• Susana Vacas, Tristan Grogan, Drew Cheng, and Ira Hofer.
• Department of Anesthesiology and Perioperative Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
• Medicine (Baltimore). 2022 Oct 21; 101 (42): e31176e31176.

MethodsThe EHR of 32734 patients >18 years of age undergoing surgery and had POD assessment were reviewed. Patient characteristics and study variables were summarized between delirium groups. We constructed univariate logistic regression models for POD using each study variable to estimate odds ratios (OR) and constructed a multivariable logistic regression model with stepwise variable selection. In order to create a clinically useful/implementable tool we created a nomogram to predict risk of delirium.ResultsOverall, we found a rate of POD of 3.7% across our study population. The Model achieved an AUC of the ROC curve of 0.83 (95% CI 0.82-0.84). We found that age, increased American Society of Anesthesiologists (ASA) score (ASA 3-4 OR 2.81, CI 1.49-5.28, P < .001), depression (OR 1.28, CI 1.12-1.47, P < .001), postoperative benzodiazepine use (OR 3.52, CI 3.06-4.06, P < .001) and urgent cases (Urgent OR 3.51, CI 2.92-4.21, P < .001; Emergent OR 3.99, CI 3.21-4.96, P < .001; Critically Emergent OR 5.30, CI 3.53-7.96, P < .001) were associated with POD.DiscussionWe were able to distinguish the contribution of individual risk factors to the development of POD. We created a clinically useful easy-to-use tool that has the potential to accurately identify those at high-risk of delirium, a first step to prevent POD.Copyright © 2022 the Author(s). Published by Wolters Kluwer Health, Inc.

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