Open AccessPrediction of early prognosis after traumatic brain injury by multifactor model
Author(s) -
Yang Bocheng,
Sun Xiaochuan,
Shi Quanhong,
Dan Wei,
Zhan Yan,
Zheng Dinghao,
Xia Yulong,
Xie Yanfeng,
Jiang Li
Publication year - 2022
Publication title -
cns neuroscience and therapeutics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.403
H-Index - 69
eISSN - 1755-5949
pISSN - 1755-5930
DOI - 10.1111/cns.13935
Subject(s) - glasgow coma scale , set (abstract data type) , test set , medicine , traumatic brain injury , artificial intelligence , computer science , surgery , psychiatry , programming language
Aims To design a model to predict the early prognosis of patients with traumatic brain injury (TBI) based on parameters that can be quickly obtained in emergency conditions from medical history, physical examination, and supplementary examinations. Methods The medical records of TBI patients who were hospitalized in two medical institutions between June 2015 and June 2021 were collected and analyzed. Patients were divided into the training set, validation set, and testing set. The possible predictive indicators were screened after analyzing the data of patients in the training set. Then prediction models were found based on the possible predictive indicators in the training set. Data of patients in the validation set and the testing set was provided to validate the predictive values of the models. Results Age, Glasgow coma scale score, Apolipoprotein E genotype, damage area, serum C‐reactive protein, and interleukin‐8 (IL‐8) levels, and Marshall computed tomography score were found associated with early prognosis of TBI patients. The accuracy of the early prognosis prediction model (EPPM) was 80%, and the sensitivity and specificity of the EPPM were 78.8% and 80.8% in the training set. The accuracy of the EPPM was 79%, and the sensitivity and specificity of the EPPM were 66.7% and 86.2% in the validation set. The accuracy of the early EPPM was 69.1%, and the sensitivity and specificity of the EPPM were 67.9% and 77.8% in the testing set. Conclusion Prediction models integrating general information, clinical manifestations, and auxiliary examination results may provide a reliable and rapid method to evaluate and predict the early prognosis of TBI patients.