ArticleBMJ open2024
Protocol for the impact of machine learning-based clinician decision support algorithims in perioperative care (IMAGINATIVE) in Singapore general hospital : a large prospective randomised controlled trial.
Article in BMJ open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. It is linked to trial NCT05809232 (Impact of Machine Learning-based Clinician Decision Support Algorithms in Perioperative Care - A Randomized Control Trial), which is not on this map. Cited by 1 paper.
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.
Impact of Machine Learning-based Clinician Decision Support Algorithms in Perioperative Care - A Randomized Control Trial (IMAGINATIVE Trial)
Who cites it
1 citing paper in PubMed.
Corrections and comments
- Erratum issued
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionAs surgical accessibility improves, the incidence of postoperative complications is expected to rise. The implementation of a precise and objective risk stratification tool holds the potential to mitigate these complications by early identification of high-risk patients. Moreover, it could address the escalating costs from resource misallocation. In Singapore General Hospital (SGH), we introduced the Combined Assessment of Risk Encountered in Surgery-Machine Learning (CARES-ML) in June 2023, focusing on predicting 30-day postoperative mortality and the need for post-surgery intensive care unit (ICU) stays. The IMAGINATIVE Trial aims to evaluate the efficacy of such systems in a large academic medical centre. METHODS AND ANALYSIS: This study adopts type 1 effectiveness-implementation study design within a randomised controlled trial framework. Patients will be randomly assigned in a 1:1 ratio to either the CARES-guided group (unblinded to risk level) or the unguided group (blinded to the risk level). A total of 9200 patients will be enrolled in the study, with the inclusion criteria encompassing individuals aged 21-100 years old undergoing elective surgeries except for neurology and cardiology surgeries at SGH. The primary outcome is to evaluate the effectiveness of the Machine Learning Clinical Decision Support (ML-CDS) algorithm in improving perioperative mortality rates when integrated into the clinical workflow. ETHICS AND DISSEMINATION: The study has been approved by the SingHealth Centralised Institutional Review Board (CIRB Ref: 2023:2114) and is registered on ClinicalTrials.gov (trial number: NCT05809232). All patients will sign an informed consent form before recruitment and translators will be made available to non-English-speaking participants. This study is funded by the National Medical Research Council, Singapore (HCSAINV22jul-0002) and the findings will be published in peer-reviewed journals and presented at academic conferences. TRIAL REGISTRATION NUMBER: NCT05809232.
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