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Year : 2020  |  Volume : 15  |  Issue : 4  |  Page : 409-415

Clinical nomogram predicting intracranial injury in pediatric traumatic brain injury

1 Division of Neurosurgery, Department of Surgery, Faculty of Medicine, Prince of Songkla University, Hat Yai, Thailand
2 Department of Computer Science, Faculty of Science, Prince of Songkla University, Hat Yai, Thailand
3 Department of Biotechnology, Faculty of Science and Technology, Thammasat University (Rangsit Campus), Khlong Luang, Thailand

Correspondence Address:
Dr. Thara Tunthanathip
Division of Neurosurgery, Department of Surgery, Faculty of Medicine Prince of Songkla University, Hat Yai, Songkhla 90110.
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/JPN.JPN_11_20

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Background: There are differences in injured mechanisms among pediatric traumatic brain injury (TBI) in developing countries. This study aimed to develop and validate clinical nomogram for predicting intracranial injury in pediatric TBI that will be implicated in balancing the unnecessary investigation in the general practice. Materials and Methods: The retrospective study was conducted in all patients who were younger than 15 years old and underwent computed tomography (CT) of the brain after TBI in southern Thailand. Injured mechanisms and clinical characteristics were identified and analyzed with binary logistic regression for predicting intracranial injury. Using random sampling without replacement, the total data was split into nomogram developing dataset (80%) and testing dataset (20%). Therefore, a nomogram was constructed and applied via the web-based application from the developing dataset. Using testing dataset, validation as binary classifiers was performed by various probabilities levels. Results: A total of 900 victims were enrolled. The mean age was 87.2 (standard deviation [SD] 57.4) months, and 65.3% of all patients injured were from road traffic accidents. The rate of positive findings in CT of the brain was 32.8%. A nomogram was developed from the significant variables, including age groups, road traffic accidents, loss of consciousness, scalp hematoma/laceration, motor weakness, signs of basilar skull fraction, low Glasgow Coma Scale score, and pupillary light reflex.Therefore, a nomogram was developed from 80% of data and was validated from 20% of data. The accuracy, sensitivity, specificity, positive, and negative predictive values of the nomogram were 0.83, 0.42, 1.00, 1.00, and 0.81 at a cutoff value of 0.5 probability. Conclusion: This study provides a clinical nomogram that will be applied to making decisions in general practice as a diagnostic tool from high specificity.


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