Predictors of Anemia Intolerance for Real-Time Transfusion Decision-Making During Resuscitation of Trauma Subjects: A Machine Learning Approach Using Heart Rate Variability.

OBJECTIVES

RBC transfusion in anemic patients with sustainable tolerance may cause harm, emphasizing the need for reliable metrics that quantify adequacy (oxygen delivery ≥ demand) and sustainability (oxygen delivery remains adequate without transfusion) of compensatory physiology. Our objective was to identify personalized predictors of anemia intolerance (inadequate and unsustainable physiologic compensation) that predict the likelihood of transfusion benefit. We studied adult trauma subjects at arrival to the emergency department, employing machine learning to evaluate ability of heart rate variability (HRV) to predict subsequent need for clinically indicated significant RBC transfusion.

DESIGN

This single-center retrospective cohort study used electronic medical records data from patients admitted to a specialized trauma care hospital between January 2016 and December 2018.

SETTING

Trauma resuscitation unit (TRU).

PATIENTS

Adult trauma subjects with at least 3 hours of stay in the TRU, without RBC transfusion during the first hour at TRU but, with receipt or nonreceipt of transfusion in the second and/or third hour were included. Availability of electrocardiogram tracings for at least 50% of the first hour of stay in the TRU was also considered for inclusion in the study.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

The primary binary outcome variable, a clinically indicated significant transfusion, was if a subject received RBC transfusion or not during the second and third hour stay in the TRU (transfusion vs. no transfusion). Patient clinical information, and HRV parameters generated from a 5-minute electrocardiogram recording during the first hour of admission were used as predictors for predicting transfusion. We evaluated five predefined prediction models for transfusion using random forest algorithm, varying the inclusion of demographic, clinical, trauma, and HRV variables. Model predictive performance was assessed using area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and Shapley analysis was conducted to identify key contributing variables. The analysis included 269 patients (126: transfusion cohort and 133: no transfusion cohort), who met the inclusion criteria. The model, which included demographic, clinical laboratory, trauma, and HRV variables, had an AUROC of 0.86, a sensitivity of 78%, and a specificity of 75% in predicting transfusion throughout the 3-hour study period. The model with only HRV variables showed comparable predictive performance (AUROC: 0.72) compared with other models with less than 35% false positive and negative rates. Among HRV parameters, lower values of log-transformed very low frequency absolute power predicted transfusion consistently.

CONCLUSIONS

HRV parameters collected during the first 5-10 minutes after admission, when combined with basic clinical information that is immediately available upon emergency admission, augmented ability to predict potential for RBC transfusion, suggesting this metric may be incorporated into structured approaches to personalized transfusion decision-making.