 2016). These associated morbidities result in lifelong therapy, increased medical care costs, as well as social and academic assistance (Glass, et al., 2016). Glass and associates (2016) reported mortality rates of 17% of infants in their prospective study, mortality increased for infants with the greatest seizure burden. A rate of 7 or more documented seizures increased mortality of infants prospectively studied from 6% to 24% (Glass, et al., 2016). Mortality from seizures is largest among infants with HIE followed by IVH, and lastly ischemic strokes (Glass, et al., 2016). NEUROLOGIC MONITORING OF NEONATES 14 Seizure detection. Neonatal seizure detection is difficult as most seizures are subclinical in nature. Formatted: Indent: First line: 0.5" Accuracy of seizure diagnosis and management is limited by multiple factors:, first,: any abnormal movement may be attributed to seizure activity therefore diagnosis of seizures is challenging in neonates (Glass, et al., 2016). Second, electrographic seizures do not often have a clinical correlation further necessitating the need for accurate diagnosis (Glass, et al., 2016). aEEG monitoring is a simplified tool with the ability to display trends, obtain real-time data, and detection of some subclinical seizures that are not detected by cEEG (Foreman & Thorngate, 2011). The inability to detect seizures that originate outside of the temporal area is a limitation of aEEG technology (Foreman & Thorngate, 2011). Current research indicates that there is strong evidence for the use of aEEG monitoring and seizure detection for several populations of high-risk infants namely encephalopathy, inborn errors of metabolism, congenital cardiac defects, and premature infants with brain injury (Foreman & Thorngate, 2011). Studies indicate that aEEG can be used in preterm infants to identify acute changes in neurologic function (Foreman & Thorngate, 2011). Once seizure activity is diagnosed and treatment is initiated, over 50% of this population sub-set will no longer exhibit clinical signs of seizures therefore EEG monitoring can detect the subclinical electrographic seizures and allow the clinician to adjust the treatment regimen (Shellhaas et al., 2011). Hypoxic Ischemic Encephalopathy The most common cause of neonatal seizures is HIE; these infants are more likely to develop seizures resulting in epilepsy and long-term cognitive deficits (Zhou, et al., 2015). The mechanism believed to be responsible for this risk is the increased levels of excitatory inotropic Formatted: Indent: First line: 0.5" NEUROLOGIC MONITORING OF NEONATES 15 glutamate receptors (Zhou, et al., 2015). Glutamate receptors are documented as having increased activation when hypoxia and/or ischemia is present (Zhou, et al., 2015). This increased activation contributes to seizure activity in infants who have suffered from HIE (Zhou, et al., 2015). Because of the increased rate of seizure activity for infants with HIE numerous studies have been conducted. These studies determine the most appropriate neurologic monitoring for infants and demonstrate the benefit appropriate monitoring provides clinicians regarding prognostic data and clinical management of seizure activity. Toffoli and associates (2015) from Italy completed a study to assess seizure identification abilities of aEEG and two-channel EEG against the traditional cEEG. The technologies were utilized in infants who suffered HIE and were undergoing therapeutic hypothermia (Toffoli, et al., 2015). Three independent researchers documented seizure identification. Their findings suggest that the two-channel EEG identified more seizures than the aEEG, however, the traditional cEEG had added value to identify seizures originating outside the parasagittal regions (Toffoli, et al., 2015). These study findings led to the combined use of cEEG and aEEG during specific times for infants with HIE in the Neurologic Monitoring algorithm. Abbott Laptook, MD is well known for his research efforts focused on HIE and brain development in infants. His synthesis of literature surrounding the use of aEEG in the NICU setting indicates that this technology is useful as a prognostic tool when background and the return of and quality of sleep-wake cycles are analyzed in infants who have suffered HIE (Laptook, 2014). The utility of aEEG has also been identified as a formidable tool to identify regional electrographic seizures especially when paired with adequately trained personnel and NEUROLOGIC MONITORING OF NEONATES 16 computerized seizure detection algorithms (Laptook, 2014). A multivariate regression study on infants who suffered HIE and examined neurodevelopmental outcomes of the infants who had seizures against those without seizure was conducted at UCSF Benioff Children's hospital (Glass, et al., 2009). Motor and cognitive outcomes were compared in infants with and without seizure activity once severity of injury was controlled for (Glass, et al., 2009). This study found that infants with clinical seizures who had HIE developed worse neurodevelopmental outcomes than those who did not develop seizures (Glass, et al., 2009). Intraventricular hemorrhage and post-hemorrhagic hydrocephalus Premature infants are at risk for development of IVH; this complication associated with prematurity is quite common. Incidence rates range from 25-45% depending on birth weight (Mukerji, Shah, & Shah, 2015). The brain of premature infants is especially susceptible to the development of hemorrhage in the germinal matrix (Mukerji, et al., 2015). The germinal matrix is a highly vascularized and cellularized region of the brain where cellular migration occurs as the brain develops (Mukerji, et al., 2008). The germinal matrix begins to involute from 28 weeks and is mostly absent by 40 weeks gestation (Perlman, 2009). Premature infants are unable to auto regulate cerebral blood flow (Mukerji, et al., 2015). This characteristic of premature infant physiology combined with the fragile nature of capillaries in the germinal matrix make them extremely susceptible to IVH (Mukerji, et al., 2015). Infants with IVH are more likely to develop post-hemorrhagic hydrocephalus (PHH) (McCrea & Ment, 2008). Infants with grade III/IV IVH develop seizures in 5-10% of cases, with 50% presenting with progression to PHH (McCrea & Ment, 2008). Formatted: Indent: First line: 0.5" NEUROLOGIC MONITORING OF NEONATES 17 A large retrospective study of preterm infants reported that infants with IVH were more likely to develop seizure activity and were found to have an increased risk of poor neurodevelopmental outcomes (Davis, et al., 2010). Neurologic monitoring of this population subset through aEEG has been shown to predict IVH (Kato, et al., 2013). Glass and associates (2012), identified major gaps in knowledge and consistency regarding management of seizures and calls for clinical studies and trials (Glass, et al., 2012). As future research efforts are undertaken it will also be vital to follow premature infants long-term to further understand the best management approach and outcomes. The neonatology community agrees that early diagnosis of seizure activity and pharmacologic treatment to stop the seizures can protect the developing brain and improve developmental outcomes (Glass, et al., 2012). Extracorporeal Membrane Oxygenation Neonates who undergo ECMO therapy suffer from significant respiratory or cardiac compromise. The most common conditions treated by ECMO include: congenital diaphragmatic hernia, congenital heart defects, meconium aspiration syndrome, severe sepsis, significant air leaks, and severe persistent pulmonary hypertension (Crowley & Stork, 2015). Infants are frequently placed on ECMO after cardiac surgery, Naim and associates (2015) conducted a quality improvement project founded on the recommendations of cEEG monitoring of infants during the immediate cardiac post-operative period; the study covered an 18-month period with a sample size of 161 neonates who met entry criteria. Electrographic Formatted: Indent: First line: 0.5" NEUROLOGIC MONITORING OF NEONATES 18 seizures were recorded in 8% of the sample without clinical correlation; the seizure activity would not have been discovered in these infants without the neurologic monitoring (Naim, et al., 2015). Seizure activity in these infants was associated with increased severity of illness; the seizure activity was concluded to be a marker of brain injury as documented by follow-up imaging (Naim, et al., 2015). Patients requiring ECMO therapy are at high risk for neurologic sequelae including hemorrhage, ischemic stroke, and seizure activity (Rao, Matsumoto, & Sankar, 2010). Infants on ECMO require clinical paralysis, these infants benefit from EEG monitoring as neurologic examinations are no longer accurate secondary to the neuromuscular blocking agents (Shellhaas, et al., 2011). Infants who are pharmacologically paralyzed have clinical conditions that place them at risk for seizure development (Hill, 2009). The value of neurologic monitoring via EEG has been documented as a valuable tool for diagnosis of seizure activities in these infants (Hill, 2009). Promising research is being conducted on the use of aEEG in non-neurologic clinical conditions that place infants at high-risk for developmental delay such as severe respiratory distress syndrome requiring ECMO therapy (Laptook, 2014). A retrospective study of patients undergoing ECMO demonstrated that EEG technology led to diagnosis of abnormal electrical activity in 75% of the patients and subclinical seizures in 62.5% (Rao, et al., 2010). Rao and associates (2010), feel that cEEG monitoring while patients undergo ECMO can also aid in treatment of seizures. Metabolic Disorders NEUROLOGIC MONITORING OF NEONATES 19 There are 20 inherited metabolic disorders likely to experience seizure activity and/or encephalopathy in the neonatal period, of these disorders 13 are considered treatable (Prasad & Hoffman, 2010). The 5 most common metabolic disorders encountered in the NICU include urea cycle disorders, nesidioblastosis, propionic, methylmalonic and isovaleric acidurias, maple syrup urine disease, and non-ketotic hyperglycemia (Prasad & Hoffman, 2010). Only nonketotic hyperglycemia is considered untreatable (Prasad & Hoffman, 2010). Clinical presentation, age of onset, and severity are highly variable and are dependent on the severity of the enzyme deficiency and region of metabolic block (Prasad & Hoffman, 2010). Seizure and encephalopathy may develop in-utero, shortly after birth, or within the first year of life (Prasad & Hoffman, 2010). The population of infants with metabolic disorders is limited in size therefore a worldwide registry has been developed to capture data. Information obtained from this database of infants with inborn errors of metabolism, who received neurologic monitoring through aEEG has been done (Theda, 2010). The neonates studied displayed abnormal aEEG patterns consistent with encephalopathy and seizure activity during acute metabolic events (Theda, 2010). This group of metabolic experts recommends neurologic monitoring of this subset of infants as a tool to aid the multidisciplinary team caring for them (Theda, 2010). The use of aEEG should be studied to perfect the use of this tool to determine severity of encephalopathy, seizure detection, assess for efficacy of treatments and allow for testing of new treatment modalities (Theda, 2010). Prasad & Hoffman (2010), suggest that aEEG and cEEG monitoring is helpful to determine not only if seizure activity is occurring but it can be used to determine if treatments are successful in quelling seizure activity. Infants with inborn errors of metabolism who have EEG recordings can also capture Formatted: Indent: First line: 0.5" NEUROLOGIC MONITORING OF NEONATES 20 abnormalities in background rhythms in addition to epileptiform discharges or seizures (Prasad & Hoffman, 2010). Need for Consistent Neurologic Monitoring Wide variation existed in clinician orders and implementation of neurologic monitoring Formatted: Indent: First line: 0.5" of at risk infants in the selected clinical sites. The services of pediatric neurology and epileptologists can be enhanced when they have access to patient data obtained from appropriate neurologic monitoring technology (B. Ostrander, personal communication, June 28, 2016). The evidence presented for aEEG and cEEG monitoring demonstrates improved seizure detection, the ability to monitor therapeutic efficacy, enhanced prognostic and diagnostic ability as well as improved outcomes in this high-risk neonatal population. Based on the literature review development of a consistent neurologic monitoring algorithm was vital and should lead to improved care in the clinical sites; data obtained through this algorithm can be utilized in the future to improve care for high-risk neonates on a regional and national level. Current practice Infants with concern for seizure activity at the implementation sites are presently monitored in a haphazard fashion for most of the high-risk clinical conditions or not at all (B. Ostrander, personal communication, June 28, 2016). Infants who suffer HIE represent the Formatted: Indent: First line: 0.5" NEUROLOGIC MONITORING OF NEONATES 21 greatest proportion of infants who benefit from neurologic monitoring at these sites, the monitoring of these infants is not always in line with current recommendations (B. Ostrander, personal communication, June 28, 2016). Pediatric neurology is not routinely notified of infants with neurologic monitoring in a timely fashion; this time delay has resulted in missed or delayed seizure identification (B. Ostrander, personal communication, June 28, 2016). Theoretical Framework: Roger's Diffusion of Innovation Theory Development and implementation of a neurologic monitoring algorithm for infants in the NICU involveds presentation of the evidence-based algorithm and clinician support of the effort. Roger's Diffusion of Innovation Theory served as a foundational framework for this project (Rogers & Scott, 1997). The neurologic monitoring algorithm was an innovative change in clinical practice at two local NICUs. After algorithm development and approval, communication of the new clinical practice was disseminated to clinicians based on Roger's theory of innovation (Rogers & Scott, 1997). Successful implementation of this new algorithm depended on clinician adoption; the new algorithm was presented through effective communication channels that captured most adoptees at each implementation site. This was achieved as the algorithm was introduced in various venues and formats targeted to encourage adoption by each person identified by Roger's theory: namely the innovators, early adopters, the early majority, late majority, and laggards (Kaminski, 2011). The implementation process founded in Roger's Diffusion of Innovation Theory allowed the student to target communications within the social system of two Neonatology groups to achieve rapid adoption of this new algorithm by most clinicians (Rogers & Scott, Formatted: Indent: First line: 0.5" NEUROLOGIC MONITORING OF NEONATES 22 1997). Implementation and Evaluation Table Objectives Implementation Evaluation Develop evidence-based Neurologic Monitoring algorithm for high-risk neonates. • Conduct literature search. • Identify clinical conditions for neurologic monitoring. • Identify testing & length for each condition. • Compare algorithm with ACNS recommendations. • Identify & address barriers for pediatric neurology notification. • Submit algorithm to Dr. Ostrander, Dr. Tufts, & Neuro NICU committee for feedback, adjust as necessary. • Algorithm approved. • Algorithm introduced to clinicians. • Request feedback from clinicians. • Resources placed at sites. • Laminated reminders attached to equipment. Assess appropriate neurologic • Submit IRB proposal for testing, length of time from comparisons. order to initiation of • Complete chart review of monitoring, and timeliness of 10 pts prior to algorithm pediatric neurology implementation. notification after presentation • Chart review of 5 pts after given. algorithm. Neurologic Monitoring algorithm introduced to clinicians at implementation sites (Level 3 & Level 4 NICUs). Disseminate results to Neuro NICU committee and poster presentation abstract submitted. • Develop presentation of findings. • Format abstract for poster presentation according to conference algorithm. • • Algorithm introduced. • Binder and resources provided. • IRB approval received. • Charts reviewed and data analyzed. • Document comparative data findings. • Findings to be presented April 3, 2017. • Abstract submitted for poster presentation. Formatted: Font color: Gray-85% Formatted: List Paragraph, Bulleted + Level: 1 + Aligned at: 0" + Indent at: 0.25" Implementation NEUROLOGIC MONITORING OF NEONATES 23 The initial Doctorate of Nursing Practice (DNP) project proposal was presented and passed, see Appendix A, and then the implementation phase of the project was begun. This was completed during the Fall 2016 semester. The first objective was implemented through completion of a literature search to determine best evidence for neurologic monitoring to capture prognostic data and aid in seizure identification. Clinical conditions for the Neurologic Monitoring algorithm were identified. The literature served as the foundation for determining the appropriate monitoring technology and length of testing using cEEG alone, aEEG alone, or a combination of both methods. A Neurologic Monitoring algorithm was developed after identifying the 8 clinical conditions most likely to suffer seizures based on ACNS recommendations, see Appendix C. A literature review was conducted to find the best evidence for neurologic monitoring and recommended duration of testing for each clinical condition. The student then outlined a table of each clinical condition, the type of EEG monitoring, and duration of testing based on the literature search. The table was then compared to the ACNS recommendations to see if the evidence matched. The table was then forwarded to the content expert for review. The content expert also had several colleagues review the content; no changes were made at that time. Once approved the contents were placed in algorithm form and presented to the Neuro-NICU steering committee for review. Wording on cEEG was changed to cVEEG (continuous video EEG) to use a term the local staff was more familiar with. The algorithm addressed local issues regarding timely ordering, application, and pediatric neurology notification of an infant with monitoring needs. The algorithm was submitted to the content expert for this project, Dr. Betsy Ostrander, for initial review and feedback. Next, the draft was sent to the project chair, Dr. Gillian Tufts. The algorithm was approved to the Formatted: Indent: First line: 0.5" NEUROLOGIC MONITORING OF NEONATES 24 Neuro NICU steering committee, a multidisciplinary team with a variety of expertise, for additional feedback regarding the algorithm or barriers that may be present at the facility. The algorithm was then forwarded to the Neonatology group for approval at both implementation sites; approval was received. The implementation process of the second objective was introduction of the algorithm to the clinicians at each implementation site through various methods of communication. The information was geared to engage a variety of individuals. To promote clinician adoption of this clinical practice change, a PowerPoint presentation of the algorithm was distributed, hard copies of the algorithm was provided to each clinician at one of the implementation sites, a laminated copy was placed in a practice binder at each implementation site as well as uploaded to an electronic reference database at one of the facilities. To further promote timely neurology notification, a laminated reminder to page the attending neurologist was placed on the EEG equipment. The third objective was completed as an IRB for retrospective patient chart audits was submitted and the project was deemed exempt, IRB oversight was not needed, see Appendix B. Once exemption was obtained chart audits were completed to compare the type of neurologic monitoring and the duration of the testing ordered, and neurology consultation and involvement. Ten patient charts were audited prior to algorithm implementation (1 chart did not fit criteria and was removed from the results) and 5 patient charts post-implementation. The fourth objective was implemented by development of a presentation to disseminate results of the chart audits surrounding neurologic monitoring practices after the algorithm was implemented. This presentation was provided to members of the Neuro NICU steering committee at one of their monthly meetings. Additionally, an abstract of the project was NEUROLOGIC MONITORING OF NEONATES 25 submitted for poster presentation to the 2017 ANN Las Vegas Conference, National Neonatal/Mother Baby/Advanced Practice Conference. Evaluation The first objective was determined to be successful when it was submitted and approved Formatted: Indent: First line: 0.5" for use at the implementation sites and was adopted by the neonatology teams as practice. Objective number two was met when the when the algorithm was introduced to clinicians at the implementation sites through a variety of mediums. The resources outlined to promote recall of the algorithm and adherence to the process outlined in the implementation steps were completed (copies of the algorithm were provided for clinicians, a copy was placed in a practice binder and uploaded to an electronic database, and reminders placed on the equipment). The third objective was satisfied after several steps were completed. First, an application was submitted to the IRB and an exemption for the chart audits awarded. Chart audits of 10 patients with HIE pre-implementation and 5 patients with HIE after implementation of the algorithm was completed to evaluate neurologic monitoring ordered, duration of testing, pediatric neurology consult ordered on admission along with a daily note while on monitoring, and seizure detection. Successful completion of the fourth objective was dissemination of the chart audit results to the Neuro NICU steering committee. In addition, an abstract for a poster presentation of the Neurologic Monitoring algorithm and findings was submitted to the ANN conference. Results Chart audits were completed on 10 patients who qualified for neurologic monitoring prior to algorithm implementation and on 5 patients pwho qualified for neurologic monitoring Formatted: Indent: First line: 0.5" NEUROLOGIC MONITORING OF NEONATES 26 post-implementation. The algorithm was implemented on October 10, 2016. The preimplementation audits were conducted on patients with HIE from January 1, 2016-October 9, 2016. Post-implementation audits were conducted on patients with HIE from October 10, 2016February 15, 2016. One of the pre-implementation infants was removed from analysis by the student at the recommendation of the content expert. T as this infant presented at day of life 9 instead of at birth and with different circumstances oand had other unusual clinical variables. The chart audits included: the type of neurologic monitoring ordered on admit, was a neurology consult ordered at admission, if a neurology note was completed daily while the infant was on EEG monitoring, was the monitoring timeframe of monitoring according to recommendations, and characteristics that would indicate neurologic monitoring and neurology consultation would benefit the infant, family, and the neonatology team. Before implementation of the algorithm 56% of patients who suffered from HIE had appropriate neurologic monitoring ordered. Three of the 9 infants had only an aEEG ordered, 1 was monitored with just only cEEG, and 5 were monitored with both aEEG and cEEG (this matches with the algorithm and evidence-based recommendations). There have been 5 infants with HIE since implementation of the algorithm. No infants were monitored with only aEEG technology, 2 (40%) were placed only on cEEG, and 3 (60%) had both aEEG and cEEG applied at admission. Post-implementation audits indicated a 4% improvement in appropriate monitoring (both aEEG and cEEG) for this clinical condition. There have been 5 infants with HIE since implementation of the algorithm. No infants were monitored with only aEEG technology, 2 (40%) were placed only on cEEG, and 3 (60%) had both aEEG and cEEG applied at admission. The audit process alsose charts were also audited to determined if a neurology consult Formatted: Indent: First line: 0.5" NEUROLOGIC MONITORING OF NEONATES 27 was ordered at admission. Pre-implementation findings included 5 of 9 infants (56%) had a neurology consult ordered at admission. Four of the 9 (44%) pre-algorithm implementation infants did not ever have a neurology consult ordered ever during hospitalization. Postimplementation audits found that all 5 (100%) of infants had a neurology consult ordered at admission and all indicated that neurology the notification was completed. Prior to implementation of the algorithm a daily neurology note was completed 78% of the time that an infant was being monitored. In 22% of the pre-implementation charts a daily note was not completed as neurology had not been notified of the study. After implementation neurology notes were in place 100% of the time an infant was placed on monitoring; this coupled with 100% of post-implementation infants having a neurologic consult ordered on admission indicates that notification of this important expert in neurologic care was contacted. Chart audits to assess if the recommended time-frame for monitoring was also conducted. Prior to implementation of the algorithm only 44% of infants were monitored for the recommended time-frame whereas post-implementation infants received appropriate monitoring in 80% of cases. The one infant who was not monitored for the correct timeframe had his cEEG removed 6 hours early because he was extremely edematous so the aEEG monitor would not remain in place. One-third of the infants both pre- and post-implementation experienced seizures. These seizures were sub-clinical in nature. Appropriate neurologic monitoring was vital in identifying seizures and initiation of pharmaceutical intervention to quell seizure activity was begun. Monitoring allowed the clinicians to determine if the chosen medication stopped seizure activity. Two infants required multiple medication changes and neurologic monitoring was vital to identification of this necessary pharmaceutical adjustment. NEUROLOGIC MONITORING OF NEONATES 28 Neurology consultations provided expert guidance to manage these infants from a neurologic standpoint and support the care team when providing families with prognostic data regarding outcomes and quality of life. A third of the infants studied expired prior to discharge; this specialty consultation combined with data from neurologic monitoring was valuable for the care team both in management and communication with family members. The data from monitoring provided additional information that clinicians utilized to inform families of neurologic damage when decisions regarding continuation of care were made. Implementation of the Neurologic Monitoring algorithm appears to have made a difference in clinician consistency of ordering appropriate testing and duration of monitoring in the short time since adoption. Most importantly, a significant improvement of neurologic consultation ordering and notification at the time of admission was impressive. Neurology involvement is vital for accurate identification of subclinical seizure diagnosis, pharmaceutical management, and expert insight into further neurologic monitoring, imaging, patient management, and outcomes. Challenges for this analysis included inconsistent nursing documentation of the time EEG monitoring was placed. A small sample size secondary to the timeframe from implementation to completion of this project. And the inability to track the timeframe from order initiation to neurology contact. Recommendations Continued use of the algorithm with a yearly chart audit of all clinical conditions covered in the algorithm will be helpful to further identify trends and opportunities for improvement. An annual training update of the algorithm provided to clinicians and registered nurses regarding the importance of timely application of monitoring and neurology involvement Formatted: Indent: First line: 0.5" NEUROLOGIC MONITORING OF NEONATES 29 will be critical in continued adherence to this evidence-based tool. Neurology and neonatal clinicians should continue to utilize monitoring data to guide seizure pharmaceutical management, weaning, and cessation of pharmaceutical treatment. Neurology and neonatology should work together to alter the algorithm if new evidence emerges from the literature. Finally, as quality improvement audits continue, data obtained from these sites regarding evidence-based neurologic monitoring, seizure identification, pharmaceutical management of seizure activity, and outcome data from the implementation sites can impact neonatal neurologic care on a national level as the information is shared with other Neonatal centers through entry into the NICHD database, conferences, and literature updates. DNP Essentials The DNP essentials applicable to this project are essential II and III. The second DNP essential is: Organizational and Systems Leadership for Quality Improvement and Systems Thinking. The third DNP essential III: Clinical Scholarship and Analytical Methods for Evidence-Based Practice. The doctorate prepared nurse practitioner has developed the ability to analyze and synthesize evidence to improve health outcomes. The DNP essential II relates and supports this DNP project through working with local NICU leaders in identifying the need for the project and in the implementation phase. Quality improvement strategies and systems thinking were applied as the algorithm was introduced to clinicians to improve practice. Chart audits were then completed and can continue to be analyzed to improve practice in the implementation sites. Formatted: Indent: First line: 0.5" NEUROLOGIC MONITORING OF NEONATES 30 The purpose of this project was to develop a clinical practice algorithm for appropriate neurologic monitoring for critically ill neonates. The process to develop the algorithm and the future research possibilities are supported through DNP essential III. Evidence-based literature was reviewed and information was then gathered, analyzed, and synthesized regarding neurologic monitoring for a variety of clinical conditions affecting neonates and the algorithm was then developed. The implementation of this guideline in a local level III and level IV NICU will provide data to guide future practice on a local and national level. Comparisons of data obtained from implementation of this algorithm may be made with national statistics as the implementation sites are part of the Neonatal Research Network. Conclusion The ACNS and literature review of the evidence supports the use of consistent neurologic monitoring technology for specific periods of time to best identify seizure activity in neonates at high-risk for seizure development. Practices at two local facilities were reported by the subject matter expert as highly variable and neurology notification was not always timely. This project served to develop a neurologic monitoring algorithm for 8 high-risk clinical conditions with recommended testing and duration along with education to promote best practice. A quality improvement effort was undertaken as retrospective chart reviews were done assessing if the neurologic monitoring algorithm improved appropriate EEG monitoring, proper monitoring timeframe, and neurology consult on admission. The charts of infants with HIE from January 2016-September 2016 (pre-implementation, n=9) and October 2016-February 2017 (post-implementation n=5) where reviewed and compared for the aforementioned aims. While Formatted: Indent: First line: 0.5" NEUROLOGIC MONITORING OF NEONATES 31 the consistency of monitoring and duration of testing seems to have more closely mirrored the algorithm post-implementation, it will be beneficial to compare similar sample sizes to more effectively analyze outcomes in the future. The most significant improvement postimplementation was neurology consultation ordered on admission with notification of the neurologist. The expertise of a pediatric neurologist is vital to improve outcomes for these atrisk infants. In addition, the timely diagnosis of subclinical seizures and pharmaceutical management will ultimately lead to improved outcomes. The prognostic data captured from the monitoring and expert input from neurology was also beneficial to the care team and families regarding withdrawal of support for infants who did not survive to discharge. Based on these limited findings, this project improved care to infants who are at risk for seizure development in the NICU through consistent appropriate monitoring and neurology involvement at admission. Continued quality improvement efforts will provide further information and solidify the impact of this algorithm. Future results can be used to guide future evidence for best practice of neurologic monitoring and seizure identification along with pharmaceutical management of high risk infants. NEUROLOGIC MONITORING OF NEONATES 32 Formatted: Left, Indent: First line: 0" References Abend, N. S., & Wusthoff, C. J. (2012). Neonatal seizures and status epilepticus. Journal of Clinical Neurophysiology: Official Publication of the American Electroencephalographic Society, 29(5), 441-448. https://doi.org/10.1097/WNP.0b013e31826bd90d Azzopardi, D. (2015). Clinical applications of cerebral function monitoring in neonates. Seminars in Fetal and Neonatal Medicine, 20(3), 154-163. http://doi.org/10.1016/j.siny.2015.02.001 Crowley M. A., & Stork, E.K. (2015) Therapy for cardiorespiratory failure in the neonate in Martin RJ, Fanaroff AA, Walsh MC, eds. Fanaroff and Martin's Neonatal-Perinatal Medicine. 10th ed. Philadelphia, PA; Elsevier, 1170-1185. Davis, A. S., Hintz, S. R., Van Meurs, K. P., Li, L., Das, A., Stoll, B. J., … Higgins, R. D. (2010). Seizures in extremely low birth weight infants are associated with adverse outcome. The Journal of Pediatrics, 157(5), 720-725.e2. http://doi.org/10.1016/j.jpeds.2010.04.065 NEUROLOGIC MONITORING OF NEONATES 33 Foreman, S. W., & Thorngate, L. (2011). 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Diffusion of innovation theory. Canadian journal of nursing Informatics, 6(2). Theory in Nursing Informatics Column. Retrieved from http://cjni.net/journal/?p=1444 Laptook, A. (2014). Amplitude integrated electroencephalogram (aEEG): has it found its niche in neonatal intensive care unit? Jornal de Pediatria, 90(2), 102-104. http://doi.org/10.1016/j.jped.2013.12.001 Kato, T., Okumura, A., Hayakawa, F., Tsuji, T., Hayashi, S., & Natsume, J. (2013). Amplitudeintegrated electroencephalogram 1 h after birth in a preterm infant with cystic periventricular leukomalacia. Brain and Development, 35(1), 75-78. https://doi.org/10.1016/j.braindev.2011.11.010 NEUROLOGIC MONITORING OF NEONATES McCrea, H. J. & Ment, L. R. (2008). The diagnosis, management, and postnatal prevention of intraventricular hemorrhage in the preterm neonate. Clinics in Perinatology 35(4), 777-792. http://doi.org/10.1016/j.clp.2008.07.014 Naim, M. Y., Gaynor, J. W., Chen, J., Nicolson, S. C., Fuller, S., Spray, T. L., … Abend, N. S. (2015). Subclinical seizures identified by postoperative electroencephalographic monitoring is common after neonatal cardiac surgery. The Journal of Thoracic and Cardiovascular Surgery, 150(1), 169-180. http://doi.org/10.1016/j.jtcvs.2015.03.045 Rahman, S., Footitt, E. J., Varadkar, S., & Clayton, P. T. (2013). Inborn errors of metabolism causing epilepsy. Developmental Medicine & Child Neurology, 55(1), 23-36. https://doi.org/10.1111/j.1469-8749.2012.04406.x Payne, E. T., Zhao, X. Y., Frndova, H., McBain, K., Sharma, R., Hutchison, J. S., & Hahn, C. D. (2014). Seizure burden is independently associated with short term outcome in critically ill children. Brain, 137(5), 1429-1438. http://doi.org/10.1093/brain/awu042 Perlman JM. The relationship between systemic hemodynamic perturbations and periventricular intraventricular hemorrhage-a historical perspective. Seminars in Pediatric Neurology 2009;16(4):191-199pmid:19945653 Prasad, A. N., & Hoffmann, G. F. (2010, May). 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Retrieved from http://apps.who.int/iris/bitstream/10665/77756/1/9789241548304_eng.pdf Appendix A: DNP Project Initial Defense 36 NEUROLOGIC MONITORING OF NEONATES 37 NEUROLOGIC MONITORING OF NEONATES 38 NEUROLOGIC MONITORING OF NEONATES 39 NEUROLOGIC MONITORING OF NEONATES 40 NEUROLOGIC MONITORING OF NEONATES Appendix B: IRB Exemption 41 NEUROLOGIC MONITORING OF NEONATES Appendix C: Neurologic Monitoring Algorithm 42 NEUROLOGIC MONITORING OF NEONATES 43 NEUROLOGIC MONITORING OF NEONATES 44 NEUROLOGIC MONITORING OF NEONATES Appendix D: Clinician Engagement Sample of tags applied to EEG equipment? NICU, has Pediatric Neurology been notified of this study? 45 NEUROLOGIC MONITORING OF NEONATES 46 NEUROLOGIC MONITORING OF NEONATES 47 NEUROLOGIC MONITORING OF NEONATES 48 NEUROLOGIC MONITORING OF NEONATES 49 NEUROLOGIC MONITORING OF NEONATES 50 NEUROLOGIC MONITORING OF NEONATES 51 NEUROLOGIC MONITORING OF NEONATES Appendix E: DNP Poster Presentation 52 NEUROLOGIC MONITORING OF NEONATES Appendix F: Dissemination PowerPoint 53 NEUROLOGIC MONITORING OF NEONATES 54 NEUROLOGIC MONITORING OF NEONATES 55 NEUROLOGIC MONITORING OF NEONATES 56 NEUROLOGIC MONITORING OF NEONATES 57 Formatted: Normal NEUROLOGIC MONITORING OF NEONATES 58 Appendix F: Dissemination PowerPoint Formatted: Font: Font color: Text 1 Formatted: Font: Font color: Text 1 Formatted: Font: Font color: Text 1 NEUROLOGIC MONITORING OF NEONATES 59 Formatted: Font: Font color: Text 1 Formatted: Font: Font color: Text 1 Formatted: Font: Font color: Text 1 NEUROLOGIC MONITORING OF NEONATES 60 Formatted: Font: Font color: Text 1 Formatted: Font: Font color: Text 1 Formatted: Font: Font color: Text 1 NEUROLOGIC MONITORING OF NEONATES 61 Formatted: Font: Font color: Text 1 Formatted: Font: Font color: Text 1 Formatted: Font: Font color: Text 1