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

## TL;DR

This study validates a machine learning algorithm for predicting postoperative delirium in older adults and makes it publicly available for use in hospitals.

## Contribution

The SURGE-Ahead algorithm is externally validated and open-sourced for real-world implementation in surgical care.

## Key findings

- The SURGE-Ahead algorithm achieved an ROC AUC of 0.86 in predicting postoperative delirium.
- The algorithm demonstrated good calibration with a Brier Score of 0.14.
- It uses preoperative data and is available on GitHub for integration into clinical settings.

## Abstract

To externally validate the performance of the SURGE-Ahead postoperative delirium (POD) prediction algorithm in older adults undergoing surgery.

The SURGE-Ahead POD algorithm showed excellent predictive ability (ROC AUC 0.86) and good calibration (Brier Score 0.14).

This validated algorithm can be readily accessed on GitHub, allowing for easy integration into various surgical environments to enhance patient care for hospitalized older adults.

The online version contains supplementary material available at 10.1007/s41999-025-01180-5.

In this prospective external validation study, we examined the performance of the Supporting SURgery with GEriatric Co-Management and AI (SURGE-Ahead) postoperative delirium (POD) prediction algorithm. SURGE-Ahead is a collaborative project that aims to develop a clinical decision support system that uses predictive models to support geriatric co-management in surgical wards. Delirium is a common complication in older adults after surgery, leading to poor outcomes and increased healthcare costs. Early and accurate prediction of POD is crucial for timely intervention and prevention strategies.

The SURGE-Ahead algorithm utilizes a linear support vector machine model with a comprehensive set of 15 clinical and demographic features. In our validation, we analyzed 173 study participants, of which 50 developed POD.

The study found that the SURGE-Ahead POD prediction algorithm yielded state-of-the-art performance, using only preoperative data, with a receiver operating characteristics area under the curve of 0.86. In addition, the SURGE-Ahead algorithm exhibited good calibration as shown by a Brier Score of 0.14. The algorithm is openly available on GitHub, facilitating its implementation and adaptation to different surgical settings.

Our findings contribute to the development of reliable POD prediction tools, ultimately supporting the improvement of patient care in hospitalized older adults.

The online version contains supplementary material available at 10.1007/s41999-025-01180-5.