A Decision-Tree Approach to Assist in Forecasting the Outcomes of the Neonatal Brain Injury

Bogdan Mihai Neamțu, Gabriela Visa, Ionela Maniu, Maria Livia Ognean, Rubén Pérez-Elvira, Andrei Dragomir, Maria Agudo, Ciprian Radu Șofariu, Mihaela Gheonea, Antoniu Pitic, Remus Brad, Claudiu Matei, Minodora Teodoru and Ciprian Băcilă
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Bogdan Mihai Neamțu: Clinical Department, Faculty of Medicine, Lucian Blaga University Sibiu, 550169 Sibiu, Romania
Gabriela Visa: Research and Telemedicine Center in Pediatric Neurology, Pediatric Clinical Hospital Sibiu, 550166 Sibiu, Romania
Ionela Maniu: Research and Telemedicine Center in Pediatric Neurology, Pediatric Clinical Hospital Sibiu, 550166 Sibiu, Romania
Maria Livia Ognean: Clinical Department, Faculty of Medicine, Lucian Blaga University Sibiu, 550169 Sibiu, Romania
Rubén Pérez-Elvira: Neuropsychophysiology Lab., NEPSA Rehabilitación Neurológica, 37003 Salamanca, Spain
Andrei Dragomir: Research and Telemedicine Center in Pediatric Neurology, Pediatric Clinical Hospital Sibiu, 550166 Sibiu, Romania
Maria Agudo: Neuropsychophysiology Lab., NEPSA Rehabilitación Neurológica, 37003 Salamanca, Spain
Ciprian Radu Șofariu: Research and Telemedicine Center in Pediatric Neurology, Pediatric Clinical Hospital Sibiu, 550166 Sibiu, Romania
Mihaela Gheonea: Neonatology Department, Craiova Clinical and Emergency County Hospital, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania
Antoniu Pitic: Department of Computer Science and Electrical Engineering, Faculty of Engineering, Lucian Blaga University Sibiu, 550025 Sibiu, Romania
Remus Brad: Department of Computer Science and Electrical Engineering, Faculty of Engineering, Lucian Blaga University Sibiu, 550025 Sibiu, Romania
Claudiu Matei: Dental and Nursing Medical Department, Faculty of Medicine, Lucian Blaga University Sibiu, 550169 Sibiu, Romania
Minodora Teodoru: Clinical Department, Faculty of Medicine, Lucian Blaga University Sibiu, 550169 Sibiu, Romania
Ciprian Băcilă: Dental and Nursing Medical Department, Faculty of Medicine, Lucian Blaga University Sibiu, 550169 Sibiu, Romania

IJERPH, 2021, vol. 18, issue 9, 1-19

Abstract: Neonatal brain injury or neonatal encephalopathy (NE) is a significant morbidity and mortality factor in preterm and full-term newborns. NE has an incidence in the range of 2.5 to 3.5 per 1000 live births carrying a considerable burden for neurological outcomes such as epilepsy, cerebral palsy, cognitive impairments, and hydrocephaly. Many scoring systems based on different risk factor combinations in regression models have been proposed to predict abnormal outcomes. Birthweight, gestational age, Apgar scores, pH, ultrasound and MRI biomarkers, seizures onset, EEG pattern, and seizure duration were the most referred predictors in the literature. Our study proposes a decision-tree approach based on clinical risk factors for abnormal outcomes in newborns with the neurological syndrome to assist in neonatal encephalopathy prognosis as a complementary tool to the acknowledged scoring systems. We retrospectively studied 188 newborns with associated encephalopathy and seizures in the perinatal period. Etiology and abnormal outcomes were assessed through correlations with the risk factors. We computed mean, median, odds ratios values for birth weight, gestational age, 1-min Apgar Score, 5-min Apgar score, seizures onset, and seizures duration monitoring, applying standard statistical methods first. Subsequently, CART (classification and regression trees) and cluster analysis were employed, further adjusting the medians. Out of 188 cases, 84 were associated to abnormal outcomes. The hierarchy on etiology frequencies was dominated by cerebrovascular impairments, metabolic anomalies, and infections. Both preterms and full-terms at risk were bundled in specific categories defined as high-risk 75–100%, intermediate risk 52.9%, and low risk 0–25% after CART algorithm implementation. Cluster analysis illustrated the median values, profiling at a glance the preterm model in high-risk groups and a full-term model in the inter-mediate-risk category. Our study illustrates that, in addition to standard statistics methodologies, decision-tree approaches could provide a first-step tool for the prognosis of the abnormal outcome in newborns with encephalopathy.

Keywords: neonatal brain injury; risk factors; abnormal outcomes; seizures; neurodevelopment; decision-tree algorithms (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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