A prediction model for massive hemorrhage in trauma: a retrospective observational study
Dublin Core
Title
A prediction model for massive hemorrhage in trauma: a retrospective observational study
Subject
Trauma, Massive hemorrhage, LASSO, Prediction model, Assisted diagnosis
Description
Background: Massive hemorrhage is the main cause of preventable death after trauma. This study aimed to establish prediction models for early diagnosis of massive hemorrhage in trauma.
Methods: Using the trauma database of Chinese PLA General Hospital, two logistic regression (LR) models were
ft to predict the risk of massive hemorrhage in trauma. Sixty-two potential predictive variables, including clinical
symptoms, vital signs, laboratory tests, and imaging results, were included in this study. Variable selection was done
using the least absolute shrinkage and selection operator (LASSO) method. The frst model was constructed based on
LASSO feature selection results. The second model was constructed based on the frst vital sign recordings of trauma
patients after admission. Finally, a web calculator was developed for clinical use.
Results: A total of 2353 patients were included in this study. There were 377 (16.02%) patients with massive hemorrhage. The selected predictive variables were heart rate (OR: 1.01; 95% CI: 1.01–1.02; P<0.001), pulse pressure (OR:
0.99; 95% CI: 0.98–0.99; P=0.004), base excess (OR: 0.90; 95% CI: 0.87–0.93; P<0.001), hemoglobin (OR: 0.95; 95% CI:
0.95–0.96; P<0.001), displaced pelvic fracture (OR: 2.13; 95% CI: 1.48–3.06; P<0.001), and a positive computed tomography scan or positive focused assessment with sonography for trauma (OR: 1.62; 95% CI: 1.21–2.18; P=0.001). Model 1,
which was developed based on LASSO feature selection results and LR, displayed excellent discrimination (AUC: 0.894;
95% CI: 0.875–0.912), good calibration (P=0.405), and clinical utility. In addition, the predictive power of model 1 was
better than that of model 2 (AUC: 0.718; 95% CI: 0.679–0.757). Model 1 was deployed as a public web tool (http://82.
156.217.249:8080/).
Conclusions: Our study developed and validated prediction models to assist medical staf in the early diagnosis of
massive hemorrhage in trauma. An open web calculator was developed to facilitate the practical application of the
research results.
Methods: Using the trauma database of Chinese PLA General Hospital, two logistic regression (LR) models were
ft to predict the risk of massive hemorrhage in trauma. Sixty-two potential predictive variables, including clinical
symptoms, vital signs, laboratory tests, and imaging results, were included in this study. Variable selection was done
using the least absolute shrinkage and selection operator (LASSO) method. The frst model was constructed based on
LASSO feature selection results. The second model was constructed based on the frst vital sign recordings of trauma
patients after admission. Finally, a web calculator was developed for clinical use.
Results: A total of 2353 patients were included in this study. There were 377 (16.02%) patients with massive hemorrhage. The selected predictive variables were heart rate (OR: 1.01; 95% CI: 1.01–1.02; P<0.001), pulse pressure (OR:
0.99; 95% CI: 0.98–0.99; P=0.004), base excess (OR: 0.90; 95% CI: 0.87–0.93; P<0.001), hemoglobin (OR: 0.95; 95% CI:
0.95–0.96; P<0.001), displaced pelvic fracture (OR: 2.13; 95% CI: 1.48–3.06; P<0.001), and a positive computed tomography scan or positive focused assessment with sonography for trauma (OR: 1.62; 95% CI: 1.21–2.18; P=0.001). Model 1,
which was developed based on LASSO feature selection results and LR, displayed excellent discrimination (AUC: 0.894;
95% CI: 0.875–0.912), good calibration (P=0.405), and clinical utility. In addition, the predictive power of model 1 was
better than that of model 2 (AUC: 0.718; 95% CI: 0.679–0.757). Model 1 was deployed as a public web tool (http://82.
156.217.249:8080/).
Conclusions: Our study developed and validated prediction models to assist medical staf in the early diagnosis of
massive hemorrhage in trauma. An open web calculator was developed to facilitate the practical application of the
research results.
Creator
Chengyu Guo, Minghui Gong, Lei Ji, Fei Pan, Hui Han, Chunping Li and Tanshi Li
Publisher
BMC Emergency Medicine
Date
(2022) 22:180
Contributor
Fajar bagus W
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Chengyu Guo, Minghui Gong, Lei Ji, Fei Pan, Hui Han, Chunping Li and Tanshi Li, “A prediction model for massive hemorrhage in trauma: a retrospective observational study,” Repository Horizon University Indonesia, accessed September 20, 2024, https://repository.horizon.ac.id/items/show/4243.