Post-operative bleeding risk stratification in cardiac pulmonary bypass patients using artificial neural network

The prediction of bleeding risk in cardiopulmonary bypass (CPB) patients plays a vital role in their postoperative management. Therefore, an artificial neural network (ANN) to analyze intra-operative laboratory data to predict postoperative bleeding was set up. The JustNN software (Neural Planner Software, Cheshire, England) was used. This ANN was trained using 15 intra-operative laboratory parameters paired with one output category - risk of bleeding, defined as units of blood components transfused in 48 hours. The ANN was trained with the first 39 CPB cases. The set of input parameters for this ANN was also determined, and the ANN was validated with the next 13 cases. The set of input parameters include five components: pro-thrombin time, platelet count, thromboelastograph-reaction time, D-Dimer, and thromboelastograph-coagulation index. The validation results show 9 cases (69.2%) with exact match, 3 cases (23.1%) with one-grading difference, and 1 case (7.7%) with two-grading difference between actual blood usage versus predicted blood usage. To the best of our knowledge, ours is the first ANN developed for post-operative bleeding risk stratification of CPB patients. With promising results, we have started using this ANN to risk-stratify our CPB patients, and it has assisted us in predicting post-operative bleeding risk.