Prediction and risk evaluation of delirium after surgery in older patients: development and internal validation of an algorithm from the prospective BioCog cohort study.
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Lammers-Lietz Florian, Akyuez Levent, Boraschi Diana, Borchers Friedrich, de Bresser Jeroen, Chatterjee Sreyoshi, Correia Marta M, de Lange Nikola M, Dschietzig Thomas Bernd, Ghosh Soumyabrata, Feinkohl Insa, Ferreira da Silva Izabela, Fislage Marinus, Fournier Anna, Gallinat Jürgen, Hadzidiakos Daniel, Hädel Sven, Halzl-Yürek Fatima, Heilmann-Heimbach Stefanie, Heinrich Maria, Hendrikse Jeroen, Hoffmann Per, Janke Jürgen, Kant Ilse M J, Kraft Angelie, Krause Roland, Kruppa-Scheetz Jochen, Kühn Simone, Lachmann Gunnar, Laubach Markus, Lippert Christoph, Menon David K, Mörgeli Rudolf, Müller Anika, Mutsaerts Henk-Jan, Nöthen Markus, Nürnberg Peter, Ofosu Kwaku, Pietzsch Malte, Piper Sophie K, Pischon Tobias, Preller Jacobus, Scheurer Konstanze, Schneider Reinhard, Scholtz Kathrin, Schreier Peter H, Slooter Arjen J C, Stamatakis Emmanuel A, von Haefen Clarissa, van Montfort Simone J T, van Dellen Edwin, Volk Hans-Dieter, Weber Simon, Wiebach Janine, Wiehe Anton, Winterer Jeanne M, Wolf Alissa, Zacharias Norman, Spies Claudia, Winterer Georg, BioCog consortium
Аннотация
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.