Br J Anaesth. 2026 Mar 17:S0007-0912(26)00052-8. doi: 10.1016/j.bja.2026.01.025. Online ahead of print.
ABSTRACT
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.
PMID:41850989 | DOI:10.1016/j.bja.2026.01.025