Prediction and risk evaluation of delirium after surgery in older patients: development and internal validation of an algorithm from the prospective BioCog cohort study

Florian Lammers-Lietz; Levent Akyuez; Diana Boraschi; Friedrich Borchers; Jeroen de Bresser; Sreyoshi Chatterjee; Marta M Correia; Nikola M de Lange; Thomas Bernd Dschietzig; Soumyabrata Ghosh; Insa Feinkohl; Izabela Ferreira da Silva; Marinus Fislage; Anna Fournier; Jürgen Gallinat; Daniel Hadzidiakos; Sven Hädel; Fatima Halzl-Yürek; Stefanie Heilmann-Heimbach; Maria Heinrich; Jeroen Hendrikse; Per Hoffmann; Jürgen Janke; Ilse M J Kant; Angelie Kraft; Roland Krause; Jochen Kruppa-Scheetz; Simone Kühn; Gunnar Lachmann; Markus Laubach; Christoph Lippert; David K Menon; Rudolf Mörgeli; Anika Müller; Henk-Jan Mutsaerts; Markus Nöthen; Peter Nürnberg; Kwaku Ofosu; Malte Pietzsch; Sophie K Piper; Tobias Pischon; Jacobus Preller; Konstanze Scheurer; Reinhard Schneider; Kathrin Scholtz; Peter H Schreier; Arjen J C Slooter; Emmanuel A Stamatakis; Simone J T van Montfort; Edwin van Dellen

doi:10.1016/j.bja.2026.01.025

Prediction and risk evaluation of delirium after surgery in older patients: development and internal validation of an algorithm from the prospective BioCog cohort study

Florian Lammers-Lietz
, Levent Akyuez
, Diana Boraschi
, Friedrich Borchers
, Jeroen de Bresser
, Sreyoshi Chatterjee
, Marta M Correia
, Nikola M de Lange
, Thomas Bernd Dschietzig
, Soumyabrata Ghosh
, Insa Feinkohl
, Izabela Ferreira da Silva
, Marinus Fislage
, Anna Fournier
, Jürgen Gallinat
, Daniel Hadzidiakos
, Sven Hädel
, Fatima Halzl-Yürek
, Stefanie Heilmann-Heimbach
, Maria Heinrich

Jeroen Hendrikse, Per Hoffmann, Jürgen Janke, Ilse M J Kant, Angelie Kraft, Roland Krause, Jochen Kruppa-Scheetz, Simone Kühn, Gunnar Lachmann, Markus Laubach, Christoph Lippert, David K Menon, Rudolf Mörgeli, Anika Müller, Henk-Jan Mutsaerts, Markus Nöthen, Peter Nürnberg, Kwaku Ofosu, Malte Pietzsch, Sophie K Piper, Tobias Pischon, Jacobus Preller, Konstanze Scheurer, Reinhard Schneider, Kathrin Scholtz, Peter H Schreier, Arjen J C Slooter, Emmanuel A Stamatakis, Simone J T van Montfort, Edwin van Dellen,

Research output: Contribution to journal › Article › Academic › peer-review

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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.

Original language	English
Pages (from-to)	1495-1508
Number of pages	14
Journal	British Journal of Anaesthesia
Volume	136
Issue number	5
Early online date	17 Mar 2026
DOIs	https://doi.org/10.1016/j.bja.2026.01.025
Publication status	Published - May 2026

Keywords

risk factors
cohort study
transcriptome
postoperative complications
postoperative delirium
neuroimaging

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10.1016/j.bja.2026.01.025Licence: CC BY-NC

PIIS0007091226000528Final published version, 1.22 MBLicence: CC BY-NC

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Lammers-Lietz, F., Akyuez, L., Boraschi, D., Borchers, F., de Bresser, J., Chatterjee, S., Correia, M. M., de Lange, N. M., Dschietzig, T. B., Ghosh, S., Feinkohl, I., Ferreira da Silva, I., Fislage, M., Fournier, A., Gallinat, J., Hadzidiakos, D., Hädel, S., Halzl-Yürek, F., Heilmann-Heimbach, S. (2026). Prediction and risk evaluation of delirium after surgery in older patients: development and internal validation of an algorithm from the prospective BioCog cohort study. British Journal of Anaesthesia, 136(5), 1495-1508. https://doi.org/10.1016/j.bja.2026.01.025

@article{ba5836eeeb0d43838152f36cc104612d,

title = "Prediction and risk evaluation of delirium after surgery in older patients: development and internal validation of an algorithm from the prospective BioCog cohort study",

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.",

keywords = "risk factors, cohort study, transcriptome, postoperative complications, postoperative delirium, neuroimaging",

author = "Florian Lammers-Lietz and Levent Akyuez and Diana Boraschi and Friedrich Borchers and \{de Bresser\}, Jeroen and Sreyoshi Chatterjee and Correia, \{Marta M\} and \{de Lange\}, \{Nikola M\} and Dschietzig, \{Thomas Bernd\} and Soumyabrata Ghosh and Insa Feinkohl and \{Ferreira da Silva\}, Izabela and Marinus Fislage and Anna Fournier and J{\"u}rgen Gallinat and Daniel Hadzidiakos and Sven H{\"a}del and Fatima Halzl-Y{\"u}rek and Stefanie Heilmann-Heimbach and Maria Heinrich and Jeroen Hendrikse and Per Hoffmann and J{\"u}rgen Janke and Kant, \{Ilse M J\} and Angelie Kraft and Roland Krause and Jochen Kruppa-Scheetz and Simone K{\"u}hn and Gunnar Lachmann and Markus Laubach and Christoph Lippert and Menon, \{David K\} and Rudolf M{\"o}rgeli and Anika M{\"u}ller and Henk-Jan Mutsaerts and Markus N{\"o}then and Peter N{\"u}rnberg and Kwaku Ofosu and Malte Pietzsch and Piper, \{Sophie K\} and Tobias Pischon and Jacobus Preller and Konstanze Scheurer and Reinhard Schneider and Kathrin Scholtz and Schreier, \{Peter H\} and Slooter, \{Arjen J C\} and Stamatakis, \{Emmanuel A\} and \{van Montfort\}, \{Simone J T\} and \{van Dellen\}, Edwin",

note = "Publisher Copyright: {\textcopyright} 2026 The Author(s). Published by Elsevier Ltd on behalf of British Journal of Anaesthesia. This is an open access article under the CC BY-NC license. http://creativecommons.org/licenses/by-nc/4.0/",

year = "2026",

month = may,

doi = "10.1016/j.bja.2026.01.025",

language = "English",

volume = "136",

pages = "1495--1508",

journal = "British Journal of Anaesthesia",

issn = "0007-0912",

publisher = "Elsevier Limited",

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Lammers-Lietz, F, Akyuez, L, Boraschi, D, Borchers, F, de Bresser, J, Chatterjee, S, Correia, MM, de Lange, NM, Dschietzig, TB, Ghosh, S, Feinkohl, I, Ferreira da Silva, I, Fislage, M, Fournier, A, Gallinat, J, Hadzidiakos, D, Hädel, S, Halzl-Yürek, F, Heilmann-Heimbach, S, Heinrich, M, Hendrikse, J, Hoffmann, P, Janke, J, Kant, IMJ, Kraft, A, Krause, R, Kruppa-Scheetz, J, Kühn, S, Lachmann, G, Laubach, M, Lippert, C, Menon, DK, Mörgeli, R, Müller, A, Mutsaerts, H-J, Nöthen, M, Nürnberg, P, Ofosu, K, Pietzsch, M, Piper, SK, Pischon, T, Preller, J, Scheurer, K, Schneider, R, Scholtz, K, Schreier, PH, Slooter, AJC, Stamatakis, EA, van Montfort, SJT, van Dellen, E 2026, 'Prediction and risk evaluation of delirium after surgery in older patients: development and internal validation of an algorithm from the prospective BioCog cohort study', British Journal of Anaesthesia, vol. 136, no. 5, pp. 1495-1508. https://doi.org/10.1016/j.bja.2026.01.025

Prediction and risk evaluation of delirium after surgery in older patients: development and internal validation of an algorithm from the prospective BioCog cohort study. / Lammers-Lietz, Florian; Akyuez, Levent; Boraschi, Diana et al.
In: British Journal of Anaesthesia, Vol. 136, No. 5, 05.2026, p. 1495-1508.

Research output: Contribution to journal › Article › Academic › peer-review

TY - JOUR

T1 - Prediction and risk evaluation of delirium after surgery in older patients

T2 - development and internal validation of an algorithm from the prospective BioCog cohort study

AU - Lammers-Lietz, Florian

AU - Akyuez, Levent

AU - Boraschi, Diana

AU - Borchers, Friedrich

AU - de Bresser, Jeroen

AU - Chatterjee, Sreyoshi

AU - Correia, Marta M

AU - de Lange, Nikola M

AU - Dschietzig, Thomas Bernd

AU - Ghosh, Soumyabrata

AU - Feinkohl, Insa

AU - Ferreira da Silva, Izabela

AU - Fislage, Marinus

AU - Fournier, Anna

AU - Gallinat, Jürgen

AU - Hadzidiakos, Daniel

AU - Hädel, Sven

AU - Halzl-Yürek, Fatima

AU - Heilmann-Heimbach, Stefanie

AU - Heinrich, Maria

AU - Hendrikse, Jeroen

AU - Hoffmann, Per

AU - Janke, Jürgen

AU - Kant, Ilse M J

AU - Kraft, Angelie

AU - Krause, Roland

AU - Kruppa-Scheetz, Jochen

AU - Kühn, Simone

AU - Lachmann, Gunnar

AU - Laubach, Markus

AU - Lippert, Christoph

AU - Menon, David K

AU - Mörgeli, Rudolf

AU - Müller, Anika

AU - Mutsaerts, Henk-Jan

AU - Nöthen, Markus

AU - Nürnberg, Peter

AU - Ofosu, Kwaku

AU - Pietzsch, Malte

AU - Piper, Sophie K

AU - Pischon, Tobias

AU - Preller, Jacobus

AU - Scheurer, Konstanze

AU - Schneider, Reinhard

AU - Scholtz, Kathrin

AU - Schreier, Peter H

AU - Slooter, Arjen J C

AU - Stamatakis, Emmanuel A

AU - van Montfort, Simone J T

AU - van Dellen, Edwin

N1 - Publisher Copyright: © 2026 The Author(s). Published by Elsevier Ltd on behalf of British Journal of Anaesthesia. This is an open access article under the CC BY-NC license. http://creativecommons.org/licenses/by-nc/4.0/

PY - 2026/5

Y1 - 2026/5

N2 - 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.

AB - 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.

KW - risk factors

KW - cohort study

KW - transcriptome

KW - postoperative complications

KW - postoperative delirium

KW - neuroimaging

UR - https://www.scopus.com/pages/publications/105036700010

U2 - 10.1016/j.bja.2026.01.025

DO - 10.1016/j.bja.2026.01.025

M3 - Article

C2 - 41850989

SN - 0007-0912

VL - 136

SP - 1495

EP - 1508

JO - British Journal of Anaesthesia

JF - British Journal of Anaesthesia

IS - 5

ER -

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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