Predicting Postoperative Delirium in Older Patients

BackgroundThe number of elective surgeries for older individuals is on the rise globally. Machine learning may improve risk assessment with an impact on surgical planning and postoperative care. Preoperative cognitive assessment may facilitate early identification of postoperative delirium (POD). This study aims to estimate the predictive ability of machine learning models for POD using pre-and/or perioperative features, with a specific focus on adding neuropsychological assessments prior to surgery. Materials and MethodsThis retrospective cohort study analyzed data from the multicenter PAWEL study and its PAWEL-R substudy, encompassing older patients ([≥]70 years) undergoing elective surgeries across five medical centers from July 2017 to April 2019. A total of 1624 patients were included, with POD diagnosis made before discharge. Data included demographics, clinical, surgical, and neuropsychological features collected pre- and perioperatively. Machine learning model performance was evaluated using the area under the receiver operating characteristic curve (AUC), with permutation testing for significance and SHapley Additive exPlanations (SHAP) to identify effective neuropsychological assessments. ResultsIn this cohort of 1624 patients, 52.3% (N=850) were male, with a mean [SD] age of 77.9 [4.9] years. Predicting POD before surgery using demographic, clinical, surgical, and neuropsychological features achieved an AUC of 0.79. Incorporating all pre- and perioperative features into the model yielded a slightly higher AUC of 0.82, with no significant difference observed (P= .19). Notably, cognitive factors alone were not strong predictors (AUC=0.61). However, specific tests within neuropsychological assessments, such as the Montreal Cognitive Assessment memory subdomain and Trail Making Test Part B, were found to be crucial for prediction according to SHAP analysis. Conclusion and RelevancePreoperative risk prediction for POD can increase risk awareness in presurgical assessment and improve postoperative management in patients with a high risk for delirium. HighlightsO_LIAnalyzed 1624 older patients ([≥]70 years) undergoing elective surgeries across five medical centers from July 2017 to April 2019. C_LIO_LIEstablished machine learning model to predict postoperative delirium before surgery. C_LIO_LIPreoperative cognition enhances predictive performance, comparable to models incorporating all pre- and perioperative features. C_LIO_LIMontreal Cognitive Assessment memory subdomain and Trail Making Test Part B drive the cognition-based prediction. C_LIO_LIPerioperative surgical features, such as the duration of the surgery, are important predictors. C_LI