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Machine Learning to Develop and Internally Validate a Predictive Model for Post-operative Delirium in a Prospective, Observational Clinical Cohort Study of Older Surgical Patients

2020; Springer Science+Business Media; Volume: 36; Issue: 2 Linguagem: Inglês

10.1007/s11606-020-06238-7

1525-1497

Annie M. Racine, Douglas Tommet, Madeline 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 K. Lange, Brandon E. Earp, Bradford C. Dickerson, Edward R. Marcantonio, Jon A. Steingrimsson, Thomas G. Travison, Sharon K. Inouye, Richard N. Jones, Steven E. Arnold, Bradford C. Dickerson, Tamara G. Fong, Richard N. Jones, Towia A. Libermann, Thomas G. Travison, Simon T. Dillon, Jacob M. Hooker, Tammy T. Hshieh, Long Ngo, Hasan H. Otu, Annie M. Racine, Alexandra Touroutoglou, Sarinnapha M. Vasunilashorn, Douglas Ayres, Gregory W. Brick, Fan Chen, Robert G. Davis, Jacob M. Drew, Richard Iorio, Fulton Kornack, Michael J. Weaver, Anthony Webber, Richard M. Wilk, David Shaff, Brett Armstrong, Angelee Banda, Sylvie Bertrand, Madeline D’Aquila, Jacqueline Gallagher, Baileigh G. Hightower, Shannon Malloy, Jacqueline Nee, Chloe K. Nobuhara, Abigail Overstreet, Bianca A. Trombetta, Baileigh G. Hightower, David Urick, Guoquan Xu, Grae Arabasz, Michael Brickhouse, Regan Butterfield, Shirley Hsu, Sara Makaretz, Judit Sore, Fan Chen, Sabrina Carretie, Ted Gruen, Katherine Tasker,

Anesthesia and Neurotoxicity Research

Our objective was to assess the performance of machine learning methods to predict post-operative delirium using a prospective clinical cohort. We 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. The 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. We 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.