Evidence map›Paper›PMID 41469047›Full record

ArticleBMJ open2025

Evaluation of visual patient predictive for enhancing level 3 situation awareness: protocol for a multicentre randomised computer-based simulation and diagnostic accuracy study (true positive rate, precision, average lead time).

Cynthia Alexandra Hunn, Hannes Bruns, Sebastian Sahli, Luca Wachtendorf, Johannes Schäfer, Stefan Schwerin, Achilles Delis, Markus Kalisch, Gaspar Dugac, Arend Rahrisch and 9 more

Abstract readClinical Trial Protocol
In one paragraph

Article in BMJ open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

19 authors.

Cynthia Alexandra HunnInstitute for Anesthesiology and Perioperative Medicine, University Hospital Zurich, Zurich, Switzerland.
Hannes BrunsInstitute for Anesthesiology and Perioperative Medicine, University Hospital Zurich, Zurich, Switzerland.
Sebastian SahliDepartment of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA.
Luca WachtendorfDepartment of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA.
Johannes SchäferDepartment of Anaesthesiology, Heidelberg University, Heidelberg, BW, Germany.
Stefan SchwerinDepartment of Anesthesiology and Intensive Care, TUM, Munich, BY, Germany.
Achilles DelisDepartment of Anesthesiology, Universitätsklinikum Bonn, Bonn, NRW, Germany.
Markus KalischSeminar for Statistics, ETH Zurich, Zürich, ZH, Switzerland.
Gaspar DugacSeminar for Statistics, ETH Zurich, Zürich, ZH, Switzerland.
Arend RahrischInstitute for Anesthesiology and Perioperative Medicine, University Hospital Zurich, Zurich, Switzerland.
Max EbenspergerDepartment of Anesthesiology and Intensive Care, TUM, Munich, BY, Germany.
Arian KarimitarDepartment of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA.
Gregor MassothDepartment of Anesthesiology, Universitätsklinikum Bonn, Bonn, NRW, Germany.
Christopher NeuhausDepartment of Anaesthesiology, Heidelberg University, Heidelberg, BW, Germany.
Alina DubatovkaDepartment of Computer Science, ETH Zurich, Zürich, ZH, Switzerland.
Christoph B NöthigerInstitute for Anesthesiology and Perioperative Medicine, University Hospital Zurich, Zurich, Switzerland.
G GasciauskaiteInstitute for Anesthesiology and Perioperative Medicine, University Hospital Zurich, Zurich, Switzerland.
Tadzio Raoul RocheInstitute for Anesthesiology and Perioperative Medicine, University Hospital Zurich, Zurich, Switzerland.
David W TschollInstitute for Anesthesiology and Perioperative Medicine, University Hospital Zurich, Zurich, Switzerland david.tscholl@usz.ch.ORCID http://orcid.org/0000-0002-3677-3705

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionVisual Patient Predictive (VPP) is an AI-based extension of the Visual Patient Avatar (VPA) that integrates deep learning models to predict upcoming vital sign deviations and display them as dashed visual elements. By explicitly showing anticipated changes, the system aims to support level 3 situation awareness-the projection of future patient states. This multicentre simulation study will evaluate whether predictive algorithms and visualisations integrated into the VPA (resulting in VPP) improve clinicians' ability to anticipate critical vital sign changes compared with conventional number-based and waveform-based monitoring and examine its effects on decision-making, confidence, workload and user acceptance. METHODS AND ANALYSIS: This investigator-initiated, randomised, within-subjects crossover, computer-based simulation trial will be conducted at five academic centres in Switzerland, Germany and the United States. Medical professionals from anaesthesiology departments will complete scenario-based prediction tasks using both VPP (as the index test) and conventional monitoring (as the reference standard) in randomised order, with the same participant evaluating both modalities and the identical underlying clinical scenario used in each condition, following video-based training and a learnability test. The primary outcome is recall (true positive rate) of vital sign deviation predictions. Secondary outcomes include average lead time, precision, prediction confidence, number and correctness of proposed interventions, perceived workload (NASA-TLX) and qualitative usability feedback. Quantitative data will be analysed using a logistic generalised linear mixed model with random intercepts for centre and participant, and a random slope for the intervention effect. Qualitative interviews will undergo thematic analysis. ETHICS AND DISSEMINATION: The leading ethics committee (Zurich, Switzerland; BASEC-Req-2023-00465) reviewed and approved the study protocol. Ethics committees at the other participating centres have obtained their respective approvals or waivers. Bonn: 2025-144-BO, Boston: 2025P000501, Heidelberg: S-376/2025, Munich: 2025-357 W-CB. As this simulation study involves only healthcare professionals performing prediction tasks based on simulated vital sign scenarios-without collection of patient data or any medically relevant personal data-it does not constitute human subjects research under applicable regulations. Study results will be disseminated through peer-reviewed publications and presentations at scientific conferences.

Indexed as

AwarenessComputer SimulationVital SignsAlgorithmsCross-Over StudiesDeep LearningHumansMulticenter Studies as TopicRandomized Controlled Trials as TopicSwitzerlandANAESTHETICSArtificial IntelligenceHealth informatics

Identifiers

PMID41469047
PMCPMC12766851

What OpenQuestion holds

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Registered trials

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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.