Background: Viscoelastic testing (VT) is recommended for guidance of hemostatic treatment (HT) in acute bleeding. Whole-blood point-of-care methods are however limited by inter- and intra-observer variability and by the lack of the possibility for subsequent measurements for quality control.

Aims: Our aim was to develop a prediction model for bedside point-of-care VT fibrinogen clot firmness (MCF) from corresponding measurements in stored plasma samples.

Methods: We assessed 100 samples (n=77 patients, n=23 healthy volunteers) with the ROTEM^TM delta (Instrumentation Laboratory) and the ClotPro^TM (Haemonetics) device. MCF was determined in whole blood and thawed plasma with the FIBTEM and the Fib-test assay. Clauss fibrinogen concentrations (analyzers/reagent: ACL TOP 500/Werfen/Fibrinogen-C/HemosIL and BCS XP/Siemens/Multifibren U, Siemens) as well as a blood count (KX-21/Sysmex) were additionally measured. Correlations were analysed and a prediction model was developed using step-wise multiple regression.

Results: The fibrinogen content of the samples covered a broad range of the Clauss fibrinogen concentration measurement (median 250, IQR 127, min/max 140-632). Strong correlations of whole blood with plasma in FIBTEM/Fibtest MCF (r=0.795, p<0.001/r=0.795, p<0.001) were observed. Multivariate analysis showed FIBTEM values in whole blood may be predicted from FIBTEM values in corresponding measurements in plasma samples solely (r²=0.69, n.s.) while Fib-test MCF values in whole blood may be predicted taking into account plasma Fib-test MCF values and the hemoglobin value of the original sample as an independent variable, inversely related to MCF (r²=0.906, p<0.001).

Bland-Altman plot showing the difference versus the average of MCF [mm] of the predicted MCF results and the MCF results of whole blood samples measured by the ClotPro® analyzer. Bias, upper and lower limits of agreement (±1,96 SD (=1.91), green dotted lines) are represented. The red line indicates equality (difference=0). Multiple linear regression analysis was used to develop a model for predicting MCF [mm] in whole blood from MCF [mm] in thawed plasma, hemoglobin concentration, red blood cell count and Clauss fibrinogen concentration (g/L). The analysis identified the MCF [mm] in thawed plasma and the hemoglobin concentration as significant predictor variables (p<0.001), with the haemoglobin concentration inversely related to MCF in the whole blood sample. The predicted Fibtest-MCFs were calculated with the following formula: Fibtest pred. = 0.821 x MCF [mm–0.846 x hemoglobin (g/L) + 5.383
Bland-Altman plot showing the difference versus the average of MCF [mm] of the predicted MCF and the MCF results of whole blood samples measured by the ROTEM delta® analyzer. Bias, upper and lower limits of agreement (± 1,96 SD (= 3.44), green dotted lines) are represented. The red line indicates equality (difference = 0). Multiple linear regression analysis was used to develop a model for predicting MCF [mm] in whole blood from MCF [mm] in thawed plasma, haemoglobin concentration, red blood cell count and Clauss fibrinogen concentration (g/L). The analysis identified the MCF [mm] in thawed plasma as the only predictor variable; the other variables were not significant in multiple regression analysis. The predicted FIBTEM-MCF was calculated with the following mathematical formula: FIBTEM pred. = 0.675 x MCF [mm] + 0.68.

Conclusions: We developed a model to reliably predict MCF from stored plasma samples, taking into account plasma MCF and the hemoglobin concentration of the original sample. Our findings first serve to contribute to quality control for VT measurement in clinical practice and research and second point to the importance of hemoglobin as a significant factor of influence in VT measurements, potentially indicating the need for defining hemoglobin-dependent treatment cut-off values for fibrinogen replacement.

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ISTH Congress Abstracts - https://abstracts.isth.org/abstract/the-red-blood-cell-paradox-in-viscoelastic-coagulation-tests-hemoglobin-concentration-as-a-confounder-for-test-reliability/