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Prediction of early prognosis after traumatic brain injury by multifactor model
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Prediction of early prognosis after traumatic brain injury by multifactor model
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Journal Article
Prediction of early prognosis after traumatic brain injury by multifactor model
2022
Overview
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. By analyzing the admission information and examination results of patients with traumatic brain injury (TBI), the factors that may affect the early prognosis of patients with TBI were obtained. Then these factors are combined by mathematics to establish a prediction model to implement the effective prediction of early prognosis of patients with TBI. The results show that this idea is feasible.
