TY  - JOUR
PY  - 2023//
TI  - Development and validation of a Bayesian network predicting neurosurgical intervention after injury in children and adolescents
JO  - Journal of trauma and acute care surgery
A1  - Sullivan, Travis M.
A1  - Sippel, Genevieve J.
A1  - Matison, Elizabeth A.
A1  - Gestrich-Thompson, Waverly V.
A1  - DeWitt, Peter E.
A1  - Carlisle, M. Adam
A1  - Oluigbo, David
A1  - Oluigbo, Chima
A1  - Bennett, Tellen D.
A1  - Burd, Randall S.
SP  - ePub
EP  - ePub
VL  - ePub
IS  - ePub
N2  - BACKGROUND: Timely surgical decompression improves functional outcomes and survival among children with traumatic brain injury (TBI) and increased intracranial pressure. Previous scoring systems for identifying the need for surgical decompression after TBI in children and adults have had several barriers to use. These barriers include the inability to generate a score with missing data, a requirement for radiographic imaging that may not be immediately available, and limited accuracy. To address these limitations, we developed a Bayesian network to predict the probability of neurosurgical intervention among injured children and adolescents (age 1 to 18 years) using physical exam findings and injury characteristics observable at hospital arrival. <br><br>METHODS: We obtained patient, injury, transportation, resuscitation, and procedure characteristics from the 2017 to 2019 Trauma Quality Improvement Project database. We trained and validated a Bayesian network to predict the probability of a neurosurgical intervention, defined as undergoing a craniotomy, craniectomy, or intracranial pressure monitor placement. We evaluated model performance using the area under the receiver operating characteristic (AUROC) and calibration curves. We evaluated the percentage of contribution of each input for predicting neurosurgical intervention using relative mutual information (RMI). <br><br>RESULTS: The final model included four predictor variables, including the Glasgow Coma Scale score (RMI 31.9%), pupillary response (RMI 11.6%), mechanism of injury (RMI 5.8%), and presence of prehospital cardiopulmonary resuscitation (RMI 0.8%). The model achieved an AUROC curve of 0.90 (95% CI 0.89, 0.91) and had a calibration slope of 0.77 (95% CI 0.29, 1.26) with a y-intercept of 0.05 (95% CI -0.14, 0.25). <br><br>CONCLUSION: We developed a Bayesian network that predicts neurosurgical intervention for all injured children using four factors immediately available on arrival. Compared to a binary threshold model, this probabilistic model may allow clinicians to stratify management strategies based on risk. LEVEL OF EVIDENCE: Prognostic, Level III.<p />  <p>Language: en</p>
LA  - en
SN  - 2163-0755
UR  - http://dx.doi.org/10.1097/TA.0000000000003935
ID  - ref1
ER  -