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Leveraging Turbidity and Thromboelastography for Complementary Clot Characterization
Published on: June 4, 2020
A Retrospective Analysis of Thromboelastometry and Conventional Laboratory Values in Nonsurgical and Noncardiac
Philipp Fischer1, Anita Lüthy2, Daniel Bolliger3
1KSA, Department of Anesthesia, Aarau, Switzerland; Faculty of Medicine, University of Basel, Basel, Switzerland.
Insights
Thromboelastometry parameters correlate well with lab tests for fibrinogen and platelets in noncardiac surgery patients. However, a predictive model derived from cardiac surgery showed significant bias, limiting its use in noncardiac settings.
Area of Science:
- Coagulation management
- Point-of-care diagnostics
- Laboratory medicine
Background:
- Thromboelastometry is utilized for guiding coagulation management, yet its validation in noncardiac surgery settings is limited.
- This study aimed to assess the correlation between thromboelastometry parameters and conventional laboratory tests in noncardiac patients.
- Additionally, the study evaluated a predictive application developed for cardiac surgery in this noncardiac cohort.
Purpose of the Study:
- To evaluate the correlation between thromboelastometry parameters and conventional laboratory tests in noncardiac patients.
- To assess the diagnostic performance of thromboelastometry for hypofibrinogenemia and thrombocytopenia.
- To validate a cardiac surgery-derived predictive application for coagulation management in noncardiac patients.
Main Methods:
- Retrospective analysis of 257 noncardiac patients with simultaneous thromboelastometry and laboratory measurements.
- Pearson correlation and Bland-Altman analyses were used to assess correlations and agreement.
- Receiver operating characteristic (ROC) analyses, including area under the ROC (AUROC), were employed to evaluate diagnostic performance for hypofibrinogenemia and thrombocytopenia.
Main Results:
- Strong correlations were observed between fibrinogen and FIBTEM A10/MCF (r=0.82).
- Platelet count showed moderate correlations with EXTEM A10, INTEM A10, and EXTEM-FIBTEM A10 (r=0.57-0.61).
- The cardiac-derived predictive model exhibited systematic underestimation and wide limits of agreement for both fibrinogen and platelets, indicating substantial bias.
Conclusions:
- Thromboelastometry parameters, specifically FIBTEM A10 for fibrinogen and EXTEM/INTEM A10 for platelets, demonstrate good correlation with conventional laboratory measures in a diverse noncardiac patient population.
- These findings support the potential utility of thromboelastometry as a point-of-care diagnostic tool in noncardiac settings.
- The cardiac-derived predictive model's significant bias and variability preclude its reliable use for patient-level estimations in noncardiac surgery, and its application in this context is not recommended.
Background:
Thromboelastometry is often used to guide coagulation management, but validation outside cardiac surgery is limited. We evaluated correlations between thromboelastometry parameters and conventional laboratory tests in noncardiac patients and assessed a cardiac surgery-derived predictive app.
Methods:
A retrospective single-center cohort of 257 patients with simultaneous thromboelastometry and laboratory measurements formed the study population. Correlations were assessed with the Pearson r and agreement with Bland-Altman analysis. Diagnostic performance for hypofibrinogenemia (≤1.5 g/L) and thrombocytopenia (≤100 × 10⁹/L) was evaluated using receiver operating characteristic (ROC) analyses with area under the ROC (AUROC) and 95% confidence intervals.
Results:
Fibrinogen correlated strongly with FIBTEM (the extrinsic activation pathway, whereby platelets are blocked in FIBTEM and the resulting clot consists only of fibrin formation and polymerization) A10 (specific point of time of 10 minutes) and FIBTEM MCF (maximal clot firmness) (r = 0.82). Platelet count correlated with EXTEM (the extrinsic activation pathway) A10 (r = 0.58), INTEM (the intrinsic pathway is activated) A10 (r = 0.61), and EXTEM-FIBTEM A10 (r = 0.57). AUROC for hypofibrinogenemia was 0.92 (FIBTEM A10), 0.89 (FIBTEM MCF), and 0.89 (EXTEM alpha). AUROC for thrombocytopenia was 0.95 (EXTEM A10), 0.96 (INTEM A10), and 0.97 (EXTEM-FIBTEM A10). Predicted versus observed values correlated at r = 0.82 for fibrinogen and r = 0.58 to 0.61 for platelets. Bland-Altman analyses showed systematic underestimation by the app with wide limits of agreement (bias +0.8 g/L for fibrinogen; +62, +50, and +50 × 10⁹/L for platelets).
Conclusions:
FIBTEM A10 (fibrinogen) and EXTEM/INTEM A10 (platelets) correlate well with conventional laboratory measures in a heterogenous noncardiac cohort, supporting thromboelastometry as a point-of-care tool. However, the cardiac-derived predictive model shows substantial bias and variability, precluding reliable patient-level estimates. The use cannot be recommended in noncardiac patients.
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