The New SUMPOT to Predict Postoperative Complications Using an Artificial Neural Network

An accurate assessment of preoperative risk may improve use of hospital resources and reduce morbidity and mortality inhigh-risk surgical patients. This study aims at implementing an automated surgical risk calculator based on Artificial NeuralNetwork technology to identify patients at risk for postoperative complications. We developed the new SUMPOT based on risk factors previously used in other scoring systems and tested it in a cohortof 560 surgical patients undergoing elective or emergency procedures and subsequently admitted to intensive care units,high-dependency units or standard wards. The whole dataset was divided into a training set, to train the predictive model, anda testing set, to assess generalization performance. The effectiveness of the Artificial Neural Network is a measure of theaccuracy in detecting those patients who will develop postoperative complications.A total of 560 surgical patients entered the analysis. Among them, 77 patients (13.7%) suffered from one or more postoperativecomplications (PoCs), while 483 patients (86.3%) did not. The trained Artificial Neural Network returned an average classificationaccuracy of 90% in the testing set. Specifically, classification accuracy was 90.2% in the control group (46 patients out of 51were correctly classified) and 88.9% in the PoC group (8 patients out of 9 were correctly classified).The Artificial Neural Network showed good performance in predicting presence/absence of postoperative complications,suggesting its potential value for perioperative management of surgical patients. Further clinical studies are required to confirmits applicability in routine clinical practice.