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

Introduction: As 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.