Introducing a machine learning algorithm for delirium prediction-the Supporting SURgery with GEriatric Co-Management and AI project (SURGE-Ahead)

Abstract

Introduction: Post-operative delirium (POD) is a common complication in older patients, with an incidence of 14-56%. To implement preventative procedures, it is necessary to identify patients at risk for POD. In the present study, we aimed to develop a machine learning (ML) model for POD prediction in older patients, in close cooperation with the PAWEL (patient safety, cost-effectiveness and quality of life in elective surgery) project.

Methods: The model was trained on the PAWEL study's dataset of 878 patients (no intervention, age ≥ 70, 209 with POD). Presence of POD was determined by the Confusion Assessment Method and a chart review. We selected 15 features based on domain knowledge, ethical considerations and a recursive feature elimination. A logistic regression and a linear support vector machine (SVM) were trained, and evaluated using receiver operator characteristics (ROC).

Results: The selected features were American Society of Anesthesiologists score, multimorbidity, cut-to-suture time, estimated glomerular filtration rate, polypharmacy, use of cardio-pulmonary bypass, the Montreal cognitive assessment subscores 'memory', 'orientation' and 'verbal fluency', pre-existing dementia, clinical frailty scale, age, recent falls, post-operative isolation and pre-operative benzodiazepines. The linear SVM performed best, with an ROC area under the curve of 0.82 [95% CI 0.78-0.85] in the training set, 0.81 [95% CI 0.71-0.88] in the test set and 0.76 [95% CI 0.71-0.79] in a cross-centre validation.

Conclusion: We present a clinically useful and explainable ML model for POD prediction. The model will be deployed in the Supporting SURgery with GEriatric Co-Management and AI project.

Keywords: delirium prediction; explainable artificial intelligence (AI); machine learning; older people; post-operative delirium; support vector machine.

Figure 1

Receiver operating characteristic curves of the linear SVM delirium prediction algorithm. Left: training set (n = 702), right: test set (n = 176). SVM = support vector machine, ROC = receiver operating characteristics, AUC = area under the curve.

Figure 2

Distribution of the individual feature importance in the training set. In the linear SVM, the individual feature importance is determined by the element-wise multiplication of the coefficient and feature vectors. As the distribution of the individual feature importance is approximately centred around 0, it is possible to represent the individual feature importance in numerical terms, where positive numbers indicate a higher risk of delirium and negative numbers a lower risk (see x-axis).