Effect of telemedicine support for intraoperative anaesthesia care on postoperative outcomes: the TECTONICS randomised clinical trial.

Background: Telemedicine may help improve care quality and patient outcomes. Telemedicine for intraoperative decision support has not been rigorously studied.

Methods: This was a single-centre randomised clinical trial of unselected adult surgical patients.

Effect of machine learning models on clinician prediction of postoperative complications: the Perioperative ORACLE randomised clinical trial.

Background: Anaesthesiologists might be able to mitigate risk if they know which patients are at greatest risk for postoperative complications. This trial examined the impact of machine learning models on clinician risk assessment.

Methods: This single-centre, prospective, randomised clinical trial enrolled surgical patients aged ≥18 yr.

Effect of Machine Learning on Anaesthesiology Clinician Prediction of Postoperative Complications: The Perioperative ORACLE Randomised Clinical Trial.

Background: Anaesthesiology clinicians can implement risk mitigation strategies if they know which patients are at greatest risk for postoperative complications. Although machine learning models predicting complications exist, their impact on clinician risk assessment is unknown.

Methods: This single-centre randomised clinical trial enrolled patients age ≥18 undergoing surgery with anaesthesiology services.

Effect of Telemedicine Support for Intraoperative Anaesthesia Care on Postoperative Outcomes: The TECTONICS Randomised Clinical Trial.

Background: Novel applications of telemedicine can improve care quality and patient outcomes. Telemedicine for intraoperative decision support has not been rigorously studied.

Methods: This single centre randomised clinical trial (RCT, clinicaltrials.

Brain-Hazardous Medications and Potential Subadequate Antidepressant Dosing in Older Surgical Patients Receiving Home Antidepressants: An Observational Study of a Large US Health System.

Background: Older surgical patients with depression often experience poor postoperative outcomes. Poor outcomes may stem from brain-hazardous medications and subadequate antidepressant dosing.

Methods: This was a retrospective, observational cohort study covering the period between January 1, 2021 and December 31, 2021.

An Intraoperative Telemedicine Program to Improve Perioperative Quality Measures: The ACTFAST-3 Randomized Clinical Trial.

Importance: Telemedicine for clinical decision support has been adopted in many health care settings, but its utility in improving intraoperative care has not been assessed.

Objective: To pilot the implementation of a real-time intraoperative telemedicine decision support program and evaluate whether it reduces postoperative hypothermia and hyperglycemia as well as other quality of care measures.

Design, Setting, And Participants: This single-center pilot randomized clinical trial (Anesthesiology Control Tower-Feedback Alerts to Supplement Treatments [ACTFAST-3]) was conducted from April 3, 2017, to June 30, 2019, at a large academic medical center in the US.

Protocol for the perioperative outcome risk assessment with computer learning enhancement (Periop ORACLE) randomized study.

More than four million people die each year in the month following surgery, and many more experience complications such as acute kidney injury. Some of these outcomes may be prevented through early identification of at-risk patients and through intraoperative risk mitigation. Telemedicine has revolutionized the way at-risk patients are identified in critical care, but intraoperative telemedicine services are not widely used in anesthesiology.

Association of a Perioperative Multicomponent Fall Prevention Intervention With Falls and Quality of Life After Elective Inpatient Surgical Procedures.

Importance: Falls after elective inpatient surgical procedures are common and have physical, emotional, and financial consequences. Close interactions between patients and health care teams before and after surgical procedures may offer opportunities to address modifiable risk factors associated with falls.

Objective: To assess whether a multicomponent intervention that incorporates education, home medication review, and home safety assessment is associated with reductions in the incidence of falls after elective inpatient surgical procedures.

Effect of electroencephalogram-guided anaesthesia administration on 1-yr mortality: follow-up of a randomised clinical trial.

Background: Intraoperative EEG suppression duration has been associated with postoperative delirium and mortality. In a clinical trial testing anaesthesia titration to avoid EEG suppression, the intervention did not decrease the incidence of postoperative delirium, but was associated with reduced 30-day mortality. The present study evaluated whether the EEG-guided anaesthesia intervention was also associated with reduced 1-yr mortality.

Abnormal preoperative cognitive screening in aged surgical patients: a retrospective cohort analysis.

Background: Preoperative cognitive dysfunction has been associated with adverse postoperative outcomes. There are limited data characterising the epidemiology of preoperative cognitive dysfunction in older surgical patients.

Methods: This retrospective cohort included all patients ≥65 yr old seen at the Washington University preoperative clinic between January 2013 and June 2018.

Preoperative Cognitive Abnormality, Intraoperative Electroencephalogram Suppression, and Postoperative Delirium: A Mediation Analysis.

Background: Postoperative delirium is a common complication that hinders recovery after surgery. Intraoperative electroencephalogram suppression has been linked to postoperative delirium, but it is unknown if this relationship is causal or if electroencephalogram suppression is merely a marker of underlying cognitive abnormalities. The hypothesis of this study was that intraoperative electroencephalogram suppression mediates a nonzero portion of the effect between preoperative abnormal cognition and postoperative delirium.

Association between postoperative complications and lingering post-surgical pain: an observational cohort study.

Background: Post-surgical pain that lingers beyond the initial few-week period of tissue healing is a major predictor of pain chronification, which leads to substantial disability and new persistent opioid analgesic use. We investigated whether postoperative medical complications increase the risk of lingering post-surgical pain.

Methods: The study population consisted of patients undergoing diverse elective surgical procedures in an academic referral centre in the USA, between September 2013 and May 2017.

Deep-learning model for predicting 30-day postoperative mortality.

Background: Postoperative mortality occurs in 1-2% of patients undergoing major inpatient surgery. The currently available prediction tools using summaries of intraoperative data are limited by their inability to reflect shifting risk associated with intraoperative physiological perturbations. We sought to compare similar benchmarks to a deep-learning algorithm predicting postoperative 30-day mortality.

Effect of Electroencephalography-Guided Anesthetic Administration on Postoperative Delirium Among Older Adults Undergoing Major Surgery: The ENGAGES Randomized Clinical Trial.

Importance: Intraoperative electroencephalogram (EEG) waveform suppression, often suggesting excessive general anesthesia, has been associated with postoperative delirium.

Objective: To assess whether EEG-guided anesthetic administration decreases the incidence of postoperative delirium.

Design, Setting, And Participants: Randomized clinical trial of 1232 adults aged 60 years and older undergoing major surgery and receiving general anesthesia at Barnes-Jewish Hospital in St Louis.

Using machine learning techniques to develop forecasting algorithms for postoperative complications: protocol for a retrospective study.

Introduction: Mortality and morbidity following surgery are pressing public health concerns in the USA. Traditional prediction models for postoperative adverse outcomes demonstrate good discrimination at the population level, but the ability to forecast an individual patient's trajectory in real time remains poor. We propose to apply machine learning techniques to perioperative time-series data to develop algorithms for predicting adverse perioperative outcomes.