Screening of biomarkers and machine learning prediction in traumatic brain injury.
Authors
Affiliations (15)
Affiliations (15)
- School of Rehabilitation, Capital Medical University, Beijing, China.
- Department of Neurological Rehabilitation, Beijing Bo'ai Hospital, China Rehabilitation Research Center, Beijing, China.
- Department of Rehabilitation Medicine, Hebei General Hospital, Shijiazhuang, Hebei Province, China.
- Department of Rehabilitation Medicine, The First Hospital of Jilin University, Changchun, Jilin Province, China.
- The Affiliated Taian City Central Hospital of Qingdao University, Taian, Shandong Province, China.
- Department of Rehabilitation Medicine, Jiangbin Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China.
- Department of Rehabilitation, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province, China.
- Department of Rehabilitation Medicine, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China.
- Department of Rehabilitation Medicine, Nanjing Drum Tower Hospital, Clinical College of Nanjing Medical University, Nanjing, Jiangsu Province, China.
- Rehabilitation Medicine Center, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China.
- Center for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China.
- Department of Rehabilitation Medicine, Guangdong Sanjiu Brain Hospital, Guangzhou, Guangdong Province, China.
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, China.
- Department of Rehabilitation Medicine, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, Sichuan Province, China.
- China Rehabilitation Research Center, Beijing, China.
Abstract
Traumatic brain injury (TBI) is a major cause of disability and mortality worldwide. Accessible blood and hormonal biomarkers may be related to injury severity and cognitive status in patients undergoing neurorehabilitation. This study used explainable machine learning to explore the associations of routine blood parameters and hormones with radiological injury severity and cognitive status in TBI. In this prospective cross-sectional multicenter study, 154 patients with TBI were enrolled. Blood samples were collected within 1 week after admission to assess routine hematological indices, liver and renal function, blood lipids, and hormone levels. Injury severity was evaluated using the Helsinki CT Score, and cognitive status was assessed using the mini-mental state examination (MMSE). After preprocessing, data were split into training and validation sets at a ratio of 7:3. LASSO regression was used for feature selection, and six machine learning models were developed. Model performance was evaluated using <i>R</i> <sup>2</sup>, mean squared error, and mean absolute error. SHapley Additive exPlanations were used for interpretation. LASSO identified eight features for the Helsinki CT Score and four for MMSE. Random forest performed best for the Helsinki CT Score (validation <i>R</i> <sup>2</sup> = 0.06), whereas CatBoost performed best for MMSE (validation <i>R</i> <sup>2</sup> = 0.103). SHAP analysis indicated that IGF-1 was an important feature in both models. IGF-1 showed a possible nonlinear association with both outcomes. Routine blood and hormonal biomarkers, particularly IGF-1, may be associated with radiological injury severity and cognitive status in TBI. These findings are exploratory and require validation in larger longitudinal studies. Chinese Clinical Trial Registry: ChiCTR2300072902. Medical Research Registration Number: MR-11-23-023826.