The establishment and validation of a prediction model for traumatic intracranial injury patients: a reliable nomogram

Jia Yi Chen; Guang Yong Jin; Long Huang Zeng; Bu Qing Ma; Hui Chen; Nan Yuan Gu; Kai Qiu; Fu Tian; Lu Pan; Wei Hu; Dong Cheng Liang

doi:10.3389/fneur.2023.1165020

The establishment and validation of a prediction model for traumatic intracranial injury patients: a reliable nomogram

Front Neurol. 2023 May 25:14:1165020. doi: 10.3389/fneur.2023.1165020. eCollection 2023.

Authors

Jia Yi Chen^#^{1

2}, Guang Yong Jin^#^{1

2}, Long Huang Zeng^#^{1

2}, Bu Qing Ma^#^{1

2}, Hui Chen^{1

2}, Nan Yuan Gu^{1

2}, Kai Qiu^{1

2}, Fu Tian^{1

2}, Lu Pan^{1

2}, Wei Hu¹, Dong Cheng Liang³

Affiliations

¹ Department of Critical Care Medicine, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China.
² Department of Critical Care Medicine, Hangzhou Geriatric Hospital, Hangzhou, China.
³ Department of Intensive Care Unit, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China.

^# Contributed equally.

Abstract

Objective: Traumatic brain injury (TBI) leads to death and disability. This study developed an effective prognostic nomogram for assessing the risk factors for TBI mortality.

Method: Data were extracted from an online database called "Multiparameter Intelligent Monitoring in Intensive Care IV" (MIMIC IV). The ICD code obtained data from 2,551 TBI persons (first ICU stay, >18 years old) from this database. R divided samples into 7:3 training and testing cohorts. The univariate analysis determined whether the two cohorts differed statistically in baseline data. This research used forward stepwise logistic regression after independent prognostic factors for these TBI patients. The optimal variables were selected for the model by the optimal subset method. The optimal feature subsets in pattern recognition improved the model prediction, and the minimum BIC forest of the high-dimensional mixed graph model achieved a better prediction effect. A nomogram-labeled TBI-IHM model containing these risk factors was made by nomology in State software. Least Squares OLS was used to build linear models, and then the Receiver Operating Characteristic (ROC) curve was plotted. The TBI-IHM nomogram model's validity was determined by receiver operating characteristic curves (AUCs), correction curve, Hosmer-Lemeshow test, integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision-curve analysis (DCA).

Result: The eight features with a minimal BIC model were mannitol use, mechanical ventilation, vasopressor use, international normalized ratio, urea nitrogen, respiratory rate, and cerebrovascular disease. The proposed nomogram (TBI-IHM model) was the best mortality prediction model, with better discrimination and superior model fitting for severely ill TBI patients staying in ICU. The model's receiver operating characteristic curve (ROC) was the best compared to the seven other models. It might be clinically helpful for doctors to make clinical decisions.

Conclusion: The proposed nomogram (TBI-IHM model) has significant potential as a clinical utility in predicting mortality in TBI patients.

Keywords: mortality; nomogram; prediction model; risk factors; traumatic brain injury.

Grants and funding

This work was supported by the Construction Fund of Medical Key Disciplines of Hangzhou (Grant Number: OO20200485), the Project of Hangzhou Science and Technology (Grant Number: 20201203B198), and Hangzhou Health Science and Technology Plan (A20220388).