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Updated: Jun 25, 2025
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Introducing a machine learning algorithm for delirium prediction-the Supporting SURgery with GEriatric Co-Management
Samuel Benovic1,2, Anna H Ajlani3,4, Christoph Leinert1,2
1Institute of Geriatric Research, Ulm University Medical Center, Ulm, Germany.
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.
