A prediction model for massive hemorrhage in trauma: a retrospective observational study.

ABSTRACT:

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 fit 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 first model was constructed based on LASSO feature selection results. The second model was constructed based on the first vital sign recordings of trauma patients after admission

SUBMITTER: Guo C 

PROVIDER: S-EPMC9661746 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature