A joint latent class model for classifying severely hemorrhaging trauma patients. (December 2015)

• Record Type: Journal Article
• Authors: Rahbar, Mohammad; Ning, Jing; Choi, Sangbum; Piao, Jin; Hong, Chuan; Huang, Hanwen; Junco, Deborah; Fox, Erin; Rahbar, Elaheh; Holcomb, John
• Is Part Of: BMC research notes. Volume 8:Number 1(2015)
• Page Start: 1
• Page End: 13
• Publication Date: 2015-12
• Subjects: Induced censoring -- Joint model -- Latent variable -- Massive transfusion -- Mixture -- Trauma; Medicine -- Periodicals; Biology -- Periodicals; 610.5
• DOI: 10.1186/s13104-015-1563-4
• Languages: English
• ISSNs: 1756-0500

Abstract

Abstract Background In trauma research, "massive transfusion" (MT), historically defined as receiving ≥10 units of red blood cells (RBCs) within 24 h of admission, has been routinely used as a "gold standard" for quantifying bleeding severity. Due to early in-hospital mortality, however, MT is subject to survivor bias and thus a poorly defined criterion to classify bleeding trauma patients. Methods Using the data from a retrospective trauma transfusion study, we applied a latent-class (LC) mixture model to identify severely hemorrhaging (SH) patients. Based on the joint distribution of cumulative units of RBCs and binary survival outcome at 24 h of admission, we applied an expectation-maximization (EM) algorithm to obtain model parameters. Estimated posterior probabilities were used for patients' classification and compared with the MT rule. To evaluate predictive performance of the LC-based classification, we examined the role of six clinical variables as predictors using two separate logistic regression models. Results Out of 471 trauma patients, 211 (45 %) were MT, while our latent SH classifier identified only 127 (27 %) of patients as SH. The agreement between the two classification methods was 73 %. A non-ignorable portion of patients (17 out of 68, 25 %) who died within 24 h were not classified as MT but the SH group included 62 patients (91 %) who died during the same period. Our comparison of the predictive models based on MT and SH revealed significant differences between the coefficients of potential predictors of patients who may be in need of activation of the massive transfusion protocol. Conclusions The traditional MT classification does not adequately reflect transfusion practices and outcomes during the trauma reception and initial resuscitation phase. Although we have demonstrated that joint latent class modeling could be used to correct for potential bias caused by misclassification of severely bleeding patients, improvement in this approach could be made in the presence of time to event data from prospective studies.