Construction and preliminary trial test of a decision-making app for pre-hospital damage control resuscitation.
10.1016/j.cjtee.2024.11.001
- Author:
Haoyang YANG
1
;
Wenqiong DU
1
;
Zhaowen ZONG
2
,
3
;
Xin ZHONG
1
;
Yijun JIA
1
;
Renqing JIANG
1
;
Chenglin DAI
1
;
Zhao YE
1
Author Information
1. State Key Laboratory of Trauma, Burn and Combined Injury, Department for Combat Casualty Care Training, Army Medical University, Chongqing, 400038, China.
2. State Key Laboratory of Trauma, Burn and Combined Injury, Department for Combat Casualty Care Training, Army Medical University, Chongqing, 400038, China. Electronic address: zongzhaowen@
3. com.
- Publication Type:Journal Article
- Keywords:
App;
Decision-making;
Pre-hospital damage control resuscitation;
Severely injured patients;
Trauma
- MeSH:
Animals;
Swine;
Resuscitation/methods*;
Mobile Applications;
Humans;
Algorithms;
Emergency Medical Services/methods*;
Male;
Decision Making;
Female
- From:
Chinese Journal of Traumatology
2025;28(5):313-318
- CountryChina
- Language:English
-
Abstract:
PURPOSE:To construct a decision-making app for pre-hospital damage control resuscitation (PHDCR) for severely injured patients, and to make a preliminary trial test on the effectiveness and usability aspects of the constructed app.
METHODS:Decision-making algorithms were first established by a thorough literature review, and were then used to be learned by computer with 3 kinds of text segmentation algorithms, i.e., dictionary-based segmentation, machine learning algorithms based on labeling, and deep learning algorithms based on understanding. B/S architecture mode and Spring Boot were used as a framework to construct the app. A total of 16 Grade-5 medical students were recruited to test the effectiveness and usability aspects of the app by using an animal model-based test on simulated PHDCR. Twelve adult Bama miniature pigs were subjected to penetrating abdominal injuries and were randomly assigned to the 16 students, who were randomly divided into 2 groups (n = 8 each): group A (decided on PHDCR by themselves) and group B (decided on PHDCR with the aid of the app). The students were asked to complete the PHDCR within 1 h, and then blood samples were taken and thromboelastography, routine coagulation test, blood cell count, and blood gas analysis were examined. The lab examination results along with the value of mean arterial pressure were used to compare the resuscitation effects between the 2 groups. Furthermore, a 4-statement-based post-test survey on a 5-point Likert scale was performed in group B students to test the usability aspects of the constructed app.
RESULTS:With the above 3 kinds of text segmentation algorithm, B/S architecture mode, and Spring Boot as the development framework, the decision-making app for PHDCR was successfully constructed. The time to decide PHDCR was (28.8 ± 3.41) sec in group B, much shorter than that in group A (87.5 ± 8.53) sec (p < 0.001). The outcomes of animals treated by group B students were much better than that by group A students as indicated by higher mean arterial pressure, oxygen saturation and fibrinogen concentration and maximum amplitude, and lower R values in group B than those in group A. The post-test survey revealed that group B students gave a mean score of no less than 4 for all 4 statements.
CONCLUSION:A decision-making app for PHDCR was constructed in the present study and the preliminary trial test revealed that it could help to improve the resuscitation effect in animal models of penetrating abdominal injury.
