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.

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.

Intraoperative individualization of positive-end-expiratory pressure through electrical impedance tomography or esophageal pressure assessment: a systematic review and meta-analysis of randomized controlled trials.

PURPOSE: This systematic review of randomized-controlled trials (RCTs) with meta-analyses aimed to compare the effects on intraoperative arterial oxygen tension to inspired oxygen fraction ratio (PaO2/FiO2), exerted by positive end-expiratory pressure (PEEP) individualized trough electrical impedance tomography (EIT) or esophageal pressure (Pes) assessment (intervention) vs. PEEP not tailored on EIT or Pes (control), in patients undergoing abdominal or pelvic surgery with an open or laparoscopic/robotic approach. METHODS: PUBMED®, EMBASE®, and Cochrane Controlled Clinical trials register were searched for observational studies and RCTs from inception to the end of August 2022. Inclusion criteria were: RCTs comparing PEEP titrated on EIT/Pes assessment vs. PEEP not individualized on EIT/Pes and reporting intraoperative PaO2/FiO2. Two authors independently extracted data from the enrolled investigations. Data are reported as mean difference and 95% confidence interval (CI). RESULTS: Six RCTs were included for a total of 240 patients undergoing general anesthesia for surgery, of whom 117 subjects in the intervention group and 123 subjects in the control group. The intraoperative mean PaO2/FiO2 was 69.6 (95%CI 32.-106.4 ) mmHg higher in the intervention group as compared with the control group with 81.4% between-study heterogeneity (p < 0.01). However, at meta-regression, the between-study heterogeneity diminished to 44.96% when data were moderated for body mass index (estimate 3.45, 95%CI 0.78-6.11, p = 0.011). CONCLUSIONS: In patients undergoing abdominal or pelvic surgery with an open or laparoscopic/robotic approach, PEEP personalized by EIT or Pes allowed the achievement of a better intraoperative oxygenation compared to PEEP not individualized through EIT or Pes. PROSPERO REGISTRATION NUMBER: CRD 42021218306, 30/01/2023.

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.

Evolution of Hybrid Intelligence and Its Application in Evidence-Based Medicine: A Review.

Modern medicine, both in clinical practice and research, has become more and more based on data, which is changing equally in type and quality with the advent and development of healthcare digitalization. The first part of the present paper aims to present the steps through which data, and subsequently clinical and research practice, have evolved from paper-based to digital, proposing a possible future of this digitalization in terms of potential applications and integration of digital tools in medical practice. Noting that digitalization is no more a possible future, but a concrete reality, there is a strong need for a new definition of evidence-based medicine, which must take into account the progressive integration of artificial intelligence (AI) in all decision-making processes. So, leaving behind the traditional research concept of human intelligence versus AI, poorly adaptable to real-world clinical practice, a Human and AI hybrid model, seen as a deep integration of AI and human thinking, is proposed as a new healthcare governance system. The second part of our review is focused on some of the major challenges the digitalization process has to face, particularly privacy issues, system complexity and opacity, and ethical concerns related to legal aspects and healthcare disparities. Analyzing these open issues, we aim to present some of the future directions that in our opinion should be pursued to implement AI in clinical practice.

One-Lung Ventilation and Postoperative Pulmonary Complications After Major Lung Resection Surgery. A Multicenter Randomized Controlled Trial.

OBJECTIVES: The effect of one-lung ventilation (OLV) strategy based on low tidal volume (TV), application of positive end-expiratory pressure (PEEP), and alveolar recruitment maneuvers (ARM) to reduce postoperative acute respiratory distress syndrome (ARDS) and pulmonary complications (PPCs) compared with higher TV without PEEP and ARM strategy in adult patients undergoing lobectomy or pneumonectomy has not been well established. DESIGN: Multicenter, randomized, single-blind, controlled trial. SETTING: Sixteen Italian hospitals. PARTICIPANTS: A total of 880 patients undergoing elective major lung resection. INTERVENTIONS: Patients were randomized to receive lower tidal volume (LTV group: 4 mL/kg predicted body weight, PEEP of 5 cmH2O, and ARMs) or higher tidal volume (HTL group: 6 mL/kg predicted body weight, no PEEP, and no ARMs). After OLV, until extubation, both groups were ventilated using a tidal volume of 8 mL/kg and a PEEP value of 5 cmH2O. The primary outcome was the incidence of in-hospital ARDS. Secondary outcomes were the in-hospital rate of PPCs, major cardiovascular events, unplanned intensive care unit (ICU) admission, in-hospital mortality, ICU length of stay, and in-hospital length of stay. MEASUREMENTS AND MAIN RESULTS: ARDS occurred in 3 of 438 patients (0.7%, 95% CI 0.1-2.0) and in 1 of 442 patients (0.2%, 95% CI 0-1.4) in the LTV and HTV group, respectively (Risk ratio: 3.03 95% CI 0.32-29, p = 0.372). Pulmonary complications occurred in 125 of 438 patients (28.5%, 95% CI 24.5-32.9) and in 136 of 442 patients (30.8%, 95% CI 26.6-35.2) in the LTV and HTV group, respectively (risk ratio: 0.93, 95% CI 0.76-1.14, p = 0.507). The incidence of major complications, in-hospital mortality, and unplanned ICU admission, ICU and in-hospital length of stay were comparable in both groups. CONCLUSIONS: In conclusion, among adult patients undergoing elective lung resection, an OLV with lower tidal volume, PEEP 5 cmH2O, and ARMs and a higher tidal volume strategy resulted in low ARDS incidence and comparable postoperative complications, in-hospital length of stay, and mortality.

Surgical outcomes and patient-centred perioperative programs.

Perioperative medicine is changing, and its goals are expanding. More and more attention is paid to the surgical experience and the patient's quality of life. Patient-reported data represent a useful tool in this context. Patient-reported outcomes measures (PROMs) and experience measures (PREMs) are among the most used categories. However, creating perioperative programs capable of integrating traditional perioperative data with these scales is not easy. New technologies, particularly artificial intelligence, thanks to their ability to recognise, interpret, process or simulate human feelings, emotions and moods, could provide the necessary tools to combine all perioperative aspects, placing the patients and their needs at the centre of the process.

From Big Data's 5Vs to clinical practice's 5Ws: enhancing data-driven decision making in healthcare.

The use of AI-based algorithms is rapidly growing in healthcare, but there is still an ongoing debate about how to manage and ensure accountability for their clinical use. While most of the studies focus on demonstrating a good algorithm performance it is important to acknowledge that several additional steps are needed for reaching an effective implementation of AI-based models in daily clinical practice, with implementation being one of the main key factors. We propose a model characterized by five questions that can guide in this process. Additionally, we believe that a hybrid intelligence, human and artificial respectively, is the new clinical paradigm that offer the most benefits for developing clinical decision support systems for bedside use.

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

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

Three Logistic Predictive Models for the Prediction of Mortality and Major Pulmonary Complications after Cardiac Surgery.

Background and Objectives: Pulmonary complications are a leading cause of morbidity after cardiac surgery. The aim of this study was to develop models to predict postoperative lung dysfunction and mortality. Materials and Methods: This was a single-center, observational, retrospective study. We retrospectively analyzed the data of 11,285 adult patients who underwent all types of cardiac surgery from 2003 to 2015. We developed logistic predictive models for in-hospital mortality, postoperative pulmonary complications occurring in the intensive care unit, and postoperative non-invasive mechanical ventilation when clinically indicated. Results: In the "preoperative model" predictors for mortality were advanced age (p < 0.001), New York Heart Association (NYHA) class (p < 0.001) and emergent surgery (p = 0.036); predictors for non-invasive mechanical ventilation were advanced age (p < 0.001), low ejection fraction (p = 0.023), higher body mass index (p < 0.001) and preoperative renal failure (p = 0.043); predictors for postoperative pulmonary complications were preoperative chronic obstructive pulmonary disease (p = 0.007), preoperative kidney injury (p < 0.001) and NYHA class (p = 0.033). In the "surgery model" predictors for mortality were intraoperative inotropes (p = 0.003) and intraoperative intra-aortic balloon pump (p < 0.001), which also predicted the incidence of postoperative pulmonary complications. There were no specific variables in the surgery model predicting the use of non-invasive mechanical ventilation. In the "intensive care unit model", predictors for mortality were postoperative kidney injury (p < 0.001), tracheostomy (p < 0.001), inotropes (p = 0.029) and PaO2/FiO2 ratio at discharge (p = 0.028); predictors for non-invasive mechanical ventilation were kidney injury (p < 0.001), inotropes (p < 0.001), blood transfusions (p < 0.001) and PaO2/FiO2 ratio at the discharge (p < 0.001). Conclusions: In this retrospective study, we identified the preoperative, intraoperative and postoperative characteristics associated with mortality and complications following cardiac surgery.

Strategies to build and maintain competence in pain management: Insights from a SIAARTI survey on educational needs among Italian anesthesiologists.

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.

Acute Respiratory Distress Syndrome in the Perioperative Period of Cardiac Surgery: Predictors, Diagnosis, Prognosis, Management Options, and Future Directions.

Acute respiratory distress syndrome (ARDS) after cardiac surgery is reported with a widely variable incidence (from 0.4%-8.1%). Cardiac surgery patients usually are affected by several comorbidities, and the development of ARDS significantly affects their prognosis. Herein, evidence regarding the current knowledge in the field of ARDS in cardiac surgery is summarized and is followed by a discussion on therapeutic strategies, with consideration of the peculiar aspects of ARDS after cardiac surgery. Prevention of lung injury during and after cardiac surgery remains pivotal. Blood product transfusions should be limited to minimize the risk, among others, of lung injury. Open lung ventilation strategy (ventilation during cardiopulmonary bypass, recruitment maneuvers, and the use of moderate positive end-expiratory pressure) has not shown clear benefits on clinical outcomes. Clinicians in the intraoperative and postoperative ventilatory settings carefully should consider the effect of mechanical ventilation on cardiac function (in particular the right ventricle). Driving pressure should be kept as low as possible, with low tidal volumes (on predicted body weight) and optimal positive end-expiratory pressure. Regarding the therapeutic options, management of ARDS after cardiac surgery challenges the common approach. For instance, prone positioning may not be easily applicable after cardiac surgery. In patients who develop ARDS after cardiac surgery, extracorporeal techniques may be a valid choice in experienced hands. The use of neuromuscular blockade and inhaled nitric oxide can be considered on a case-by-case basis, whereas the use of aggressive lung recruitment and oscillatory ventilation should be discouraged.

Associations Between Expiratory Flow Limitation and Postoperative Pulmonary Complications in Patients Undergoing Cardiac Surgery.

OBJECTIVES: To determine whether driving pressure and expiratory flow limitation are associated with the development of postoperative pulmonary complications (PPCs) in cardiac surgery patients. DESIGN: Prospective cohort study. SETTING: University Hospital San Raffaele, Milan, Italy. PARTICIPANTS: Patients undergoing elective cardiac surgery. MEASUREMENTS AND MAIN RESULTS: The primary endpoint was the occurrence of a predefined composite of PPCs. The authors determined the association among PPCs and intraoperative ventilation parameters, mechanical power and energy load, and occurrence of expiratory flow limitation (EFL) assessed with the positive end-expiratory pressure test. Two hundred patients were enrolled, of whom 78 (39%) developed one or more PPCs. Patients with PPCs, compared with those without PPCs, had similar driving pressure (mean difference [MD] -0.1 [95% confidence interval (CI), -1.0 to 0.7] cmH2O, p = 0.561), mechanical power (MD 0.5 [95% CI, -0.3 to 1.1] J/m, p = 0.364), and total energy load (MD 95 [95% CI, -78 to 263] J, p = 0.293), but they had a higher incidence of EFL (51% v 38%, p = 0.005). Only EFL was associated independently with the development of PPCs (odds ratio 2.46 [95% CI, 1.28-4.80], p = 0.007). CONCLUSIONS: PPCs occurred frequently in this patient population undergoing cardiac surgery. PPCs were associated independently with the presence of EFL but not with driving pressure, total energy load, or mechanical power.

Level of Diffusion and Training of Lung Ultrasound during the COVID-19 Pandemic - A National Online Italian Survey (ITALUS) from the Lung Ultrasound Working Group of the Italian Society of Anesthesia, Analgesia, Resuscitation, and Intensive Care (SIAARTI).

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.

Point-of-care ultrasound training for residents in anaesthesia and critical care: results of a national survey comparing residents and training program directors' perspectives.

BACKGROUND: Point-of-care ultrasound (POCUS) has become an essential tool for anaesthesia and critical care physicians and dedicated training is mandatory. This survey describes the current state of Italian residency training programs through the comparison of residents' and directors' perspective. METHODS: Observational prospective cross-sectional study: 12-question national e-survey sent to Italian directors of anaesthesia and critical care residency programs (N = 40) and residents (N = 3000). Questions focused on POCUS teaching (vascular access, transthoracic echocardiography, focused assessment for trauma, transcranial Doppler, regional anaesthesia, lung and diaphragm ultrasound), organization (dedicated hours, teaching tools, mentors), perceived adequacy/importance of the training and limiting factors. RESULTS: Five hundred seventy-one residents and 22 directors completed the survey. Bedside teaching (59.4-93.2%) and classroom lessons (29.7-54.4%) were the most frequent teaching tools. Directors reported higher participation in research projects (p < 0.05 for all techniques but focused assessment for trauma) and simulation (p < 0.05 for all techniques but transthoracic echocardiography). Use of online teaching was limited (< 10%); however, 87.4% of residents used additional web-based tools. Consultants were the most frequent mentors, with different perspectives between residents (72.0%) and directors (95.5%; p = 0.013). Residents reported self-training more frequently (48.5 vs. 9.1%; p < 0.001). Evaluation was mainly performed at the bedside; a certification was not available in most cases (< 10%). Most residents perceived POCUS techniques as extremely important. Residents underestimated the relevance given by directors to ultrasound skills in their evaluation and the minimal number of exams required to achieve basic competency. Overall, the training was considered adequate for vascular access only (62.2%). Directors mainly agreed on the need of ultrasound teaching improvement in all fields. Main limitations were the absence of a standardized curriculum for residents and limited mentors' time/expertise for directors. CONCLUSION: POCUS education is present in Italian anaesthesia and critical care residency programs, although with potential for improvement. Significant discrepancies between residents' and directors' perspectives were identified.

Is lung ultrasound score a useful tool to monitoring and handling moderate and severe COVID-19 patients in the general ward? An observational pilot study.

Lung ultrasound is a well-established diagnostic tool in acute respiratory failure, and it has been shown to be particularly suited for the management of COVID-19-associated respiratory failure. We present exploratory analyses on the diagnostic and prognostic performance of lung ultrasound score (LUS) in general ward patients with moderate-to-severe COVID-19 pneumonia receiving O2 supplementation and/or noninvasive ventilation. From March 10 through May 1, 2020, 103 lung ultrasound exams were performed by our Forward Intensive Care Team (FICT) on 26 patients (18 males and 8 females), aged 62 (54 - 76) and with a Body Mass Index (BMI) of 30.9 (28.7 - 31.5), a median 6 (5 - 9) days after admission to the COVID-19 medical unit of the University Hospital of Parma, Italy. All patients underwent chest computed tomography (CT) the day of admission. The initial LUS was 16 (11 - 21), which did not significantly correlate with initial CT scans, probably due to rapid progression of the disease and time between CT scan on admission and first FICT evaluation; conversely, LUS was significantly correlated with PaO2/FiO2 ratio throughout patient follow-up [R = - 4.82 (- 6.84 to - 2.80; p < 0.001)]. The area under the receiving operating characteristics curve of LUS for the diagnosis of moderate-severe disease (PaO2/FiO2 ratio ≤ 200 mmHg) was 0.73, with an optimal cutoff value of 11 (positive predictive value: 0.98; negative predictive value: 0.29). Patients who eventually needed invasive ventilation and/or died during admission had significantly higher LUS throughout their stay.

Effects of Varying Levels of Inspiratory Assistance with Pressure Support Ventilation and Neurally Adjusted Ventilatory Assist on Driving Pressure in Patients Recovering from Hypoxemic Respiratory Failure.

BACKGROUND: Driving pressure can be readily measured during assisted modes of ventilation such as pressure support ventilation (PSV) and neurally adjusted ventilatory assist (NAVA). The present prospective randomized crossover study aimed to assess the changes in driving pressure in response to variations in the level of assistance delivered by PSV vs NAVA. METHODS: 16 intubated adult patients, recovering from hypoxemic acute respiratory failure (ARF) and undergoing assisted ventilation, were randomly subjected to six 30-min-lasting trials. At baseline, PSV (PSV100) was set with the same regulation present at patient enrollment. The corresponding level of NAVA (NAVA100) was set to match the same inspiratory peak of airway pressure obtained in PSV100. Therefore, the level of assistance was reduced and increased by 50% in both ventilatory modes (PSV50, NAVA50; PSV150, NAVA150). At the end of each trial, driving pressure obtained in response to four short (2-3 s) end-expiratory and end-inspiratory occlusions was analyzed. RESULTS: Driving pressure at PSV50 (6.6 [6.1-7.8] cmH2O) was lower than that recorded at PSV100 (7.9 [7.2-9.1] cmH2O, P = 0.005) and PSV150 (9.9 [9.1-13.2] cmH2O, P < 0.0001). In NAVA, driving pressure at NAVA50 was reduced compared to NAVA150 (7.7 [5.1-8.1] cmH2O vs 8.3 [6.4-11.4] cmH2O, P = 0.013), whereas there were no changes between baseline and NAVA150 (8.5 [6.3-9.8] cmH2O vs 8.3 [6.4-11.4] cmH2O, P = 0.331, respectively). Driving pressure at PSV150 was higher than that observed in NAVA150 (P = 0.011). CONCLUSIONS: NAVA delivers better lung-protective ventilation compared to PSV in hypoxemic ARF patients. TRIAL REGISTRATION NUMBER AND DATE OF REGISTRATION: The present trial was prospectively registered at www.clinicatrials.gov (NCT03719365) on 24 October 2018.

Effect of awake prone position on diaphragmatic thickening fraction in patients assisted by noninvasive ventilation for hypoxemic acute respiratory failure related to novel coronavirus disease.

BACKGROUND: Awake prone position is an emerging rescue therapy applied in patients undergoing noninvasive ventilation (NIV) for acute hypoxemic respiratory failure (ARF) related to novel coronavirus disease (COVID-19). Although applied to stabilize respiratory status, in awake patients, the application of prone position may reduce comfort with a consequent increase in the workload imposed on respiratory muscles. Thus, we primarily ascertained the effect of awake prone position on diaphragmatic thickening fraction, assessed through ultrasound, in COVID-19 patients undergoing NIV. METHODS: We enrolled all COVID-19 adult critically ill patients, admitted to intensive care unit (ICU) for hypoxemic ARF and undergoing NIV, deserving of awake prone positioning as a rescue therapy. Exclusion criteria were pregnancy and any contraindication to awake prone position and NIV. On ICU admission, after NIV onset, in supine position, and at 1 h following awake prone position application, diaphragmatic thickening fraction was obtained on the right side. Across all the study phases, NIV was maintained with the same setting present at study entry. Vital signs were monitored throughout the entire study period. Comfort was assessed through numerical rating scale (0 the worst comfort and 10 the highest comfort level). Data were presented in median and 25th-75th percentile range. RESULTS: From February to May 2021, 20 patients were enrolled and finally analyzed. Despite peripheral oxygen saturation improvement [96 (94-97)% supine vs 98 (96-99)% prone, p = 0.008], turning to prone position induced a worsening in comfort score from 7.0 (6.0-8.0) to 6.0 (5.0-7.0) (p = 0.012) and an increase in diaphragmatic thickening fraction from 33.3 (25.7-40.5)% to 41.5 (29.8-50.0)% (p = 0.025). CONCLUSIONS: In our COVID-19 patients assisted by NIV in ICU, the application of awake prone position improved the oxygenation at the expense of a greater diaphragmatic thickening fraction compared to supine position. Trial registration ClinicalTrials.gov, number NCT04904731. Registered on 05/25/2021, retrospectively registered. https://clinicaltrials.gov/ct2/show/NCT04904731 .

Association of weaning failure from mechanical ventilation with transthoracic echocardiography parameters: a systematic review and meta-analysis.

BACKGROUND: Weaning from mechanical ventilation is a challenging step during recovery from critical illness. Weaning failure or early reintubation are associated with increased morbidity and mortality, exposing patients to life-threatening complications. Cardiac dysfunction represents the most common cause of weaning failure. We conducted a systematic review and meta-analysis to evaluate the association between transthoracic echocardiographic parameters and weaning failure. METHODS: We performed a systematic search of MEDLINE and EMBASE screening for prospective studies providing echocardiographic data collected just before the beginning of spontaneous breathing trial and outcome of the weaning attempt. We primarily focused on parameters currently recommended for evaluation of left ventricular (LV) systolic or diastolic dysfunction. RESULTS: We included 11 studies in our primary analysis, which included data on LV ejection fraction (LVEF, n=10 studies) and parameters recommended for the assessment of LV diastolic function (E/e' ratio n=10; E/A ratio n=9; E wave n=8; and e' wave n=7). Weaning failure was significantly associated to a higher E/e' ratio (standardised mean difference [SMD]=1.70, 95% confidence interval [CI; 0.78-2.62]; P<0.001), lower e' wave (SMD=-1.22, 95% CI [-2.33 to -0.11]; P=0.03), and higher E wave (SMD=0.97, 95% CI [0.29-1.65]; P=0.005). We found no association between weaning failure and LVEF (SMD=-0.86, 95% CI [-1.92-0.20]; P=0.11) and E/A ratio (SMD=0.00, 95% CI [-0.30-0.31]; P=0.98). CONCLUSIONS: Weaning failure is associated with parameters indicating worse LV diastolic function (E/e', e' wave, E wave) and increased LV filling pressure (E/e' ratio). The association between weaning failure and LV systolic dysfunction as evaluated by LVEF is more unclear. More studies are needed to clarify this aspect and regarding the role of right ventricular function.