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

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Listed:
  • Bogdan Mihai Neamțu

    (Clinical Department, Faculty of Medicine, Lucian Blaga University Sibiu, 550169 Sibiu, Romania
    Department of Computer Science and Electrical Engineering, Faculty of Engineering, Lucian Blaga University Sibiu, 550025 Sibiu, Romania
    Research and Telemedicine Center in Pediatric Neurology, Pediatric Clinical Hospital Sibiu, 550166 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
    Department of Mathematics and Informatics, Faculty of Sciences, Lucian Blaga University Sibiu, 550012 Sibiu, Romania)

  • Maria Livia Ognean

    (Clinical Department, Faculty of Medicine, Lucian Blaga University Sibiu, 550169 Sibiu, Romania
    Neonatology Department, Sibiu Clinical and Emergency County Hospital, Lucian Blaga University Sibiu, 550245 Sibiu, Romania)

  • Rubén Pérez-Elvira

    (Neuropsychophysiology Lab., NEPSA Rehabilitación Neurológica, 37003 Salamanca, Spain
    Biological and Health Psychology Department, Universidad Autónoma de Madrid, 280048 Madrid, Spain)

  • Andrei Dragomir

    (Research and Telemedicine Center in Pediatric Neurology, Pediatric Clinical Hospital Sibiu, 550166 Sibiu, Romania
    The N.1 Institute for Health, National University of Singapore, 28, Medical Dr. #05-COR, Singapore 117456, Singapore)

  • 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
    Dr. Gheorghe Preda Psychiatric Hospital, 550082 Sibiu, Romania)

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.

Suggested Citation

  • 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 & Cla, 2021. "A Decision-Tree Approach to Assist in Forecasting the Outcomes of the Neonatal Brain Injury," IJERPH, MDPI, vol. 18(9), pages 1-19, April.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:9:p:4807-:d:546957
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    References listed on IDEAS

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    1. Hapfelmeier, A. & Hothorn, T. & Ulm, K., 2012. "Recursive partitioning on incomplete data using surrogate decisions and multiple imputation," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1552-1565.
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    Cited by:

    1. Špela But & Brigita Celar & Petja Fister, 2023. "Tackling Neonatal Sepsis—Can It Be Predicted?," IJERPH, MDPI, vol. 20(4), pages 1-13, February.
    2. Chia-Tien Hsu & Kai-Chih Pai & Lun-Chi Chen & Shau-Hung Lin & Ming-Ju Wu, 2023. "Machine Learning Models to Predict the Risk of Rapidly Progressive Kidney Disease and the Need for Nephrology Referral in Adult Patients with Type 2 Diabetes," IJERPH, MDPI, vol. 20(4), pages 1-16, February.
    3. He Li & Yefei Liu & Rong Zhao & Xiaofang Zhang & Zhaonian Zhang, 2022. "How Did the Risk of Poverty-Stricken Population Return to Poverty in the Karst Ecologically Fragile Areas Come into Being?—Evidence from China," Land, MDPI, vol. 11(10), pages 1-20, September.

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