Development and Validation of a Dynamic Prediction Model for Massive Hemorrhage in Trauma.
Emerg Med Int
; 2022: 9438159, 2022.
Article
em En
| MEDLINE
| ID: mdl-36506794
ABSTRACT
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
/
Screening_studies
Idioma:
En
Revista:
Emerg Med Int
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
China