The Heparin-Antithrombin Product: A Novel Value for Pediatric Extracorporeal Anticoagulation

Abstract

Hematologic complications are a source of morbidity and mortality for patients receiving extracorporeal membrane oxygenation (ECMO) support. There is no consensus strategy for monitoring anticoagulation for children supported with ECMO. This study evaluated a novel measurement of anticoagulation for children on ECMO. This was a single-center observational study of children supported with ECMO from 2015 to 2020. Each patient's current unfractionated heparin dose was multiplied by the current antithrombin III (AT) level to obtain a novel anticoagulation value, the heparin-antithrombin product (HAP). This value was compared with the heparin dose, AT, and activated clotting time (ACT) to predict anti-Xa value using linear correlation and decision tree methods. Data were obtained from 128 patients supported with ECMO. The HAP value was more highly correlated with anti-Xa level than heparin dose, AT level, and ACT. This correlation was highest in the neonatal population (r = .7). The variable importance metrics from the regression tree and random forest models both identified the HAP value as the most influential predictor variable for anti-Xa value. The HAP value is more highly correlated with the anti-Xa level than heparin dose, AT level, or ACT. Further research is needed to evaluate the effectiveness of the HAP value as a measurement of anticoagulation for children on ECMO.

Keywords: anticoagulation; critical care medicine.; extracorporeal membrane oxygenation; pediatrics.

Figure 1.

Bar chart showing correlation values by lab and age category.

Figure 2.

Scatterplot of anti-Xa value by heparin dosing with regression line (top left). Scatterplot of anti-Xa value by antithrombin III (AT) level with regression line (top right). Scatterplot of anti-Xa value by heparin-antithrombin product (HAP) value with regression line (bottom).

Figure 3.

Regression tree diagram for predicting anti-Xa value.

Figure 4.

Variable importance values for the simple regression tree (left), and amount of increase in node purity for each variable in the random forest model (right).