Background

Postoperative delirium (POD) affects ∼20% of older surgical patients. It is associated with poor clinical outcome and increased mortality. We aimed to identify the major POD risk factors and to develop and validate a multivariate algorithm for individual POD risk prediction and risk evaluation in the very early postoperative period.

Methods

BioCog is a prospective cohort study conducted in the anaesthesiology departments of two tertiary care centres in Germany and The Netherlands. Patients aged ≥65 yr with no preoperative dementia (Mini-Mental Status Examination ≥24) undergoing surgery with an expected duration of at least 60 min were enrolled and screened for POD according to DSM 5 until the seventh postoperative day. Clinical, neuropsychological, neuroimaging data, and blood were measured before and after surgery. We evaluated several models by sequentially adding blocks of variables. Gradient-boosted trees (GBT) with nested cross-validation were used for POD prediction. Model accuracy (area under the receiver-operating curve, AUC) and calibration were assessed (Brier score).

Results

Out of 929 patients, 184 (20%) experienced POD. A GBT algorithm using both preoperative data, characteristics of the intervention, and postoperative changes in laboratory parameters achieved the highest AUC (0.83, [0.79-0.86]) with a Brier score of 0.12 (0.12-0.13).

Conclusions

Models combining preoperative with precipitating factors during surgery predict POD with high accuracy. This suggests that the resulting algorithms eventually may become useful to support clinical decision-making.

Clinical trial registration

NCT02265263.

Copyright © 2026 The Author(s). Published by Elsevier Ltd.. All rights reserved.