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

Autor:	Benovic S; Institute of Geriatric Research, Ulm University Medical Center, Ulm, Germany.; Agaplesion Bethesda Clinic Ulm, Ulm, Germany., Ajlani AH; Institute of the History, Philosophy and Ethics of Medicine, Ulm University, Ulm, Germany.; Department of Sociology with a Focus on Innovation and Digitalization, Institute of Sociology, Johannes Kepler University Linz, Linz, Austria., Leinert C; Institute of Geriatric Research, Ulm University Medical Center, Ulm, Germany.; Agaplesion Bethesda Clinic Ulm, Ulm, Germany., Fotteler M; Agaplesion Bethesda Clinic Ulm, Ulm, Germany.; DigiHealth Institute, Neu-Ulm University of Applied Sciences, Neu-Ulm, Germany., Wolf D; Institute of Medical Systems Biology, Ulm University, Ulm, Germany., Steger F; Institute of the History, Philosophy and Ethics of Medicine, Ulm University, Ulm, Germany., Kestler H; Institute of Medical Systems Biology, Ulm University, Ulm, Germany., Dallmeier D; Institute of Geriatric Research, Ulm University Medical Center, Ulm, Germany.; Department of Epidemiology, Boston University School of Public Health, Boston, USA., Denkinger M; Institute of Geriatric Research, Ulm University Medical Center, Ulm, Germany.; Agaplesion Bethesda Clinic Ulm, Ulm, Germany., Eschweiler GW; Geriatric Center, University Hospital Tübingen, Tubingen, Germany.; Department of Psychiatry and Psychotherapy, Tübingen University Hospital, Tübingen, Germany., Thomas C; Department of Psychiatry and Psychotherapy, Tübingen University Hospital, Tübingen, Germany.; Department of Geriatric Psychiatry and Psychotherapy, Klinikum Stuttgart, Stuttgart, Germany., Kocar TD; Institute of Geriatric Research, Ulm University Medical Center, Ulm, Germany.; Agaplesion Bethesda Clinic Ulm, Ulm, Germany.
Jazyk:	angličtina
Zdroj:	Age and ageing [Age Ageing] 2024 May 01; Vol. 53 (5).
DOI:	10.1093/ageing/afae101
Abstrakt:	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. (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Geriatrics Society.)
Databáze:	MEDLINE
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