Protocol for the Effectiveness of an Anesthesiology Control Tower System in Improving Perioperative Quality Metrics and Clinical Outcomes: the TECTONICS randomized, pragmatic trial

Abstract

Introduction: Perioperative morbidity is a public health priority, and surgical volume is increasing rapidly. With advances in technology, there is an opportunity to research the utility of a telemedicine-based control center for anesthesia clinicians that assess risk, diagnoses negative patient trajectories, and implements evidence-based practices. Objectives: The primary objective of this trial is to determine whether an anesthesiology control tower (ACT) prevents clinically relevant adverse postoperative outcomes including 30-day mortality, delirium, respiratory failure, and acute kidney injury. Secondary objectives are to determine whether the ACT improves perioperative quality of care metrics including management of temperature, mean arterial pressure, mean airway pressure with mechanical ventilation, blood glucose, anesthetic concentration, antibiotic redosing, and efficient fresh gas flow. Methods and analysis: We are conducting a single center, randomized, controlled, phase 3 pragmatic clinical trial. A total of 58 operating rooms are randomized daily to receive support from the ACT or not. All adults (eighteen years and older) undergoing surgical procedures in these operating rooms are included and followed until 30 days after their surgery. Clinicians in operating rooms randomized to ACT support receive decision support from clinicians in the ACT. In operating rooms randomized to no intervention, the current standard of anesthesia care is delivered. The intention-to-treat principle will be followed for all analyses. Differences between groups will be presented with 99% confidence intervals; p-values <0.005 will be reported as providing compelling evidence, and p-values between 0.05 and 0.005 will be reported as providing suggestive evidence. Registration: TECTONICS is registered on ClinicalTrials.gov, NCT03923699; registered on 23 April 2019.

Keywords: Anesthesiology; Artificial Intelligence; Decision Support; Forecasting Algorithms; Machine Learning; Randomized Controlled Trial; Telemedicine.

Conflict of interest statement

No competing interests were disclosed.

Copyright: © 2019 King CR et al.

Figures

Figure 1.. Schematic of study design, patient activity flow.

Figure 2.. Summary overview data for a hypothetical patient (AlertWatch® ACT Dashboard).

Figure 3.. The key workflow and process components of TECTONICS.

The team in the ACT receives data form the electronic health record, web-interfaced monitors in the operating room (OR), video cameras in the OR, multipath convolutional neural network machine learning algorithms, and alerting software has been customized to provide maximum utility in an ACT. The team weaves together disparate data strands, and collaboratively formulates a plan to address the patient’s risk and optimize outcomes. The plan is discussed collegially with OR clinicians, who exercise judgement in delivering the best individualized perioperative management to each surgical patient. Dynamic data from OR patient monitors. (The photo was taken in our prototype ACT). CRNA, certified registered nurse anesthetist.