ORIGINAL ARTICLE   

Journal of Neurosurgical Sciences 2021 February;65(1):69-74

DOI: 10.23736/S0390-5616.17.04123-6

Copyright © 2017 EDIZIONI MINERVA MEDICA

lingua: Inglese

Artificial neural network predicts hemorrhagic contusions following decompressive craniotomy in traumatic brain injury

Jian-Li WANG, Guo-Liang JIN, Zi-Gang YUAN ✉

BACKGROUND: This study aimed to explore relevant factors of hemorrhagic contusions following decompressive craniotomy (DC) in traumatic brain injury (TBI) and create an artificial neural network (ANN) prediction model of the risk factors of hemorrhagic contusions.
METHODS: This study analyzed 425 patients with TBI who underwent DC in the Neurosurgery Department of Shaoxing People’s Hospital between 2009 and 2014. Patients were divided into two groups according to the first postoperative CT scans: hemorrhage group and non-hemorrhage group. Gender, age, preoperative situations (Initial Rotterdam CT Score, GCS Score, pupillary response, laboratory data and preoperative hematoma), the time gap between trauma and DC, postoperative CT scans, and Glasgow Outcome Scale (GOS) scores were recorded. ANN was used to predict hematoma. Correlation analysis was used to state the relationship between increased hemorrhage volumes and GOS scores.
RESULTS: The ANN prediction model was established. This model included 11 parameters: initial Rotterdam CT score, GCS score, C-reactive protein, age, the time gap between trauma and DC, pupillary response, platelet count, bone-flap size, glucose level, hernia magnitude and preoperative hematoma volume. The overall predictive accuracy of the model was 73.0%.
CONCLUSIONS: Initial Rotterdam CT scores and GCS scores may predict the risk of expansion contusions following DC. The ANN prediction model has a high accuracy to forecast hemorrhage.

KEY WORDS: Brain Injuries, traumatic; Subarachnoid hemorrhage, traumatic; Decompressive craniectomy