184 – Meta-Analysis, Latent Class Analysis, Interrater Agreement, and Control Charts in Epidemiology
A Latent Class Model for Defining Severe Hemorrhage
Hanwen Huang
The University of Texas Health Science Center
Deborah del Junco
The University of Texas Health Science Center
Jing Ning
MD Anderson Cancer Center
Mohammad Rahbar
The University of Texas Health Science Center at Houston
There is no diagnostic test to identify trauma patients who have had severe hemorrhage (SH) and may need a massive transfusion protocol (MTP). However, several predictive models have been developed based on the traditional definition of massive transfusion, which is transfusion of 10 units of red blood cells (RBCs) within 24 hours of Emergency Department (ED) admission. This definition excludes patients with severe bleeding who died before a 10th unit of RBCs could be transfused, resulting in survival bias. The lack of a valid definition for severe hemorrhage calls these prediction models into question. We proposed a latent class model for identifying a subgroup of patients with SH.We developed an EM algorithm for estimating the posterior probability of being an SH patient based on information at ED admission, blood product utilization, and survival status during the first 24 hours. We assessed the performance of our latent class model in classifying SH patients and compare to the traditional massive transfusion definition using data from a retrospective trauma transfusion study.
