ArticleComputer methods in biomechanics and biomedical engineering. Imaging & visualization2022
Using machine learning to predict perfusionists' critical decision-making during cardiac surgery.
Article in Computer methods in biomechanics and biomedical engineering. Imaging & visualization, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.
- 2024 EACTS/EACTAIC/EBCP Guidelines on cardiopulmonary bypass in adult cardiac surgery.British journal of anaesthesia · 2025Guideline
- Human-Centered design requirements for clinical decision support in cardiac surgery perfusion.Human factors in healthcare · 2026Article
- Artificial Intelligence in Adult Cardiovascular Medicine and Surgery: Real-World Deployments and Outcomes.Journal of personalized medicine · 2026Review
- 2024 EACTS/EACTAIC/EBCP Guidelines on cardiopulmonary bypass in adult cardiac surgery.Interdisciplinary cardiovascular and thoracic surgery · 2025Article
- 2024 EACTS/EACTAIC/EBCP Guidelines on cardiopulmonary bypass in adult cardiac surgery.European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery · 2025Article
- Behavioral sciences applied to acute care teams: a research agenda for the years ahead by a European research network.BMC health services research · 2024Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
Abstract
The cardiac surgery operating room is a high-risk and complex environment in which multiple experts work as a team to provide safe and excellent care to patients. During the cardiopulmonary bypass phase of cardiac surgery, critical decisions need to be made and the perfusionists play a crucial role in assessing available information and taking a certain course of action. In this paper, we report the findings of a simulation-based study using machine learning to build predictive models of perfusionists' decision-making during critical situations in the operating room (OR). Performing 30-fold cross-validation across 30 random seeds, our machine learning approach was able to achieve an accuracy of 78.2% (95% confidence interval: 77.8% to 78.6%) in predicting perfusionists' actions, having access to only 148 simulations. The findings from this study may inform future development of computerised clinical decision support tools to be embedded into the OR, improving patient safety and surgical outcomes.
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What OpenQuestion holds
Registered trials
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.