• Jan Menzenbach, Andrea Kirfel, Vera Guttenthaler, Johanna Feggeler, Tobias Hilbert, Arcangelo Ricchiuto, Christian Staerk, Andreas Mayr, Mark Coburn, Maria Wittmann, and PROPDESC Collaboration Group.
• Department of Anesthesiology and Intensive Care Medicine, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany. Electronic address: jan.menzenbach@ukbonn.de.
• J Clin Anesth. 2022 Jun 1; 78: 110684.

Study ObjectiveTo develop and validate a pragmatic risk screening score for postoperative delirium (POD) based on routine preoperative data.DesignProspective observational monocentric trial.SettingPreoperative data and POD assessment were collected from cardiac and non-cardiac surgical patients at a German university hospital. Data-driven modelling approaches (step-wise vs. component-wise gradient boosting on complete and restricted predictor set) were compared to predictor selection by experts (investigators vs. external Delphi survey).PatientsInpatients (≥60 years) scheduled for elective surgery lasting more than 60 min.MeasurementsPOD was assessed daily during first five postoperative or post-sedation days with confusion assessment method for intensive and standard care unit (CAM-ICU/CAM), 4 'A's test (4AT) and Delirium Observation Screening (DOS) scale.Main ResultsFrom 1023 enrolled patients, 978 completed observations were separated in development (n = 600; POD incidence 22.2%) and validation (n = 378; POD incidence 25.7%) cohorts. Data-driven approaches generated models containing laboratory values, surgical discipline and several items on cognitive and quality of life assessment, which are time consuming to collect. Boosting on complete predictor set yielded the highest bootstrapped prediction accuracy (AUC 0.767) by selecting 12 predictors, with substantial dependence on cardiac surgery. Investigators selected via univariate comparison age, ASA and NYHA classification, surgical risk as well as ´serial subtraction´ and ´sentence repetition´ of the Montreal Cognitive Assessment (MoCA) to enable rapid collection of their risk score for preoperative screening. This investigator model provided slightly lower bootstrapped prediction accuracy (AUC 0.746) but proved to have robust results on validation cohort (AUC 0.725) irrespective of surgical discipline. Simplification of the investigator model by scaling and rounding of regression coefficients into the PROPDESC score achieved a comparable precision on the validation cohort (AUC 0.729).ConclusionsThe PROPDESC score showed promising performance on a separate validation cohort in predicting POD based on routine preoperative data. Suitability for universal screening needs to be shown in a large external validation.Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.

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