Beyond Hemoglobin Thresholds: A Retrospective Cohort Analysis of RBC Transfusion Decisions in ICU Patients

Blood transfusion is an essential therapy for patients receiving intensive care. The objective of the study was to better characterize the factors influencing the decision to transfuse RBCs during intensive care.

Retrospective cohort analysis using a new analytic method called decision time-interval analysis, which segments care into objectively defined sequential time intervals.

Three ICUs during the period from January 2018 to June 2023.

Adult ICU patients 18 years old or older.

None.

The pre-study planned outcome was the likelihood of RBC transfusion during each time interval of care. We analyzed 199,296 decision time-intervals occurring during 19,439 ICU encounters of 18,544 patients ranging in age from 18 to 103 years. The pre-transfusion hemoglobin concentration did not fully account for the likelihood of transfusion. Multivariable logistic regression analysis using either generalized linear mixed-effects or generalized estimating equations was used to model the impact of other clinical factors present at the time of the decision to transfuse for patients with hemoglobins in the 7-9 g/dL range. Factors that were significantly associated with an increased likelihood of transfusion included a decline in hemoglobin from the prior value, chest tube drainage, concurrent transfusion of plasma or platelets, prior RBC transfusion, use of extracorporeal membrane oxygenation, vasopressors, mechanical ventilation, renal replacement therapy, patient age, and male sex. The odds ratios for the principal clinical factors were significantly different in medical, surgical, and cardiac surgery ICUs.

The decision to transfuse RBC during ICU care is multifactorial and not adequately explained by the pre-transfusion hemoglobin concentration alone. Our findings have direct relevance for clinical guidelines regarding transfusion care, the interpretation of prior studies of transfusion based on hemoglobin concentration, and the opportunity for predictive analytics to improve patient outcomes.