# Integrated Coagulation Sensing to Predict Hemorrhage and Guide Transfusions

> **NIH NIH R01** · MASSACHUSETTS GENERAL HOSPITAL · 2020 · $781,557

## Abstract

ABSTRACT
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.

## Key facts

- **NIH application ID:** 9989890
- **Project number:** 5R01HL142272-03
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Seemantini K Nadkarni
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $781,557
- **Award type:** 5
- **Project period:** 2018-08-15 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9989890

## Citation

> US National Institutes of Health, RePORTER application 9989890, Integrated Coagulation Sensing to Predict Hemorrhage and Guide Transfusions (5R01HL142272-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9989890. Licensed CC0.

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