Multiple electrode aggregometry and thromboelastography in thrombocytopenic patients with haematological malignancies.

BACKGROUND

In thrombocytopenic patients better assessment of bleeding risk than that provided by platelet count alone is required. Multiplate aggregometry and thromboelastography (TEG) could be used, but information on their role in such patients is limited. The primary aim of this study was to investigate the feasibility of Multiplate analyses in patients with haematological malignancies. A secondary aim was to explore whether a multiple logistic regression model combining Multiplate, TEG, clinical and laboratory variables was associated with risk of bleeding.

MATERIALS AND METHODS

This was an exploratory, prospective observational study of thrombocytopenic patients with haematological malignancies. Total platelet count (TPC), white blood cell count, C-reactive protein (CRP) level, temperature and bleeding status were recorded daily. TEG and Multiplate analyses with four agonists were performed on weekdays.

RESULTS

Ten patients were enrolled into the study. The median number of days in a study period was 21. Bleeding was observed on 64 of 298 study days. TPC <20×10/L and <10×10/L occurred on 119 and 25 days, respectively. When TPC was <33×10/L, many samples showed no aggregation, regardless of bleeding status. Despite this, the odds of World Health Organization (WHO) grade 2 bleeding decreased significantly as aggregation increased and Multiplate had a negative predictive value (NPV) of 96% and a positive predictive value (PPV) of 19% for significant bleeding. In the multiple logistic regression model collagen-activated Multiplate aggregation, TEG angle, TEG reaction time and CRP significantly affected the odds of WHO grade 2 bleeding. The combined model had a NPV of 99% and a PPV of 19%.

DISCUSSION

Our findings suggest that the markers of platelet function and haemostasis provided by Multiplate aggregometry and TEG may add information to support prediction of bleeding, although platelet count still remains the most accessible analysis for routine testing.