Closed-loop isoflurane administration with bispectral index in open heart surgery: Randomized controlled trial with manual control

Rationale and design of a randomized controlled clinical trial; Titration of Oxygen Levels (TOOL) during mechanical ventilation

BACKGROUND

Both hyperoxemia and hypoxemia are deleterious in critically ill patients. Targeted oxygenation is recommended to prevent both of these extremes, however this has not translated to the bedside. Hyperoxemia likely persists more than hypoxemia due to absence of immediate discernible adverse effects, cognitive biases and delay in prioritization of titration.

METHODS

We present the methodology for the Titration Of Oxygen Levels (TOOL) trial, an open label, randomized controlled trial of an algorithm-based FiO2 titration with electronic medical record-based automated alerts. We hypothesize that the study intervention will achieve targeted oxygenation by curbing episodes of hyperoxemia while preventing hypoxemia. In the intervention arm, electronic alerts will be used to titrate FiO2 if SpO2 is ≥94% with FiO2 levels ≥0.4 over 45 min. FiO2 will be titrated per standard practice in the control arm. This study is being carried out with deferred consent. The sample size to determine efficacy is 316 subjects, randomized in a 1:1 ratio to the intervention vs. control arm. The primary outcome is proportion of time during mechanical ventilation spent with FiO2 ≥ 0.4 and SpO2 ≥ 94%. We will also assess proportion of time during mechanical ventilation spent with SpO2 < 88%, duration of mechanical ventilation, length of ICU and hospital stay, hospital mortality, and adherence to electronic alerts as secondary outcomes.

CONCLUSION

This study is designed to evaluate the efficacy of a high fidelity, bioinformatics-based, electronic medical record derived electronic alert system to improve targeted oxygenation in mechanically ventilated patients by reducing excessive FiO2 exposure.

Artificial intelligence and anesthesia: A narrative review

Rapid advances in Artificial Intelligence (AI) have led to diagnostic, therapeutic, and intervention-based applications in the field of medicine. Today, there is a deep chasm between AI-based research articles and their translation to clinical anesthesia, which needs to be addressed. Machine learning (ML), the most widely applied arm of AI in medicine, confers the ability to analyze large volumes of data, find associations, and predict outcomes with ongoing learning by the computer. It involves algorithm creation, testing and analyses with the ability to perform cognitive functions including association between variables, pattern recognition, and prediction of outcomes. AI-supported closed loops have been designed for pharmacological maintenance of anesthesia and hemodynamic management. Mechanical robots can perform dexterity and skill-based tasks such as intubation and regional blocks with precision, whereas clinical-decision support systems in crisis situations may augment the role of the clinician. The possibilities are boundless, yet widespread adoption of AI is still far from the ground reality. Patient-related “Big Data” collection, validation, transfer, and testing are under ethical scrutiny. For this narrative review, we conducted a PubMed search in 2020-21 and retrieved articles related to AI and anesthesia. After careful consideration of the content, we prepared the review to highlight the growing importance of AI in anesthesia. Awareness and understanding of the basics of AI are the first steps to be undertaken by clinicians. In this narrative review, we have discussed salient features of ongoing AI research related to anesthesia and perioperative care.

Optimizing antimicrobial use: challenges, advances and opportunities

An optimal antimicrobial dose provides enough drug to achieve a clinical response while minimizing toxicity and development of drug resistance. There can be considerable variability in pharmacokinetics, for example, owing to comorbidities or other medications, which affects antimicrobial pharmacodynamics and, thus, treatment success. Although current approaches to antimicrobial dose optimization address fixed variability, better methods to monitor and rapidly adjust antimicrobial dosing are required to understand and react to residual variability that occurs within and between individuals. We review current challenges to the wider implementation of antimicrobial dose optimization and highlight novel solutions, including biosensor-based, real-time therapeutic drug monitoring and computer-controlled, closed-loop control systems. Precision antimicrobial dosing promises to improve patient outcome and is important for antimicrobial stewardship and the prevention of antimicrobial resistance.

Machine Learning, Deep Learning, and Closed Loop Devices-Anesthesia Delivery

With the tremendous volume of data captured during surgeries and procedures, critical care, and pain management, the field of anesthesiology is uniquely suited for the application of machine learning, neural networks, and closed loop technologies. In the past several years, this area has expanded immensely in both interest and clinical applications. This article provides an overview of the basic tenets of machine learning, neural networks, and closed loop devices, with emphasis on the clinical applications of these technologies.

Microneedle Array-Based Platforms for Future Theranostic Applications

Theranostics involves finding the biomarkers of a disease, fighting them through site specific drug delivery and following them for prognosis of the disease. Microneedle array technology has been used for drug delivery and extended for continuous monitoring of analytes present in the skin compartment. We envisage the use of microneedle arrays for future theranostic applications. The potential of combining microneedle array-based drug delivery and diagnostics as part of closed-loop control system for the management of diseases and delivery of precision drugs in individual patients is reported in this paper.

Delivering precision antimicrobial therapy through closed-loop control systems

Sub-optimal exposure to antimicrobial therapy is associated with poor patient outcomes and the development of antimicrobial resistance. Mechanisms for optimizing the concentration of a drug within the individual patient are under development. However, several barriers remain in realizing true individualization of therapy. These include problems with plasma drug sampling, availability of appropriate assays, and current mechanisms for dose adjustment. Biosensor technology offers a means of providing real-time monitoring of antimicrobials in a minimally invasive fashion. We report the potential for using microneedle biosensor technology as part of closed-loop control systems for the optimization of antimicrobial therapy in individual patients.

Closed-loop Administration of General Anaesthesia: From Sensor to Medical Device

Closed-loop administration devices for general anaesthesia have become a common subject of clinical research over the last decade and appear more and more acceptable in clinical practice. They encompass various therapeutic needs of the anesthetized patient, e. g. fluid administration, hypnotic and analgesic drug administration, myorelaxation. Multiple clinical trials involving closed-loop devices have underscored their safety, but data concerning their clinical benefit to the patient are still lacking. As the marketing of various devices increases, clinicians need to understand how comparisons between these devices can be made: the measure of performance error and wobble are technical but have also a clinical meaning, to which clinical outcomes can be added, such as drug consumption and maintenance of hemodynamic parameters (e. g. heart rate and blood pressure) within predefined ranges. Clinicians using closed-loop devices need especially to understand how various physiological signals lead to specific drug adaptations, which means that they switch from decision making to supervision of general anaesthesia.

Evaluation of closed-loop anesthesia delivery for propofol anesthesia in pediatric cardiac surgery

OBJECTIVE

The objective of this study was to compare the feasibility of closed-loop anesthesia delivery with manual control of propofol in pediatric patients during cardiac surgery.

METHODS

Forty ASA II-III children, undergoing elective cardiac surgery under cardiopulmonary bypass (CPB) in a tertiary care hospital, were randomized to receive propofol either through a closed-loop anesthesia delivery system (CL group) or through traditional manual control (manual group) to achieve a target BIS of 50. Patients were induced and subsequently maintained with a propofol infusion. The propofol usage and the efficacy of closed-loop system in controlling BIS within ±10 of the target were compared with that of manual control.

RESULTS

The maintenance of BIS within ±10 of target and intraoperative hemodynamic stability were similar between the two groups. However, induction dose of propofol was less in the CL group (2.06 ± 0.79 mg·kg(-1) ) than the manual group (2.95 ± 1.03 mg·kg(-1) ) (P = 0.006) with less overshoot of BIS during induction in the closed-loop group (P = 0.007). Total propofol used in the off-CPB period was less in the CL group (6.29 ± 2.48 mg·kg(-1) h(-1) vs 7.82 ± 2.1 mg·kg(-1) h(-1) ) (P = 0.037). Phenylephrine use in the pre-CPB period was more in the manual group (16.92 ± 10.92 μg·kg(-1) vs 5.79 ± 5.98 μg·kg(-1) ) (P = 0.014). Manual group required a median of 18 (range 8-29) dose adjustments per hour, while the CL group required none.

CONCLUSION

This study demonstrated the feasibility of closed-loop controlled propofol anesthesia in children, even in challenging procedures such as cardiac surgery. Closed-loop system needs further and larger evaluation to establish its safety and efficacy.