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A Decision-Tree Approach to Assist in Forecasting the Outcomes of the Neonatal Brain Injury.
Neamțu, Bogdan Mihai; Visa, Gabriela; Maniu, Ionela; Ognean, Maria Livia; Pérez-Elvira, Rubén; Dragomir, Andrei; Agudo, Maria; Șofariu, Ciprian Radu; Gheonea, Mihaela; Pitic, Antoniu; Brad, Remus; Matei, Claudiu; Teodoru, Minodora; Bacila, Ciprian.
  • Neamțu BM; Clinical Department, Faculty of Medicine, Lucian Blaga University Sibiu, 550169 Sibiu, Romania.
  • Visa G; Department of Computer Science and Electrical Engineering, Faculty of Engineering, Lucian Blaga University Sibiu, 550025 Sibiu, Romania.
  • Maniu I; Research and Telemedicine Center in Pediatric Neurology, Pediatric Clinical Hospital Sibiu, 550166 Sibiu, Romania.
  • Ognean ML; Research and Telemedicine Center in Pediatric Neurology, Pediatric Clinical Hospital Sibiu, 550166 Sibiu, Romania.
  • Pérez-Elvira R; Research and Telemedicine Center in Pediatric Neurology, Pediatric Clinical Hospital Sibiu, 550166 Sibiu, Romania.
  • Dragomir A; Department of Mathematics and Informatics, Faculty of Sciences, Lucian Blaga University Sibiu, 550012 Sibiu, Romania.
  • Agudo M; Clinical Department, Faculty of Medicine, Lucian Blaga University Sibiu, 550169 Sibiu, Romania.
  • Șofariu CR; Neonatology Department, Sibiu Clinical and Emergency County Hospital, Lucian Blaga University Sibiu, 550245 Sibiu, Romania.
  • Gheonea M; Neuropsychophysiology Lab., NEPSA Rehabilitación Neurológica, 37003 Salamanca, Spain.
  • Pitic A; Biological and Health Psychology Department, Universidad Autónoma de Madrid, 280048 Madrid, Spain.
  • Brad R; Research and Telemedicine Center in Pediatric Neurology, Pediatric Clinical Hospital Sibiu, 550166 Sibiu, Romania.
  • Matei C; The N.1 Institute for Health, National University of Singapore, 28, Medical Dr. #05-COR, Singapore 117456, Singapore.
  • Teodoru M; Neuropsychophysiology Lab., NEPSA Rehabilitación Neurológica, 37003 Salamanca, Spain.
  • Bacila C; Research and Telemedicine Center in Pediatric Neurology, Pediatric Clinical Hospital Sibiu, 550166 Sibiu, Romania.
Int J Environ Res Public Health ; 18(9)2021 04 30.
Article in English | MEDLINE | ID: covidwho-1231474
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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Brain Injuries / Epilepsy Type of study: Etiology study / Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Limits: Female / Humans / Infant / Infant, Newborn / Pregnancy Language: English Year: 2021 Document Type: Article Affiliation country: Ijerph18094807

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Brain Injuries / Epilepsy Type of study: Etiology study / Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Limits: Female / Humans / Infant / Infant, Newborn / Pregnancy Language: English Year: 2021 Document Type: Article Affiliation country: Ijerph18094807