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Updated: Apr 11, 2026

An Experimental Paradigm for the Prediction of Post-Operative Pain PPOP

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SURGE-ahead postoperative delirium prediction: external validation and open-source library.

Thomas Derya Kocar^1,2, Philip Wolf³, Christoph Leinert^4,5

¹Institute for Geriatric Research, AGAPLESION Bethesda Ulm, Ulm University Medical Center, Zollernring 26, 89073, Ulm, Germany. thomas.kocar@uni-ulm.de.

European Geriatric Medicine

|March 11, 2025

Summary

The Supporting SURgery with GEriatric Co-Management and AI (SURGE-Ahead) algorithm accurately predicts postoperative delirium (POD) in older adults using preoperative data. This validated AI tool aids in early intervention for better patient outcomes.

Keywords:

Artificial intelligence Delirium prediction Explainable AI Machine learning Postoperative delirium

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Last Updated: Apr 11, 2026

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Area of Science:

Geriatric Medicine
Artificial Intelligence in Healthcare
Surgical Patient Care

Background:

Postoperative delirium (POD) is a frequent complication in older surgical patients.
POD is associated with adverse outcomes and increased healthcare costs.
Accurate and early POD prediction is vital for effective management.

Purpose of the Study:

To externally validate the performance of the SURGE-Ahead predictive algorithm for postoperative delirium.
To assess the algorithm's utility in supporting geriatric co-management in surgical settings.
To evaluate the algorithm's predictive accuracy using preoperative data.

Main Methods:

Prospective external validation study.
Utilized the SURGE-Ahead algorithm, a linear support vector machine model with 15 features.
Analyzed data from 173 surgical participants, 50 of whom developed POD.

Main Results:

The SURGE-Ahead algorithm demonstrated state-of-the-art performance with an AUC of 0.86.
The algorithm showed good calibration, indicated by a Brier Score of 0.14.
The algorithm is openly available on GitHub for broader implementation.

Conclusions:

The validated SURGE-Ahead algorithm reliably predicts POD using preoperative data.
This AI tool can enhance patient care for hospitalized older adults.
Findings support the development of robust POD prediction tools for surgical settings.