A novel machine learning-assisted clinical diagnosis support model for early identification of pancreatic injuries in patients with blunt abdominal trauma: a cross-national study
Abstract. Background. The recognition of pancreatic injury in blunt abdominal trauma is often severely delayed in clinical practice. The aim of this study was to develop a machine learning model to support clinical diagnosis for early detection of abdominal trauma. Methods. We retrospectively analyz...
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| Format: | Article |
| Language: | English |
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Wolters Kluwer Health/LWW
2023-12-01
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| Series: | Emergency and Critical Care Medicine |
| Online Access: | http://journals.lww.com/10.1097/EC9.0000000000000096 |
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| author | Sai Huang Xuan Zhang Bo Yang Yue Teng Li Mao Lili Wang Jing Wang Xuan Zhou Li Chen Yuan Yao Cong Feng |
| author_facet | Sai Huang Xuan Zhang Bo Yang Yue Teng Li Mao Lili Wang Jing Wang Xuan Zhou Li Chen Yuan Yao Cong Feng |
| author_sort | Sai Huang |
| collection | DOAJ |
| description | Abstract. Background. The recognition of pancreatic injury in blunt abdominal trauma is often severely delayed in clinical practice. The aim of this study was to develop a machine learning model to support clinical diagnosis for early detection of abdominal trauma.
Methods. We retrospectively analyzed of a large intensive care unit database (Medical Information Mart for Intensive Care [MIMIC]-IV) for model development and internal validation of the model, and performed outer validation based on a cross-national data set. Logistic regression was used to develop three models (PI-12, PI-12-2, and PI-24). Univariate and multivariate analyses were used to determine variables in each model. The primary outcome was early detection of a pancreatic injury of any grade in patients with blunt abdominal trauma in the first 24 hours after hospitalization.
Results. The incidence of pancreatic injuries was 5.56% (n = 18) and 6.06% (n = 6) in the development (n = 324) and internal validation (n = 99) cohorts, respectively. Internal validation cohort showed good discrimination with an area under the receiver operator characteristic curve (AUC) value of 0.84 (95% confidence interval [CI]: 0.71–0.96) for PI-24. PI-24 had the best AUC, specificity, and positive predictive value (PPV) of all models, and thus it was chosen as the final model to support clinical diagnosis. PI-24 performed well in the outer validation cohort with an AUC value of 0.82 (95% CI: 0.65–0.98), specificity of 0.97 (95% CI: 0.91–1.00), and PPV of 0.67 (95% CI: 0.00–1.00).
Conclusion. A novel machine learning-based model was developed to support clinical diagnosis to detect pancreatic injuries in patients with blunt abdominal trauma at an early stage. |
| format | Article |
| id | doaj-art-c87c43942b364763b5d331fdfe03534b |
| institution | OA Journals |
| issn | 2097-0617 2693-860X |
| language | English |
| publishDate | 2023-12-01 |
| publisher | Wolters Kluwer Health/LWW |
| record_format | Article |
| series | Emergency and Critical Care Medicine |
| spelling | doaj-art-c87c43942b364763b5d331fdfe03534b2025-08-20T02:26:10ZengWolters Kluwer Health/LWWEmergency and Critical Care Medicine2097-06172693-860X2023-12-013414214810.1097/EC9.0000000000000096202312000-00002A novel machine learning-assisted clinical diagnosis support model for early identification of pancreatic injuries in patients with blunt abdominal trauma: a cross-national studySai Huang0Xuan Zhang1Bo Yang2Yue Teng3Li Mao4Lili Wang5Jing Wang6Xuan Zhou7Li Chen8Yuan Yao9Cong Feng10a Department of Hematology, Fifth Medical Center of Chinese PLA General Hospital, Beijing, Chinac Hospital Management Institute, Medical Innovation Research Department, Chinese PLA General Hospital, Beijing, Chinad Department of General Thoracic Surgery, First Medical Center of Chinese PLA General Hospital, Beijing, Chinae Department of Emergency Medicine, General Hospital of Northern Theatre Command, Shenyang, Liaoning, Chinac Hospital Management Institute, Medical Innovation Research Department, Chinese PLA General Hospital, Beijing, Chinaf Department of General Medicine, First Medical Center of Chinese PLA General Hospital, Beijing, Chinaf Department of General Medicine, First Medical Center of Chinese PLA General Hospital, Beijing, Chinag Department of Emergency, Hainan Hospital of Chinese PLA General Hospital, Sanya, Hainan, Chinaf Department of General Medicine, First Medical Center of Chinese PLA General Hospital, Beijing, Chinac Hospital Management Institute, Medical Innovation Research Department, Chinese PLA General Hospital, Beijing, Chinab National Clinical Research Center of Geriatric Diseases, Chinese PLA General Hospital, Beijing, ChinaAbstract. Background. The recognition of pancreatic injury in blunt abdominal trauma is often severely delayed in clinical practice. The aim of this study was to develop a machine learning model to support clinical diagnosis for early detection of abdominal trauma. Methods. We retrospectively analyzed of a large intensive care unit database (Medical Information Mart for Intensive Care [MIMIC]-IV) for model development and internal validation of the model, and performed outer validation based on a cross-national data set. Logistic regression was used to develop three models (PI-12, PI-12-2, and PI-24). Univariate and multivariate analyses were used to determine variables in each model. The primary outcome was early detection of a pancreatic injury of any grade in patients with blunt abdominal trauma in the first 24 hours after hospitalization. Results. The incidence of pancreatic injuries was 5.56% (n = 18) and 6.06% (n = 6) in the development (n = 324) and internal validation (n = 99) cohorts, respectively. Internal validation cohort showed good discrimination with an area under the receiver operator characteristic curve (AUC) value of 0.84 (95% confidence interval [CI]: 0.71–0.96) for PI-24. PI-24 had the best AUC, specificity, and positive predictive value (PPV) of all models, and thus it was chosen as the final model to support clinical diagnosis. PI-24 performed well in the outer validation cohort with an AUC value of 0.82 (95% CI: 0.65–0.98), specificity of 0.97 (95% CI: 0.91–1.00), and PPV of 0.67 (95% CI: 0.00–1.00). Conclusion. A novel machine learning-based model was developed to support clinical diagnosis to detect pancreatic injuries in patients with blunt abdominal trauma at an early stage.http://journals.lww.com/10.1097/EC9.0000000000000096 |
| spellingShingle | Sai Huang Xuan Zhang Bo Yang Yue Teng Li Mao Lili Wang Jing Wang Xuan Zhou Li Chen Yuan Yao Cong Feng A novel machine learning-assisted clinical diagnosis support model for early identification of pancreatic injuries in patients with blunt abdominal trauma: a cross-national study Emergency and Critical Care Medicine |
| title | A novel machine learning-assisted clinical diagnosis support model for early identification of pancreatic injuries in patients with blunt abdominal trauma: a cross-national study |
| title_full | A novel machine learning-assisted clinical diagnosis support model for early identification of pancreatic injuries in patients with blunt abdominal trauma: a cross-national study |
| title_fullStr | A novel machine learning-assisted clinical diagnosis support model for early identification of pancreatic injuries in patients with blunt abdominal trauma: a cross-national study |
| title_full_unstemmed | A novel machine learning-assisted clinical diagnosis support model for early identification of pancreatic injuries in patients with blunt abdominal trauma: a cross-national study |
| title_short | A novel machine learning-assisted clinical diagnosis support model for early identification of pancreatic injuries in patients with blunt abdominal trauma: a cross-national study |
| title_sort | novel machine learning assisted clinical diagnosis support model for early identification of pancreatic injuries in patients with blunt abdominal trauma a cross national study |
| url | http://journals.lww.com/10.1097/EC9.0000000000000096 |
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