Evaluating the Feasibility of ChatGPT in Healthcare: An Analysis of Multiple Clinical and Research Scenarios

This paper aims to highlight the potential applications and limits of a large language model (LLM) in healthcare. ChatGPT is a recently developed LLM that was trained on a massive dataset of text for dialogue with users. Although AI-based language models like ChatGPT have demonstrated impressive capabilities, it is uncertain how well they will perform in real-world scenarios, particularly in fields such as medicine where high-level and complex thinking is necessary. Furthermore, while the use of ChatGPT in writing scientific articles and other scientific outputs may have potential benefits, important ethical concerns must also be addressed. Consequently, we investigated the feasibility of ChatGPT in clinical and research scenarios: (1) support of the clinical practice, (2) scientific production, (3) misuse in medicine and research, and (4) reasoning about public health topics. Results indicated that it is important to recognize and promote education on the appropriate use and potential pitfalls of AI-based LLMs in medicine.

The efficacy of Epigallocatechin-3-gallate (green tea) in the treatment of Alzheimer's disease: an overview of pre-clinical studies and translational perspectives in clinical practice

Alzheimer's disease (AD) is a neurodegenerative disorder and the most common form of dementia characterized by cognitive and memory impairment. One of the mechanism involved in the pathogenesis of AD, is the oxidative stress being involved in AD's development and progression. In addition, several studies proved that chronic viral infections, mainly induced by Human herpesvirus 1 (HHV-1), Cytomegalovirus (CMV), Human herpesvirus 2 (HHV-2), and Hepatitis C virus (HCV) could be responsible for AD's neuropathology. Despite the large amount of data regarding the pathogenesis of Alzheimer's disease (AD), a very limited number of therapeutic drugs and/or pharmacological approaches, have been developed so far. It is important to underline that, in recent years, natural compounds, due their antioxidants and anti-inflammatory properties have been largely studied and identified as promising agents for the prevention and treatment of neurodegenerative diseases, including AD. The ester of epigallocatechin and gallic acid, (-)-Epigallocatechin-3-Gallate (EGCG), is the main and most significantly bioactive polyphenol found in solid green tea extract. Several studies showed that this compound has important anti-inflammatory and antiatherogenic properties as well as protective effects against neuronal damage and brain edema. To date, many studies regarding the potential effects of EGCG in AD's treatment have been reported in literature. The purpose of this review is to summarize the in vitro and in vivo pre-clinical studies on the use of EGCG in the prevention and the treatment of AD as well as to offer new insights for translational perspectives into clinical practice.

Delayed Emergence from Anesthesia: What We Know and How We Act

The emergence from anesthesia is the stage of general anesthesia featuring the patient's progression from the unconsciousness status to wakefulness and restoration of consciousness. This complex process has precise neurobiology which differs from that of induction. Despite the medications commonly used in anesthesia allow recovery in a few minutes, a delay in waking up from anesthesia, called delayed emergence, may occur. This phenomenon is associated with delays in the operating room, and an overall increase in costs. Together with the emergence delirium, the phenomenon represents a manifestation of inadequate emergence. Nevertheless, in delayed emergence, the transition from unconsciousness to complete wakefulness usually occurs along a normal trajectory, although slowed down. On the other hand, this awakening trajectory could proceed abnormally, possibly culminating in the manifestation of emergence delirium. Clinically, delayed emergence often represents a challenge for clinicians who must make an accurate diagnosis of the underlying cause to quickly establish appropriate therapy. This paper aimed at presenting an update on the phenomenon, analyzing its causes. Diagnostic and therapeutic strategies are addressed. Finally, therapeutic perspectives on the "active awakening" are reported.

The Breakthrough of Large Language Models Release for Medical Applications: 1-Year Timeline and Perspectives

Within the domain of Natural Language Processing (NLP), Large Language Models (LLMs) represent sophisticated models engineered to comprehend, generate, and manipulate text resembling human language on an extensive scale. They are transformer-based deep learning architectures, obtained through the scaling of model size, pretraining of corpora, and computational resources. The potential healthcare applications of these models primarily involve chatbots and interaction systems for clinical documentation management, and medical literature summarization (Biomedical NLP). The challenge in this field lies in the research for applications in diagnostic and clinical decision support, as well as patient triage. Therefore, LLMs can be used for multiple tasks within patient care, research, and education. Throughout 2023, there has been an escalation in the release of LLMs, some of which are applicable in the healthcare domain. This remarkable output is largely the effect of the customization of pre-trained models for applications like chatbots, virtual assistants, or any system requiring human-like conversational engagement. As healthcare professionals, we recognize the imperative to stay at the forefront of knowledge. However, keeping abreast of the rapid evolution of this technology is practically unattainable, and, above all, understanding its potential applications and limitations remains a subject of ongoing debate. Consequently, this article aims to provide a succinct overview of the recently released LLMs, emphasizing their potential use in the field of medicine. Perspectives for a more extensive range of safe and effective applications are also discussed. The upcoming evolutionary leap involves the transition from an AI-powered model primarily designed for answering medical questions to a more versatile and practical tool for healthcare providers such as generalist biomedical AI systems for multimodal-based calibrated decision-making processes. On the other hand, the development of more accurate virtual clinical partners could enhance patient engagement, offering personalized support, and improving chronic disease management.

Dissecting the mechanisms and molecules underlying the potential carcinogenicity of red and processed meat in colorectal cancer (CRC): an overview on the current state of knowledge

Meat is a crucial nutrient for human health since it represents a giant supply of proteins, minerals, and vitamins. On the opposite hand, the intake of red and processed meat is taken into account dangerous due to its potential of carcinogenesis and cancer risk improvement, particularly for colorectal cancer (CRC), although it has been reported that also the contaminations of beef infected by oncogenic bovine viruses could increase colorectal cancer's risk. Regarding the mechanisms underlying the potential carcinogenicity of red and processed meat, different hypotheses have been proposed. A suggested mechanism describes the potential role of the heterocyclic amines (HACs) and polycyclic aromatic hydrocarbons (PHAs) in carcinogenesis induced by DNA mutation. Another hypothesis states that heme, through the lipid peroxidation process and therefore the formation of N-nitroso compounds (NOCs), produces cytotoxic and genotoxic aldehydes, resulting in carcinogenesis. Furthermore, a recent proposed hypothesis, is based on the combined actions between the N-Glycolylneuraminic acid (Neu5Gc) and genotoxic compounds. The purpose of this narrative review is to shed a light on the mechanisms underlying the potential carcinogenicity of red and processed meat, by summarizing the data reported in literature on this topic.

Postoperative delirium and postoperative cognitive dysfunction: updates in pathophysiology, potential translational approaches to clinical practice and further research perspectives

Postoperative delirium, the onset of confusion/delirium during the early postoperative phase, is the most common postoperative complication in older adults and represents a medical emergency that requires immediate assessment and treatment. On the other hand, the term postoperative cognitive decline or dysfunction refers to a wide spectrum of clinical conditions featuring a decline in a variety of neuropsychological domains including memory, executive functioning, and speed of processing emerging week to months after anesthesia and surgery. Postoperative delirium and postoperative cognitive decline represent two distinct clinical entities along the continuum of cognitive impairment after anesthesia and surgery. All patients experiencing delirium does however not develop postoperative dysfunction and likewise all patients showing various degree of postoperative decline have not explicitly exhibited postoperative delirium. Although neuroinflammation seems to be main cause, the exact pathophysiology of both phenomena is very difficult to ascertain because it involves the crossroads of many mechanisms. This paper aimed to provide an update in the pathophysiology of these conditions with special attention to the roles of the neuroinflammation, the microglia activation, the surgical stress response, the impairment of neurotransmitter systems, and the direct neurotoxic effect of general anesthetic drugs. Potential translational approaches to clinical practice and further research perspectives are also given.

COVID-Pain: Acute and Late-Onset Painful Clinical Manifestations in COVID-19 - Molecular Mechanisms and Research Perspectives

Although the respiratory manifestations of COVID-19 are predominant, signs and symptoms of an extra-pulmonary involvement are usually encompassed among the clinical picture of the disease. Several painful manifestations can occur during the acute phase but also as short- or long-term complications. Myalgia, joint pain, sore throat, abdominal pain, chest pain, and headache usually accompany respiratory symptoms, but they can also occur as isolated clinical findings or can be expressed regardless of the severity of COVID-19. On these premises, given the vast spectrum of clinical manifestations and the complexity of their pathogenesis, it would be more appropriate to refer to "COVID-pain", an umbrella term useful for encompassing all these clinical manifestations in a separate chapter of the disease. In this scenario, we addressed the topic from a molecular perspective, trying to provide explanations for the underlying pathophysiological processes. Consequently, this narrative review is aimed at dissecting the mechanisms of acute and chronic painful manifestations, summarizing fundamental concepts on the matter, controversies, current research gaps, and potential developments in this field.

Dissecting the Potential Roles of Nigella sativa and Its Constituent Thymoquinone on the Prevention and on the Progression of Alzheimer's Disease

Several nutraceuticals have been investigated for preventing or retarding the progression of different neurodegenerative diseases, including Alzheimer's disease (AD). Because Nigella sativa (NS) and its isolated compound thymoquinone (TQ) have significant anti-oxidant and anti-inflammatory proprieties, they could represent effective neuroprotective agents. The purpose of this manuscript is to analyze and to recapitulate the results of in vitro and in vivo studies on the potential role of NS/TQ in AD's prevention and treatment. The level of evidence for each included animal study has been assessed by using a modified CAMARADES (Collaborative Approach to Meta-Analysis and Review of Animal Data from Experimental Studies) 10-item checklist. We used MEDLINE and EMBASE databases to screen relevant articles published up to July 2017. A manual search was also performed. The database search yielded 38 studies, of which 18 were included in this manuscript. Results from these approaches suggest that NS or TQ could represent an effective strategy against AD due to the balancing of oxidative processes and the binding to specific intracellular targets. The overall effects mainly regard the prevention of hippocampal pyramidal cell loss and the increased cognitive functions.

The role of general anesthetics and the mechanisms of hippocampal and extra-hippocampal dysfunctions in the genesis of postoperative cognitive dysfunction

Postoperative cognitive dysfunction (POCD) is a multifactorial process with a huge number of predisposing, causal, and precipitating factors. In this scenario, the neuroinflammation and the microglial activation play a pivotal role by triggering and amplifying a complex cascade involving the immuno-hormonal activation, the micro circle alterations, the hippocampal oxidative stress activation and, finally, an increased blood-brain barrier's permeability. While the role of anesthetics in the POCD's genesis in humans is debated, a huge number of preclinical studies have been conducted on the topic and many mechanisms have been proposed to explain the potential neurodegenerative effects of general anesthetics. Probably, the problem concerns on what we are searching for and how we are searching and, surprisingly, preclinical studies showed that anesthetics may also manifest neuroprotective properties. The aim of this paper is to offer an overview on the potential impact of general anesthetics on POCD. Mechanisms of hippocampal and extra-hippocampal dysfunction due to neuroinflammation are discussed, whereas further research perspectives are also given.

The challenge of pain assessment in children with cognitive disabilities: Features and clinical applicability of different observational tools

Children with cognitive disabilities are at greater risk of experiencing pain. It has been shown that this paediatric population often receive inadequate pain management. Pain may be very difficult to assess, especially in a defined subgroup with non-communicating intellectual disability or severe cognitive disability. Accordingly, several observational pain assessment tools have been proposed to overcome this issue. Due to the absence of an ideal measurement tool, accurate pain assessment requires, after a case-by-case analysis, selecting the more appropriate tool or a variety of combined instruments. The aim of this work is to provide a comprehensive review of the pain assessment tools commonly used in cognitively impaired children. Critical discussion on features and clinical applicability may suggest how to overcome this difficult challenge. Furthermore, this review will help further research aiming to design new instruments and to improve already-in-use tools.

Role of Nigella sativa and Its Constituent Thymoquinone on Chemotherapy-Induced Nephrotoxicity: Evidences from Experimental Animal Studies

BACKGROUND

Most chemotherapeutic drugs are known to cause nephrotoxicity. Therefore, new strategies have been considered to prevent chemotherapy-induced nephrotoxicity. It is of note that Nigella sativa (NS), or its isolated compound Thymoquinone (TQ), has a potential role in combating chemotherapy-induced nephrotoxicity.

AIM

To analyze and report the outcome of experimental animal studies on the protective effects of NS/TQ on chemotherapy-associated kidney complications.

DESIGN

Standard systematic review and narrative synthesis.

DATA SOURCES

MEDLINE, EMBASE databases were searched for relevant articles published up to March 2017. Additionally, a manual search was performed. Criteria for a study's inclusion were: conducted in animals, systematic reviews and meta-analysis, containing data on nephroprotective effects of NS/TQ compared to a placebo or other substance. All strains and genders were included.

RESULTS

The database search yielded 71 studies, of which 12 (cisplatin-induced nephrotoxicity 8; methotrexate-induced nephrotoxicity 1; doxorubicin-induced nephrotoxicity 2; ifosfamide-induced nephrotoxicity 1) were included in this review.

CONCLUSIONS

Experimental animal studies showed the protective effect of NS, or TQ, on chemotherapy-induced nephrotoxicity. These effects are caused by decreasing lipid peroxidation and increasing activity of antioxidant enzymes in renal tissue of chemotherapy-treated animals.

A Delphi Consensus Approach for the Management of Chronic Pain during and after the COVID-19 Era

Due to a lack of published evidence on the topic, a modified Delphi approach was used to develop recommendations useful for chronic pain management during and after the COVID-19 pandemic. Focusing on the available literature and personal clinical expertise, an Italian board of nine professionals from different disciplines identified four main topics: prevention of chronic pain, treatment of chronic pain, consequences of inadequate treatment, and perspectives. They elaborated a semi-structured questionnaire. A multidisciplinary panel of experts in the field of pain management was requested to comment on the statements. Based on the answers provided, a structured questionnaire was prepared (Round 1). It included 21 statements divided into three categories (organizational issues; diagnosis and therapies; telemedicine and future perspectives). A five-point Likert scale was adopted. The threshold for consensus was set at a minimum of 70% of the number of respondents (level of agreement ≥ 4, Agree or Strongly Agree). A final questionnaire with rephrasing of the statements that did not reach the consensus threshold was elaborated (Round 2). A total of 29 clinicians were included in the panel. Twenty clinicians (69%) responded in both the first and second round. After two rounds, consensus (≥70%) was achieved in 20 out of 21 statements. The lack of consensus was recorded for the statement regarding the management of post-COVID pain (55%; Median 4; IQR 2.3). Another statement on telemedicine reached the threshold in the first round (70%), but the value was not confirmed in Round 2 (65%; Median 4; IQR 2). Most of the proposed items reached consensus, suggesting the need to make organizational changes, the structuring of careful diagnostic and therapeutic pathways, and the application of new technologies in pain medicine. Long-COVID-19 care is an issue that needs further research. Remote assistance for chronic pain must be regulated.

Artificial Intelligence for Automatic Pain Assessment: Research Methods and Perspectives

Although proper pain evaluation is mandatory for establishing the appropriate therapy, self-reported pain level assessment has several limitations. Data-driven artificial intelligence (AI) methods can be employed for research on automatic pain assessment (APA). The goal is the development of objective, standardized, and generalizable instruments useful for pain assessment in different clinical contexts. The purpose of this article is to discuss the state of the art of research and perspectives on APA applications in both research and clinical scenarios. Principles of AI functioning will be addressed. For narrative purposes, AI-based methods are grouped into behavioral-based approaches and neurophysiology-based pain detection methods. Since pain is generally accompanied by spontaneous facial behaviors, several approaches for APA are based on image classification and feature extraction. Language features through natural language strategies, body postures, and respiratory-derived elements are other investigated behavioral-based approaches. Neurophysiology-based pain detection is obtained through electroencephalography, electromyography, electrodermal activity, and other biosignals. Recent approaches involve multimode strategies by combining behaviors with neurophysiological findings. Concerning methods, early studies were conducted by machine learning algorithms such as support vector machine, decision tree, and random forest classifiers. More recently, artificial neural networks such as convolutional and recurrent neural network algorithms are implemented, even in combination. Collaboration programs involving clinicians and computer scientists must be aimed at structuring and processing robust datasets that can be used in various settings, from acute to different chronic pain conditions. Finally, it is crucial to apply the concepts of explainability and ethics when examining AI applications for pain research and management.

Satisfaction with Telemedicine for Cancer Pain Management: A Model of Care and Cross-Sectional Patient Satisfaction Study

Background: Since cancer pain requires complex modalities of care, the proper strategy for addressing its telemedicine-based management should be better defined. This study aimed to trace a pathway for a progressive implementation of the telemedicine process for the treatment of pain in the setting of cancer patients. Methods: The features of the model were investigated to dissect the dropout from the telemedicine pathway. A cross-sectional patient satisfaction study was conducted. The degree of satisfaction was evaluated through a developed 22-item questionnaire (Likert scale 0−7). Results: A total of 375 video consultations for 164 patients (mean age 62.9 years) were performed through remote consultations for cancer pain management between March 2021 and February 2022. After the exclusion of 72 patients, 92 (56.1%) were included in the analysis. The dropout ratio was 8.7%. The number of visits and pharmacological therapies for neuropathic pain correlated with the risk for readmission (p < 0.05). Overall, the satisfaction was very high (mean > 5.5 for all items). Conclusion: Feedback from patients reflected high satisfaction rates with the care provided. A methodological approach based on the degree of satisfaction combined with the analysis of the pathways can help to implement the quality of a service provided through telemedicine. While not without limitations, our hybrid protocol can be useful for addressing cancer pain through a patient-centered approach.

Potential application of the Kampo medicine goshajinkigan for prevention of chemotherapy-induced peripheral neuropathy

Chemotherapy-induced peripheral neuropathy (CIPN) is one of the most common and severe adverse effects related to cancer treatment. Unfortunately, although several agents and protocols have been proposed, no prophylactic strategies have yet to be proven useful. Therefore, new alternative therapies have been considered for CIPN prevention. Herbal medicine in Japan, called Kampo medicine, is derived from traditional Chinese medicine. Goshajinkigan (GJG) is a Kampo medicine, that is comprised of ten herbs. The aim of this work is to analyse the results of pre-clinical and clinical studies on the potential applications of GJG in CIPN prevention.

Mechanisms underlying brain monitoring during anesthesia: limitations, possible improvements, and perspectives

Currently, anesthesiologists use clinical parameters to directly measure the depth of anesthesia (DoA). This clinical standard of monitoring is often combined with brain monitoring for better assessment of the hypnotic component of anesthesia. Brain monitoring devices provide indices allowing for an immediate assessment of the impact of anesthetics on consciousness. However, questions remain regarding the mechanisms underpinning these indices of hypnosis. By briefly describing current knowledge of the brain's electrical activity during general anesthesia, as well as the operating principles of DoA monitors, the aim of this work is to simplify our understanding of the mathematical processes that allow for translation of complex patterns of brain electrical activity into dimensionless indices. This is a challenging task because mathematical concepts appear remote from clinical practice. Moreover, most DoA algorithms are proprietary algorithms and the difficulty of exploring the inner workings of mathematical models represents an obstacle to accurate simplification. The limitations of current DoA monitors - and the possibility for improvement - as well as perspectives on brain monitoring derived from recent research on corticocortical connectivity and communication are also discussed.

Envisioning gamification in anesthesia, pain management, and critical care: basic principles, integration of artificial intelligence, and simulation strategies

Unlike traditional video games developed solely for entertainment purposes, game-based learning employs intentionally crafted approaches that seamlessly merge entertainment and educational content, resulting in captivating and effective learning encounters. These pedagogical methods include serious video games and gamification. Serious games are video games utilized as tools for acquiring crucial (serious) knowledge and skills. On the other hand, gamification requires integrating gaming elements (game mechanics) such as points, leaderboards, missions, levels, rewards, and more, into a context that may not be associated with video gaming activities. They can be dynamically (game dynamics) combined developing various strategic approaches. Operatively, gamification adopts simulation elements and leverages the interactive nature of gaming to teach players specific skills, convey knowledge, or address real-world issues. External incentives stimulate internal motivation. Therefore, these techniques place the learners in the central role, allowing them to actively construct knowledge through firsthand experiences.Anesthesia, pain medicine, and critical care demand a delicate interplay of technical competence and non-technical proficiencies. Gamification techniques can offer advantages to both domains. Game-based modalities provide a dynamic, interactive, and highly effective opportunity to learn, practice, and improve both technical and non-technical skills, enriching the overall proficiency of anesthesia professionals. These properties are crucial in a discipline where personal skills, human factors, and the influence of stressors significantly impact daily work activities. Furthermore, gamification can also be embraced for patient education to enhance comfort and compliance, particularly within pediatric settings (game-based distraction), and in pain medicine through stress management techniques. On these bases, the creation of effective gamification tools for anesthesiologists can present a formidable opportunity for users and developers.This narrative review comprehensively examines the intricate aspects of gamification and its potentially transformative influence on the fields of anesthesiology. It delves into theoretical frameworks, potential advantages in education and training, integration with artificial intelligence systems and immersive techniques, and also addresses the challenges that could arise within these contexts.

Open Issues and Practical Suggestions for Telemedicine in Chronic Pain

Telemedicine represents a major opportunity to facilitate continued assistance for patients with chronic pain and improve their access to care. Preliminary data show that an improvement can be expected of the monitoring, treatment adherence, assessment of treatment effect including the emotional distress associated with pain. Moreover, this approach seems to be convenient and cost-effective, and particularly suitable for personalized treatment. Nevertheless, several open issues must be highlighted such as identification of assessment tools, implementation of monitoring instruments, and ability to evaluate personal needs and expectations. Open questions exist, such as how to evaluate the need for medical intervention and interventional procedures, and how to define when a clinical examination is required for certain conditions. In this context, it is necessary to establish dynamic protocols that provide the right balance between face-to-face visits and telemedicine. Useful tips are provided to start an efficient experience. More data are needed to develop precise operating procedures. In the meantime, the first experiences from such settings can pave the way to initiate effective care pathways in chronic pain.

Different Machine Learning Approaches for Implementing Telehealth-Based Cancer Pain Management Strategies

Background: The most effective strategy for managing cancer pain remotely should be better defined. There is a need to identify those patients who require increased attention and calibrated follow-up programs. Methods: Machine learning (ML) models were developed using the data prospectively obtained from a single-center program of telemedicine-based cancer pain management. These models included random forest (RF), gradient boosting machine (GBM), artificial neural network (ANN), and the LASSO−RIDGE algorithm. Thirteen demographic, social, clinical, and therapeutic variables were adopted to define the conditions that can affect the number of teleconsultations. After ML validation, the risk analysis for more than one remote consultation was assessed in target individuals. Results: The data from 158 patients were collected. In the training set, the accuracy was about 95% and 98% for ANN and RF, respectively. Nevertheless, the best accuracy on the test set was obtained with RF (70%). The ML-based simulations showed that young age (<55 years), lung cancer, and occurrence of breakthrough cancer pain help to predict the number of remote consultations. Elderly patients (>75 years) with bone metastases may require more telemedicine-based clinical evaluations. Conclusion: ML-based analyses may enable clinicians to identify the best model for predicting the need for more remote consultations. It could be useful for calibrating care interventions and resource allocation.

Bibliometric Network Analysis on Rapid-Onset Opioids for Breakthrough Cancer Pain Treatment

BACKGROUND AND OBJECTIVES

Proper breakthrough cancer pain (BTcP) management is of pivotal importance. Although rapid-acting, oral and nasal transmucosal, fentanyl formulations (rapid-onset opioids, ROOs) are licensed for BTcP treatment, not all guidelines recommend their use. Presumably, some research gaps need to be bridged to produce solid evidence. We present a bibliometric network analysis on ROOs for BTcP treatment.

METHODS

Documents were retrieved from the Web of Science (WOS) online database. The string was "rapid onset opioids" or "transmucosal fentanyl" and "breakthrough cancer pain". Year of publication, journal metrics (impact factor and quartile), title, document type, topic, and clinical setting (in-patients, outpatients, and palliative care) were extracted. The software tool VOSviewer (version 1.6.17) was used to analyze the semantic network analyzes, bibliographic coupling, journals analysis, and research networks.

RESULTS

502 articles were found in WOS. A declining trend in published articles from 2014 to 2021 was observed. Approximately 50% of documents regard top quartile (Q1) journals. Most articles focused on ROOs efficacy, but abuse and misuse issues are poorly addressed. With respect to article type, we calculated 132 clinical investigations. The semantic network analysis found interconnections between the terms "breakthrough cancer pain," "opioids," and "cancers." The top co-cited article was published in 2000 and addressed pain assessment. The largest number of partnerships regarded the United States, Italy, and England.

CONCLUSION

In this research area, most articles are published in top-ranked journals. Nevertheless, paramount topics should be better addressed, and the implementation of research networks is needed.

Multidimensional Statistical Technique for Interpreting the Spontaneous Breakthrough Cancer Pain Phenomenon. A Secondary Analysis from the IOPS-MS Study

Breakthrough cancer pain (BTcP) is a temporary exacerbation of pain that "breaks through" a phase of adequate pain control by an opioid-based therapy. The non-predictable BTcP (NP-BTcP) is a subtype of BTcP that occurs in the absence of any specific activity. Since NP-BTcP has an important clinical impact, this analysis is aimed at characterizing the NP-BTcP phenomenon through a multidimensional statistical technique. This is a secondary analysis based on the Italian Oncologic Pain multiSetting-Multicentric Survey (IOPS-MS). A correlation analysis was performed to characterize the NP-BTcP profile about its intensity, number of episodes per day, and type. The multiple correspondence analysis (MCA) determined the identification of four groups (phenotypes). A univariate analysis was performed to assess differences between the four phenotypes and selected covariates. The four phenotypes represent the hierarchical classification according to the status of NP-BTcP: from the best (phenotype 1) to the worst (phenotype 4). The univariate analysis found a significant association between the onset time >10 min in the phenotype 1 (37.3%)' vs. the onset > 10 min in phenotype 4 (25.8%) (p < 0.001). Phenotype 1 was characterized by the gastrointestinal type of cancer (26.4%) with respect to phenotype 4, where the most frequent cancer affected the lung (28.8%) (p < 0.001). Phenotype 4 was mainly managed with rapid-onset opioids, while in phenotype 1, many patients were treated with oral, subcutaneous, or intravenous morphine (56.4% and 44.4%, respectively; p = 0.008). The ability to characterize NP-BTcP can offer enormous benefits for the management of this serious aspect of cancer pain. Although requiring validation, this strategy can provide many indications for identifying the diagnostic and therapeutic gaps in NP-BTcP management.

Exploring Artificial Intelligence in Anesthesia: A Primer on Ethics, and Clinical Applications

The field of anesthesia has always been at the forefront of innovation and technology, and the integration of Artificial Intelligence (AI) represents the next frontier in anesthesia care. The use of AI and its subtypes, such as machine learning, has the potential to improve efficiency, reduce costs, and ameliorate patient outcomes. AI can assist with decision making, but its primary advantage lies in empowering anesthesiologists to adopt a proactive approach to address clinical issues. The potential uses of AI in anesthesia can be schematically grouped into clinical decision support and pharmacologic and mechanical robotic applications. Tele-anesthesia includes strategies of telemedicine, as well as device networking, for improving logistics in the operating room, and augmented reality approaches for training and assistance. Despite the growing scientific interest, further research and validation are needed to fully understand the benefits and limitations of these applications in clinical practice. Moreover, the ethical implications of AI in anesthesia must also be considered to ensure that patient safety and privacy are not compromised. This paper aims to provide a comprehensive overview of AI in anesthesia, including its current and potential applications, and the ethical considerations that must be considered to ensure the safe and effective use of the technology.

Strengths and Weaknesses of Cancer Pain Management in Italy: Findings from a Nationwide SIAARTI Survey

OBJECTIVES

Despite guidelines, a large percentage of cancer patients continue to suffer from ineffectively treated pain. The authors undertook this survey to assess the strengths and weaknesses of cancer pain management in Italy.

DESIGN

This was a prospectively administered survey.

PARTICIPANTS

The participants were anesthesiologists of the Italian Society of Anesthesia, Analgesia, Resuscitation and Intensive Care (SIAARTI).

INTERVENTION

A 58-item questionnaire covered the demographics and features of cancer pain management in the Italian context.

RESULTS

The authors received responses from 611 pain therapists of 279 centers. Only 22% of physicians are exclusively pain therapists. Seventy-five percent are specialists in anesthesiology, intensive care, and pain medicine. Most pain centers are hospital or university facilities (78%). The strengths of cancer pain management in Italy are the careful opioid prescriptions, the use of strategies for the treatment of neuropathic pain, patient/healthcare provider partnerships, and breakthrough cancer pain management. Weaknesses to be addressed include poor adherence to guidelines, inadequate attention toward the patient's quality of life, insufficient use of minimally invasive techniques, lack of teamwork approaches, inappropriate timing of pain specialist engagement, and poor telemedicine use.

CONCLUSIONS

Despite several strengths, further efforts are needed to improve the care of patients suffering from cancer pain in Italy.

Anesthetic dreaming, anesthesia awareness and patient satisfaction after deep sedation with propofol target controlled infusion: A prospective cohort study of patients undergoing day case breast surgery

BACKGROUND

Anesthetic dreaming and anesthesia awareness are well distinct phenomena. Although the incidence of intraoperative awareness is more common among patients who reported a dream after surgery, the exact correlation between the two phenomena remains an unsolved rebus. The main purpose of this study was to investigate anesthetic dreaming, anesthesia awareness and psychological consequences eventually occurred under deep sedation. Intraoperative dreaming experiences were correlated with dream features in natural sleep.

METHODS

Fifty-one patients, undergoing surgical excision of fibroadenomas under a Bispectral index-guided deep sedation anesthesia with propofol target controlled infusion, were enrolled into this prospective study. Psychological assessment was performed through the State Trait Anxiety Inventory. A questionnaire was adopted to register dreaming and anesthesia awareness. Data were collected after emergence (t0), 24 hours (t1), 1 month (t2), 6 months (t3).

RESULTS

Six patients (12%) reported anesthetic dreaming at t0 confirming the response at each subsequent evaluation. One patient (2%) confirmed dreaming during anesthesia in all, but denied it at t0. There was a high correlation between the intraoperative dream contents and the features of dreams in natural sleep. No cases of anesthesia awareness were detected. A similar level of satisfaction was observed in dreaming and no-dreaming patients.

CONCLUSIONS

Anesthetic dreaming does not seem to influence satisfaction of patients undergoing deep sedation with propofol target controlled infusion. A psychological assessment would seem to improve the evaluation of possible psychological consequences in dreamer patient.

Chemotherapy-related cognitive impairment: mechanisms, clinical features and research perspectives

The term chemotherapy-related cognitive impairment (CRCI), or cognitive dysfunction, or chemo fog, or chemo brain, is referred to a decline in a variety of neuropsychological tasks after chemotherapy, or following other anticancer treatments such as radiation therapy or surgery, in patients with non-central nervous system cancers. Furthermore, several pieces of evidence suggest that clinical manifestations of cognitive impairment may occur in cancer patients, prior to chemotherapy or in those not treated with cancer therapies. In these circumstances, it should be more appropriate to use the term cancer-related cognitive dysfunction. Because there is no consensus about its definition and diagnostic criteria, no specific test for CRCI diagnose exists. Whatever the cause, this manifestation of central nervous system toxicity is of increasing concern as the survival rates for cancer have improved steadily and, in turn, cognitive dysfunction can negatively impact the patients and cancer survivors' quality of life. The aim of this work is to offer an overview of the topic and recommendations for future research.