Association between spontaneous breathing trial methods and reintubation in adult critically ill patients: A systematic review and network meta-analysis of randomized controlled trials.

BACKGROUND: Reintubation is associated with higher risk of mortality. There is no clear evidence on the best spontaneous breathing trial (SBT) method to reduce the risk of reintubation. RESEARCH QUESTION: Are different methods of conducting SBT in critically ill patients associated with different risk of reintubation compared to T-tube? STUDY DESIGN AND METHODS: We conducted a systematic review and Bayesian network meta-analysis of randomized controlled trials (RCTs) investigating the effects of different SBT methods on reintubation. We surveyed PubMed, MEDLINE, CINAHL and CENTRAL databases from inception to 26th January 2024. The Surface Under the Cumulative Ranking curve (SUCRA) was used to determine the likelihood that an intervention was ranked as the best. Pairwise comparisons were also investigated by frequentist meta-analysis. Certainty of the evidence was assessed according to the GRADE approach. RESULTS: A total of 22 RCTs were included, for a total of 6196 patients. The network included nine nodes, with 13 direct pairwise comparisons. About 71% of the patients were allocated to T-tube and PSV-ZEEP, with 2135 and 2101 patients, respectively. The only intervention with a significantly lower risk of reintubation compared to T-tube was high flow oxygen (HFO) (RR 0.23, CrI 0.09 to 0.51, moderate quality evidence). HFO was associated with the highest probability of being the best intervention for reducing the risk of reintubation (81.86%, SUCRA 96.42), followed by continuous positive airway pressure (11.8%, SUCRA 76.75). INTERPRETATION: HFO SBT was associated with a lower risk of reintubation in comparison to other SBT methods. The results of our analysis should be considered with caution due to the low number of studies that investigated HFO SBT, and potential clinical heterogeneity related to co-interventions. Further trials should be performed to confirm the results on larger cohorts of patients and assess specific subgroups.

Application of AI in urolithiasis risk of infection: a scoping review.

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.

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.

The role and mechanism of PDZ binding kinase in hypobaric and hypoxic acute lung injury.

Background: Acute lung injury (ALI) caused by hypobaric hypoxia (HH) is frequently observed in high-altitude areas, and it is one of the leading causes of death in high-altitude-related diseases due to its rapid onset and progression. However, the pathogenesis of HH-related ALI (HHALI) remains unclear, and effective treatment approaches are currently lacking. Methods: A new mouse model of HHALI developed by our laboratory was used as the study subject (Chinese patent No. ZL 2021 1 1517241 X). Real-time quantitative polymerase chain reaction (RT-qPCR) was used to detect the messenger RNA (mRNA) expression levels of PDZ-binding kinase (PBK), sirtuin 1 (SIRT1), and PTEN-induced kinase 1 (PINK1) in mouse lung tissue. Hematoxylin and eosin staining was used to observe the main types of damage and damaged cells in lung tissue, and the lung injury score was used for quantification. The wet-dry (W/D) ratio was used to measure lung water content. Enzyme-linked immunosorbent assay was used to detect changes in inflammatory factors and oxidative stress markers in the lungs. Western blotting verified the expression of various mitochondrial autophagy-related proteins. The 5,5',6,6'-tetrachloro-1,1',3,3'-tetraethylbenzimi-dazoylcarbocyanine iodide (JC-1) method was used determined the health status of mitochondria based on changes in mitochondrial membrane potential. Transmission electron microscopy was used to directly observe the morphology of mitochondria. Multicolor immunofluorescence was used to observe the levels of mitochondrial autophagy markers. Other signaling pathways and molecular mechanisms that may play a role in epithelial cells were analyzed via through RNA sequencing. Results: Low pressure and hypoxia caused pathological changes in mouse lung tissue, mainly ALI, leading to increased levels of inflammatory factors and intensified oxidative stress response in the lungs. Overexpression of PBK was found to alleviate HHALI, and activation of the p53 protein was shown to abrogate this therapeutic effect, while activation of SIRT1 protein reactivated this therapeutic effect. The therapeutic effect of PBK on HHALI is achieved via the activation of mitochondrial autophagy. Finally, RNA sequencing demonstrated that besides mitochondrial autophagy, PBK also exerts other functions in HHALI. Conclusions: Overexpression of PBK inhibits the expression of p53 and activates SIRT1-PINK1 axis mediated mitochondrial autophagy to alleviate HHALI.

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.

The Future of Artificial Intelligence Using Images and Clinical Assessment for Difficult Airway Management.

Artificial intelligence (AI) algorithms, particularly deep learning, are automatic and sophisticated methods that recognize complex patterns in imaging data providing high qualitative assessments. Several machine-learning and deep-learning models using imaging techniques have been recently developed and validated to predict difficult airways. Despite advances in AI modeling. In this review article, we describe the advantages of using AI models. We explore how these methods could impact clinical practice. Finally, we discuss predictive modeling for difficult laryngoscopy using machine-learning and the future approach with intelligent intubation devices.

Artificial Intelligence for Personalized Perioperative Medicine.

The development of artificial intelligence (AI) is disruptive and unstoppable, also in medicine. Because of the enormous quantity of data recorded during continuous monitoring and the peculiarity of our specialty where stratification and mitigation risk are some of the core aspects, anesthesiology and postoperative intensive care are fertile fields where new technologies find ample room for expansion. Recently, research efforts have focused on the development of a holistic technology that globally embraces the entire perioperative period rather than a fragmented approach where AI is developed to carry out specific tasks. This could potentially revolutionize the perioperative medicine we know today. In fact, AI will be able to expand clinician's ability to interpret, adapt, and ultimately act in a complex reality with facets that are too complex to be managed all at the same time and in a holistic manner. With the support of new tools, as healthcare professionals we have the moral obligation to govern this transition, allowing an ethical and sustainable development of these technologies and avoiding being overwhelmed by them. We should welcome this transhumanist tension which does not aim at the replacement of human capabilities or even at the integration of these but rather at the expansion of a "single intelligence".

Wearable devices for postoperative monitoring in surgical ward and the chain of liability.

Perioperative medicine is undergoing many changes with the introduction of new technologies. Wearable devices are among them. These novel tools are providing an additional possibility for perioperative monitoring. However, in order to ensure that the introduction of wearable device in surgical wards does not lead to additional challenges for healthcare professionals, a careful implementation plan should be drawn up by a multidisciplinary team. In addition, a chain of liability should also be established a priori to facilitate their use and avoid ambiguity in the occurrence of a critical event.

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.

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.

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.