| dc.description.abstract | Background: Postoperative excessive bleeding is still one of the most common complications
in cardiac surgery and is associated with increased morbidity and mortality. Major bleeding
is a partially modifiable risk factor, and adequate preventive treatment strategies should be
used to reduce the incidence of this complication. Prophylactic drug treatments can reduce
postoperative bleeding. Prediction modelling strategies can be used for identifying patients
at higher risk of bleeding after cardiac surgery and thereby contribute to implementation of
prophylactic therapies and early interventions. Genetic variation may contribute to
postoperative bleeding after cardiac surgery, and genetic information could make the
prediction of excessive postoperative bleeding more precise.
Aims: The overall aim of the thesis was to contribute to better patient care through
increased knowledge about postoperative bleeding and about the treatment of this
complication in high-risk groups. More specifically, the aim in Study I was to investigate the
effects of tranexamic acid on postoperative blood loss and transfusion requirements in
elderly patients undergoing complex cardiac surgery. In Study II the aims were 1) to
investigate if risk prediction for postoperative bleeding was possible using clinical variables,
by validating the Papworth Bleeding Risk Score and by developing local risk prediction
models, and 2) to compare the usefulness of the universal definition of postoperative
bleeding proposed by Dyke et al with the definition used in the Papworth Bleeding Risk
Score. In Study III the main aim was to validate some earlier identified genetic
polymorphisms associated with bleeding after cardiac surgery, and to explore other
functional polymorphisms central in the coagulation and fibrinolysis systems or in plateletmembrane
receptors related to postoperative bleeding. A secondary aim was to evaluate
the additional predictive ability with inclusion of genetic information in risk prediction
models based only on clinical variables, thereby investigating whether genetic susceptibility
is a clinically relevant factor for further studies.
Methods: A prospective, randomized, double-blind, placebo-controlled, parallel-group trial
was used to investigate the effect of tranexamic acid in patients, 70 years or older,
undergoing aortic valve replacement (AVR) or coronary artery bypass grafting (CABG). For
Study II and III we used a prospectively recorded local database. All patients included in the
studies underwent cardiac surgery with cardiopulmonary bypass at St. Olavs University Hospital, Trondheim, Norway. The predictive ability of the Papworth Bleeding Risk Score was
validated, using the universal definition of perioperative bleeding proposed by Dyke et al
and the definition used to develop the Papworth Bleeding Risk Score. Clinical prediction
models were constructed using multivariate logistic regression analyses, using the two
mentioned endpoints. Genetic associations were performed using allelic association tests
and logistic regression. Multivariate logistic regression was used to evaluate whether the
genetic information could improve the model with clinical variables.
Results: The total number of red cell transfusions given was significantly larger in the
placebo group of elderly patients compared with the tranexamic acid group (5 vs. 3
respectively, p = 0.049). When using both the Papworth Bleeding Risk Score and the locally
developed risk prediction models, the positive predictive value was low (0.15 and 0.12
respectively) in the high-risk group and the negative predictive value was high (0.98 and 0.95
respectively) in the low-risk group. We identified five significant SNPs associated with
postoperative bleeding. Addition of the genetic covariates to a logistic regression model with
clinical variables significantly improved the model (p<0.01).
Conclusions: Tranexamic acid reduced the need for red cell transfusion in elderly patients
undergoing combined AVR and CABG surgery. Neither the Papworth Bleeding Risk Score nor
the locally developed risk prediction models were suitable for identifying the patients at high
risk of severe postoperative bleeding after cardiac surgery, but could more precisely identify
patients at low risk of bleeding. None of the two endpoint definitions for postoperative
bleeding were clearly preferable. We identified five SNPs associated with postoperative
bleeding. Inclusion of genetic information to a risk prediction model with only clinical
variables improved the model, indicating that genetic predisposition is relevant for
postoperative bleeding after cardiac surgery. | nb_NO |
