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Background and Objective: We hypothesize that lung ultrasound scores (LUS) can help stratify the cardiac risk of elderly patients undergoing orthopedic surgery for hip fracture, adding value to the Revised Cardiac Risk Index (RCRI), the American Society of Anesthesiologists Physical Status (ASA-PS) and the National Surgical Quality Improvement Program Myocardial infarction and Cardiac arrest (NSQIP-MICA)., Methods: Prospective, observational multicenter study of 11 Italian hospitals on patients aged >65 years with hip fractures needing urgent surgery. Subjects with major adverse cardiovascular events (MACE) in the previous 6 months or with ongoing acute heart failure were excluded. Trained anesthesiologists obtained preoperative LUS scores during preoperative evaluation. ROC curve analysis and comparison were used to evaluate test accuracy., Results: A total of 877 patients were enrolled in the study period. 108 MACE events occurred in 98 patients, with an overall incidence of 11.2%. LUS score was higher in complicated than non-complicated patients, 11.6 ± 6.64 vs. 4.97 ± 4.90 (p < 0.001). Preoperative LUS score ≥8 showed both better AUC (0.78) and accuracy (0.76) in predicting MACE than the RCRI scores (p < 0.001), MICA scores (p = 0.001) and ASA classes (p < 0.001). LUS sensitivity was 0.71, specificity was 0.76, negative predictive value was 0.95. LUS score ≥8 showed an OR for MACE of 5.81[95% CI 3.55-9.69] at multivariate analysis. 91 patients (10.4%) experienced postoperative pneumonia showing a preoperative LUS score higher in the non-pneumonia group, p <  0.001., Conclusions: The preoperative LUS score, with its high negative predictive value, could improve patients' risk stratification when used alone or add further value to the RCRI score., Registration: Registered at clinicaltrials.gov as NCT04074876., (Copyright © 2024. Published by Elsevier Masson SAS.)

Background: The integration of telemedicine in pain management represents a significant advancement in healthcare delivery, offering opportunities to enhance patient access to specialized care, improve satisfaction, and streamline chronic pain management. Despite its growing adoption, there remains a lack of comprehensive data on its utilization in pain therapy, necessitating a deeper understanding of physicians' perspectives, experiences, and challenges., Methods: A survey was conducted in Italy between January 2024 and May 2024. Specialist center members of the SIAARTI were sent an online questionnaire testing the state of the art of telemedicine for pain medicine., Results: One-hundred thirty-one centers across Italy reveal varied adoption rates, with 40% routinely using telemedicine. Regional disparities exist, with Northern Italy showing higher adoption rates. Barriers include the absence of protocols, resource constraints, and bureaucratic obstacles. Despite challenges, telemedicine has shown positive impacts on service delivery, with increased service volume reported. Technological capabilities, including image sharing and teleconsultation with specialists, indicate promising interdisciplinary potential., Conclusions: The integration of advanced telemedicine software utilizing artificial intelligence holds promise for enhancing telemonitoring and alert systems, potentially leading to more proactive and personalized pain management strategies., (© 2024. The Author(s).)

Background: Postoperative pulmonary complications (PPCs) remain a challenge after esophagectomy. Despite improvement in surgical and anesthesiological management, PPCs are reported in as many as 40% of patients. The main aim of this study is to investigate whether early application of high-flow nasal cannula (HFNC) after extubation will provide benefit in terms of reduced PPC frequency compared to standard oxygen therapy., Methods: Patients aged 18-85 years undergoing esophagectomy for cancer treatment with radical intent, excluding those with American Society of Anesthesiologists (ASA) score >3 and severe systemic comorbidity (cardiac, pulmonary, renal or hepatic disease) will be randomized at the end of surgery to receive HFNC or standard oxygen therapy (Venturi mask or nasal goggles) after early extubation (within 12 hours after the end of surgery) for 48 hours. The main postoperative goals are to obtain SpO 2 ≥94% and adequate pain control. Oxygen therapy after 48 hours will be stopped unless the physician deems it necessary. In case of respiratory clinical worsening, patients will be supported with the most appropriate tool (noninvasive ventilation or invasive mechanical ventilation). Pulmonary [pneumonia, pleural effusion, pneumothorax, atelectasis, acute respiratory distress syndrome (ARDS), tracheo-bronchial injury, air leak, reintubation, and/or respiratory failure] complications will be recorded as main outcome. Secondary outcomes, including cardiovascular, surgical, renal and infective complications will also be recorded. The primary analysis will be carried out on 320 patients (160 per group) and performed on an intention-to-treat (ITT) basis, including all participants randomized into the treatment groups, regardless of protocol adherence. The primary outcome, the PPC rate, will be compared between the two treatment groups using a chi-square test for categorical data, or Fisher's exact test will be used if the assumptions for the chi-square test are not met., Discussion: Recent evidence demonstrated that early application of HFNC improved the respiratory rate oxygenation index (ROX index) after esophagectomy but did not reduce PPCs. This randomized controlled multicenter trial aims to assess the potential effect of the application of HFNC versus standard oxygen over PPCs in patients undergoing esophagectomy., Trial Registration: This study is registered at clinicaltrial.gov NCT05718284, dated 30 January 2023., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-24-575/coif). The authors have no conflicts of interest to declare., (2024 Journal of Thoracic Disease. All rights reserved.)

Background: Lung ultrasonography (LUS) is a non-invasive imaging method used to diagnose and monitor conditions such as pulmonary edema, pneumonia, and pneumothorax. It is precious where other imaging techniques like CT scan or chest X-rays are of limited access, especially in low- and middle-income countries with reduced resources. Furthermore, LUS reduces radiation exposure and its related blood cancer adverse events, which is particularly relevant in children and young subjects. The score obtained with LUS allows semi-quantification of regional loss of aeration, and it can provide a valuable and reliable assessment of the severity of most respiratory diseases. However, inter-observer reliability of the score has never been systematically assessed. This study aims to assess experienced LUS operators' agreement on a sample of video clips showing predefined findings., Methods: Twenty-five anonymized video clips comprehensively depicting the different values of LUS score were shown to renowned LUS experts blinded to patients' clinical data and the study's aims using an online form. Clips were acquired from five different ultrasound machines. Fleiss-Cohen weighted kappa was used to evaluate experts' agreement., Results: Over a period of 3 months, 20 experienced operators completed the assessment. Most worked in the ICU (10), ED (6), HDU (2), cardiology ward (1), or obstetric/gynecology department (1). The proportional LUS score mean was 15.3 (SD 1.6). Inter-rater agreement varied: 6 clips had full agreement, 3 had 19 out of 20 raters agreeing, and 3 had 18 agreeing, while the remaining 13 had 17 or fewer people agreeing on the assigned score. Scores 0 and score 3 were more reproducible than scores 1 and 2. Fleiss' Kappa for overall answers was 0.87 (95% CI 0.815-0.931, p < 0.001)., Conclusions: The inter-rater agreement between experienced LUS operators is very high, although not perfect. The strong agreement and the small variance enable us to say that a 20% tolerance around a measured value of a LUS score is a reliable estimate of the patient's true LUS score, resulting in reduced variability in score interpretation and greater confidence in its clinical use., (© 2024. The Author(s).)

Introduction: Cardiac arrest (CA) is the third leading cause of death, with persistently low survival rates despite medical advancements. This article evaluates the potential of emerging technologies to enhance CA management over the next decade, using predictions from the AI tools ChatGPT-4 and Gemini Advanced., Methods: We conducted an exploratory literature review to envision the future of cardiopulmonary arrest (CA) management. Utilizing ChatGPT-4 and Gemini Advanced, we predicted implementation timelines for innovations in early recognition, CPR, defibrillation, and post-resuscitation care. We also consulted the AI to assess the consistency and reproducibility of the predictions., Results: We extrapolate that healthcare may embrace new technologies, such as comprehensive monitoring of vital signs to activate the emergency system (wireless detectors, smart speakers, and wearable devices), use new innovative early CPR and early AED devices (robot CPR, wearable AEDs, and immersive reality), and post-resuscitation care monitoring (brain-computer interface). These technologies could enhance timely life-saving interventions for cardiac arrest. However, there are many ethical and practical challenges, particularly in maintaining patient privacy and equity. The two AI tools made different predictions, with a horizon for implementation ranging between three and eight years., Conclusion: Integrating advanced monitoring technologies and AI-driven tools offers hope in improving CA management. A balanced approach involving rigorous scientific validation and ethical oversight is necessary. Collaboration among technologists, medical professionals, ethicists, and policymakers is crucial to use these innovations ethically to reduce CA incidence and enhance outcomes. Further research is needed to enhance the reliability of AI predictive capabilities., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: Burnout is a maladaptive response to chronic stress, particularly prevalent among clinicians. Anesthesiologists are at risk of burnout, but the role of maladaptive traits in their vulnerability to burnout remains understudied., Methods: A secondary analysis was performed on data from the Italian Association of Hospital Anesthesiologists, Pain Medicine Specialists, Critical Care, and Emergency (AAROI-EMAC) physicians. The survey included demographic data, burnout assessment using the Maslach Burnout Inventory (MBI) and subscales (emotional exhaustion, MBI-EE; depersonalization, MBI-DP; personal accomplishment, MBI-PA), and evaluation of personality disorders (PDs) based on DSM-IV (Diagnostic and Statistical Manual of Mental Disorders Fourth Edition) criteria using the assessment of DSM-IV PDs (ADP-IV). We investigated the aggregated scores of maladaptive personality traits as predictor variables of burnout. Subsequently, the components of personality traits were individually assessed., Results: Out of 310 respondents, 300 (96.77%) provided complete information. The maladaptive personality traits global score was associated with the MBI-EE and MBI-DP components. There was a significant negative correlation with the MBI-PA component. Significant positive correlations were found between the MBI-EE subscale and the paranoid (r = 0.42), borderline (r = 0.39), and dependent (r = 0.39) maladaptive personality traits. MBI-DP was significantly associated with the passive-aggressive (r = 0.35), borderline (r = 0.33), and avoidant (r = 0.32) traits. Moreover, MBI-PA was negatively associated with dependent (r =  - 0.26) and avoidant (r =  - 0.25) maladaptive personality features., Conclusions: There is a significant association between different maladaptive personality traits and the risk of experiencing burnout among anesthesiologists. This underscores the importance of understanding and addressing personality traits in healthcare professionals to promote their well-being and prevent this serious emotional, mental, and physical exhaustion state., (© 2024. The Author(s).)

Introduction: Artificial intelligence and machine learning are the new frontier in urology; they can assist the diagnostic work-up and in prognostication bring superior to the existing nomograms. Infectious events and in particular the septic risk, are one of the most common and in some cases life threatening complication in patients with urolithiasis. We performed a scoping review to provide an overview of the current application of AI in prediction the infectious complications in patients affected by urolithiasis., Evidence Acquisition: A systematic scoping review of the literature was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses for Scoping Reviews (PRISMA-ScR) guidelines by screening Medline, PubMed, and Embase to detect pertinent studies., Evidence Synthesis: A total of 467 articles were found, of which nine met the inclusion criteria and were considered. All studies are retrospective and published between 2021 and 2023. Only two studies performed an external validation of the described models. The main event considered is urosepsis in four articles, urinary tract infection in two articles and diagnosis of infection stones in three articles. Different AI models were trained, each of which exploited several types and numbers of variables. All studies reveal good performance. Random forest and artificial neural networks seem to have higher AUC, specificity and sensibility and perform better than the traditional statistical analysis., Conclusions: Further prospective and multi-institutional studies with external validation are needed to better clarify which variables and AI models should be integrated in our clinical practice to predict infectious events.

This systematic review examines the recent use of artificial intelligence, particularly machine learning, in the management of operating rooms. A total of 22 selected studies from February 2019 to September 2023 are analyzed. The review emphasizes the significant impact of AI on predicting surgical case durations, optimizing post-anesthesia care unit resource allocation, and detecting surgical case cancellations. Machine learning algorithms such as XGBoost, random forest, and neural networks have demonstrated their effectiveness in improving prediction accuracy and resource utilization. However, challenges such as data access and privacy concerns are acknowledged. The review highlights the evolving nature of artificial intelligence in perioperative medicine research and the need for continued innovation to harness artificial intelligence's transformative potential for healthcare administrators, practitioners, and patients. Ultimately, artificial intelligence integration in operative room management promises to enhance healthcare efficiency and patient outcomes., (© 2024. The Author(s).)

Background: Blood pressure has become one of the most important vital signs to monitor in the perioperative setting. Recently, the Italian Society of Anesthesia Analgesia Resuscitation and Intensive Care (SIAARTI) recommended, with low level of evidence, continuous monitoring of blood pressure during the intraoperative period. Continuous monitoring allows for early detection of hypotension, which may potentially lead to a timely treatment. Whether the ability to detect more hypotension events by continuous noninvasive blood pressure (C-NiBP) monitoring can improve patient outcomes is still unclear. Here, we report the rationale, study design, and statistical analysis plan of the niMON trial, which aims to evaluate the effect of intraoperative C-NiBP compared with intermittent (I-NiBP) monitoring on postoperative myocardial and renal injury., Methods: The niMon trial is an investigator-initiated, multicenter, international, open-label, parallel-group, randomized clinical trial. Eligible patients will be randomized in a 1:1 ratio to receive C-NiBP or I-NiBP as an intraoperative monitoring strategy. The proportion of patients who develop myocardial injury in the first postoperative week is the primary outcome; the secondary outcomes are the proportions of patients who develop postoperative AKI, in-hospital mortality rate, and 30 and 90 postoperative days events. A sample size of 1265 patients will provide a power of 80% to detect a 4% absolute reduction in the rate of the primary outcome., Conclusions: The niMON data will provide evidence to guide the choice of the most appropriate intraoperative blood pressure monitoring strategy., Clinical Trial Registration: Clinical Trial Registration: NCT05496322, registered on the 5 th of August 2023., (© 2024. The Author(s).)

Introduction: Cardiac arrest leaves witnesses, survivors, and their relatives with a multitude of questions. When a young or a public figure is affected, interest around cardiac arrest and cardiopulmonary resuscitation (CPR) increases. ChatGPT allows everyone to obtain human-like responses on any topic. Due to the risks of accessing incorrect information, we assessed ChatGPT accuracy in answering laypeople questions about cardiac arrest and CPR., Methods: We co-produced a list of 40 questions with members of Sudden Cardiac Arrest UK covering all aspects of cardiac arrest and CPR. Answers provided by ChatGPT to each question were evaluated by professionals for their accuracy, by professionals and laypeople for their relevance, clarity, comprehensiveness, and overall value on a scale from 1 (poor) to 5 (excellent), and for readability., Results: ChatGPT answers received an overall positive evaluation (4.3 ± 0.7) by 14 professionals and 16 laypeople. Also, clarity (4.4 ± 0.6), relevance (4.3 ± 0.6), accuracy (4.0 ± 0.6), and comprehensiveness (4.2 ± 0.7) of answers was rated high. Professionals, however, rated overall value (4.0 ± 0.5 vs 4.6 ± 0.7; p = 0.02) and comprehensiveness (3.9 ± 0.6 vs 4.5 ± 0.7; p = 0.02) lower compared to laypeople. CPR-related answers consistently received a lower score across all parameters by professionals and laypeople. Readability was 'difficult' (median Flesch reading ease score of 34 [IQR 26-42])., Conclusions: ChatGPT provided largely accurate, relevant, and comprehensive answers to questions about cardiac arrest commonly asked by survivors, their relatives, and lay rescuers, except CPR-related answers that received the lowest scores. Large language model will play a significant role in the future and healthcare-related content generated should be monitored., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Background: Randomized clinical trials in non-critically ill COVID-19 patients showed that therapeutic-dose heparin increased survival with reduced organ support as compared with usual-care thromboprophylaxis, albeit with increased bleeding risk. The purpose of the study is to assess the safety of intermediate dose enoxaparin in hospitalized patients with moderate to severe COVID-19., Methods: A phase II single-arm interventional prospective study including patients receiving intermediate dose enoxaparin once daily according to body weight: 60 mg for 45-60 kg, 80 mg for 61-100 kg or 100 mg for > 100 kg for 14 days, with dose adjustment according to anti-factor Xa activity (target range: 0.4-0.6 UI/ml); an observational cohort (OC) included patients receiving enoxaparin 40 mg day for comparison. Follow-up was 90 days. Primary outcome was major bleeding within 30 and 90 days after treatment onset. Secondary outcome was the composite of all-cause 30 and 90-day mortality rates, disease severity at the end of treatment, intensive care unit (ICU) admission and length of ICU stay, length of hospitalization. All outcomes were adjudicated by an independent committee and analyzed before and after propensity score matching (PSm)., Results: Major bleeding was similar in IC (1/98 1.02%) and in the OC (none), with only one event observed in a patient receiving concomitantly anti-platelet therapy. The composite outcome was observed in 53/98 patients (54%) in the IC and 132/203 (65%) patients in the OC (p = 0.07) before PSm, while it was observed in 50/90 patients (55.6%) in the IC and in 56/90 patients (62.2%) in the OC after PSm (p = 0.45). Length of hospitalization was lower in the IC than in OC [median 13 (IQR 8-16) vs 14 (11-21) days, p = 0.001], however it lost statistical significance after PSm (p = 0.08). At 30 days, two patients had venous thrombosis and two pulmonary embolism in the OC. Time to first negative RT-PCR were similar in the two groups., Conclusions: Weight adjusted intermediate dose heparin with anti-FXa monitoring is safe with potential positive impact on clinical course in COVID-19 non-critically ill patients., Trial Registration: The study INHIXACOVID19 was registred on ClinicalTrials.gov with the trial registration number (TRN) NCT04427098 on 11/06/2020., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Background and Aim: The Nursing undergraduate degree educational program represents an intensive and complex course, and includes a number of professionalizing practical internships, and for these reasons it requires an action to support and improve. Coaching is based on the premise that people have personal strengths and abilities which, through a interview, can be directed to solving their problems. Several studies demonstrate the efficacy of Health Coaching in different University, but never have been measured benefits regard skills improving. The objective of the study is to assess the impact of a health coaching program on the nursing students., Method: A pre-post quasi-experimental study was conducted, involving the activation of a Health Coaching Program for 25 nursing students selected through convenience sampling, based on their fulfillment of the inclusion criteria. The Health Coaching Program was administered by the Health Coaching Academy., Result: This study also evaluated parameters such as: level of concentration in study, motivation, problem solving and reorganization skills, study organization skills, psycho-physical-emotional state comprehension, decision-making skills and self-esteem, noting a statistically significant increase post-HC program. A statistically significant improvement was also found in the students' perception of their own stress management skills after the course., Conclusion: This study strengthens the hypothesis that HC programs contribute to improving performance of nursing students. Those conclusions need to be corroborated by future studies on the topic to further support the hypothesis that programs of HC within the learning nursing contexts can lead to a tangible benefit for students of the health professions.

Rationale: Asteoarthritis (OA) is a leading cause of chronic pain in the elderly population and is often associated with emotional comorbidities such as anxiety and depression. Despite age is a risk factor for both OA and mood disorders, preclinical studies are mainly conducted in young adult animals., Objectives: Here, using young adult (11-week-old) and older adult (20-month-old) mice, we evaluate in a monosodium-iodoacetate-(MIA)-induced OA model the development of anxio-depressive-like behaviors and whether brain neuroinflammation may underlie the observed changes. We also test whether an effective pain treatment may prevent behavioral and biochemical alterations., Methods: Mechanical allodynia was monitored throughout the experimental protocol, while at the end of protocol (14 days), anxio-depressive-like behaviors and cognitive dysfunction were assessed. Neuroinflammatory condition was evaluated in prefrontal cortex, hippocampus and hypothalamus. Serum IFNγ levels were also measured. Moreover, we test the efficacy of a 1-week treatment with morphine (2.5 mg/kg) on pain, mood alterations and neuroinflammation., Results: We observed that young adult and older adult controls (CTRs) mice had comparable allodynic thresholds and developed similar allodynia after MIA injection. Older adult CTRs were characterized by altered behavior in the tests used to assess the presence of depression and cognitive impairment and by elevated neuroinflammatory markers in brain areas compared to younger ones. The presence of pain induced depressive-like behavior and neuroinflammation in adult young mice, anxiety-like behavior in both age groups and worsened neuroinflammation in older adult mice. Morphine treatment counteracted pain, anxio-depressive behaviors and neuroinflammatory activation in both young adult and older adult mice., Conclusions: Here, we demonstrated that the presence of chronic pain in young adult mice induces mood alterations and supraspinal biochemical changes and aggravates the alterations already evident in older adult animals. A treatment with morphine, counteracting the pain, prevents the development of anxio-depressive disorders and reduces neuroinflammation., (© 2023. The Author(s).)

Background: Four-hundred forty-nine patients affected by Covid-19 were hospitalized at the Rome Military Hospital between March 2020 and July 2022. Depending on the severity of the disease, they were assigned either to the Functional Health Emergency Unit - if suffering from interstitial pneumonia with a clinical manifestation of dyspnea associated with peripheral oxygen saturation  < 92%, and oxygen atmospheric pressure therapy - or to the intensive care unit - if the blood gas-lytic index P/F (ratio between partial pressure of arterial O2 and inspired fraction of O2) was below 150. This prospective observation and monocentric study aim to verify the outcome (healing/death) of early use of remdesivir in pneumonia patients., Results: The results highlight the importance of the adoption of remdesivir in the initial stages of infection to prevent the systemic spread and viral multiplication and, in the subsequent phase, a cytokine storm resulting in acute respiratory failure and multiorgan failure. The use of the drug in the most advanced stages of the disease is not associated with a real impact on patient outcomes. Therefore, there is a statistically significant correspondence between the early use of remdesivir in the treatment of SARS-CoV-2 disease - in addition to guidelines therapies - and a favorable clinical outcome., Conclusions: This work shows therapeutic efficacy in the first 5 days of intravenous administration of remdesivir, following the loading dose. It is also necessary to underline the different behaviors of the drug when administered late in patients undergoing mechanical ventilation, compared to those who only needed low-flow oxygen therapy, whose share of recovery - decidedly relevant - reaches statistical significance., (© 2023. Italian Society of Anesthesia, Analgesia, Resuscitation and Intensive Care.)

Artificial intelligence refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. In the present review we chose ten influential papers from the last five years and through Kintsugi, shed the light on recent evolution of artificial intelligence in anesthesiology. A comprehensive search in in Medline, Embase, Web of Science and Scopus databases was conducted. Each author searched the databases independently and created a list of six articles that influenced their clinical practice during this period, with a focus on their area of competence. During a subsequent step, each researcher presented his own list and most cited papers were selected to create the final collection of ten articles. In recent years purely methodological works with a cryptic technology (black-box) represented by the intact and static vessel, translated to a "modern artificial intelligence" in clinical practice and comprehensibility (glass-box). The purposes of this review are to explore the ten most cited papers about artificial intelligence in anesthesiology and to understand how and when it should be integrated in clinical practice.

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., Competing Interests: The authors declare that there are no conflicts of interest., (Copyright © 2023 Marco Cascella et al.)

Background: The utilization of artificial intelligence (AI) in healthcare has significant potential to revolutionize the delivery of medical services, particularly in the field of telemedicine. In this article, we investigate the capabilities of a specific deep learning model, a generative adversarial network (GAN), and explore its potential for enhancing the telemedicine approach to cancer pain management., Materials and Methods: We implemented a structured dataset comprising demographic and clinical variables from 226 patients and 489 telemedicine visits for cancer pain management. The deep learning model, specifically a conditional GAN, was employed to generate synthetic samples that closely resemble real individuals in terms of their characteristics. Subsequently, four machine learning (ML) algorithms were used to assess the variables associated with a higher number of remote visits., Results: The generated dataset exhibits a distribution comparable to the reference dataset for all considered variables, including age, number of visits, tumor type, performance status, characteristics of metastasis, opioid dosage, and type of pain. Among the algorithms tested, random forest demonstrated the highest performance in predicting a higher number of remote visits, achieving an accuracy of 0.8 on the test data. The simulations based on ML indicated that individuals who are younger than 45 years old, and those experiencing breakthrough cancer pain, may require an increased number of telemedicine-based clinical evaluations., Conclusion: As the advancement of healthcare processes relies on scientific evidence, AI techniques such as GANs can play a vital role in bridging knowledge gaps and accelerating the integration of telemedicine into clinical practice. Nonetheless, it is crucial to carefully address the limitations of these approaches., (© 2023. The Author(s).)

Purpose: Fulfilling educational needs in pain management should be a lifelong process, even involving physicians board certified in pain medicine such as the anesthesiologists/pain therapists. The aim of the study was to investigate Italian anesthesiologists' self-perceived competency, confidence, and interest to attend educational programs in relation to their seniority in pain management., Methods: SIAARTI members were sent an online questionnaire addressing the following items: education, skills (both soft and hard skills), technical expertise and engaged to participate between December 2020 and January 2021. Participants rated their competence based on the following range (no knowledge, knowledge, competence) while their agreement to attend educational courses was assessed using a 5-point Likert-type scale., Results: Less than one in four participants declare to be dedicated to pain medicine activity with greater proportion among older (over 61 years) compared to younger ones (31-40 years). Regarding cancer and chronic noncancer pain a positive gradient of self-perceived competence has been observed in relation to seniority. In contrast, no gradient of self-perceived competence was reported about musculoskeletal and low back pain. Participants self-perceived competent in both opioid use and prevention of opioid-related adverse event while feeling less competent when managing drugs with abuse potential. The lowest competence has been observed in pediatric pain along with the lowest interest to attend educational courses. Participants were much and very much interested to education regarding cancer, noncancer, musculoskeletal, and low back pain, invasive analgesic procedures but less regarding items for which they declared less competence, such as use of pain scales, pain management in children, and use of drugs with abuse potential., Conclusion: This work provides first evidence of a summative assessment of competency and related educational needs' profile of anesthesiologists/pain therapists thus paving the way for developing a nationwide educational program to improve chronic pain care in Italy., (© 2023 The Authors. Pain Practice published by Wiley Periodicals LLC on behalf of World Institute of Pain.)

Artificial intelligence (AI) is a powerful tool that can assist researchers and clinicians in various settings. However, like any technology, it must be used with caution and awareness as there are numerous potential pitfalls. To provide a creative analogy, we have likened research to the PAC-MAN classic arcade video game. Just as the protagonist of the game is constantly seeking data, researchers are constantly seeking information that must be acquired and managed within the constraints of the research rules. In our analogy, the obstacles that researchers face are represented by "ghosts", which symbolize major ethical concerns, low-quality data, legal issues, and educational challenges. In short, clinical researchers need to meticulously collect and analyze data from various sources, often navigating through intricate and nuanced challenges to ensure that the data they obtain are both precise and pertinent to their research inquiry. Reflecting on this analogy can foster a deeper comprehension of the significance of employing AI and other powerful technologies with heightened awareness and attentiveness.

Background: Investigating the health-related quality of life (HRQoL) after intensive care unit (ICU) discharge is necessary to identify possible modifiable risk factors. The primary aim of this study was to investigate the HRQoL in COVID-19 critically ill patients one year after ICU discharge., Methods: In this multicenter prospective observational study, COVID-19 patients admitted to nine ICUs from 1 March 2020 to 28 February 2021 in Italy were enrolled. One year after ICU discharge, patients were required to fill in short-form health survey 36 (SF-36) and impact of event-revised (IES-R) questionnaire. A multivariate linear or logistic regression analysis to search for factors associated with a lower HRQoL and post-traumatic stress disorded (PTSD) were carried out, respectively., Results: Among 1003 patients screened, 343 (median age 63 years [57-70]) were enrolled. Mechanical ventilation lasted for a median of 10 days [2-20]. Physical functioning (PF 85 [60-95]), physical role (PR 75 [0-100]), emotional role (RE 100 [33-100]), bodily pain (BP 77.5 [45-100]), social functioning (SF 75 [50-100]), general health (GH 55 [35-72]), vitality (VT 55 [40-70]), mental health (MH 68 [52-84]) and health change (HC 50 [25-75]) describe the SF-36 items. A median physical component summary (PCS) and mental component summary (MCS) scores were 45.9 (36.5-53.5) and 51.7 (48.8-54.3), respectively, considering 50 as the normal value of the healthy general population. In all, 109 patients (31.8%) tested positive for post-traumatic stress disorder, also reporting a significantly worse HRQoL in all SF-36 domains. The female gender, history of cardiovascular disease, liver disease and length of hospital stay negatively affected the HRQoL. Weight at follow-up was a risk factor for PTSD (OR 1.02, p = 0.03)., Conclusions: The HRQoL in COVID-19 ARDS (C-ARDS) patients was reduced regarding the PCS, while the median MCS value was slightly above normal. Some risk factors for a lower HRQoL have been identified, the presence of PTSD is one of them. Further research is warranted to better identify the possible factors affecting the HRQoL in C-ARDS.

Knee osteoarthritis is a common cause of pain and disability in old subjects. Pain may predispose to the development of frailty. Studies on mechanisms underlying pain in osteoarthritis models during aging are lacking. In this work, we used the monosodium iodoacetate model of osteoarthritis in adult (11-week-old) and old (20-month-old) C57BL/6J mice to compare hypersensitivity, locomotion, neuroinflammation, and the effects of morphine treatment. After osteoarthritis induction in adult and old mice, weight-bearing asymmetry, mechanical allodynia, and thermal hyperalgesia similarly developed, while locomotion and frailty were more affected in old than in adult animals. When behavioral deficits were present, the animals were treated for 7 days with morphine. This opioid counteracts the behavioral alterations and the frailty index worsening both in adult and old mice. To address the mechanisms that underlie pain, we evaluated neuroinflammatory markers and proinflammatory cytokine expression in the sciatic nerve, DRGs, and spinal cord. Overexpression of cytokines and glia markers were present in osteoarthritis adult and old mice, but the activation was qualitatively and quantitatively more evident in aged mice. Morphine was able to counteract neuroinflammation in both age groups. We demonstrate that old mice are more vulnerable to pain's detrimental effects, but prompt treatment is successful at mitigating these effects.

Left ventricular (LV) diastolic dysfunction is a commonly encountered condition and its impact on the anesthesia and the intensive care population is often underestimated. The study of the diastole is known as "diastology" and comprises four phases: isovolumetric relaxation, early filling phase, diastasis, and late filling phase. Diastolic function needs at least the same attention as systolic function, since its alteration has been associated with worse prognosis. Notwithstanding, many physicians consider the assessment of diastolic function too much complex. In this context, the latest 2016 guideline have simplified the assessment of diastolic function. In this educational review, we approach diastolic dysfunction with didactic purposes. First, we use a metaphor to consider the LV as a glass that progressively changes its shape and height along the disease course, resembling variable end-diastolic pressures and volumes at different stages while progressing with diastolic dysfunction. We guide readers in the process of diagnosis and grading of LV diastolic dysfunction, with description of pathophysiological changes in LV relaxation and consequently in the pressure gradient between the left-sided heart chambers. In the second part, starting from physiology we move towards suggestions for the clinical management of anesthesia and intensive care patients with diastolic dysfunction under different scenarios (hypo- and hypervolemia, weaning, sepsis, tachycardia and arrhythmias, right ventricular dysfunction).

Background and Aim: Artificial intelligence was born to allow computers to learn and control their environment, trying to imitate the human brain structure by simulating its biological evolution. Artificial intelligence makes it possible to analyze large amounts of data (big data) in real-time, providing forecasts that can support the clinician's decisions. This scenario can include diagnosis, prognosis, and treatment in anesthesiology, intensive care medicine, and pain medicine. Machine Learning is a subcategory of AI. It is based on algorithms trained for decisions making that automatically learn and recognize patterns from data. This article aims to offer an overview of the potential application of AI in anesthesiology and analyzes the operating principles of machine learning Every Machine Learning pathway starts from task definition and ends in model application., Conclusions: High-performance characteristics and strict quality controls are needed during its progress. During this process, different measures can be identified (pre-processing, exploratory data analysis, model selection, model processing and evaluation). For inexperienced operators, the process can be facilitated by ad hoc tools for data engineering, machine learning, and analytics.

Introduction: Hyperoxemia during cardiac arrest (CA) may increase chances of successful resuscitation. However, episodes of severe hyperoxemia after intensive care unit admission occurs frequently (up to 60%), and these have been associated with higher mortality in CA patients. The impact of severe hyperoxemia on neurological outcome is more unclear., Evidence Acquisition: We conducted a systematic review and meta-analysis on Pubmed and EMBASE to evaluate the effects of severe hyperoxemia according to arterial blood gas analysis on neurological outcome and mortality in patients resuscitated from CA and admitted to intensive care unit., Evidence Synthesis: Thirteen observational studies were included, eight of them reporting data on neurological outcome and ten on mortality. Most studies reported odds ratio adjusted for confounders. Severe hyperoxemia was associated with worse neurological outcome (OR 1.37 [95%CI 1.01,1.86], P=0.04) and higher mortality at longest follow-up (OR 1.32 [95%CI 1.11,1.57], P=0.002). Subgroup analyses according to timing of hyperoxemia showed that any hyperoxemia during the first 36 hours was associated with worse neurological outcome (OR 1.52 [95%CI 1.12,2.08], P=0.008) and higher mortality (OR 1.40 [95%CI 1.18,1.66], P=0.0001), whilst early hyperoxemia was not (neurological: P=0.29; mortality: P=0.19). Sensitivity analyses mostly confirmed the results of the primary analyses., Conclusions: Severe hyperoxemia is associated with worse neurological outcome and lower survival in CA survivors admitted to intensive care unit. Clinical efforts should be made to avoid severe hyperoxemia during at least the first 36 hours after cardiac arrest.

Purpose:  The goal of this survey was to describe the use and diffusion of lung ultrasound (LUS), the level of training received before and during the COVID-19 pandemic, and the clinical impact LUS has had on COVID-19 cases in intensive care units (ICU) from February 2020 to May 2020., Materials and Methods:  The Italian Lung Ultrasound Survey (ITALUS) was a nationwide online survey proposed to Italian anesthesiologists and intensive care physicians carried out after the first wave of the COVID-19 pandemic. It consisted of 27 questions, both quantitative and qualitative., Results:  807 responded to the survey. The median previous LUS experience was 3 years (IQR 1.0-6.0). 473 (60.9 %) reported having attended at least one training course on LUS before the COVID-19 pandemic. 519 (73.9 %) reported knowing how to use the LUS score. 404 (52 %) reported being able to use LUS without any supervision. 479 (68.2 %) said that LUS influenced their clinical decision-making, mostly with respect to patient monitoring. During the pandemic, the median of patients daily evaluated with LUS increased 3-fold (p < 0.001), daily use of general LUS increased from 10.4 % to 28.9 % (p < 0.001), and the daily use of LUS score in particular increased from 1.6 % to 9.0 % (p < 0.001)., Conclusion:  This survey showed that LUS was already extensively used during the first wave of the COVID-19 pandemic by anesthesiologists and intensive care physicians in Italy, and then its adoption increased further. Residency programs are already progressively implementing LUS teaching. However, 76.7 % of the sample did not undertake any LUS certification., Competing Interests: Francesco Mojoli, fee for lectures (Hamilton Medical, GE Healthcare, SEDA Spa). Consultancy agreement between University of Pavia and Hamilton Medical.Silvia Mongodi, received fees for lectures by GE healthcare, outside the present work., (The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commecial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).)

Background . It was previously reported that health care professionals working in the fields of anesthesiology and emergency are at higher risk of burnout. However, the correlations between burnout, alexithymia, and other psychological symptoms are poorly investigated. Furthermore, there is a lack of evidence on which risk factors, specific to the work of anesthetists and intensivists, can increase the risk of burnout, and which are useful for developing remedial health policies. Methods . This cross-sectional study was conducted in 2020 on a sample of 300 professionals recruited from AAROI-EMAC subscribers in Italy. Data collection instruments were a questionnaire on demographic, education, job characteristics and well-being, the Maslach Burnout Inventory Tool, the Toronto Alexithymia Scale, the Symptom Checklist-90-R, and the Rosenberg Self-Esteem Scale administered during refresher courses in anesthesiology. Correlations between burnout and physical and psychological symptoms were searched. Results . With respect to burnout, 29% of individuals scored at high risk on emotional exhaustion, followed by 36% at moderate-high risk. Depersonalization high and moderate-high risk were scored by 18.7% and 34.3% of individuals, respectively. Burnout personal accomplishment was scored by 34.7% of respondents. The highest mean scores of burnout dimensions were related to dissatisfaction with one's career, conflicting relationships with surgeons, and, finally, difficulty in explaining one's work to patients. Conclusions . Burnout rates in Italian anesthesiologists and intensivists have been worrying since before the COVID-19 pandemic. Anesthesiologists with higher levels of alexithymia are more at risk for burnout. It is therefore necessary to take urgent health policy measures..

The hypothesis that pain can predispose to frailty development has been recently investigated in several clinical studies suggesting that frailty and pain may share some mechanisms. Both pain and frailty represent important clinical and social problems and both lack a successful treatment. This circumstance is mainly due to the absence of in-depth knowledge of their pathological mechanisms. Evidence of shared pathways between frailty and pain are preliminary. Indeed, many clinical studies are observational and the impact of pain treatment, and relative pain-relief, on frailty onset and progression has never been investigated. Furthermore, preclinical research on this topic has yet to be performed. Specific researches on the pain-frailty relation are needed. In this narrative review, we will attempt to point out the most relevant findings present in both clinical and preclinical literature on the topic, with particular attention to genetics, epigenetics and inflammation, in order to underline the existing gaps and the potential future interventional strategies. The use of pain and frailty animal models discussed in this review might contribute to research in this area., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Background: To produce statements based on the available evidence and an expert consensus (as members of the Lung Ultrasound Working Group of the Italian Society of Analgesia, Anesthesia, Resuscitation, and Intensive Care, SIAARTI) on the use of lung ultrasound for the management of patients with COVID-19 admitted to the intensive care unit., Methods: A modified Delphi method was applied by a panel of anesthesiologists and intensive care physicians expert in the use of lung ultrasound in COVID-19 intensive critically ill patients to reach a consensus on ten clinical questions concerning the role of lung ultrasound in the following: COVID-19 diagnosis and monitoring (with and without invasive mechanical ventilation), positive end expiratory pressure titration, the use of prone position, the early diagnosis of pneumothorax- or ventilator-associated pneumonia, the process of weaning from invasive mechanical ventilation, and the need for radiologic chest imaging., Results: A total of 20 statements were produced by the panel. Agreement was reached on 18 out of 20 statements (scoring 7-9; "appropriate") in the first round of voting, while 2 statements required a second round for agreement to be reached. At the end of the two Delphi rounds, the median score for the 20 statements was 8.5 [IQR 8.9], and the agreement percentage was 100%., Conclusion: The Lung Ultrasound Working Group of the Italian Society of Analgesia, Anesthesia, Resuscitation, and Intensive Care produced 20 consensus statements on the use of lung ultrasound in COVID-19 patients admitted to the ICU. This expert consensus strongly suggests integrating lung ultrasound findings in the clinical management of critically ill COVID-19 patients., (© 2021. The Author(s).)

Objectives Epiduroscopy is a minimally invasive procedure that is used in pain therapy to treat lumbar and root pain that is resistant to medical and infiltrative therapies. The indications for periduroscopy are partly shared with those of spinal cord stimulation (SCS): failed back surgery syndrome (FBSS) and stenosis of the vertebral canal in particular. The costs and risks of periduroscopy are considerably lower than those of SCS. The purpose of this study is to evaluate the clinical and economic advantages of integrating periduroscopy as a step prior to SCS for patients with severe lumbar or radicular pain that is unresponsive to pharmacological and infiltrative treatments. Materials and Methods Patients were enrolled if they had FBSS and spinal stenosis with indications for SCS and accepted periduroscopy treatment before the possible SCS trial. Patients were followed up for 24 months with evaluations of clinical data on the day after the procedure and at one and 24 months. The pain trend, satisfaction with the periduroscopy procedure, and the incidence of SCS implants in the study period were analyzed. Results A total of 106 patients were enrolled. Immediately after the procedure and in the first month, the reduction of pain and the level of patient satisfaction were high, but they were drastically reduced at 24 months with a progressive reappearance of symptoms that substantially overlapped with the pre-surgery levels. At 24 months, 48% of the patients underwent a neurostimulation trial, and a significant percentage of them were able to avoid the implantation of an SCS. Conclusions Periduroscopy appears to be rational as a step prior to SCS in terms of the improvement of pain symptoms in the short term, the definitive results in a significant percentage of patients, and the significant economic savings for the health system., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2021, Marchesini et al.)