The goal of this project is to develop an integrated coagulation instrument approach for use at the point-of-care to predict bleeding events and tailor blood component transfusion after cardiac surgery. Severe bleeding, frequently a result of impaired coagulation or coagulopathy, occurs in over 50% of patients after complex cardiac surgeries. Multiple factors including the depletion of clotting factors, impaired platelet function and the systemic activation of fibrinolytic pathways contribute to the development of coagulopathy. To manage defective coagulation, blood components are transfused to correct bleeding abnormalities. Inadequate or delayed transfusion can lead to life-threatening blood loss and organ failure, while overuse of allogenic blood may cause acute lung injury, renal failure and increased mortality after cardiac surgery. In order to achieve optimal outcome and save lives, clinical tools that can rapidly predict major bleeding following cardiac surgery are essential. Unfortunately, laboratory tests are ineffective in the context of rapidly changing coagulation conditions in critically-ill cardiac patients, resulting in transfusion triggers that are often imprecise, inadequate and in many cases, clinically unnecessary. Together, these factors pose detrimental complications for patients, and place a large burden on healthcare costs by wasting a scarce resource leading to blood product shortage for patients in need. This dire situation has prompted a Class I recommendation by the Society of Thoracic Surgeons for tailoring transfusion decisions via point-of-care coagulation tests that are supplemented with integrated transfusion algorithms. Our proposal directly addresses this recommendation. Here, we propose a novel approach termed iCoagLab that measures multiple coagulation parameters within less than 10 minutes at the bedside to identify patients at an elevated risk of bleeding after cardiac surgery, tailor transfusion requirements and monitor hemostasis during treatment. The technique involves placing a few drops of whole blood in a small cartridge. A laser source illuminates the blood sample and a camera images laser speckle patterns reflected from the sample over time. By analyzing laser speckle intensity fluctuations during coagulation, we can simultaneously quantify multiple coagulation metrics including prothrombin time, activated clotting time, thrombin generation rate, fibrin polymerization, fibrinolysis, and platelet function. The optical device will be supplemented by an algorithm that combines information from multiplexed coagulation parameters to predict bleeding severity and identify transfusion strategies tailored to the individual patient. In the final phase of our work, we will conduct studies to evaluate the accuracy and utility of the new iCoagLab approach to predict bleeding risk and transfusion requirements in patients admitted to the cardiac ICU following surgery.
Over a quarter of patients require blood transfusions after cardiac surgery. Blood transfusions, although carry a low risk of infection, are often associated with several complications including acute lung injury, renal failure and increase mortality, thus motivating the need for new approaches to reduce unnecessary transfusions. By permitting the quantification of multiplexed coagulation parameters, the coagulation sensing strategy developed in this proposal will advance clinical capability for the timely prediction of bleeding events and provide the capability to guide transfusions based on individual patient needs.
