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Does Rotational Thromboelastometry (ROTEM) Improve Prediction of Bleeding After Cardiac Surgery?

Lee, Grace C. BS*; Kicza, Adrienne M. BA*; Liu, Kuang-Yu PhD*; Nyman, Charles B. MBBCh*; Kaufman, Richard M. MD; Body, Simon C. MBChB, MPH*

doi: 10.1213/ANE.0b013e31825e7c39
Cardiovascular Anesthesiology: Research Reports

BACKGROUND: Coagulopathy and massive bleeding are severe complications of cardiac surgery, particularly in procedures requiring prolonged cardiopulmonary bypass (CPB). There is huge variability in transfusion practices across hospitals and providers in cross-sectional studies. This variability may indicate unguided decision-making, perhaps attributable to lack of reliable, predictive laboratory testing of coagulopathy to guide transfusion practice. Rotational thromboelastometry (ROTEM) measures multiple coagulation parameters and may provide value from its ease of use, rapid results, and measurement of several steps in the coagulation pathway. Yet, the predictive value and utility of ROTEM remains unclear. In this study, we investigated ROTEM's predictive value for chest tube drainage after cardiac surgery.

METHODS: Three hundred twenty-one patients undergoing cardiac surgery involving CPB were enrolled. Patient data were obtained from medical records, including chest tube output (CTO) from post-CPB through the first 8 postoperative hours. Perioperative and postoperative blood samples were collected for ROTEM analysis. Three measures of CTO were used as the primary end points for assessing coagulopathy: (i) continuous CTO; (ii) CTO dichotomized at 600 mL (75th percentile); and (iii) CTO dichotomized at 910 mL (90th percentile). Clinical and hematological variables, excluding ROTEM data, that were significantly correlated (P < 0.05) with continuous CTO were included in a stepwise regression model (model 1). An additional model that contained ROTEM variables in addition to the variables from model 1 was created (model 2). Significance in subsequent analyses was declared at P < 0.0167 to account for the 3 CTO end points. Net reclassification index was used to assess overall value of ROTEM data.

RESULTS: For continuous CTO, ROTEM variables improved the model's predictive ability (P < 0.0001). For CTO dichotomized at 600 mL (75th percentile), ROTEM did not improve the area under the receiver operating characteristic curve (AUC) (P = 0.03). Similarly, for CTO dichotomized at 910 mL (90th percentile), ROTEM did not improve the AUC (P = 0.23). Net reclassification index similarly indicated that ROTEM results did not improve overall classification of patients (P = 0.12 for CTO ≥600 mL; P = 0.08 for CTO ≥910 mL).

CONCLUSIONS: These results suggest that ROTEM data do not substantially improve a model's ability to predict chest tube drainage, beyond frequently used clinical and laboratory parameters. Although several ROTEM parameters were individually associated with CTO, they did not significantly improve goodness of fit when added to statistical models comprising only clinical and routine laboratory parameters. ROTEM does not seem to improve prediction of chest tube drainage after cardiac surgery involving CPB, although its use in guiding transfusion during cardiac surgery remains to be determined.

Published ahead of print June 19, 2012 Supplemental Digital Content is available in the text.

From the Departments of *Anesthesiology, Perioperative and Pain Medicine, and Pathology, Brigham and Women's Hospital, Boston, Massachusetts.

Funding: A ROTEM machine and disposables were provided free of charge by Tem Inc. No aspect of the study design, conduct, analysis, or interpretation was determined by Tem Inc. Dr. Body received minor consultant support from Tem Inc. Ms. Lee's support was provided by a grant from Harvard Medical School.

Conflicts of Interest: See Disclosures at the end of the article.

Reprints will not be available from the authors.

Address correspondence to Simon C. Body, MBChB, MPH, Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital, 75 Francis St., CWN L1, Boston, MA 02115. Address e-mail to body@zeus.bwh.harvard.edu.

Accepted April 12, 2012

Published ahead of print June 19, 2012

Coagulopathy and massive bleeding are severe complications of cardiac surgery, particularly occurring after procedures requiring prolonged cardiopulmonary bypass (CPB).13 Transfusion of coagulation factors and platelets is a necessary therapy for coagulopathy, but has been associated with increased morbidity and mortality after cardiac surgery,4,5 with evidence that concomitant red blood cell transfusion is an independent risk factor.6,7

Cross-sectional studies show wide variability in transfusion practices among different geographic regions, hospitals, and even individual providers.7 This variability has persisted for decades, despite the introduction and repeated revisions of guidelines, indicating a lack of consensus in transfusion decision-making.811 These wide and persistent differences in transfusion practices may indicate a lack of reliable and predictive laboratory testing of coagulation function to guide clinicians to appropriate transfusion practices. Notably, currently used basic laboratory-based coagulation tests often have delayed results and poorly guide rational coagulation factor and platelet use. Implementation of rapid and predictive laboratory testing into consensus-based blood transfusion algorithms may allow clinicians to rationally practice transfusion medicine.1214

A novel point-of-care test that uses rotational thromboelastometry (ROTEM) (Tem Systems, Durham, NC) to monitor multiple coagulation parameters is frequently used in Europe because of its ease of use and rapid results.1519 ROTEM measures clotting time and clot amplitude when citrated whole blood is mixed with activators of specific portions of the coagulation cascade. Yet, the extent to which the use of ROTEM, in addition to laboratory-based coagulation tests, improves prediction of bleeding and guides transfusion is unclear. In this study, we hypothesized that one or more ROTEM parameters would improve prediction of bleeding after cardiac surgery. We anticipated that such knowledge could potentially enable clinicians to rationally and effectively treat coagulopathy after cardiac surgery requiring CPB.

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METHODS

Study Design and Subjects

In a prospectively collected cohort of 321 patients undergoing cardiac surgery requiring CPB at Brigham and Women's Hospital (Boston, MA), we examined the association between ROTEM parameters and chest tube output (CTO). This study was approved by the Partners IRB as a discarded blood and medical records protocol, not requiring individual patient consent for the use of blood normally discarded after routine coagulation testing.

During the first phase of the study (part 1; n = 218), samples from patients who had surgery scheduled for the morning were collected. At midcourse review of the study, few cases of bleeding and an overall low transfusion rate were observed. For the second phase of the study (part 2; n = 103), only patients at high risk for bleeding were enrolled. High-risk inclusion criteria consisted of reoperative surgery, operations on >1 valve, operations involving hypothermic circulatory arrest, and administration of clopidogrel within 7 days before surgery. Clinicians caring for patients were not provided with ROTEM results. No institutional clinical or transfusion practices were changed during the study period. No institutional transfusion algorithm was in place at the time of the study. All patients received ε-aminocaproic acid, 7.5 g as an initial loading dose after induction of anesthesia, followed by 1.5 g/h, irrespective of body weight.

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Data and Sample Collection

Patients' medical records were reviewed for demographics, surgical procedure, laboratory test results, CTO, and blood product transfusions. One milliliter of citrated whole blood was obtained from specimens collected as part of routine clinical care (5 minutes after post-CPB protamine administration, and upon arrival in the intensive care unit).

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ROTEM Analysis

Coagulation was assessed using the ROTEM thromboelastometry analyzer (Tem Systems). Descriptions of the ROTEM technology have been published previously.15,17,20,21 extrinsic rotational thromboelastometry (EXTEM), intrinsic rotational thromboelastometry (INTEM), and fibrinogen rotational thromboelastometry (FIBTEM) tests were run on the samples. Each ROTEM test requires 300 μL of citrated whole blood. Pilot studies with citrated whole blood samples demonstrated that removing a 1-mL whole blood aliquot from a 2.7-mL blue top tube leaves behind adequate sample volume for standard-of-care prothrombin time, partial thromboplastin time (PTT), and fibrinogen testing without affecting the results of the prothrombin time, PTT, or fibrinogen assays (unpublished data). The residual 1-mL volume was obtained within 30 minutes of phlebotomy. Pilot studies also showed that ROTEM results are unchanged for samples that have been at room temperature for up to 6 hours. Quality control tests were run every week. Reference ranges for ROTEM values were obtained from the United States package inserts, which cite unpublished studies of healthy blood donors and are comparable to published reference ranges.22

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Statistical Methods

Statistical analyses were performed using JMP 9.0 (SAS Institute, Cary, NC) and R 2.13.1 (R Foundation for Statistical Computing, Vienna, Austria). The ROTEM and non-ROTEM parameters significantly correlated with total CTO (intraoperative CTO plus CTO during the first 8 postoperative hours) (P < 0.05) were determined using the Wilcoxon rank sum test and linear regression models. Continuous variables were stratified at their 90th percentile. These covariates were placed into a forward stepwise regression model for continuous CTO, which found the ROTEM and non-ROTEM parameters most predictive of total CTO. The stepwise function added the covariates to the model in order of significance of the individual covariate's correlation with total CTO, with a P value threshold of 0.25 for entering into the model and 0.1 for leaving the model. Two statistical models for predicting CTO were created: model 1 contained clinical and laboratory predictors of CTO while not including ROTEM variables, whereas model 2 included ROTEM variables associated with CTO, in addition to the covariates from model 1. Goodness of fit analyses were conducted, including adjusted R2 calculations based on linear regressions using CTO as a continuous variable. F tests were conducted to compare the fit of models 1 and 2.

The predictive accuracy of the models was also assessed using CTO as a dichotomous variable (stratified at the 75th and 90th percentile of CTO), conducting nominal logistic regressions, and comparing nested models using generalized Nagelkerke R2 values.23 Significance was again determined by assessing the F distribution, with significance declared at P < 0.0167 to account for the 3 end points used (continuous CTO, and CTO dichotomized at the 75th and 90th percentiles).

Using the dichotomous CTO outcome variable, area under the receiver operating characteristic curve (AUC) for each model was also calculated and compared using the Harrell miscellaneous package (version 3.8-3) of R. Because it has become increasingly clear that AUC is not a very sensitive measure to evaluate improvement of a model provided by including additional predictors, we examined the clinical importance of the change in model prediction for individual patients using net reclassification index (NRI) for the ROTEM model.24

In NRI analyses, the outcomes were considered “events” if CTO was more than or equal to the 75th or 90th percentile. NRI was determined by:

CV

CV

where:

CV

CV

CV

CV

CV

CV

CV

CV

and P indicates a probability. “Moving up” indicates that an event was reclassified correctly, whereas “moving down” implies incorrect reclassification. The definitions are reversed for nonevents: moving up implies incorrect reclassification, whereas moving down indicates correct reclassification. The significance of the NRI was assessed by calculating the z score with the following equation24:

CV

CV

With these analyses, we aimed to assess the extent to which adding ROTEM parameters to traditional risk factors alters the accuracy of predicting bleeding in patients undergoing cardiac surgery involving CPB.

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RESULTS

Demographic, operative characteristics, hematology laboratory results, and ROTEM results are summarized in Tables 1 and 2. Some patients lacked complete ROTEM data because surgery was completed late in the day and samples were not collected, the volume of unused blood was too small, or samples were discarded. Sixteen patients lacked intraoperative, but not postoperative, CTO information and were thus assigned the group median (120 mL) for intraoperative CTO to increase the number of analyzable patients. All patients had postoperative CTO information. Missing post-CPB FIBTEM α angle (ALP) values from 45 patients were imputed using a linear regression that included the post-CPB FIBTEM clotting time (CT), A5, and AR5 values.

Table 1

Table 1

Table 2

Table 2

Clinical and laboratory variables (not including ROTEM parameters) that were independently associated with CTO were entered into a forward stepwise model. Because of strong colinearity with other variables in the model, post-CPB international normalized ratio (INR) and activated PTT (aPTT) were not included. The resulting covariates (model 1) are listed in Table 3. ROTEM parameters that were associated with total CTO (P < 0.05) were added to model 1 to determine their additional predictive value. Post-CPB EXTEM A10, post-CPB INTEM clot formation time, and intensive care unit–arrival INTEM clot formation time were excluded because of their strong colinearity with other ROTEM variables being entered into the model. Post-CPB INTEM CT (a measure of time before clot formation begins when the intrinsic clotting pathway is stimulated) and post-CPB FIBTEM ALP (α angle, a measure of the speed of clot formation when the extrinsic clotting pathway is stimulated and platelets are inhibited) were significantly associated with total CTO (Table 3), over and above the variables identified in model 1. Inclusion of ROTEM-derived post-CPB INTEM CT and post-CPB FIBTEM ALP (adjusted R2 = 0.275) into model 2 significantly improved prediction over model 1 (adjusted R2 = 0.207), which contained only clinical data (P < 0.001) (Table 3).

Table 3

Table 3

A similar analysis was performed using an outcome of CTO dichotomized at the 75th percentile of total CTO (600 mL). Models were compared by conducting nominal logistic regression and calculating the AUC (Fig. 1). Inclusion of ROTEM data in model 2 (AUC = 0.776, Bayesian information criterion [BIC] = 252.655) did not significantly improve model performance over model 1 (AUC = 0.726, BIC = 252.752) containing only clinical data, after adjusting for multiple comparisons (P = 0.03). However, Nagelkerke R2 values (0.188 in model 1; 0.251 in model 2) and F test calculations indicated that model 2 significantly improved prediction of CTO over clinical and routine laboratory data alone (model 1) (P < 0.001). Model 2 had a sensitivity of 47%, specificity of 86%, positive predictive value (PPV) of 53%, and negative predictive value (NPV) of 83%.

Figure 1

Figure 1

Similar results were seen when dichotomizing CTO at the 90th percentile (910 mL) (Fig. 2). Model 2 (AUC = 0.836, BIC = 157.270) did not significantly outperform model 1 (AUC = 0.815, BIC = 150.849) (P = 0.23), although the difference in Nagelkerke R2 values estimated by F test was significant (P = 0.01). In this case, model 2 had a sensitivity of 18%, specificity of 98%, PPV of 50%, and NPV of 91%.

Figure 2

Figure 2

We explored the disparate results from the AUC and Nagelkerke R2 using net reclassification improvement from the additional value of ROTEM over the solely clinical and laboratory model. For a cutpoint at the 75th percentile of CTO (600 mL), the NRI was 0.078 (P = 0.12). Eight patients were correctly reclassified by inclusion of ROTEM data but 5 patients were incorrectly reclassified, indicating that ROTEM was unable to provide clinically useful additional predictive value over the clinical model for CTO ≥600 mL (Fig. 3). For a cutpoint at the 90th percentile of CTO (910 mL), the NRI was 0.136 (P = 0.08). Three patients were correctly reclassified by inclusion of ROTEM data and no patients were incorrectly reclassified, indicating that ROTEM was again unable to provide significant, clinically useful, additional predictive value over the clinical model for CTO ≥910 mL (Fig. 4).

Figure 3

Figure 3

Figure 4

Figure 4

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CONCLUSIONS

Bleeding and coagulopathy are complications of cardiac surgery, particularly in procedures involving prolonged CPB. This study assessed the ability of ROTEM data to predict which patients are at high risk of bleeding after cardiac surgery with CPB. Our results suggest that ROTEM data do not substantially improve prediction of chest tube drainage, beyond frequently used clinical and laboratory parameters. Although several ROTEM parameters were individually associated with CTO, they did not significantly improve predictive accuracy when added to statistical models comprising clinical and routine laboratory parameters.

Several methods of comparing clinical and laboratory predictors of CTO (model 1) to model 2, which additionally included ROTEM data, yielded equivocal results as to ROTEM's predictive efficacy. Using CTO as a continuous outcome, inclusion of ROTEM data significantly improved prediction of CTO. When CTO was dichotomized at the 75th and 90th percentiles, Nagelkerke R2 and F tests also indicated that inclusion of ROTEM data led to significant improvement in prediction of CTO. However, AUC and net reclassification improvement measures of significance indicated that inclusion of ROTEM data did not significantly improve prediction over clinical and laboratory data alone.

Despite the inconsistency of the results, the AUC and NRI are thought to be more accurate measures of predictive model performance.24 With their incorporation of sensitivity, specificity, and reclassification tables, they tend to be more conservative and clinically applicable than other statistical tests of significance. A criticism of the AUC is that large independent associations between a new model covariate and the outcome are required to lead to significant changes in the AUC. Nevertheless, the concurrent insignificance of our NRI results strengthens our conclusion that ROTEM data do not significantly improve the model's ability to predict bleeding.

A strength of this study is that the ROTEM results were not provided to clinicians caring for the patients. This allowed robust interpretation of the value of ROTEM in predicting CTO, without therapies being based on the ROTEM values that might have affected CTO. This strength is also a limitation, because it precluded assessment of the value of ROTEM in determining transfusion outcomes. Other limitations of this study include the relatively low amount of bleeding (median = 390 mL, interquartile range = 260–600 mL) and few patients with severe blood loss, even after selecting patients at high risk of bleeding during the second part of the study. AUC, NRI, sensitivity, and PPV estimates will have been affected by the low CTO. Another important limitation of this study is that it is a single-center study. Clinical and transfusion practices particular to this institution likely affected CTO and ROTEM values obtained in this study. It is possible that institutions with higher average CTO or transfusion rates may see larger effect sizes of ROTEM variables with a CTO outcome.

Despite these limitations, our results are consistent with previous studies assessing the ability of ROTEM to predict bleeding after cardiac surgery. One study of 58 patients undergoing a coronary artery bypass graft procedure found that ROTEM parameters had high NPV and specificity, but low PPV and sensitivity. The investigators concluded that postoperative ROTEM data poorly predicted massive postoperative bleeding.25 The authors of another study of 150 patients with cardiac surgery involving CPB, which found that ROTEM had PPVs ranging from 63% to 73% and sensitivities ranging from 86% to 95%, concluded that ROTEM was not sufficiently sensitive and specific to use in screening for patients at high risk of bleeding.26 These findings are not surprising given the numerous factors that can contribute to massive blood loss in the setting of cardiac surgery, such as surgical bleeding, hypothermia, and acidosis, which ROTEM parameters would not assess.

Common measures of coagulopathy, such as platelet count, fibrinogen, INR, and PTT, have also been shown to be poor predictors of bleeding, but are often used in the management of bleeding.26,27 Despite its poor prediction of CTO, ROTEM may have value in reducing transfusion by more accurately determining coagulation status than common measures of coagulation.14,19 Several studies found that providing ROTEM values to clinicians reduced transfusion of coagulation products.28,29 A meta-analysis of 9 randomized controlled trials of 776 patients found that use of ROTEM was significantly associated with decreased bleeding and decreased platelet and plasma transfusions, but postoperative mortality remained the same.30,31 Although some institutions report incorporating ROTEM parameters into their transfusion algorithms,32,33 future studies are needed to determine the efficacy of adding ROTEM data to these guidelines.34

Although ROTEM parameters may not improve clinicians' ability to predict bleeding after cardiac surgery, ROTEM results seem to be equally or more strongly associated with bleeding than current clinical and laboratory data, as indicated by our data and prior studies.19,25,26,30 ROTEM technology has advantages over current laboratory tests and thromboelastrography, because of its ease of use and more rapid provision of results than conventional laboratory tests such as fibrinogen, INR, and PTT. Additionally, because of its variety of tests, ROTEM can suggest a cause for the patient's coagulopathy, such as rebound heparinization, hyperfibrinolysis, functional thrombocytopenia, or factor deficiency.17,20,3539 These diagnoses are rarely obtainable with conventional laboratory tests.

In this study, we evaluated the extent to which adding ROTEM to traditional bleeding risk factors improves classification of risk of bleeding after cardiac surgery using AUC and NRI measures. Although some ROTEM parameters were independently associated with CTO, including them in models of bleeding did not significantly improve the models' predictivity. Nevertheless, ROTEM technology quickly provides a global assessment of coagulopathy that may be effective in the management of bleeding after cardiac surgery involving CPB.

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DISCLOSURES

Name: Grace C. Lee, BS.

Contribution: This author helped design the study, conduct the study, analyze the data, and write the manuscript.

Attestation: Grace C. Lee has seen the original study data, reviewed the analysis of the data, and approved the final manuscript.

Conflicts of Interest: The author has no conflicts of interest to declare.

Name: Adrienne M. Kicza, BA.

Contribution: This author helped conduct the study.

Attestation: Adrienne M. Kicza approved the final manuscript.

Conflicts of Interest: The author has no conflicts of interest to declare.

Name: Kuang-Yu Liu, PhD.

Contribution: This author helped analyze the data.

Attestation: Kuang-Yu Liu reviewed the analysis of the data and approved the final manuscript.

Conflicts of Interest: The author has no conflicts of interest to declare.

Name: Charles B. Nyman, MBBCh.

Contribution: This author helped design the study and conduct the study.

Attestation: Charles B. Nyman approved the final manuscript.

Conflicts of Interest: The author has no conflicts of interest to declare.

Name: Richard M. Kaufman, MD.

Contribution: This author helped design the study and conduct the study.

Attestation: Richard M. Kaufman approved the final manuscript.

Conflicts of Interest: The author has no conflicts of interest to declare.

Name: Simon C. Body, MBChB, MPH.

Contribution: This author helped design the study, analyze the data, and write the manuscript.

Attestation: Simon C. Body has seen the original study data, reviewed the analysis of the data, approved the final manuscript, and is the author responsible for archiving the study files.

Conflicts of Interest: Simon C. Body consulted for Tem Inc. Dr. Body received minor consultant support from Tem Inc.

This manuscript was handled by: Jerrold H. Levy, MD, FAHA.

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