Innovative technologies applied to anesthesia: how will they impact the way clinicians practice?

Enhanced labor pain monitoring using machine learning and ECG waveform analysis for uterine contraction-induced pain

OBJECTIVES

This study aims to develop an innovative approach for monitoring and assessing labor pain through ECG waveform analysis, utilizing machine learning techniques to monitor pain resulting from uterine contractions.

METHODS

The study was conducted at National Taiwan University Hospital between January and July 2020. We collected a dataset of 6010 ECG samples from women preparing for natural spontaneous delivery (NSD). The ECG data was used to develop an ECG waveform-based Nociception Monitoring Index (NoM). The dataset was divided into training (80%) and validation (20%) sets. Multiple machine learning models, including LightGBM, XGBoost, SnapLogisticRegression, and SnapDecisionTree, were developed and evaluated. Hyperparameter optimization was performed using grid search and five-fold cross-validation to enhance model performance.

RESULTS

The LightGBM model demonstrated superior performance with an AUC of 0.96 and an accuracy of 90%, making it the optimal model for monitoring labor pain based on ECG data. Other models, such as XGBoost and SnapLogisticRegression, also showed strong performance, with AUC values ranging from 0.88 to 0.95.

CONCLUSIONS

This study demonstrates that the integration of machine learning algorithms with ECG data significantly enhances the accuracy and reliability of labor pain monitoring. Specifically, the LightGBM model exhibits exceptional precision and robustness in continuous pain monitoring during labor, with potential applicability extending to broader healthcare settings.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT04461704.

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.

Clinician Monitoring

Since the first public demonstration of general anesthesia in 1846, anesthesiology has seen major advancements as a specialty. These include both important technological improvements and the development and implementation of internationally accepted patient safety standards. Together, these ultimately resulted in the recognition of anesthesiology as the leading medical specialty advocating for patient safety. Modern-day anesthesiology faces a new challenge of automated anesthesia delivery. Despite evidence for a more refined and precise delivery of anesthesia through this platform, there is currently no substitute for the presence of an appropriately trained anesthesia clinician to manage the complex interplay of human factors and patient safety in the perioperative setting.

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.

Regulatory Considerations for Physiological Closed-Loop Controlled Medical Devices Used for Automated Critical Care: Food and Drug Administration Workshop Discussion Topics

Part of the mission of the Center for Devices and Radiological Health (CDRH) at the US Food and Drug Administration is to facilitate medical device innovation. Therefore, CDRH plays an important role in helping its stakeholders such as manufacturers, health care professionals, patients, patient advocates, academia, and other government agencies navigate the regulatory landscape for medical devices. This is particularly important for innovative physiological closed-loop controlled (PCLC) devices used in critical care environments, such as intensive care units, emergency settings, and battlefield environments. CDRH's current working definition of a PCLC medical device is a medical device that incorporates physiological sensor(s) for automatic manipulation of a physiological variable through actuation of therapy that is conventionally made by a clinician. These emerging devices enable automatic therapy delivery and may have the potential to revolutionize the standard of care by ensuring adequate and timely therapy delivery with improved performance in high workload and high-stress environments. For emergency response and military applications, automatic PCLC devices may play an important role in reducing cognitive overload, minimizing human error, and enhancing medical care during surge scenarios (ie, events that exceed the capability of the normal medical infrastructure). CDRH held an open public workshop on October 13 and 14, 2015 with the aim of fostering an open discussion on design, implementation, and evaluation considerations associated with PCLC devices used in critical care environments. CDRH is currently developing regulatory recommendations and guidelines that will facilitate innovation for PCLC devices. This article highlights the contents of the white paper that was central to the workshop and focuses on the ensuing discussions regarding the engineering, clinical, and human factors considerations.

The Ayre's T-piece turns 80: A 21st century review

Ayre's T-piece, first introduced 80 years ago, continues to be widely used in pediatric anesthesia despite colossal advances in equipment and technology. We present a review of its history, advantages, and disadvantages, and place in modern-day clinical practice.

Medical devices early assessment methods: systematic literature review

OBJECTIVES

The aim of this study was to get an overview of current theory and practice in early assessments of medical devices, and to identify aims and uses of early assessment methods used in practice.

METHODS

A systematic literature review was conducted in September 2013, using computerized databases (PubMed, Science Direct, and Scopus), and references list search. Selected articles were categorized based on their type, objective, and main target audience. The methods used in the application studies were extracted and mapped throughout the early stages of development and for their particular aims.

RESULTS

Of 1,961 articles identified, eighty-three studies passed the inclusion criteria, and thirty were included by searching reference lists. There were thirty-one theoretical papers, and eighty-two application papers included. Most studies investigated potential applications/possible improvement of medical devices, developed early assessment framework or included stakeholder perspective in early development stages. Among multiple qualitative and quantitative methods identified, only few were used more than once. The methods aim to inform strategic considerations (e.g., literature review), economic evaluation (e.g., cost-effectiveness analysis), and clinical effectiveness (e.g., clinical trials). Medical devices were often in the prototype product development stage, and the results were usually aimed at informing manufacturers.

CONCLUSIONS

This study showed converging aims yet widely diverging methods for early assessment during medical device development. For early assessment to become an integral part of activities in the development of medical devices, methods need to be clarified and standardized, and the aims and value of assessment itself must be demonstrated to the main stakeholders for assuring effective and efficient medical device development.

Monitoring the nociception level: a multi-parameter approach

The aim of the present study was to develop and validate an objective index for nociception level (NoL) of patients under general anesthesia, based on a combination of multiple physiological parameters. Twenty-five patients scheduled for elective surgery were enrolled. For clinical reference of NoL, the combined index of stimulus and analgesia was defined as a composite of the surgical stimulus level and a scaled effect-site concentration of opioid. The physiological parameters heart rate, heart rate variability (0.15-0.4 Hz band power), plethysmograph wave amplitude, skin conductance level, number of skin conductance fluctuations, and their time derivatives, were extracted. Two techniques to incorporate these parameters into a single index representing the NoL have been proposed: NoLlinear, based on an ordinary linear regression, and NoLnon-linear, based on a non-linear Random Forest regression. NoLlinear and NoLnon-linear significantly increased after moderate to severe noxious stimuli (Wilcoxon rank test, p < 0.01), while the individual parameters only partially responded. Receiver operating curve analysis showed that NoL index based on both techniques better discriminated noxious and non-noxious surgical events [area under curve (AUC) = 0.97] compared with individual parameters (AUC = 0.56-0.74). NoLnon-linear better ranked the level of nociception compared with NoLlinear (R = 0.88 vs. 0.77, p < 0.01). These results demonstrate the superiority of multi-parametric approach over any individual parameter in the evaluation of nociceptive response. In addition, advanced non-linear technique may have an advantage over ordinary linear regression for computing NoL index. Further research will define the usability of the NoL index as a clinical tool to assess the level of nociception during general anesthesia.

Clinical review: Does it matter which hemodynamic monitoring system is used?

Hemodynamic monitoring and management has greatly improved during the past decade. Technologies have evolved from very invasive to non-invasive, and the philosophy has shifted from a static approach to a functional approach. However, despite these major changes, the critical care community still has potential to improve its ability to adopt the most modern standards of research methodology in order to more effectively evaluate new monitoring systems and their impact on patient outcome. Today, despite the huge enthusiasm raised by new hemodynamic monitoring systems, there is still a big gap between clinical research studies evaluating these monitors and clinical practice. A few studies, especially in the perioperative period, have shown that hemodynamic monitoring systems coupled with treatment protocols can improve patient outcome. These trials are small and, overall, the corpus of science related to this topic does not yet fit the standard of clinical research methodology encountered in other specialties such as cardiology and oncology. Larger randomized trials or quality improvement processes will probably answer questions related to the real impact of these systems.