• Annie M Racine, Douglas Tommet, Madeline L D'Aquila, Tamara G Fong, Yun Gou, Patricia A Tabloski, Eran D Metzger, Tammy T Hshieh, Eva M Schmitt, Sarinnapha M Vasunilashorn, Lisa Kunze, Kamen Vlassakov, Ayesha Abdeen, Jeffrey Lange, Brandon Earp, Bradford C Dickerson, Edward R Marcantonio, Jon Steingrimsson, Thomas G Travison, Sharon K Inouye, Richard N Jones, and , the RISE Study Group.
• Aging Brain Center, Institute for Aging Research, Boston, MA, USA.
• J Gen Intern Med. 2021 Feb 1; 36 (2): 265273265-273.

BackgroundOur objective was to assess the performance of machine learning methods to predict post-operative delirium using a prospective clinical cohort.MethodsWe analyzed data from an observational cohort study of 560 older adults (≥ 70 years) without dementia undergoing major elective non-cardiac surgery. Post-operative delirium was determined by the Confusion Assessment Method supplemented by a medical chart review (N = 134, 24%). Five machine learning algorithms and a standard stepwise logistic regression model were developed in a training sample (80% of participants) and evaluated in the remaining hold-out testing sample. We evaluated three overlapping feature sets, restricted to variables that are readily available or minimally burdensome to collect in clinical settings, including interview and medical record data. A large feature set included 71 potential predictors. A smaller set of 18 features was selected by an expert panel using a consensus process, and this smaller feature set was considered with and without a measure of pre-operative mental status.ResultsThe area under the receiver operating characteristic curve (AUC) was higher in the large feature set conditions (range of AUC, 0.62-0.71 across algorithms) versus the selected feature set conditions (AUC range, 0.53-0.57). The restricted feature set with mental status had intermediate AUC values (range, 0.53-0.68). In the full feature set condition, algorithms such as gradient boosting, cross-validated logistic regression, and neural network (AUC = 0.71, 95% CI 0.58-0.83) were comparable with a model developed using traditional stepwise logistic regression (AUC = 0.69, 95% CI 0.57-0.82). Calibration for all models and feature sets was poor.ConclusionsWe developed machine learning prediction models for post-operative delirium that performed better than chance and are comparable with traditional stepwise logistic regression. Delirium proved to be a phenotype that was difficult to predict with appreciable accuracy.

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