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Yu M, Wang S, He K, Teng F, Deng J, Guo S, Yin X, Lu Q, Gu W. Predicting the complexity and mortality of polytrauma patients with machine learning models. Sci Rep 2024; 14:8302. [PMID: 38594313 PMCID: PMC11004111 DOI: 10.1038/s41598-024-58830-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Accepted: 04/03/2024] [Indexed: 04/11/2024] Open
Abstract
We aim to develop machine learning (ML) models for predicting the complexity and mortality of polytrauma patients using clinical features, including physician diagnoses and physiological data. We conducted a retrospective analysis of a cohort comprising 756 polytrauma patients admitted to the intensive care unit (ICU) at Pizhou People's Hospital Trauma Center, Jiangsu, China between 2020 and 2022. Clinical parameters encompassed demographics, vital signs, laboratory values, clinical scores and physician diagnoses. The two primary outcomes considered were mortality and complexity. We developed ML models to predict polytrauma mortality or complexity using four ML algorithms, including Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN) and eXtreme Gradient Boosting (XGBoost). We assessed the models' performance and compared the optimal ML model against three existing trauma evaluation scores, including Injury Severity Score (ISS), Trauma Index (TI) and Glasgow Coma Scale (GCS). In addition, we identified several important clinical predictors that made contributions to the prognostic models. The XGBoost-based polytrauma mortality prediction model demonstrated a predictive ability with an accuracy of 90% and an F-score of 88%, outperforming SVM, RF and ANN models. In comparison to conventional scoring systems, the XGBoost model had substantial improvements in predicting the mortality of polytrauma patients. External validation yielded strong stability and generalization with an accuracy of up to 91% and an AUC of 82%. To predict polytrauma complexity, the XGBoost model maintained its performance over other models and scoring systems with good calibration and discrimination abilities. Feature importance analysis highlighted several clinical predictors of polytrauma complexity and mortality, such as Intracranial hematoma (ICH). Leveraging ML algorithms in polytrauma care can enhance the prognostic estimation of polytrauma patients. This approach may have potential value in the management of polytrauma patients.
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Affiliation(s)
- Meiqi Yu
- School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing, 210023, Jiangsu, China
- Jiangsu Province Engineering Research Center of TCM Intelligence Health Service, Nanjing University of Chinese Medicine, Nanjing, 210023, Jiangsu, China
| | - Shen Wang
- Department of Orthopedics and Traumatology, Peking University People's Hospital, Beijing, 100044, China
| | - Kai He
- School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing, 210023, Jiangsu, China
- Jiangsu Province Engineering Research Center of TCM Intelligence Health Service, Nanjing University of Chinese Medicine, Nanjing, 210023, Jiangsu, China
| | - Fei Teng
- Trauma Center, Pizhou People's Hospital, Xuzhou, 221300, Jiangsu, China
| | - Jin Deng
- Department of Orthopedics and Traumatology, Peking University People's Hospital, Beijing, 100044, China
| | - Shuhang Guo
- Department of Orthopedics and Traumatology, Peking University People's Hospital, Beijing, 100044, China
| | - Xiaofeng Yin
- Department of Orthopedics and Traumatology, Peking University People's Hospital, Beijing, 100044, China.
- Key Laboratory of Trauma and Neural Regeneration (Peking University), Ministry of Education, 100044, Beijing, China.
- National Center for Trauma Medicine, 100044, Beijing, China.
| | - Qingguo Lu
- Trauma Center, Pizhou People's Hospital, Xuzhou, 221300, Jiangsu, China.
| | - Wanjun Gu
- School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing, 210023, Jiangsu, China.
- Jiangsu Province Engineering Research Center of TCM Intelligence Health Service, Nanjing University of Chinese Medicine, Nanjing, 210023, Jiangsu, China.
- Collaborative Innovation Center of Jiangsu Province of Cancer Prevention and Treatment of Chinese Medicine, Nanjing University of Chinese Medicine, Nanjing, 210023, Jiangsu, China.
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