Physiologic Responses to Red Blood Cell Transfusion in Critically-Ill Pediatric Patients at a University PICU between 2014-2015

In critical care units across the world blood transfusion decisions currently rely heavily on a patient’s hemoglobin concentration, though there is a consensus that clinical judgment also plays an important role. There is a lack of quantitative data to drive these clinical judgements, causing them to be based more on clinician’s experience than insights from the data.

Furthermore, for patients in the Pediatric Intensive Care Unit (PICU), characterization of correlations between the decision to transfuse and effects on physiologic variables and clinically significant outcomes require further investigation.

Our objective is to identify key physiological features that significantly change after transfusion in PICU patients, to ultimately model these changes, and to predict adverse outcomes in order to provide clinicians with more data-driven insights to aid transfusion decisions. In order to accomplish this, we present a 15-month retrospective electronic health record cohort study. We obtained time series data from 2156 pediatric patients admitted to the Johns Hopkins Hospital PICU from July 2014 to October 2015. To remove confounding effects from previous procedures, only the first transfusions in their first PICU visit were analyzed. Preliminary variables investigated include median heart rate (HR), respiratory rate (RR), and peripheral capillary oxygen saturation (SpO2). Transfusions were categorized based on the patient’s pre-transfusion hemoglobin levels in the clinically relevant categories: <5 g/dl, 5-7 g/dl, and ≥7 g/dl. A paired Wilcoxon Ranked Sum test on the two-hour window comparing before and after transfusion was performed.

Our preliminary analysis determined that median HR significantly decreased after transfusion within a two-hour window, whereas median RR and SpO2 exhibited no significant difference. This study validates our unbiased, exploratory method for statistically identifying physiologic variables that change after transfusion. Initial generalized linear models that predict the post-transfusion states of these three variables have been created taking in a combination of the three variables with demographic information such as age, race, and sex as input variables. The correlation coefficients between the actual and predicted values of these models are 0.8009, 0.8157, 0.6497 respectively. It can be noted that the HR and RR models perform significantly better than the model for SpO2. Additional input variables will be incorporated and further models that predict a greater number of variables will be developed to better predict the physiologic state of the patients as well as the probability of adverse outcomes after transfusion.