Machine learning methods for perioperative anesthetic management in cardiac surgery patients: a scoping review

Machine learning-augmented interventions in perioperative care: a systematic review and meta-analysis

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, I2=75%) and relative hypotension (n=208, P<0.0001, I2=0%); NoL led to significantly lower mean pain scores in the post-anaesthesia care unit (PACU) (n=191, P=0.004, I2=19%). NoL showed no significant impact on intraoperative opioid consumption (n=339, P=0.31, I2=92%) or PACU opioid consumption (n=339, P=0.11, I2=0%). No significant difference in hospital length of stay (n=361, P=0.81, I2=0%) and PACU stay (n=267, P=0.44, I2=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).

Blood Pressure Goals in Critically Ill Patients

Blood pressure goals in the intensive care unit (ICU) have been extensively investigated in large datasets and have been associated with various harm thresholds at or greater than a mean pressure of 65 mm Hg. While it is difficult to perform interventional randomized trials of blood pressure in the ICU, important evidence does not support defense of a higher pressure, except in retrospective database analyses. Perfusion pressure may be a more important target than mean pressure, even more so in the vulnerable patient population. In the cardiac ICU, blood pressure targets are tailored to specific cardiac pathophysiology and patient characteristics. Generally, the goal is to maintain adequate blood pressure within a certain range to support cardiac function and to ensure end organ perfusion. Individualized targets demand the use of both invasive and noninvasive monitoring modalities and frequent titration of medications and/or mechanical circulatory support where necessary. In this review, we aim to identify appropriate blood pressure targets in the ICU, recognizing special patient populations and outlining the risk factors and predictors of end organ failure.