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ArticleEuropean geriatric medicine2025

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

Thomas Derya Kocar, Philip Wolf, Christoph Leinert, Simone Brefka, Marina L Fotteler, Adriane Uihlein, Felix Wezel, Martin Wehling, Nuh Rahbari, Hans Kestler and 3 more

Abstract readValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in European geriatric medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Thomas Derya KocarInstitute for Geriatric Research, AGAPLESION Bethesda Ulm, Ulm University Medical Center, Zollernring 26, 89073, Ulm, Germany. thomas.kocar@uni-ulm.de.ORCID http://orcid.org/0009-0001-5842-5146
Philip WolfInstitute of Medical Systems Biology, Ulm University, Ulm, Germany.
Christoph LeinertInstitute for Geriatric Research, AGAPLESION Bethesda Ulm, Ulm University Medical Center, Zollernring 26, 89073, Ulm, Germany.
Simone BrefkaInstitute for Geriatric Research, AGAPLESION Bethesda Ulm, Ulm University Medical Center, Zollernring 26, 89073, Ulm, Germany.
Marina L FottelerDigihealth Institute, Neu-Ulm University of Applied Sciences, Neu-Ulm, Germany.
Adriane UihleinDepartment of Orthopedic Trauma, Hand, Plastic and Reconstruction Surgery, Ulm University Medical Center, Ulm, Germany.
Felix WezelDepartment of Urology and Pediatric Urology, University Hospital Ulm, Ulm, Germany.
Martin WehlingClinical Pharmacology, University of Heidelberg, Mannheim, Germany.
Nuh RahbariDepartment of Surgery, Ulm University Hospital, Ulm, Germany.
Hans KestlerInstitute of Medical Systems Biology, Ulm University, Ulm, Germany.
Florian GebhardDepartment of Orthopedic Trauma, Hand, Plastic and Reconstruction Surgery, Ulm University Medical Center, Ulm, Germany.
Dhayana DallmeierGeriatric Center Ulm, Ulm, Germany.
Michael DenkingerInstitute for Geriatric Research, AGAPLESION Bethesda Ulm, Ulm University Medical Center, Zollernring 26, 89073, Ulm, Germany.

Funding

Bundesministerium für Bildung und Forschung 01GY2101
6 · The paper itself

Abstract

purposeIn 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.

methodsThe 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.

resultsThe 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.

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

Indexed as

Decision Support Systems, ClinicalDeliriumPostoperative ComplicationsAgedAged, 80 and overAlgorithmsFemaleGeriatric AssessmentHumansMaleProspective StudiesROC CurveSupport Vector MachineArtificial intelligenceDelirium predictionExplainable AIMachine learningPostoperative delirium

Identifiers

PMID40064822
PMCPMC12174281

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.