An Automated Algorithm Incorporating Poincaré Analysis Can Quantify the Severity of Opioid-Induced Ataxic Breathing

Quantified Ataxic Breathing Can Detect Opioid-Induced Respiratory Depression Earlier in Normal Volunteers Infused with Remifentanil

BACKGROUND

Ataxic breathing (AB) is a well-known manifestation of opioid effects in animals and humans, but is not routinely included in monitoring for opioid-induced respiratory depression (OIRD). We quantified AB in normal volunteers receiving increasing doses of remifentanil. We used a support vector machine (SVM) learning approach with features derived from a modified Poincaré plot. We tested the hypothesis that AB may be found when bradypnea and reduced mental status are not present.

METHODS

Twenty-six healthy volunteers (13 female) received escalating target effect-site concentrations of remifentanil with a low baseline dose of propofol to simulate typical breathing patterns in drowsy patients who had received parenteral opioids. We derived respiratory rate (RR) from respiratory inductance plethysmography, mental alertness from the Modified Observer's Assessment of Alertness/Sedation Scale (MOAA/S), and AB severity on a 0 to 4 scale (categories ranging from none to severe) from the SVM. The primary outcome measure was sensitivity and specificity for AB to detect OIRD.

RESULTS

All respiratory measurements were obtained from unperturbed subjects during steady state in 121 assessments with complete data. The sensitivity of AB for detecting OIRD by the conventional method was 92% and specificity was 28%. As expected, 69 (72%) of the instances not diagnosed as OIRD using conventional measures were observed to have at least moderate AB.

CONCLUSIONS

AB was frequently present in the absence of traditionally detected OIRD as defined by reduced mental alertness (MOAA/S score of <4) and bradypnea (RR <8 breaths/min). These results justify the need for future trials to explore replicability with other opioids and clinical utility of AB as an add-on measure in recognizing OIRD.

Artificial intelligence in pediatric airway - A scoping review

Artificial intelligence is an ever-growing modality revolutionizing the field of medical science. It utilizes various computational models and algorithms and helps out in different sectors of healthcare. Here, in this scoping review, we are trying to evaluate the use of Artificial intelligence (AI) in the field of pediatric anesthesia, specifically in the more challenging domain, the pediatric airway. Different components within the domain of AI include machine learning, neural networks, deep learning, robotics, and computer vision. Electronic databases like Google Scholar, Cochrane databases, and Pubmed were searched. Different studies had heterogeneity of age groups, so all studies with children under 18 years of age were included and assessed. The use of AI was reviewed in the preoperative, intraoperative, and postoperative domains of pediatric anesthesia. The applicability of AI needs to be supplemented by clinical judgment for the final anticipation in various fields of medicine.

Fentanyl and neostigmine delivered to mouse prefrontal cortex differentially alter breathing

Opioids impair many functions modulated by the prefrontal cortex (PFC), including wakefulness, cognition, and breathing. In contrast, cholinergic activity in the PFC increases wakefulness. This study tested the hypothesis that microinjecting the opioid fentanyl and the acetylcholinesterase inhibitor neostigmine into the PFC of awake C57BL/6J male mice (n = 27) alters breathing. The lateral and medial PFC were unilaterally microinjected with saline (control) and fentanyl. The medial PFC received additional microinjections of neostigmine. The results show that fentanyl caused site-specific changes in breathing. Fentanyl delivered to the lateral PFC significantly decreased minute ventilation variability, whereas fentanyl delivered to the medial PFC significantly increased tidal volume and duty cycle. Neostigmine microinjected into the medial PFC significantly increased respiratory rate, tidal volume, and minute ventilation. A final series of experiments revealed that decreased minute ventilation caused by systemic fentanyl administration was mitigated by PFC microinjection of neostigmine.

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.

Current Applications of Artificial Intelligence in Bariatric Surgery

The application of artificial intelligence technologies is growing in several fields of healthcare settings. The aim of this article is to review the current applications of artificial intelligence in bariatric surgery. We performed a review of the literature on Scopus, PubMed and Cochrane databases, screening all relevant studies published until September 2021, and finally including 36 articles. The use of machine learning algorithms in bariatric surgery is explored in all steps of the clinical pathway, from presurgical risk-assessment and intraoperative management to complications and outcomes prediction. The models showed remarkable results helping physicians in the decision-making process, thus improving the quality of care, and contributing to precision medicine. Several legal and ethical hurdles should be overcome before these methods can be used in common practice.

Buprenorphine differentially alters breathing among four congenic mouse lines as a function of dose, sex, and leptin status

The opioid buprenorphine alters breathing and the cytokine leptin stimulates breathing. Obesity increases the risk for respiratory disorders and can lead to leptin resistance. This study tested the hypothesis that buprenorphine causes dose-dependent changes in breathing that vary as a function of obesity, leptin status, and sex. Breathing measures were acquired from four congenic mouse lines: female and male wild type C57BL/6J (B6) mice, obese db/db and ob/ob mice with leptin dysfunction, and male B6 mice with diet-induced obesity. Mice were injected intraperitoneally with saline (control) and five doses of buprenorphine (0.1, 0.3, 1.0, 3.0, 10 mg/kg). Buprenorphine caused dose-dependent decreases in respiratory frequency while increasing tidal volume, minute ventilation, and respiratory duty cycle. The effects of buprenorphine varied significantly with leptin status and sex. Buprenorphine decreased minute ventilation variability in all mice. The present findings highlight leptin status as an important modulator of respiration and encourage future studies aiming to elucidate the mechanisms through which leptin status alters breathing.

Artificial intelligence and telemedicine in anesthesia: potential and problems

INTRODUCTION

The application of novel technologies like Artificial Intelligence (AI), Machine Learning (ML) and telemedicine in anesthesiology could play a role in transforming the future of health care. In the present review we discuss the current applications of AI and telemedicine in anesthesiology and perioperative care, exploring their potential influence and the possible hurdles.

EVIDENCE ACQUISITION

AI technologies have the potential to deeply impact all phases of perioperative care from accurate risk prediction to operating room organization, leading to increased cost-effective care quality and better outcomes. Telemedicine is reported as a successful mean within the anaesthetic pathway, including preoperative evaluation, remote patient monitoring, and postoperative care.

EVIDENCE SYNTHESIS

The utilization of AI and telemedicine is promising encouraging results in perioperative management, nevertheless several hurdles remain to be overcome before these tools could be integrated in our daily practice.

CONCLUSIONS

AI models and telemedicine can significantly influence all phases of perioperative care, helping physicians in the development of precision medicine.

Analysis of respiratory kinematics: a method to characterize breaths from motion signals

Breathing motion (respiratory kinematics) can be characterized by the interval and depth of each breath, and by magnitude-synchrony relationships between locations. Such characteristics and their breath-by-breath variability might be useful indicators of respiratory health. To enable breath-by-breath characterization of respiratory kinematics, we developed a method to detect breaths using motion sensors. In 34 volunteers who underwent maximal exercise testing, we used 8 motion sensors to record upper rib, lower rib and abdominal kinematics at 3 exercise stages (rest, lactate threshold and exhaustion). We recorded volumetric air flow signals using clinical exercise laboratory equipment and synchronized them with kinematic signals. Using instantaneous phase landmarks from the analytic representation of kinematic and flow signals, we identified individual breaths and derived respiratory rate (RR) signals at 1Hz. To evaluate the fidelity of kinematics-derived RR, we calculated bias, limits of agreement, and cross-correlation coefficients (CCC) relative to flow-derived RR. To identify coupling between kinematics and flow, we calculated the Shannon entropy of the relative frequency with which flow landmarks were distributed over the phase of the kinematic cycle. We found good agreement in the kinematics-derived and flow-derived RR signals [bias (95% limit of agreement) = 0.1 (± 7) breaths/minute; CCC median (IQR) = 0.80 (0.48 - 0.91)]. In individual signals, kinematics and flow were well-coupled (entropy 0.9-1.4 across sensors), but the relationship varied within (by exercise stage) and between individuals. The final result was that the flow landmarks did not consistently localize to any particular phase of the kinematic signals (entropy 2.2-3.0 across sensors). The Analysis of Respiratory Kinematics method can yield highly resolved respiratory rate signals by separating individual breaths. This method will facilitate characterization of clinically significant breathing motion patterns on a breath-by-breath basis. The relationship between respiratory kinematics and flow is much more complex than expected, varying between and within individuals.

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.

Automated Classification of Whole Body Plethysmography Waveforms to Quantify Breathing Patterns

Whole body plethysmography (WBP) monitors respiratory rate and depth but conventional analysis fails to capture the diversity of waveforms. Our first purpose was to develop a waveform cluster analysis method for quantifying dynamic changes in respiratory waveforms. WBP data, from adult Sprague-Dawley rats, were sorted into time domains and principle component analysis was used for hierarchical clustering. The clustering method effectively sorted waveforms into categories including sniffing, tidal breaths of varying duration, and augmented breaths (sighs). We next used this clustering method to quantify breathing after opioid (fentanyl) overdose and treatment with ampakine CX1942, an allosteric modulator of AMPA receptors. Fentanyl caused the expected decrease in breathing, but our cluster analysis revealed changes in the temporal appearance of inspiratory efforts. Ampakine CX1942 treatment shifted respiratory waveforms toward baseline values. We conclude that this method allows for rapid assessment of breathing patterns across extended data recordings. Expanding analyses to include larger portions of recorded WBP data may provide insight on how breathing is affected by disease or therapy.

Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions

Neurological disorders significantly outnumber diseases in other therapeutic areas. However, developing drugs for central nervous system (CNS) disorders remains the most challenging area in drug discovery, accompanied with the long timelines and high attrition rates. With the rapid growth of biomedical data enabled by advanced experimental technologies, artificial intelligence (AI) and machine learning (ML) have emerged as an indispensable tool to draw meaningful insights and improve decision making in drug discovery. Thanks to the advancements in AI and ML algorithms, now the AI/ML-driven solutions have an unprecedented potential to accelerate the process of CNS drug discovery with better success rate. In this review, we comprehensively summarize AI/ML-powered pharmaceutical discovery efforts and their implementations in the CNS area. After introducing the AI/ML models as well as the conceptualization and data preparation, we outline the applications of AI/ML technologies to several key procedures in drug discovery, including target identification, compound screening, hit/lead generation and optimization, drug response and synergy prediction, de novo drug design, and drug repurposing. We review the current state-of-the-art of AI/ML-guided CNS drug discovery, focusing on blood-brain barrier permeability prediction and implementation into therapeutic discovery for neurological diseases. Finally, we discuss the major challenges and limitations of current approaches and possible future directions that may provide resolutions to these difficulties.

Effects of opioids on respiration assessed by a contact-free unconstraint respiratory monitor with load cells under the bed in patients with advanced cancer

Nocturnal periodic breathing of chronic opioid users has been predominantly documented by the use of polysomnography. No previous studies have assessed the opioid effects of respiratory rhythms throughout the day without the use of physical restraint. We recently developed a contact-free unconstraint vital sign monitoring system with four load cells placed under the bed legs, which allows continuous measurements of respiratory change at the center of gravity on the bed. We aimed to reveal details of the patient's 24-h respiratory status under a monitoring system and to test the hypothesis that respiratory rhythm abnormalities are opioid dose-dependent and worsen during the night time. Continuous 48-h respiratory measurements were successfully performed in 51 patients with advanced cancer (12 opioid-free patients and 39 opioid-receiving patients). Medians of respiratory variables with minimal body movement artifacts were calculated for each 8-h split time period. Compared with opioid-free patients, opioid-receiving patients had slower respiratory rate with higher respiratory rate irregularity without changing tidal centroid shift regardless of the time period. Irregular ataxic breathing was only identified in opioid-receiving patients (33%, P = 0.023) whereas incidence rate of periodic breathing did not differ between the groups. Multivariate regression analyses revealed that opioid dose was an independent risk factor for occurrence of irregular breathing [odds ratio 1.81 (95% CI: 1.39-2.36), P < 0.001], and ataxic breathing [odds ratio 2.08 (95% CI: 1.60-2.71), P < 0.001]. Females developed the ataxic breathing at lower opioid dose compared with males. We conclude that respiratory rhythm irregularity is a predominant specific feature of opioid dose-dependent respiratory depression particularly in female patients with advanced cancer.NEW & NOTEWORTHY Through usage of a novel contact-free unconstraint vital sign monitoring system with four load cells placed under the bed legs allowing continuous measurements of respiratory changes of center of gravity on the bed, this study is the first to assess detailed respiratory characteristics throughout day and night periods without interference of daily activities in patients with advanced cancer receiving opioids. Respiratory rhythm irregularity is a predominant specific feature of opioid dose-dependent respiratory depression particularly in female patients with advanced cancer.

The Importance of Respiratory Rate Monitoring: From Healthcare to Sport and Exercise

Respiratory rate is a fundamental vital sign that is sensitive to different pathological conditions (e.g., adverse cardiac events, pneumonia, and clinical deterioration) and stressors, including emotional stress, cognitive load, heat, cold, physical effort, and exercise-induced fatigue. The sensitivity of respiratory rate to these conditions is superior compared to that of most of the other vital signs, and the abundance of suitable technological solutions measuring respiratory rate has important implications for healthcare, occupational settings, and sport. However, respiratory rate is still too often not routinely monitored in these fields of use. This review presents a multidisciplinary approach to respiratory monitoring, with the aim to improve the development and efficacy of respiratory monitoring services. We have identified thirteen monitoring goals where the use of the respiratory rate is invaluable, and for each of them we have described suitable sensors and techniques to monitor respiratory rate in specific measurement scenarios. We have also provided a physiological rationale corroborating the importance of respiratory rate monitoring and an original multidisciplinary framework for the development of respiratory monitoring services. This review is expected to advance the field of respiratory monitoring and favor synergies between different disciplines to accomplish this goal.