A latent class model for defining severe hemorrhage

Experience from the PROMMTT study

Mohammad H. Rahbar, Deborah J. Del Junco, Hanwen Huang, Jing Ning, Erin E. Fox, Xuan Zhang, Martin A. Schreiber, Karen J. Brasel, Eileen M. Bulger, Charles E. Wade, Bryan A. Cotton, Herb A. Phelan, Mitchell J. Cohen, John G. Myers, Louis H. Alarcon, Peter Muskat, John B. Holcomb

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

BACKGROUND: Several predictive models have been developed to identify trauma patients who have had severe hemorrhage (SH) and may need a massive transfusion (MT) protocol. However, almost all these models define SH as the transfusion of 10 or more units of red blood cells (RBCs) within 24 hours of emergency department admission (also known as MT). This definition excludes some patients with SH, especially those who die before a 10th unit of RBCs could be transfused, which calls the validity of these prediction models into question. We show how a latent class model could improve the accuracy of identifying the SH patients. METHODS: Modeling SH classification as a latent variable, we estimate the posterior probability of a patient in SH based on emergency department admission variables (systolic blood pressure, heart rate, pH, hemoglobin), the 24-hour blood product use (plasma/RBC and platelet/ RBC ratios), and 24-hour survival status. We define the SH subgroup as those having a posterior probability of 0.5 or greater. We compare our new classification of SH with that of the traditional MT using data from PROMMTT study. RESULTS: Of the 1,245 patients, 913 had complete data, which were used in the latent class model. About 25.3% of patients were classified as SH. The overall agreement between the MTand SH classifications was 83.8%. However, among 49 patients who died before receiving the 10th unit of RBCs, 41 (84%) were classified as SH. Seven (87.5%) of the remaining eight patients who were not classified as SH had head injury. CONCLUSION: Our definition of SH based on the aforementioned latent class model has an advantage of improving on the traditional MT definition by identifying SH patients who die before receiving the 10th unit of RBCs. We recommend further improvements to more accurately classify SH patients, which could replace the traditional definition ofMT for use in developing prediction algorithms.

Original languageEnglish (US)
JournalJournal of Trauma and Acute Care Surgery
Volume75
Issue number1 SUPPL1
DOIs
StatePublished - 2013

Fingerprint

Hemorrhage
Erythrocytes
Hospital Emergency Service
Blood Pressure
Craniocerebral Trauma
Hemoglobins
Blood Platelets
Heart Rate
Survival
Wounds and Injuries

Keywords

  • Hemorrhage
  • Latent class analysis
  • Massive transfusion
  • PROMMTT
  • Trauma

ASJC Scopus subject areas

  • Critical Care and Intensive Care Medicine
  • Surgery

Cite this

Rahbar, M. H., Del Junco, D. J., Huang, H., Ning, J., Fox, E. E., Zhang, X., ... Holcomb, J. B. (2013). A latent class model for defining severe hemorrhage: Experience from the PROMMTT study. Journal of Trauma and Acute Care Surgery, 75(1 SUPPL1). https://doi.org/10.1097/TA.0b013e31828fa3d3

A latent class model for defining severe hemorrhage : Experience from the PROMMTT study. / Rahbar, Mohammad H.; Del Junco, Deborah J.; Huang, Hanwen; Ning, Jing; Fox, Erin E.; Zhang, Xuan; Schreiber, Martin A.; Brasel, Karen J.; Bulger, Eileen M.; Wade, Charles E.; Cotton, Bryan A.; Phelan, Herb A.; Cohen, Mitchell J.; Myers, John G.; Alarcon, Louis H.; Muskat, Peter; Holcomb, John B.

In: Journal of Trauma and Acute Care Surgery, Vol. 75, No. 1 SUPPL1, 2013.

Research output: Contribution to journalArticle

Rahbar, MH, Del Junco, DJ, Huang, H, Ning, J, Fox, EE, Zhang, X, Schreiber, MA, Brasel, KJ, Bulger, EM, Wade, CE, Cotton, BA, Phelan, HA, Cohen, MJ, Myers, JG, Alarcon, LH, Muskat, P & Holcomb, JB 2013, 'A latent class model for defining severe hemorrhage: Experience from the PROMMTT study', Journal of Trauma and Acute Care Surgery, vol. 75, no. 1 SUPPL1. https://doi.org/10.1097/TA.0b013e31828fa3d3
Rahbar, Mohammad H. ; Del Junco, Deborah J. ; Huang, Hanwen ; Ning, Jing ; Fox, Erin E. ; Zhang, Xuan ; Schreiber, Martin A. ; Brasel, Karen J. ; Bulger, Eileen M. ; Wade, Charles E. ; Cotton, Bryan A. ; Phelan, Herb A. ; Cohen, Mitchell J. ; Myers, John G. ; Alarcon, Louis H. ; Muskat, Peter ; Holcomb, John B. / A latent class model for defining severe hemorrhage : Experience from the PROMMTT study. In: Journal of Trauma and Acute Care Surgery. 2013 ; Vol. 75, No. 1 SUPPL1.
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abstract = "BACKGROUND: Several predictive models have been developed to identify trauma patients who have had severe hemorrhage (SH) and may need a massive transfusion (MT) protocol. However, almost all these models define SH as the transfusion of 10 or more units of red blood cells (RBCs) within 24 hours of emergency department admission (also known as MT). This definition excludes some patients with SH, especially those who die before a 10th unit of RBCs could be transfused, which calls the validity of these prediction models into question. We show how a latent class model could improve the accuracy of identifying the SH patients. METHODS: Modeling SH classification as a latent variable, we estimate the posterior probability of a patient in SH based on emergency department admission variables (systolic blood pressure, heart rate, pH, hemoglobin), the 24-hour blood product use (plasma/RBC and platelet/ RBC ratios), and 24-hour survival status. We define the SH subgroup as those having a posterior probability of 0.5 or greater. We compare our new classification of SH with that of the traditional MT using data from PROMMTT study. RESULTS: Of the 1,245 patients, 913 had complete data, which were used in the latent class model. About 25.3{\%} of patients were classified as SH. The overall agreement between the MTand SH classifications was 83.8{\%}. However, among 49 patients who died before receiving the 10th unit of RBCs, 41 (84{\%}) were classified as SH. Seven (87.5{\%}) of the remaining eight patients who were not classified as SH had head injury. CONCLUSION: Our definition of SH based on the aforementioned latent class model has an advantage of improving on the traditional MT definition by identifying SH patients who die before receiving the 10th unit of RBCs. We recommend further improvements to more accurately classify SH patients, which could replace the traditional definition ofMT for use in developing prediction algorithms.",
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AU - Del Junco, Deborah J.

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AU - Ning, Jing

AU - Fox, Erin E.

AU - Zhang, Xuan

AU - Schreiber, Martin A.

AU - Brasel, Karen J.

AU - Bulger, Eileen M.

AU - Wade, Charles E.

AU - Cotton, Bryan A.

AU - Phelan, Herb A.

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