Development and Validation of a Dynamic Prediction Model for Massive Hemorrhage in Trauma
Objectives. Early warning prediction of massive hemorrhages can greatly reduce mortality in trauma patients. This study aimed to develop and validate dynamic prediction models for massive hemorrhage in trauma patients. Methods. Based on vital signs (e.g., heart rate, respiratory rate, pulse pressure...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2022-01-01
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| Series: | Emergency Medicine International |
| Online Access: | http://dx.doi.org/10.1155/2022/9438159 |
| _version_ | 1811177900393627648 |
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| author | Chengyu Guo Maolin Tian Minghui Gong Fei Pan Hui Han Chunping Li Tanshi Li |
| author_facet | Chengyu Guo Maolin Tian Minghui Gong Fei Pan Hui Han Chunping Li Tanshi Li |
| author_sort | Chengyu Guo |
| collection | DOAJ |
| description | Objectives. Early warning prediction of massive hemorrhages can greatly reduce mortality in trauma patients. This study aimed to develop and validate dynamic prediction models for massive hemorrhage in trauma patients. Methods. Based on vital signs (e.g., heart rate, respiratory rate, pulse pressure, and peripheral oxygen saturation) time-series data and the gated recurrent unit algorithm, we characterized a group of models to flexibly and dynamically predict the occurrence of massive hemorrhages in the subsequent T hours (where T = 1, 2, and 3). Models were evaluated in terms of accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, and the area under the curve (AUC). Results. Results show that of the 2205 trauma patients selected for model development, a total of 265 (12.02%) had a massive hemorrhage. The AUCs of the model in the 1-h-group, 2-h-group, and 3-h-group were 0.763 (95% CI: 0.708–0.820), 0.775 (95% CI: 0.728–0.823), and 0.756 (95% CI: 0.715–0.797), respectively. Finally, the models were used in a web calculator and information system for the hospital emergency department. Conclusions. This study developed and validated a group of dynamic prediction models based on vital sign time-series data and a deep-learning algorithm to assist medical staff in the early diagnosis and dynamic prediction of a future massive hemorrhage in trauma. |
| first_indexed | 2024-04-11T06:09:03Z |
| format | Article |
| id | doaj.art-cee7343567bd459a914f0413bf7e2e70 |
| institution | Directory Open Access Journal |
| issn | 2090-2859 |
| language | English |
| last_indexed | 2024-04-11T06:09:03Z |
| publishDate | 2022-01-01 |
| publisher | Hindawi Limited |
| record_format | Article |
| series | Emergency Medicine International |
| spelling | doaj.art-cee7343567bd459a914f0413bf7e2e702022-12-22T04:41:23ZengHindawi LimitedEmergency Medicine International2090-28592022-01-01202210.1155/2022/9438159Development and Validation of a Dynamic Prediction Model for Massive Hemorrhage in TraumaChengyu Guo0Maolin Tian1Minghui Gong2Fei Pan3Hui Han4Chunping Li5Tanshi Li6School of MedicineSchool of Information EngineeringSchool of SoftwareDepartment of EmergencyDepartment of EmergencySchool of SoftwareSchool of MedicineObjectives. Early warning prediction of massive hemorrhages can greatly reduce mortality in trauma patients. This study aimed to develop and validate dynamic prediction models for massive hemorrhage in trauma patients. Methods. Based on vital signs (e.g., heart rate, respiratory rate, pulse pressure, and peripheral oxygen saturation) time-series data and the gated recurrent unit algorithm, we characterized a group of models to flexibly and dynamically predict the occurrence of massive hemorrhages in the subsequent T hours (where T = 1, 2, and 3). Models were evaluated in terms of accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, and the area under the curve (AUC). Results. Results show that of the 2205 trauma patients selected for model development, a total of 265 (12.02%) had a massive hemorrhage. The AUCs of the model in the 1-h-group, 2-h-group, and 3-h-group were 0.763 (95% CI: 0.708–0.820), 0.775 (95% CI: 0.728–0.823), and 0.756 (95% CI: 0.715–0.797), respectively. Finally, the models were used in a web calculator and information system for the hospital emergency department. Conclusions. This study developed and validated a group of dynamic prediction models based on vital sign time-series data and a deep-learning algorithm to assist medical staff in the early diagnosis and dynamic prediction of a future massive hemorrhage in trauma.http://dx.doi.org/10.1155/2022/9438159 |
| spellingShingle | Chengyu Guo Maolin Tian Minghui Gong Fei Pan Hui Han Chunping Li Tanshi Li Development and Validation of a Dynamic Prediction Model for Massive Hemorrhage in Trauma Emergency Medicine International |
| title | Development and Validation of a Dynamic Prediction Model for Massive Hemorrhage in Trauma |
| title_full | Development and Validation of a Dynamic Prediction Model for Massive Hemorrhage in Trauma |
| title_fullStr | Development and Validation of a Dynamic Prediction Model for Massive Hemorrhage in Trauma |
| title_full_unstemmed | Development and Validation of a Dynamic Prediction Model for Massive Hemorrhage in Trauma |
| title_short | Development and Validation of a Dynamic Prediction Model for Massive Hemorrhage in Trauma |
| title_sort | development and validation of a dynamic prediction model for massive hemorrhage in trauma |
| url | http://dx.doi.org/10.1155/2022/9438159 |
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