Abstract
BACKGROUND: We lack evidence on the cumulative effectiveness of machine learning (ML)-driven interventions in perioperative settings. Therefore, we conducted a systematic review to appraise the evidence on the impact of ML-driven interventions on perioperative outcomes. METHODS: Ovid MEDLINE, CINAHL, Embase, Scopus, PubMed, and ClinicalTrials.gov were searched to identify randomised controlled trials (RCTs) evaluating the effectiveness of ML-driven interventions in surgical inpatient populations. The review was registered with PROSPERO (CRD42023433163) and conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Meta-analysis was conducted for outcomes with two or more studies using a random-effects model, and vote counting was conducted for other outcomes. RESULTS: Among 13 included RCTs, three types of ML-driven interventions were evaluated: Hypotension Prediction Index (HPI) (n=5), Nociception Level Index (NoL) (n=7), and a scheduling system (n=1). Compared with the standard care, HPI led to a significant decrease in absolute hypotension (n=421, P=0.003, I(2)=75%) and relative hypotension (n=208, P<0.0001, I(2)=0%); NoL led to significantly lower mean pain scores in the post-anaesthesia care unit (PACU) (n=191, P=0.004, I(2)=19%). NoL showed no significant impact on intraoperative opioid consumption (n=339, P=0.31, I(2)=92%) or PACU opioid consumption (n=339, P=0.11, I(2)=0%). No significant difference in hospital length of stay (n=361, P=0.81, I(2)=0%) and PACU stay (n=267, P=0.44, I(2)=0) was found between HPI and NoL. CONCLUSIONS: HPI decreased the duration of intraoperative hypotension, and NoL decreased postoperative pain scores, but no significant impact on other clinical outcomes was found. We highlight the need to address both methodological and clinical practice gaps to ensure the successful future implementation of ML-driven interventions. SYSTEMATIC REVIEW PROTOCOL: CRD42023433163 (PROSPERO).