Machine Learning to Develop and Internally Validate a Predictive Model for Post-operative Delirium in a Prospective, Obs
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ND: Our objective was to assess the performance of machine learning methods to predict postoperative delirium using a prospective clinical cohort. METHODS: 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
Sharon K. Inouye and Richard N. Jones contributed equally as co-senior authors Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11606-020-06238-7) contains supplementary material, which is available to authorized users. Received June 26, 2019 Accepted September 11, 2020
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. RESULTS: 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
Racine et al.: Machine Learning to Predict Post-operative Delirium
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. CONCLUSIONS: 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. KEY WORDS: machine learning; statistical learning; model prediction; delirium; post-operative.
Abbreviations BIDMC BWH HMS HSL MGH PI UNL
Beth Israel Deaconess Medical Center Brigham and Women’s Hospital Harvard Medical School Hebrew SeniorLife Massachusetts General Hospital principal investigator University of Nebraska-Lincoln
J Gen Intern Med DOI: 10.1007/s11606-020-06238-7 © Society of General Internal Medicine 2020
INTRODUCTION
Delirium, the most common post-operative complication in adults over the age of 65, has an incidence of 15–25% after major elective surgery.1 Delirium is associated with both shortand long-term clinical and functional complication
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