Background: Postoperative Delirium (POD) is a disturbing reality for patients and their families. Absence of easy-to-use and accurate risk scores prompted us to retrospectively extract data from the electronic health records (EHR) to identify clinical factors associated with POD. We seek to create a multivariate nomogram to predict the risk of POD based upon the most significant clinical factors. Methods: The EHR of 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. Results: Overall, 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 ASA score (ASA 3-4 OR 2.81, CI1.49-5.28, p<0.001), depression (OR 1.28, CI1.12-1.47, p<0.001), postoperative benzodiazepine use (OR 3.52, CI3.06-4.06, p<0.001) and urgent cases (Urgent OR 3.51, CI2.92-4.21, p<0.001; Emergent OR 3.99, CI3.21-4.96, p<0.001; Critically Emergent OR 5.30, CI3.53-7.96, p<0.001) were associated with POD. Conclusions: We 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.

Ira Hofer is the founder and President of Extrico health a company that helps hospitals leverage data from their electronic health record for decision making purposes. Dr. Hofer receives research support and serves as a consultant for Merck.

This study was supported by the National Institutes of Health, National Institute of General Medical Sciences under Award Number K23GM132795 (Vacas), National Institutes of Aging under award number R21AG070269 (Vacas) and National Heart, Lung and Blood Institute under Award Number K01HL150318 (Hofer). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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