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Predictive accuracy of diagnostic tests for excessive bleeding in cardiac surgery: The COPTIC-C study
Weiqi Liao1, Robert Grant1, Florence Y Lai1
1Department of Cardiovascular Sciences, University of Leicester, UK.
Insights
Adding biomarkers for multimorbidity and aging slightly improved bleeding prediction after cardiac surgery. However, these additions offered minimal gains over standard coagulation tests alone.
Area of Science:
- Cardiovascular Surgery
- Hematology
- Biomarker Discovery
Background:
- Predicting clinically important bleeding after cardiac surgery is crucial.
- Current prediction models rely on coagulation tests.
- The role of multimorbidity and biological aging biomarkers is unclear.
Purpose of the Study:
- To evaluate if biomarkers of multimorbidity and biological aging enhance bleeding prediction accuracy.
- To compare point-of-care viscoelastometry and laboratory coagulation tests with added biomarkers.
Main Methods:
- A predictive accuracy study of 2437 cardiac surgery patients (COPTIC study).
- Utilized thromboelastography (TEG), rotational thromboelastometry (ROTEM), aggregometry, blood counts, and aging/multimorbidity biomarkers.
- Developed and validated logistic regression models using 10-fold cross-validation.
Main Results:
- The best model incorporating TEG/ROTEM and other biomarkers achieved an AUROC of 0.694.
- The optimal model using laboratory tests, blood counts, and aging/multimorbidity biomarkers had an AUROC of 0.701.
- Predictive accuracy for specific bleeding components like red cell or procoagulant transfusion was higher, but overall clinical utility did not improve.
Conclusions:
- Biomarkers of multimorbidity and biological aging provide only marginal improvements in predicting bleeding after cardiac surgery.
- Existing bleeding definitions may encompass diverse patient phenotypes and underlying disease processes.
Background:
We tested the hypothesis that the addition of biomarkers of multimorbidity and biological aging would improve the predictive accuracy of point-of-care viscoelastometry or laboratory tests of coagulation for clinically important bleeding following cardiac surgery.
Study Design And Methods:
This predictive accuracy study included 2437 participants in the coagulation and platelet laboratory testing in cardiac surgery (COPTIC study) with complete clinical, TEG 5000 thromboelastography, ROTEM, multiplate aggregometry, full blood count, laboratory reference tests of coagulopathy, and biomarkers of biological aging and multimorbidity. Models with different biomarkers to predict the composite primary outcome, clinically important bleeding, were developed using logistic regression and internally validated using 10-fold cross-validation. Discrimination, calibration, and clinical utility of the models were assessed comprehensively.
Results:
For the composite primary outcome, the AUROC for the best predictive model using TEG or ROTEM plus other biomarkers was 0.694 (0.612-0.775). The best predictive model overall included laboratory reference tests of coagulation, full blood count results, and biomarkers of multimorbidity and aging, AUROC = 0.701 (0.620-0.781), although clinical utility was not superior to using laboratory reference tests alone. Discrimination was higher for individual components of the primary outcome: large volume (≥4 units) red cell transfusion 0.754 (0.602-0.903) and large volume procoagulant transfusion 0.723 (0.590-0.857), but not for excess loss in drains/re-sternotomy 0.701 (0.613-0.788). Calibration was generally good among the models.
Discussion:
The addition of biomarkers of multimorbidity and biological aging yielded only small improvements in model predictive accuracy for bleeding over tests of coagulation. Existing clinical definitions of bleeding likely represent heterogeneous phenotypes and disease mechanisms.
