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- Title
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.
- Authors
Racine, Annie M.; Tommet, Douglas; D'Aquila, Madeline L.; Fong, Tamara G.; Gou, Yun; Tabloski, Patricia A.; Metzger, Eran D.; Hshieh, Tammy T.; Schmitt, Eva M.; Vasunilashorn, Sarinnapha M.; Kunze, Lisa; Vlassakov, Kamen; Abdeen, Ayesha; Lange, Jeffrey; Earp, Brandon; Dickerson, Bradford C.; Marcantonio, Edward R.; Steingrimsson, Jon; Travison, Thomas G.; Inouye, Sharon K.
- Abstract
<bold>Background: </bold>Our objective was to assess the performance of machine learning methods to predict post-operative delirium using a prospective clinical cohort.<bold>Methods: </bold>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.<bold>Results: </bold>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.<bold>Conclusions: </bold>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.
- Subjects
MACHINE learning; OLDER patients; RECEIVER operating characteristic curves; DELIRIUM; PREDICTION models; CLINICAL prediction rules; DIAGNOSIS of delirium; RESEARCH; RESEARCH methodology; MEDICAL cooperation; EVALUATION research; COMPARATIVE studies; RESEARCH funding; LOGISTIC regression analysis; LONGITUDINAL method
- Publication
JGIM: Journal of General Internal Medicine, 2021, Vol 36, Issue 2, p265
- ISSN
0884-8734
- Publication type
journal article
- DOI
10.1007/s11606-020-06238-7