Opportunities and barriers for telemedicine in pain management: insights from a SIAARTI survey among Italian pain physicians.

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.

Effect on post-operative pulmonary complications frequency of high flow nasal oxygen versus standard oxygen therapy in patients undergoing esophagectomy for cancer: study protocol for a randomized controlled trial-OSSIGENA study.

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 SpO2 ≥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.

Lung ultrasound among Expert operator'S: ScOring and iNter-rater reliability analysis (LESSON study) a secondary COWS study analysis from ITALUS group.

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.

Maladaptive personality traits are associated with burnout risk in Italian anesthesiologists and intensivists: a secondary analysis from a cross-sectional study.

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.

Cardiac arrest and cardiopulmonary resuscitation in the next decade: Predicting and shaping the impact of technological innovations.

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.

Algor-ethics: charting the ethical path for AI in critical care.

The integration of Clinical Decision Support Systems (CDSS) based on artificial intelligence (AI) in healthcare is groundbreaking evolution with enormous potential, but its development and ethical implementation, presents unique challenges, particularly in critical care, where physicians often deal with life-threating conditions requiring rapid actions and patients unable to participate in the decisional process. Moreover, development of AI-based CDSS is complex and should address different sources of bias, including data acquisition, health disparities, domain shifts during clinical use, and cognitive biases in decision-making. In this scenario algor-ethics is mandatory and emphasizes the integration of 'Human-in-the-Loop' and 'Algorithmic Stewardship' principles, and the benefits of advanced data engineering. The establishment of Clinical AI Departments (CAID) is necessary to lead AI innovation in healthcare, ensuring ethical integrity and human-centered development in this rapidly evolving field.

Continuous vs intermittent Non-Invasive blood pressure MONitoring in preventing postoperative organ failure (niMON): study protocol for an open-label, multicenter randomized trial.

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 5th of August 2023.

Artificial Intelligence in Operating Room Management.

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.

Health coaching for undergraduate nursing students: a pilot study for an action research.

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.

Remdesivir administration for SARS-CoV-2 pneumonia in ICU and non-ICU patients: outcome and posttreatment differences - the Italian Military Hospital experience.

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.

Supraspinal neuroinflammation and anxio-depressive-like behaviors in young- and older- adult mice with osteoarthritis pain: the effect of morphine.

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.

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.

Utilizing an artificial intelligence framework (conditional generative adversarial network) to enhance telemedicine strategies for cancer pain management.

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.

The Clinical Researcher Journey in the Artificial Intelligence Era: The PAC-MAN's Challenge.

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.