The Adelaide Score: An artificial intelligence measure of readiness for discharge after general surgery

• Published in: ANZ journal of surgery Vol. 93; no. 9; pp. 2119 - 2124
• Main Authors: Kovoor, Joshua G, Bacchi, Stephen, Gupta, Aashray K, Stretton, Brandon, Malycha, James, Reddi, Benjamin A, Liew, Danny, O'Callaghan, Patrick G, Beltrame, John F, Zannettino, Andrew C, Jones, Karen L, Horowitz, Michael, Dobbins, Christopher, Hewett, Peter J, Trochsler, Markus I, Maddern, Guy J
• Format: Journal Article
• Language: English
• Published: Australia Blackwell Publishing Ltd 01.09.2023
• Subjects: Algorithms; Artificial intelligence; Classification; Datasets; Derivation; Machine learning; Model accuracy; Patients; Regression analysis; Regression models; Surgery; discharge; general surgery; prediction; systems; artificial intelligence
• ISSN: 1445-1433; 1445-2197; 1445-2197
• DOI: 10.1111/ans.18546

This study aimed to examine the performance of machine learning algorithms for the prediction of discharge within 12 and 24 h to produce a measure of readiness for discharge after general surgery. Consecutive general surgery patients at two tertiary hospitals, over a 2-year period, were included. Observation and laboratory parameter data were stratified into training, testing and validation datasets. Random forest, XGBoost and logistic regression models were evaluated. Each ward round note time was taken as a different event. Primary outcome was classification accuracy of the algorithmic model able to predict discharge within the next 12 h on the validation data set. 42 572 ward round note timings were included from 8826 general surgery patients. Discharge occurred within 12 h for 8800 times (20.7%), and within 24 h for 9885 (23.2%). For predicting discharge within 12 h, model classification accuracies for derivation and validation data sets were: 0.84 and 0.85 random forest, 0.84 and 0.83 XGBoost, 0.80 and 0.81 logistic regression. For predicting discharge within 24 h, model classification accuracies for derivation and validation data sets were: 0.83 and 0.84 random forest, 0.82 and 0.81 XGBoost, 0.78 and 0.79 logistic regression. Algorithms generated a continuous number between 0 and 1 (or 0 and 100), representing readiness for discharge after general surgery. A derived artificial intelligence measure (the Adelaide Score) successfully predicts discharge within the next 12 and 24 h in general surgery patients. This may be useful for both treating teams and allied health staff within surgical systems.