Prediction of Bleeding After Cardiac Surgery: Comparison of Model Performances: A Prospective Observational Study

ObjectivesPrimary aims were to (1) perform external validation of the Papworth Bleeding Risk Score, and (2) compare the usefulness of the Dyke et al universal definition of perioperative bleeding with that used in the Papworth Bleeding Risk Score. A secondary aim was to use a locally developed logistic prediction model for severe postoperative bleeding to investigate whether prediction could be improved with inclusion of the variable “surgeon” or selected intraoperative variables.DesignSingle-center prospective observational study.SettingUniversity hospital.Participants7,030 adults undergoing cardiac surgery.InterventionsNone.Measurements and Main ResultsPapworth Bleeding Risk Score could identify the group of patients with low risk of postoperative bleeding, with negative predictive value of 0.98, when applying the Papworth Score on this population. The positive predictive value was low; only 15% of the patients who were rated high risk actually suffered from increased postoperative bleeding when using the Papworth Score on this population. Using the universal definition of perioperative bleeding proposed by Dyke et al, 28% of patients in the Papworth high-risk group exceeded the threshold of excessive bleeding in this population. The local models showed low ability for discrimination (area under the receiver operating characteristics curve<0.75). Addition of the factor “surgeon” or selected intraoperative variables did not substantially improve the models.ConclusionPrediction of risk for excessive bleeding after cardiac surgery was not possible using clinical variables only, independent of endpoint definition and inclusion of the variable “surgeon” or of selected intraoperative variables. These findings may be due to incomplete understanding of the causative factors underlying excessive bleeding.