Abstract
An accurate assessment of preoperative risk may improve use of hospital resources and reduce morbidity and mortality in high-risk surgical patients. This study aims at implementing an automated surgical risk calculator based on Artificial Neural Network 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 cohort of 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, and a testing set, to assess generalization performance. The effectiveness of the Artificial Neural Network is a measure of the accuracy 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 postoperative complications (PoCs), while 483 patients (86.3%) did not. The trained Artificial Neural Network returned an average classification accuracy of 90% in the testing set. Specifically, classification accuracy was 90.2% in the control group (46 patients out of 51 were 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 confirm its applicability in routine clinical practice.
Highlights
An accurate assessment of preoperative risk may improve use of hospital resources and reduce morbidity and mortality in high-risk surgical patients
A clear body of evidence shows that elective postoperative intensive care units (ICUs) admission reduces the incidence of postoperative complications (PoCs), while delayed or emergency admission to ICU/highdependency units (HDUs) following surgery may lead to worse o utcomes[6]
A prognostic tool should be easy-to-use and directly applicable at the bedside. With these concepts in mind, we explored the potential of using new technologies to develop a new tool for risk assessment, named SUMPOT (SUrgical and Medical POstoperative complications prediction Tool)
Summary
An accurate assessment of preoperative risk may improve use of hospital resources and reduce morbidity and mortality in high-risk surgical patients. This study aims at implementing an automated surgical risk calculator based on Artificial Neural Network technology to identify patients at risk for postoperative complications. The Artificial Neural Network showed good performance in predicting presence/absence of postoperative complications, suggesting its potential value for perioperative management of surgical patients. Appropriate perioperative planning of elective post-operative admission to intensive care units (ICUs), highdependency units (HDUs) or standard wards after non-cardiac surgery may improve postoperative outcomes in patients at risk for postoperative complications (PoCs)[1,2,3,4,5]. The more recent American College of Surgeon-Veterans Affairs National Surgical Quality Improvement Program surgical risk calculator (ACS-NSQIP) appears to be the most reliable system currently available, but the input of data may be cumbersome It requires a precise preoperative definition of the ongoing surgery. Spearman’s correlation test performed on patients in the validation set showed a strong correlation between higher ASPRA scores and severity of PoCs, as defined by the Clavien-Dindo c lassification[13]
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