Machine Learning Model for Predicting Acute Respiratory Failure in Individuals With Moderate-to-Severe Traumatic Brain Injury

Rui Na Ma; Yi Xuan He; Fu Ping Bai; Zhi Peng Song; Ming Sheng Chen; Min Li

doi:10.3389/fmed.2021.793230

Machine Learning Model for Predicting Acute Respiratory Failure in Individuals With Moderate-to-Severe Traumatic Brain Injury

Front Med (Lausanne). 2021 Dec 24:8:793230. doi: 10.3389/fmed.2021.793230. eCollection 2021.

Authors

Rui Na Ma¹, Yi Xuan He², Fu Ping Bai³, Zhi Peng Song², Ming Sheng Chen², Min Li²

Affiliations

¹ Department of Pulmonary and Critical Care Medicine, The Second Affiliated Hospitals of Fourth Military Medical University, Xi'an, China.
² Neurocritical Care Unit, Department of Neurosurgery, The Second Affiliated Hospitals of Fourth Military Medical University, Xi'an, China.
³ Department of Neurosurgery, Lin Fen Center Hospital, Lin Fen, China.

Abstract

Background: There is a high incidence of acute respiratory failure (ARF) in moderate or severe traumatic brain injury (M-STBI), worsening outcomes. This study aimed to design a predictive model for ARF. Methods: Adult patients with M-STBI [3 ≤ Glasgow Coma Scale (GCS) ≤ 12] with a definite history of brain trauma and abnormal head on CT images, obtained from September 2015 to May 2017, were included. Patients with age >80 years or <18 years, multiple injuries with TBI upon admission, or pregnancy (in women) were excluded. Two models based on machine learning extreme gradient boosting (XGBoost) or logistic regression, respectively, were developed for predicting ARF within 48 h upon admission. These models were evaluated by out-of-sample validation. The samples were assigned to the training and test sets at a ratio of 3:1. Results: In total, 312 patients were analyzed including 132 (42.3%) patients who had ARF. The GCS and the Marshall CT score, procalcitonin (PCT), and C-reactive protein (CRP) on admission significantly predicted ARF. The novel machine learning XGBoost model was superior to logistic regression model in predicting ARF [area under the receiver operating characteristic (AUROC) = 0.903, 95% CI, 0.834-0.966 vs. AUROC = 0.798, 95% CI, 0.697-0.899; p < 0.05]. Conclusion: The XGBoost model could better predict ARF in comparison with logistic regression-based model. Therefore, machine learning methods could help to develop and validate novel predictive models.

Keywords: XGBoost model; acute respiratory failure; logistic regression; machine learning; traumatic brain injury.