Article ; Online: The new SUMPOT to predict postoperative complications using an Artificial Neural Network.
2021 Volume 11, Issue 1, Page(s) 22692
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 ... ...
| 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. |
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| MeSH term(s) | Area Under Curve ; Cohort Studies ; Elective Surgical Procedures/adverse effects ; Emergency Treatment/adverse effects ; Female ; Hospitalization ; Humans ; Intensive Care Units ; Machine Learning ; Male ; Middle Aged ; Neural Networks, Computer ; Postoperative Complications/epidemiology ; Postoperative Complications/etiology ; Predictive Value of Tests ; Prognosis ; ROC Curve ; Retrospective Studies ; Risk Assessment/methods ; Risk Factors |
| Language | English |
| Publishing date | 2021-11-22 |
| Publishing country | England |
| Document type | Journal Article ; Observational Study |
| ZDB-ID | 2615211-3 |
| ISSN | 2045-2322 ; 2045-2322 |
| ISSN (online) | 2045-2322 |
| ISSN | 2045-2322 |
| DOI | 10.1038/s41598-021-01913-z |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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