Feasibility of Automated Propofol Sedation for Transcatheter Aortic Valve Implantation

Artificial intelligence and its clinical application in Anesthesiology: a systematic review

PURPOSE

Application of artificial intelligence (AI) in medicine is quickly expanding. Despite the amount of evidence and promising results, a thorough overview of the current state of AI in clinical practice of anesthesiology is needed. Therefore, our study aims to systematically review the application of AI in this context.

METHODS

A systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched Medline and Web of Science for articles published up to November 2022 using terms related with AI and clinical practice of anesthesiology. Articles that involved animals, editorials, reviews and sample size lower than 10 patients were excluded. Characteristics and accuracy measures from each study were extracted.

RESULTS

A total of 46 articles were included in this review. We have grouped them into 4 categories with regard to their clinical applicability: (1) Depth of Anesthesia Monitoring; (2) Image-guided techniques related to Anesthesia; (3) Prediction of events/risks related to Anesthesia; (4) Drug administration control. Each group was analyzed, and the main findings were summarized. Across all fields, the majority of AI methods tested showed superior performance results compared to traditional methods.

CONCLUSION

AI systems are being integrated into anesthesiology clinical practice, enhancing medical professionals' skills of decision-making, diagnostic accuracy, and therapeutic response.

Robotic Anesthesia: A Vision for 2050

The last 2 decades have brought important developments in anesthetic technology, including robotic anesthesia. Anesthesiologists titrate the administration of pharmacological agents to the patients' physiology and the needs of surgery, using a variety of sophisticated equipment (we use the term "pilots of the human biosphere"). In anesthesia, increased safety seems coupled with increased technology and innovation. This article gives an overview of the technological developments over the past decades, both in terms of pharmacological and mechanical robots, which have laid the groundwork for robotic anesthesia: target-controlled drug infusion systems, closed-loop administration of anesthesia and sedation, mechanical robots for intubation, and the latest development in the world of communication with the arrival of artificial intelligence (AI)-derived chatbots are presented.

[Big data and artificial intelligence in anesthesia : Reality or fiction?]

Big data and artificial intelligence are buzzwords that everyone is talking about and yet always provide a touch of science fiction to the scenery. What is the status of these topics in anesthesia? Are the first robots already rolling through the corridors while doctors are getting bored as all the work has been done? Spoiler alert! We are still far away from achieving this. Initially, paper charts and analogue notes stand in the way of comprehensive digitization. Source systems need to be merged and data standardized, harmonized and validated. Therefore, the friendly android that is rolling towards us, waving and holding a freshly brewed cup of coffee in our thoughts will have to wait; however, a glimpse of the future is already evident in some clinics and the first promising developments are already showing what could be the standard tomorrow. Learning algorithms calculate the length of stay individually for each patient in the intensive care unit (ICU), reducing negative consequences such as readmission and mortality. The field of ultrasound technology for regional anesthesia and closed-loop anesthesia systems is also demonstrating the benefits of artificial intelligence (AI)-assisted technologies in practice. The efforts are diverse and ambitious but they repeatedly collide with privacy challenges and significant capital expenditure, which weigh heavily on an already financially strained healthcare system; however, anyone who listens carefully to the medical staff knows that robots are not what they would expect and the buzzwords big data and artificial intelligence might be less science fiction than initially assumed.

A survey of pregnant patients' perspectives on the implementation of artificial intelligence in clinical care

OBJECTIVE

To evaluate and understand pregnant patients' perspectives on the implementation of artificial intelligence (AI) in clinical care with a focus on opportunities to improve healthcare technologies and healthcare delivery.

MATERIALS AND METHODS

We developed an anonymous survey and enrolled patients presenting to the labor and delivery unit at a tertiary care center September 2019-June 2020. We investigated the role and interplay of patient demographic factors, healthcare literacy, understanding of AI, comfort levels with various AI scenarios, and preferences for AI use in clinical care.

RESULTS

Of the 349 parturients, 57.6% were between the ages of 25-34 years, 90.1% reported college or graduate education and 69.2% believed the benefits of AI use in clinical care outweighed the risks. Cluster analysis revealed 2 distinct groups: patients more comfortable with clinical AI use (Pro-AI) and those who preferred physician presence (AI-Cautious). Pro-AI patients had a higher degree of education, were more knowledgeable about AI use in their daily lives and saw AI use as a significant advancement in medicine. AI-Cautious patients reported a lack of human qualities and low trust in the technology as detriments to AI use.

DISCUSSION

Patient trust and the preservation of the human physician-patient relationship are critical in moving forward with AI implementation in healthcare. Pregnant individuals are cautiously optimistic about AI use in their care.

CONCLUSION

Our findings provide insights into the status of AI use in perinatal care and provide a platform for driving patient-centered innovations.

A Century of Technology in Anesthesia & Analgesia

Technological innovation has been closely intertwined with the growth of modern anesthesiology as a medical and scientific discipline. Anesthesia & Analgesia, the longest-running physician anesthesiology journal in the world, has documented key technological developments in the specialty over the past 100 years. What began as a focus on the fundamental tools needed for effective anesthetic delivery has evolved over the century into an increasing emphasis on automation, portability, and machine intelligence to improve the quality, safety, and efficiency of patient care.

Recent advances in the technology of anesthesia

The practice of anesthesiology is inextricably dependent upon technology. Anesthetics were first made possible, then increasingly safe, and now more scalable and efficient in part due to advances in monitoring and delivery technology. Herein, we discuss salient advances of the last three years in the technology of anesthesiology.

Consumer technology and telemedicine have exploded onto the scene of outpatient medicine, and perioperative management is no exception. Preoperative evaluations have been done via teleconference, and copious consumer-generated health data is available. Regulators have acknowledged the vast potential found in the transfer of consumer technology to medical practice, but issues of privacy, data ownership/security, and validity remain.

Inside the operating suite, monitoring has become less invasive, and clinical decision support systems are common. These technologies are susceptible to the “garbage in, garbage out” conundrum plaguing artificial intelligence, but they will improve as network latency decreases. Automation looms large in the future of anesthesiology as closed-loop anesthesia delivery systems are being tested in combination (moving toward a comprehensive system).

Moving forward, consumer health companies will search for applications of their technology, and loosely regulated health markets will see earlier adoption of next-generation technology. Innovations coming to anesthesia will need to account for human factors as the anesthesia provider is increasingly considered a component of the patient care apparatus.

Pain Detection with Bioimpedance Methodology from 3-Dimensional Exploration of Nociception in a Postoperative Observational Trial

Although the measurement of dielectric properties of the skin is a long-known tool for assessing the changes caused by nociception, the frequency modulated response has not been considered yet. However, for a rigorous characterization of the biological tissue during noxious stimulation, the bioimpedance needs to be analyzed over time as well as over frequency. The 3-dimensional analysis of nociception, including bioimpedance, time, and frequency changes, is provided by ANSPEC-PRO device. The objective of this observational trial is the validation of the new pain monitor, named as ANSPEC-PRO. After ethics committee approval and informed consent, 26 patients were monitored during the postoperative recovery period: 13 patients with the in-house developed prototype ANSPEC-PRO and 13 with the commercial device MEDSTORM. At every 7 min, the pain intensity was measured using the index of Anspec-pro or Medstorm and the 0-10 numeric rating scale (NRS), pre-surgery for 14 min and post-anesthesia for 140 min. Non-significant differences were reported for specificity-sensitivity analysis between ANSPEC-PRO (AUC = 0.49) and MEDSTORM (AUC = 0.52) measured indexes. A statistically significant positive linear relationship was observed between Anspec-pro index and NRS (r2 = 0.15, p < 0.01). Hence, we have obtained a validation of the prototype Anspec-pro which performs equally well as the commercial device under similar conditions.

Transcatheter aortic valve implantation: General anesthesia using transesophageal echocardiography does not decrease the incidence of paravalvular leaks compared to sedation alone

Background:

Transcatheter aortic valve implantation (TAVI) is a valid option for patients with severe aortic stenosis judged to be at high surgical risk. For this procedure, there is no agreement on the appropriate type of anesthesia. Sedation offers several advantages, but general anesthesia (GA) leads to less paravalvular leaks (PVLs) probably because of the transesophageal echocardiography (TEE) guidance. The objective was to compare the incidence of PVL among patients receiving conscious sedation (TAVI-S) and patients receiving GA (TAVI-GA). We made the hypothesis that a referral center does not necessitate TAVI-GA to reduce the incidence of moderate-to-severe PVL.

Aim:

The primary outcome was the incidence of moderate-to-severe PVL at 30 days after the implantation.

Design and Setting:

This study design was a retrospective observational trial in a university hospital.

Methods:

The TAVI-S group underwent the procedure under conscious sedation. In the TAVI-GA group, an endotracheal tube and a TEE probe were inserted. After the valve deployment, PVL was assessed by hemodynamic and fluoroscopic measurements in the TAVI-S group. TEE was also used in the TAVI-GA group to evaluate the presence of PVL. When PVL was moderate or severe according to the Valve Academic Research Consortium criteria.

Results:

TAVI-S and TAVI-GA were accomplished in 168 (67.5%) and 81 (32.5%) patients, respectively. Our results show no difference between the two groups regarding the incidence and grade of PVL.

Conclusion:

Performing TAVI under GA with TEE guidance is not associated with a lower incidence of moderate and severe PVL.

New markers for early detection of acute kidney injury after transcatheter aortic valve implantation

BACKGROUND

Acute kidney injury (AKI) is a frequent complication after a transcatheter aortic valve implantation (TAVI). Biomarkers such as urinary G1 cell cycle arrest proteins (TIMP-2 and IGFBP7) and sonographic evaluation (Doppler Renal Resistive Index [RRI]) have been advocated to predict AKI at an early stage after a TAVI-procedure. The primary aim was to determine the predictive value of these markers to detect AKI after a TAVI-procedure at an early phase.

PATIENTS AND METHODS

In a prospective observational study, 62 consecutive patients were scheduled for a TAVI. AKI was assessed based on the KDIGO criteria. Biomarkers and RRI were measured concomitantly before TAVI, at the first micturition post-implantation and the first micturition on the morning after the procedure.

RESULTS

Twenty-two patients (35%) developed AKI. On the first day after the TAVI-procedure, urinary TIMP-2 and IGFBP7 concentrations increased significantly in patients who developed AKI (0.1, [interquartile] [0.1-0.35] to 0.40 [0.10-1.00] vs. 0.2 [0.1-0.5] to 0.10 [0.10-0.20], P=0.012) with an area under the receiver-operating characteristic curve of 0.71 [0.55-0.83]. Sensitivity was 0.57 and specificity was 0.83 for a cut-off value of 0.35. No significant increases in RRI were found in patients who developed AKI.

CONCLUSIONS

Based on the current guidelines for the diagnosis of AKI, the urinary proteins TIMP-2 and IGFBP7 do not detect AKI at an early stage accurately in patients undergoing a TAVI-procedure.