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Introduction: Oncological patients face numerous challenges throughout their cancer journey while navigating complex medical information. The advent of AI-based conversational models like ChatGPT (San Francisco, OpenAI) represents an innovation in oncological patient management. Methods: We conducted a comprehensive review of the literature on the use of ChatGPT in providing tailored information and support to patients with various types of cancer, including head and neck, liver, prostate, breast, lung, pancreas, colon, and cervical cancer. Results and Discussion: Our findings indicate that, in most instances, ChatGPT responses were accurate, dependable, and aligned with the expertise of oncology professionals, especially for certain subtypes of cancers like head and neck and prostate cancers. Furthermore, the system demonstrated a remarkable ability to comprehend patients’ emotional responses and offer proactive solutions and advice. Nevertheless, these models have also showed notable limitations and cannot serve as a substitute for the role of a physician under any circumstances. Conclusions: Conversational models like ChatGPT can significantly enhance the overall well-being and empowerment of oncological patients. Both patients and healthcare providers must become well-versed in the advantages and limitations of these emerging technologies.

This exploratory retrospective study aims to quantitatively compare the image quality of unenhanced brain computed tomography (CT) reconstructed with an iterative (AIDR-3D) and a deep learning-based (AiCE) reconstruction algorithm. After a preliminary phantom study, AIDR-3D and AiCE reconstructions (0.5 mm thickness) of 100 consecutive brain CTs acquired in the emergency setting on the same 320-detector row CT scanner were retrospectively analyzed, calculating image noise reduction attributable to the AiCE algorithm, artifact indexes in the posterior cranial fossa, and contrast-to-noise ratios (CNRs) at the cortical and thalamic levels. In the phantom study, the spatial resolution of the two datasets proved to be comparable; conversely, AIDR-3D reconstructions showed a broader noise pattern. In the human study, median image noise was lower with AiCE compared to AIDR-3D (4.7 vs. 5.3, p < 0.001, median 19.6% noise reduction), whereas AIDR-3D yielded a lower artifact index than AiCE (7.5 vs. 8.4, p < 0.001). AiCE also showed higher median CNRs at the cortical (2.5 vs. 1.8, p < 0.001) and thalamic levels (2.8 vs. 1.7, p < 0.001). These results highlight how image quality improvements granted by deep learning-based (AiCE) and iterative (AIDR-3D) image reconstruction algorithms vary according to different brain areas.

Computed Tomography Urography (CTU) is a multiphase CT examination optimized for imaging kidneys, ureters, and bladder, complemented by post-contrast excretory phase imaging. Different protocols are available for contrast administration and image acquisition and timing, with different strengths and limits, mainly related to kidney enhancement, ureters distension and opacification, and radiation exposure. The availability of new reconstruction algorithms, such as iterative and deep-learning-based reconstruction has dramatically improved the image quality and reducing radiation exposure at the same time. Dual-Energy Computed Tomography also has an important role in this type of examination, with the possibility of renal stone characterization, the availability of synthetic unenhanced phases to reduce radiation dose, and the availability of iodine maps for a better interpretation of renal masses. We also describe the new artificial intelligence applications for CTU, focusing on radiomics to predict tumor grading and patients’ outcome for a personalized therapeutic approach. In this narrative review, we provide a comprehensive overview of CTU from the traditional to the newest acquisition techniques and reconstruction algorithms, and the possibility of advanced imaging interpretation to provide an up-to-date guide for radiologists who want to better comprehend this technique.

Lymphedema is a chronic progressive disorder that significantly compromises patients’ quality of life. In Western countries, it often results from cancer treatment, as in the case of post-radical prostatectomy lymphedema, where it can affect up to 20% of patients, with a significant disease burden. Traditionally, diagnosis, assessment of severity, and management of disease have relied on clinical assessment. In this landscape, physical and conservative treatments, including bandages and lymphatic drainage have shown limited results. Recent advances in imaging technology are revolutionizing the approach to this disorder: magnetic resonance imaging has shown satisfactory results in differential diagnosis, quantitative classification of severity, and most appropriate treatment planning. Further innovations in microsurgical techniques, based on the use of indocyanine green to map lymphatic vessels during surgery, have improved the efficacy of secondary LE treatment and led to the development of new surgical approaches. Physiologic surgical interventions, including lymphovenous anastomosis (LVA) and vascularized lymph node transplant (VLNT), are going to face widespread diffusion. A combined approach to microsurgical treatment provides the best results: LVA is effective in promoting lymphatic drainage, bridging VLNT delayed lymphangiogenic and immunological effects in the lymphatic impairment site. Simultaneous VLNT and LVA are safe and effective for patients with both early and advanced stages of post-prostatectomy LE. A new perspective is now represented by the combination of microsurgical treatments with the positioning of nano fibrillar collagen scaffolds (BioBridgeTM) to favor restoring the lymphatic function, allowing for improved and sustained volume reduction. In this narrative review, we proposed an overview of new strategies for diagnosing and treating post-prostatectomy lymphedema to get the most appropriate and successful patient treatment with an overview of the main artificial intelligence applications in the prevention, diagnosis, and management of lymphedema.

This study aimed to investigate the role of the thymus in influencing long-term outcomes of COVID-19 by comparing the thymic appearance in patients with and without COVID-19 pulmonary sequelae at chest computed tomography (CT). A total of 102 adult patients previously hospitalized for COVID-19 underwent a follow-up chest CT three months after discharge. Pulmonary sequelae and thymic appearance were independently assessed by two experienced radiologists. The thymus was detectable in 55/102 patients (54%), with only 7/55 (13%) having any kind of pulmonary sequelae, compared to 33 out of 47 (70%, p < 0.001) in patients without thymic visibility, as confirmed in age-stratified analysis and at logistic regression analysis, where thymic involution had a 9.3 odds ratio (95% CI 3.0–28.2, p < 0.001) for the development of pulmonary sequelae. These results support the hypothesis that thymic reactivation plays a protective role against adverse long-term outcomes of COVID-19.

The application of artificial intelligence (AI) is accelerating the paradigm shift towards patient-tailored brain tumor management, achieving optimal onco-functional balance for each individual. AI-based models can positively impact different stages of the diagnostic and therapeutic process. Although the histological investigation will remain difficult to replace, in the near future the radiomic approach will allow a complementary, repeatable and non-invasive characterization of the lesion, assisting oncologists and neurosurgeons in selecting the best therapeutic option and the correct molecular target in chemotherapy. AI-driven tools are already playing an important role in surgical planning, delimiting the extent of the lesion (segmentation) and its relationships with the brain structures, thus allowing precision brain surgery as radical as reasonably acceptable to preserve the quality of life. Finally, AI-assisted models allow the prediction of complications, recurrences and therapeutic response, suggesting the most appropriate follow-up. Looking to the future, AI-powered models promise to integrate biochemical and clinical data to stratify risk and direct patients to personalized screening protocols.

Ultrasound elastography (USE) or elastosonography is an ultrasound-based, non-invasive imaging method for assessing tissue elasticity. The different types of elastosonography are distinguished according to the mechanisms used for estimating tissue elasticity and the type of information they provide. In strain imaging, mechanical stress is applied to the tissue, and the resulting differential strain between different tissues is used to provide a qualitative assessment of elasticity. In shear wave imaging, tissue elasticity is inferred through quantitative parameters, such as shear wave velocity or longitudinal elastic modulus. Shear waves can be produced using a vibrating mechanical device, as in transient elastography (TE), or an acoustic impulse, which can be highly focused, as in point-shear wave elastography (p-SWE), or directed to multiple zones in a two-dimensional area, as in 2D-SWE. A general understanding of the basic principles behind each technique is important for clinicians to improve data acquisition and interpretation. Major clinical applications include chronic liver disease, breast lesions, thyroid nodules, lymph node malignancies, and inflammatory bowel disease. The integration of artificial intelligence tools could potentially overcome some of the main limitations of elastosonography, such as operator dependence and low specificity, allowing for its effective integration into clinical workflow.

The advent of artificial intelligence (AI) represents a real game changer in today’s landscape of breast cancer imaging. Several innovative AI-based tools have been developed and validated in recent years that promise to accelerate the goal of real patient-tailored management. Numerous studies confirm that proper integration of AI into existing clinical workflows could bring significant benefits to women, radiologists, and healthcare systems. The AI-based approach has proved particularly useful for developing new risk prediction models that integrate multi-data streams for planning individualized screening protocols. Furthermore, AI models could help radiologists in the pre-screening and lesion detection phase, increasing diagnostic accuracy, while reducing workload and complications related to overdiagnosis. Radiomics and radiogenomics approaches could extrapolate the so-called imaging signature of the tumor to plan a targeted treatment. The main challenges to the development of AI tools are the huge amounts of high-quality data required to train and validate these models and the need for a multidisciplinary team with solid machine-learning skills. The purpose of this article is to present a summary of the most important AI applications in breast cancer imaging, analyzing possible challenges and new perspectives related to the widespread adoption of these new tools.

Dynamic digital radiography (DDR) is a high-resolution radiographic imaging technique using pulsed X-ray emission to acquire a multiframe cine-loop of the target anatomical area. The first DDR technology was orthostatic chest acquisitions, but new portable equipment that can be positioned at the patient’s bedside was recently released, significantly expanding its potential applications, particularly in chest examination. It provides anatomical and functional information on the motion of different anatomical structures, such as the lungs, pleura, rib cage, and trachea. Native images can be further analyzed with dedicated post-processing software to extract quantitative parameters, including diaphragm motility, automatically projected lung area and area changing rate, a colorimetric map of the signal value change related to respiration and motility, and lung perfusion. The dynamic diagnostic information along with the significant advantages of this technique in terms of portability, versatility, and cost-effectiveness represents a potential game changer for radiological diagnosis and monitoring at the patient’s bedside. DDR has several applications in daily clinical practice, and in this narrative review, we will focus on chest imaging, which is the main application explored to date in the literature. However, studies are still needed to understand deeply the clinical impact of this method.

We report the case of a 29-year-old patient without medical history presenting with dysphonia associated with left unilateral vocal cord paralysis. The patient underwent a contrast-enhanced computed tomography with an angiographic arterial phase of the head, neck and chest, and the only significant finding was the presence of a large, aberrant right bronchial artery originating directly from the aortic arch, where the recurrent left laryngeal nerve loops. After excluding alternative etiologies, the hypothesis of neurovascular conflict between this vessel and the recurrent left laryngeal nerve was formulated. To the best of our knowledge, this is the first case reported in the literature. Thanks to its high spatial resolution, contrast-enhanced computed tomography is the examination of choice for the study of anatomical variants and should be included in the routine work-up of patients presenting with unilateral vocal cord paralysis.

Lymphedema treatment has traditionally been based on conservative therapies. In recent years, the introduction of two microsurgical techniques—venolymphatic anastomoses and lymph-node transplantation—have revolutionized the therapeutic approach to this disease. However, these surgical techniques only provide a partial volume reduction, and thus, they require further improvement. A new device called BioBridgeTM, an implantable biocompatible and biodegradable mesh, made up of aligned nanofibrillar collagen scaffolds that mimic the collagen matrix, could significantly improve the benefits of these surgical strategies, favoring lymphatic regeneration. In this article, we describe our experience with three patients affected by primary lymphedema, two treated with a lymph-node transplant, and one with venolymphatic anastomosis. All interventions involved the placement of nanocollagen scaffolds. With each patient, non-contrast magnetic resonance lymphography was performed for surgical planning and there was an imaging and a clinical follow-up at 1 year. All patients showed a significant reduction in the affected areas with the demonstration of new lymphatic vessels at MR imaging follow-up. This was a preliminary experience, and it showed the potential benefit of positioning nonfibrillar scaffolds in combination with other microsurgical therapies for lymphedema.

Abstract Computed tomography (CT) is considered the gold standard technique for the assessment of trauma patients with suspected involvement of the eye and orbit. These traumas can result in dramatic consequences to visual function, ocular motility, and aesthetics. CT is a quick and widely available imaging modality, which provides a detailed evaluation of the orbital bony and soft tissue structures, an accurate assessment of the globes, and is used to guide the patients’ treatment planning. For a timely and accurate diagnosis, radiologists should be aware of fracture patterns and possible associated complications, ocular detachments and hemorrhages, and different appearances of intraorbital foreign bodies. This educational review aims to describe all post-traumatic orbital abnormalities that can be identified on CT, providing a list of tips and a diagnostic flowchart to help radiologists deal with this complex condition.

Rapid-paced development and adaptability of artificial intelligence algorithms have secured their almost ubiquitous presence in the field of oncological imaging. Artificial intelligence models have been created for a variety of tasks, including risk stratification, automated detection and segmentation of lesions, characterization, grading and staging, prediction of prognosis, and treatment response. Soon, artificial intelligence could become an essential part of every step of oncological workup and patient management. Integration of neural networks and deep learning into radiological artificial intelligence algorithms allow for extrapolating imaging features otherwise inaccessible to human operators and pave the way to truly personalized management of oncological patients. Although a significant proportion of currently available artificial intelligence solutions belong to basic and translational cancer imaging research, their progressive transfer to clinical routine is imminent, contributing to the development of a personalized approach in oncology. We thereby review the main applications of artificial intelligence in oncological imaging, describe the example of their successful integration into research and clinical practice, and highlight the challenges and future perspectives that will shape the field of oncological radiology.

Purpose: The mental foramen (MF) is important in dental surgery procedures for preventing possible iatrogenic lesions and for anesthetic procedures. This paper aims at expanding knowledge on the metrical characteristics that are useful for the correct anatomical assessment of the MF. Materials and Methods: On 100 CBCT scans (50 males and 50 females), height, width, depth, linear distance from the inferior and superior mandibular edge and position according to teeth and dental apices were analyzed. Differences according to sex and the side for each metrical measurement and for teeth and dental apices were assessed through a two-way ANOVA test and Chi-square test, respectively. Pearson’s test and a one-way ANOVA test were used to test correlations among the chosen parameters (p < 0.01). Results: The depth and distance from the inferior and superior mandibular edges showed sexual dimorphism (p < 0.01). Height and width were positively related, as well as depth and distance from the inferior mandibular edge in females (p < 0.01). Conclusions: Novel data about the anatomical position of the mental foramen are described, which are useful for the management of surgical procedures.

Digital twins are virtual replicas of physical objects or systems. This new technology is increasingly being adopted in industry to improve the monitoring and efficiency of products and organizations. In healthcare, digital human twins (DHTs) represent virtual copies of patients, including tissues, organs, and physiological processes. Their application has the potential to transform patient care in the direction of increasingly personalized data-driven medicine. The use of DHTs can be integrated with digital twins of healthcare institutions to improve organizational management processes and resource allocation. By modeling the complex multi-omics interactions between genetic and environmental factors, DHTs help monitor disease progression and optimize treatment plans. Through digital simulation, DHT models enable the selection of the most appropriate molecular therapy and accurate 3D representation for precision surgical planning, together with augmented reality tools. Furthermore, they allow for the development of tailored early diagnosis protocols and new targeted drugs. Furthermore, digital twins can facilitate medical training and education. By creating virtual anatomy and physiology models, medical students can practice procedures, enhance their skills, and improve their understanding of the human body. Overall, digital twins have immense potential to revolutionize healthcare, improving patient care and outcomes, reducing costs, and enhancing medical research and education. However, challenges such as data security, data quality, and data interoperability must be addressed before the widespread adoption of digital twins in healthcare. We aim to propose a narrative review on this hot topic to provide an overview of the potential applications of digital twins to improve treatment and diagnostics, but also of the challenges related to their development and widespread diffusion.

Background and purpose: Accurate instance segmentation of teeth in panoramic dental X-rays is a challenging task due to variations in tooth morphology and overlapping regions. In this study, we propose a new algorithm, for instance, segmentation of the different teeth in panoramic dental X-rays. Methods: An instance segmentation model was trained using the architecture of a Mask Region-based Convolutional Neural Network (Mask-RCNN). The data for the training, validation, and testing were taken from the Tuft dental database (1000 panoramic dental radiographs). The number of the predicted label was 52 (20 deciduous and 32 permanent). The size of the training, validation, and test sets were 760, 190, and 70 images, respectively, and the split was performed randomly. The model was trained for 300 epochs, using a batch size of 10, a base learning rate of 0.001, and a warm-up multistep learning rate scheduler (gamma = 0.1). Data augmentation was performed by changing the brightness, contrast, crop, and image size. The percentage of correctly detected teeth and Dice in the test set were used as the quality metrics for the model. Results: In the test set, the percentage of correctly classified teeth was 98.4%, while the Dice score was 0.87. For both the left mandibular central and lateral incisor permanent teeth, the Dice index result was 0.91 and the accuracy was 100%. For the permanent teeth right mandibular first molar, mandibular second molar, and third molar, the Dice indexes were 0.92, 0.93, and 0.78, respectively, with an accuracy of 100% for all three different teeth. For deciduous teeth, the Dice indexes for the right mandibular lateral incisor, right mandibular canine, and right mandibular first molar were 0.89, 0.91, and 0.85, respectively, with an accuracy of 100%. Conclusions: A successful instance segmentation model for teeth identification in panoramic dental X-ray was developed and validated. This model may help speed up and automate tasks like teeth counting and identifying specific missing teeth, improving the current clinical practice.

The incidental finding of small renal masses in CT and MRI examinations can present a diagnostic challenge. Renal cell carcinoma (RCC) and angiomyolipoma (AML) are the most common incidental malignant and benign renal lesions but may present with similar US features. US elastography is a non-invasive technique that can assess tissue elasticity, has shown promising results in many clinical settings, and could be able to differentiate between benign and malignant renal lesions based on tissue stiffness. The purpose of this article is to systematically review the applications of US elastography in the characterization of solid renal masses and to derive and compare the summary estimates of different stiffness values across different lesion subtypes. In December 2022, a systematic search was carried out on the MEDLINE (PubMed) and EMBASE databases to retrieve studies on the application of US elastography in the characterization of solid renal masses. After article selection by three researchers, 14 studies entered qualitative synthesis. A total of 1190 patients were included, and the elastography data of 959 lesions were examined: 317/959 (33%) benign and 642/959 (67%) malignant. Among the malignancies, 590 (91%) were RCC, whereas, among the 317 benign lesions, 244 (77%) were AML. All lesions were classified using a histopathological (biopsy or operative specimen) or imaging (US follow-up/CT/MRI) reference standard. After data extraction and methodological quality evaluation, quantitative synthesis was performed on 12 studies, 4 using strain elastography (SE) and 8 using shear wave elastography (SWE), with single- and double-arm random-effects meta-analyses. Lesion stiffness measured with SE was available in four studies, with an RCC strain ratio higher than the AML strain ratio both in an indirect comparison (Cochran’s Q test p = 0.014) and in a direct comparison (p = 0.021). Conversely, the SWE measurements of RCC and AML stiffness did not significantly differ either at an indirect comparison (p = 0.055) or direct comparison (p = 0.114).

Dual-energy computed tomography (DECT) is a medical imaging technique that uses two different energy levels of X-rays to provide more detailed information about the composition of tissues and materials within the body. DECT technology is still relatively new but different applications have been developed for the urological field. For example, it can be used to analyze the composition of stones to determine whether they are likely to be made up of calcium oxalate, uric acid, or other minerals. This information can help guide treatment decisions, as different types of stones may require different approaches to treatment. The availability of iodine maps helps the differentiation between benign and malignant tumors of the urinary tract, including the bladder, kidneys, and ureters. The iodine maps generated by DECT can provide information on tumor vascularity, which can help in staging and determining the aggressiveness of the tumor. DECT can assess blood flow in the kidneys and detect vascular disorders such as renal artery stenosis or aneurysms. This can be critical for early detection and management of these disorders, which can reduce the risk of renal failure and improve outcomes. DECT is a valuable tool in the urological field that can provide clinicians with detailed and accurate information for the diagnosis and treatment planning of various urological conditions. In this narrative review, we propose an overview of the possible application of DECT in the field of urological diseases.

Congenital atresia of the left main coronary artery is a rare coronary anomaly, in which the left coronary ostium is absent and the blood supply to the entire heart is provided by the right coronary artery. We show the images of the coronary CT angiography performed by a 56-year-old man, with evidence of this vascular abnormality. Coronary CT Angiography has gained a major role in coronary anomalies assessment, thank to high spatial and temporal resolution.

Bone age is an indicator of bone maturity and is useful for the treatment of different pediatric conditions as well as for legal issues. Bone age can be assessed by the analysis of different skeletal segments and teeth and through several methods; however, traditional bone age assessment is a complicated and time-consuming process, prone to inter- and intra-observer variability. There is a high demand for fully automated systems, but creating an accurate and reliable solution has proven difficult. Deep learning technology, machine learning, and Convolutional Neural Networks-based systems, which are rapidly evolving, have shown promising results in automated bone age assessment. We provide the background of bone age estimation, its usefulness and traditional methods of assessment, and review the currently artificial-intelligence-based solutions for bone age assessment and the future perspectives of these applications.

Due to its widespread availability, low cost, feasibility at the patient’s bedside and accessibility even in low-resource settings, chest X-ray is one of the most requested examinations in radiology departments. Whilst it provides essential information on thoracic pathology, it can be difficult to interpret and is prone to diagnostic errors, particularly in the emergency setting. The increasing availability of large chest X-ray datasets has allowed the development of reliable Artificial Intelligence (AI) tools to help radiologists in everyday clinical practice. AI integration into the diagnostic workflow would benefit patients, radiologists, and healthcare systems in terms of improved and standardized reporting accuracy, quicker diagnosis, more efficient management, and appropriateness of the therapy. This review article aims to provide an overview of the applications of AI for chest X-rays in the emergency setting, emphasizing the detection and evaluation of pneumothorax, pneumonia, heart failure, and pleural effusion.

Emergency Radiology is a unique branch of imaging, as rapidity in the diagnosis and management of different pathologies is essential to saving patients’ lives. Artificial Intelligence (AI) has many potential applications in emergency radiology: firstly, image acquisition can be facilitated by reducing acquisition times through automatic positioning and minimizing artifacts with AI-based reconstruction systems to optimize image quality, even in critical patients; secondly, it enables an efficient workflow (AI algorithms integrated with RIS–PACS workflow), by analyzing the characteristics and images of patients, detecting high-priority examinations and patients with emergent critical findings. Different machine and deep learning algorithms have been trained for the automated detection of different types of emergency disorders (e.g., intracranial hemorrhage, bone fractures, pneumonia), to help radiologists to detect relevant findings. AI-based smart reporting, summarizing patients’ clinical data, and analyzing the grading of the imaging abnormalities, can provide an objective indicator of the disease’s severity, resulting in quick and optimized treatment planning. In this review, we provide an overview of the different AI tools available in emergency radiology, to keep radiologists up to date on the current technological evolution in this field.

Lung cancer is one of the malignancies with higher morbidity and mortality. Imaging plays an essential role in each phase of lung cancer management, from detection to assessment of response to treatment. The development of imaging-based artificial intelligence (AI) models has the potential to play a key role in early detection and customized treatment planning. Computer-aided detection of lung nodules in screening programs has revolutionized the early detection of the disease. Moreover, the possibility to use AI approaches to identify patients at risk of developing lung cancer during their life can help a more targeted screening program. The combination of imaging features and clinical and laboratory data through AI models is giving promising results in the prediction of patients’ outcomes, response to specific therapies, and risk for toxic reaction development. In this review, we provide an overview of the main imaging AI-based tools in lung cancer imaging, including automated lesion detection, characterization, segmentation, prediction of outcome, and treatment response to provide radiologists and clinicians with the foundation for these applications in a clinical scenario.

Abstract Background Mental health symptoms among healthcare professionals (HCP) in intensive care units (ICUs) are a significant concern affecting both HCP well-being and patient care outcomes. Cross-sectional studies among members of the European Society of Intensive Care Medicine (ESICM) report up to 50% burnout rates. Determinants of burnout include communication, team cohesion, psychological support, and well-being promotion. We designed the 'Hello Bundle' intervention to mitigate burnout among ICU-HCPs by fostering positive social interactions and a supportive work environment. This justification synthesizes evidence from social psychology, positive psychology, and healthcare communication research to support the intervention. The 'Hello Bundle' aims to enhance interpersonal relationships, improve team cohesion, and reduce burnout rates. The six components include: Hello campaign posters, email reminders, integrating greetings in morning huddles, hello jars, lead-by-example initiatives, and a daily updated hello board in each ICU. This protocol describes a cluster randomized controlled trial to evaluate the effectiveness of the intervention. Methods This protocol describes a cluster randomized controlled trial (RCT) conducted among ESICM-affiliated ICUs, consisting of at least 73 clusters with in average of 50 respondents per cluster, totaling approximately 7300 participants. Intervention clusters will implement the 6-component Hello Bundle between October 14 and November 10, 2024, while control clusters will be wait-listed to receive the intervention in January 2025 after the RCT concludes. Clusters will be matched based on ICU size (fewer or more than 20 beds), region, and average 2023 mortality. The primary outcome is the proportion of HCPs with burnout between intervention and control clusters at the end of the intervention. Secondary outcomes include comparing the following between clusters: (1) number of HCPs with high emotional exhaustion; (2) number with high depersonalization; (3) number with loss of accomplishment; (4) perception of ethical climate (5) satisfaction at work (VAS); (6) professional conflicts; (7) intention to leave the ICU (VAS); (8) patient-centered care rating; (9) family-centered care rating. The last secondary outcome is the comparison of burnout rates before and after the intervention in the intervention cluster. Outcomes will be based on HCP reports collected within four weeks before and after the intervention. Discussion This is the first large trial of healthcare communication, social, and positive psychology intervention among ICU-HCPs. It holds the potential to provide valuable insights into effective strategies for addressing burnout in ICU settings, ultimately benefiting both HCPs and patients. Trial registration: This trial was registered on ClinicalTrials.Gov on June 18, 2024. Registration: NCT06453616.

Abstract Background This study aimed to develop prognostic models for predicting the need for invasive mechanical ventilation (IMV) in intensive care unit (ICU) patients with COVID-19 and compare their performance with the Respiratory rate-OXygenation (ROX) index. Methods A retrospective cohort study was conducted using data collected between March 2020 and August 2021 at three hospitals in Rio de Janeiro, Brazil. ICU patients aged 18 years and older with a diagnosis of COVID-19 were screened. The exclusion criteria were patients who received IMV within the first 24 h of ICU admission, pregnancy, clinical decision for minimal end-of-life care and missing primary outcome data. Clinical and laboratory variables were collected. Multiple logistic regression analysis was performed to select predictor variables. Models were based on the lowest Akaike Information Criteria (AIC) and lowest AIC with significant p values. Assessment of predictive performance was done for discrimination and calibration. Areas under the curves (AUC)s were compared using DeLong’s algorithm. Models were validated externally using an international database. Results Of 656 patients screened, 346 patients were included; 155 required IMV (44.8%), 191 did not (55.2%), and 207 patients were male (59.8%). According to the lowest AIC, arterial hypertension, diabetes mellitus, obesity, Sequential Organ Failure Assessment (SOFA) score, heart rate, respiratory rate, peripheral oxygen saturation (SpO2), temperature, respiratory effort signals, and leukocytes were identified as predictors of IMV at hospital admission. According to AIC with significant p values, SOFA score, SpO2, and respiratory effort signals were the best predictors of IMV; odds ratios (95% confidence interval): 1.46 (1.07–2.05), 0.81 (0.72–0.90), 9.13 (3.29–28.67), respectively. The ROX index at admission was lower in the IMV group than in the non-IMV group (7.3 [5.2–9.8] versus 9.6 [6.8–12.9], p

Abstract Background Fluid administration is the first line treatment in intensive care unit (ICU) patients with sepsis and septic shock. While fluid boluses administration can be titrated by predicting preload dependency, the amount of other forms of fluids may be more complex to be evaluated. We conducted a retrospective analysis in a tertiary hospital, to assess the ratio between fluids given as boluses and total administered fluid intake during early phases of ICU stay, and to evaluate the impact of fluid strategy on ICU mortality. Data related to fluid administration during the first four days of ICU stay were exported from an electronic health records system (ICCA®, Philips Healthcare). Demographic data, severity score, norepinephrine dose at ICU admission, overall fluid balance and the percentage of different fluid components of the overall volume administered were included in a multivariable logistic regression model, evaluating the association with ICU survival. Results We analyzed 220 patients admitted with septic shock and sepsis-induced hypotension from 1st July 2021 to 31st December 2023. Fluid boluses and maintenance represented 49.3% ± 22.8 of the overall fluid intake, being balanced solution the most represented (40.4% ± 22.0). The fluid volume for drug infusion represented 34.0% ± 2.9 of the total fluid intake, while oral or via nasogastric tube fluid intake represented 18.0% ± 15.7 of the total fluid intake. Fluid volume given as boluses represented 8.6% of the total fluid intake over the four days, with a reduction from 25.1% ± 24.0 on Day 1 to 4.8% ± 8.7 on Day 4. A positive fluid balance [OR 1.167 (1.029–1.341); p = 0.021] was the most important factor associated with ICU mortality. Non-survivors (n = 66; 30%) received a higher amount of overall inputs than survivors only on Day 1 [2493 mL vs. 1855 mL; p = 0.022]. Conclusions This retrospective analysis of fluids given over the early phases of septic shock and sepsis-induced hypotension showed that the overall volume given by boluses ranges from about 25% on Day 1 to about 5% on Day 4 from ICU admission. Our data confirms that a positive fluid balance over the first 4 days of ICU is associated with mortality.

Abstract Background Value-based healthcare (VBHC) is an approach that focuses on delivering the highest possible value for patients while driving cost efficiency in health services. It emphasizes improving patient outcomes and experiences while optimizing the use of resources, shifting the healthcare system’s focus from the volume of services to the value delivered. Our study assessed the effectiveness of implementing a VBHC-principled, tailored preoperative evaluation in enhancing patient care and outcomes, as well as reducing healthcare costs. Methods We employed a quality improvement, before-and-after approach to assessing the effects of implementing VBHC strategies on the restructuring of the preoperative evaluation clinics at Humanitas Research Hospital. The intervention introduced a VBHC-tailored risk matrix during the postintervention phase (year 2021), and the results were compared with those of the preintervention phase (2019). The primary study outcome was the difference in the number of preoperative tests and visits at baseline and after the VBHC approach. Secondary outcomes were patient outcomes and costs. Results A total of 9722 patients were included: 5242 during 2019 (baseline) and 4,480 during 2021 (VBHC approach). The median age of the population was 63 (IQR 51–72), 23% of patients were classified as ASA 3 and 4, and 26.8% (2,955 cases) were day surgery cases. We found a considerable decrease in the number of preoperative tests ordered for each patient [6.2 (2.5) vs 5.3 (2.6) tests, p

Background: Perivascular adipose tissue (PVAT) attenuation has emerged as a novel biomarker for identifying high-risk arterial plaques due to its association with inflammation. Recognizing the systemic nature of atherosclerosis and its link with major cardiovascular events in coronary disease, this study evaluated PVAT attenuation in the peripheral arteries using CT imaging to expand the understanding of its diagnostic and prognostic potential. Methods: a retrospective analysis of 53 consecutive patients who underwent CT angiography, examining PVAT density across five primary peripheral arterial segments. A 5 mm region of interest adjacent to the vascular wall was analyzed by two blinded readers, with reproducibility coefficients calculated to determine the reliability of the measurements. For the statistical analyses, mean values were derived from these measurements. The patients were stratified into four groups based on the degree of arterial stenosis: 70%. PVAT density comparisons between these groups were performed using the Kruskal–Wallis test and the pairwise Mann–Whitney U test with Holm–Bonferroni correction for multiple comparisons. Results: the Kruskal–Wallis test revealed statistically significant disparities in PVAT density across the categorically differentiated stenosis groups (p < 0.001), indicating an association between PVAT density and arterial stenosis severity. This association was especially pronounced in the external iliac, common femoral, superficial femoral, and popliteal arteries, where the p-values were consistently below 0.05. Subsequent pairwise analyses utilizing the Mann–Whitney U test with Holm–Bonferroni correction affirmed these findings, in particular for the external iliac, common femoral, superficial femoral and popliteal arteries (p < 0.05). Conclusions: our findings reinforce the correlation between increased PVAT density and the degree of arterial stenosis, supporting the clinical value of PVAT as a non-invasive biomarker for cardiovascular risk stratification and potentially guiding therapeutic interventions.

Climate change and other environmental consequences of socio-economic activities require a more sustainable and circular growth. At the same time, the limitation of the earth resource demands industries to improve resource efficiency and increase the rate of recycling of materials. There are several sustainable and circular alternatives that the industries may adopt. However, the question is that among these alternatives, which one should be selected for implementation for the highest sustainable and circular benefits. This study introduces a novel tool for assessing the sustainability and circularity of biomass-based energy supply chains, integrating multi-criteria decision-making methods with life cycle thinking approach. It evaluates five alternatives using a sustainability and circularity indicators, offering new insights into the deloyment of circular business models at companies in biomass-based energy supply chain. The tool is also applied to a specific rice straw supply chain in Italy, to assess the sustainability and circularity of five alternatives and outrank them. The results indicated that not all the alternatives are better in terms of supporting sustainable development and circular economy, compared to the baseline business model. In this supply chain, the extended lifetime for digestate from the aerobic digestion plant is the most ‘sustainable and circular’ alternative, while the capture of carbon dioxide from the same plant and its use for microalgae cultivation is the least ‘sustainable and circular’ alternative. A sensitivity analysis was conducted on different weighting sets during the assessment. It indicated that the priority of the decision makers can slightly change the outrank of the alternatives and the magnitude of the outranks.

Abstract Purpose To identify key components and variations in family-centered care practices. Methods A cross-sectional study, conducted across ESICM members. Participating ICUs completed a questionnaire covering general ICU characteristics, visitation policies, team-family interactions, and end-of-life decision-making. The primary outcome, self-rated family-centeredness, was assessed using a visual analog scale. Additionally, respondents completed the Maslach Burnout Inventory and the Ethical Decision Making Climate Questionnaire to capture burnout dimensions and assess the ethical decision-making climate. Results The response rate was 53% (respondents from 359/683 invited ICUs who actually open the email); participating healthcare professionals (HCPs) were from Europe (62%), Asia (9%), South America (6%), North America (5%), Middle East (4%), and Australia/New Zealand (4%). The importance of family-centeredness was ranked high, median 7 (IQR 6–8) of 10 on VAS. Significant differences were observed across quartiles of family centeredness, including in visitation policies availability of a waiting rooms, family rooms, family information leaflet, visiting hours, night visits, sleep in the ICU, and in team-family interactions, including daily information, routine day-3 conference, and willingness to empower nurses and relatives. Higher family centeredness correlated with family involvement in rounds, participation in patient care and end-of-life practices. Burnout symptoms (41% of respondents) were negatively associated with family-centeredness. Ethical climate and willingness to empower nurses were independent predictors of family centeredness. Conclusions This study emphasizes the need to prioritize healthcare providers’ mental health for enhanced family-centered care. Further research is warranted to assess the impact of improving the ethical climate on family-centeredness.

Abstract Background Premorbid conditions influence the outcome of acutely ill adult patients aged 80 years and over who are admitted to the ICU. The aim of this study was to determine the influence of such premorbid conditions on 6 month survival. Methods Prospective cohort study in 242 ICUs from 22 countries including patients 80 years or above, admitted over a 6 months period to an ICU between May 2018 and May 2019. Only emergency (acute) ICU admissions in adult patients ≥ 80 years of age were eligible. Patients who were admitted after planned/elective surgery were excluded. We measured the Clinical Frailty Scale (CFS), the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE), disability with the Katz activities of daily living (ADL) score, comorbidities and a Polypharmacy Score (CPS). Results Overall, the VIP2 study included 3920 patients. During ICU stay 1191 patients died (30.9%), and another 436 patients (11.1%) died after ICU discharge but within the first 30 days of admission, and an additional 895 patients died hereafter but within the first 6 months after admission (22.8%). The 6 months mortality was 64%. The median CFS was 4 (IQR 3–6). Frailty (CFS ≥ 5) was present in 26.6%. Cognitive decline (IQCODE above 3.5) was found in 30.2%. The median IQCODE was 3.19. A Katz ADL of 4 or less was present in 27.7%. Patients who surviving > 6 months were slightly younger (median age survivors 84 with IQR 81–86) than patients dying within the first 6 months (median age 84, IQR 82–87, p = 0.013), were less frequently frail (CFS > 5 in 19% versus 34%, p

Abstract Background Naturally occurring colorectal cancers (CRC) in rhesus macaques share many features with their human counterparts and are useful models for cancer immunotherapy; but mechanistic data are lacking regarding the comparative molecular pathogenesis of these cancers. Methods We conducted state-of-the-art imaging including CT and PET, clinical assessments, and pathological review of 24 rhesus macaques with naturally occurring CRC. Additionally, we molecularly characterized these tumors utilizing immunohistochemistry (IHC), microsatellite instability assays, DNAseq, transcriptomics, and developed a DNA methylation-specific qPCR assay for MLH1, CACNA1G, CDKN2A, CRABP1, and NEUROG1, human markers for CpG island methylator phenotype (CIMP). We furthermore employed Monte-Carlo simulations to in-silico model alterations in DNA topology in transcription-factor binding site-rich promoter regions upon experimentally demonstrated DNA methylation. Results Similar cancer histology, progression patterns, and co-morbidities could be observed in rhesus as reported for human CRC patients. IHC identified loss of MLH1 and PMS2 in all cases, with functional microsatellite instability. DNA sequencing revealed the close genetic relatedness to human CRCs, including a similar mutational signature, chromosomal instability, and functionally-relevant mutations affecting KRAS (G12D), TP53 (R175H, R273*), APC, AMER1, ALK, and ARID1A. Interestingly, MLH1 mutations were rarely identified on a somatic or germline level. Transcriptomics not only corroborated the similarities of rhesus and human CRCs, but also demonstrated the significant downregulation of MLH1 but not MSH2, MSH6, or PMS2 in rhesus CRCs. Methylation-specific qPCR suggested CIMP-positivity in 9/16 rhesus CRCs, but all 16/16 exhibited significant MLH1 promoter hypermethylation. DNA hypermethylation was modelled to affect DNA topology, particularly propeller twist and roll profiles. Modelling the DNA topology of a transcription factor binding motif (TFAP2A) in the MLH1 promoter that overlapped with a methylation-specific probe, we observed significant differences in DNA topology upon experimentally shown DNA methylation. This suggests a role of transcription factor binding interference in epigenetic silencing of MLH1 in rhesus CRCs. Conclusions These data indicate that epigenetic silencing suppresses MLH1 transcription, induces the loss of MLH1 protein, abrogates mismatch repair, and drives genomic instability in naturally occurring CRC in rhesus macaques. We consider this spontaneous, uninduced CRC in immunocompetent, treatment-naïve rhesus macaques to be a uniquely informative model for human CRC. Graphical abstract

IntroductionThis study investigates the role of Fibroblast Activation Protein (FAP)-positive cancer-associated fibroblasts (FAP+CAF) in shaping the tumor immune microenvironment, focusing on its association with immune cell functionality and cytokine expression patterns.MethodsUtilizing immunohistochemistry, we observed elevated FAP+CAF density in metastatic versus primary renal cell carcinoma (RCC) tumors, with higher FAP+CAF correlating with increased T cell infiltration in RCC, a unique phenomenon illustrating the complex interplay between tumor progression, FAP+CAF density, and immune response.ResultsAnalysis of immune cell subsets in FAP+CAF-rich stromal areas further revealed significant correlations between FAP+ stroma and various T cell types, particularly in RCC and non-small cell lung cancer (NSCLC). This was complemented by transcriptomic analyses, expanding the range of stromal and immune cell subsets interrogated, as well as to additional tumor types. This enabled evaluating the association of these subsets with tumor infiltration, tumor vascularization and other components of the tumor microenvironment. Our comprehensive study also encompassed cytokine, angiogenesis, and inflammation gene signatures across different cancer types, revealing heterogeneous cellular composition, cytokine expressions and angiogenic profiles. Through cytokine pathway profiling, we explored the relationship between FAP+CAF density and immune cell states, uncovering potential immunosuppressive circuits that limit anti-tumor activity in tumor-resident immune cells.ConclusionsThese findings underscore the complexity of tumor biology and the necessity for personalized therapeutic and patient enrichment approaches. The insights gathered from FAP+CAF prevalence, immune infiltration, and gene signatures provide valuable perspectives on tumor microenvironments, aiding in future research and clinical strategy development.

Abstract 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.

Abstract Background The intraoperative period is often characterized by hemodynamic instability, and intraoperative hypotension is a common complication. The optimal mean arterial pressure (MAP) target in hypertensive patients is still not clear. We hereby describe the protocol and detailed statistical analysis plan for the high versus standard blood pressure target in hypertensive high-risk patients undergoing elective major abdominal surgery: the HISTAP randomized clinical trial. The HISTAP trial aims at addressing whether the use of a higher intraoperative MAP target in high-risk hypertensive surgical patients scheduled for elective abdominal surgery would improve postoperative outcomes, as compared to the standard and recommended perioperative MAP, by using a composite outcome including a 30-day mortality from surgical intervention and at least one major organ dysfunction or new onset of sepsis and septic shock occurring 7 days after surgery. Methods The HISTAP trial is an investigator-initiated, pragmatic, parallel-grouped, randomized, stratified, analyst-blinded trial with adequate allocation sequence generation, and allocation concealment. We will allocate 636 patients to a MAP target ≥ 80 mmHg (treatment group) or to a MAP target ≥65 mmHg (control group). The primary outcome is a composite outcome including a 30-day mortality from the operation and major organ complications. Secondary outcomes are mortality at 30 days, intensive care unit (ICU) length of stay, ICU readmission, Sequential Organ Failure Assessment (SOFA) scores recorded up to postoperative day 7, overall intraoperative fluid balance, vasopressors use, and the need for reoperation. An unadjusted χ 2 test will be used for the primary outcome analysis. A Cox proportional hazards model will be used to adjust the association between the primary outcome and baseline covariates. Conclusions The HISTAP trial results will provide important evidence to guide clinicians’ choice regarding the intraoperative MAP target in high-risk hypertensive patients scheduled for elective abdominal surgery.

Transitioning to a circular economy is crucial for sustainable energy development; yet, current energy supply chains lack comprehensive assessment tools. This study introduces the Holistic Multi-Indicator Decision Support System (HMI_DSS), an innovative tool grounded in life cycle thinking and advanced multi-criteria decision-making methodologies, including Entropy and PROMETHEE II. The HMI_DSS quantifies and assesses sustainability and circularity in energy systems by employing 49 indicators, with a focus on energy efficiency and greenhouse gas emissions. A case study on the rice straw energy supply chain for biogas production illustrates the tool’s effectiveness, comparing a baseline scenario to an alternative. The results show that the global warming potential (GWP) of the baseline is 122 gCO2eq/kWh, while the alternative is 116 gCO2eq/kWh. However, the baseline scenario has lower energy consumption (1.72 × 107 MJ annually) than the alternative (1.98 × 107 MJ). Overall, the alternative outperforms the baseline in terms of sustainability and circularity. The HMI_DSS offers a flexible and robust framework for evaluating trade-offs in energy systems, providing valuable insights for energy companies and researchers in adopting circular economy principles to achieve sustainable development.

Concentrating solar power (CSP) technologies have the potential to reduce the carbon emissions in the economy and energy sector. The growing significance of solar energy sources in addressing climate change highlights the necessity for thorough assessments of their environmental impacts. This paper explores two different life-cycle impact assessment methods, ReCiPe and Product Environmental Footprint, using CSP plants with various receiver systems and heat-transfer fluids as a case study. In terms of the overall life-cycle impact, solar towers are shown to have advantages over parabolic troughs. Most of the life-cycle impacts of solar towers are lower than those of parabolic troughs, ranging from 8% to 112%, except for human toxicity and land use impacts. However, there is not much difference between the studied heat-transfer fluids, with the variance of most impacts being less than around 1%. The single-score results indicates that the ReCiPe method assigns significance to human health impacts, while the product environmental footprint method gives equal attention to all aspects. Meanwhile the comparison of components’ contributions quantified by the two methods shows the same results for more than half of the impact categories.

IntroductionFibroblast activation protein (FAP) is predominantly upregulated in various tumor microenvironments and scarcely expressed in normal tissues.MethodsWe analyzed FAP across 1216 tissue samples covering 23 tumor types and 70 subtypes.ResultsElevated FAP levels were notable in breast, pancreatic, esophageal, and lung cancers. Using immunohistochemistry and RNAseq, a correlation between FAP gene and protein expression was found. Evaluating FAP’s clinical significance, we assessed 29 cohorts from 12 clinical trials, including both mono and combination therapies with the PD-L1 inhibitor atezolizumab and chemotherapy. A trend links higher FAP expression to poorer prognosis, particularly in RCC, across both treatment arms. However, four cohorts showed improved survival with high FAP, while in four others, FAP had no apparent survival impact.ConclusionsOur results emphasize FAP’s multifaceted role in therapy response, suggesting its potential as a cancer immunotherapy biomarker.

Abstract Background Early mobilisation (EM) is an intervention that may improve the outcome of critically ill patients. There is limited data on EM in COVID-19 patients and its use during the first pandemic wave. Methods This is a pre-planned subanalysis of the ESICM UNITE-COVID, an international multicenter observational study involving critically ill COVID-19 patients in the ICU between February 15th and May 15th, 2020. We analysed variables associated with the initiation of EM (within 72 h of ICU admission) and explored the impact of EM on mortality, ICU and hospital length of stay, as well as discharge location. Statistical analyses were done using (generalised) linear mixed-effect models and ANOVAs. Results Mobilisation data from 4190 patients from 280 ICUs in 45 countries were analysed. 1114 (26.6%) of these patients received mobilisation within 72 h after ICU admission; 3076 (73.4%) did not. In our analysis of factors associated with EM, mechanical ventilation at admission (OR 0.29; 95% CI 0.25, 0.35; p = 0.001), higher age (OR 0.99; 95% CI 0.98, 1.00; p ≤ 0.001), pre-existing asthma (OR 0.84; 95% CI 0.73, 0.98; p = 0.028), and pre-existing kidney disease (OR 0.84; 95% CI 0.71, 0.99; p = 0.036) were negatively associated with the initiation of EM. EM was associated with a higher chance of being discharged home (OR 1.31; 95% CI 1.08, 1.58; p = 0.007) but was not associated with length of stay in ICU (adj. difference 0.91 days; 95% CI − 0.47, 1.37, p = 0.34) and hospital (adj. difference 1.4 days; 95% CI − 0.62, 2.35, p = 0.24) or mortality (OR 0.88; 95% CI 0.7, 1.09, p = 0.24) when adjusted for covariates. Conclusions Our findings demonstrate that a quarter of COVID-19 patients received EM. There was no association found between EM in COVID-19 patients' ICU and hospital length of stay or mortality. However, EM in COVID-19 patients was associated with increased odds of being discharged home rather than to a care facility. Trial registration ClinicalTrials.gov: NCT04836065 (retrospectively registered April 8th 2021).

Abstract Background Non-invasive ventilation (NIV) has been commonly used to treat acute respiratory failure due to COVID-19. In this study we aimed to compare outcomes of older critically ill patients treated with NIV before and during the COVID-19 pandemic. Methods We analysed a merged cohort of older adults admitted to intensive care units (ICUs) due to respiratory failure. Patients were enrolled into one of two prospective observational studies: before COVID-19 (VIP2—2018 to 2019) and admitted due to COVID-19 (COVIP—March 2020 to January 2023). The outcomes included: 30-day mortality, intubation rate and NIV failure (death or intubation within 30 days). Results The final cohort included 1986 patients (1292 from VIP2, 694 from COVIP) with a median age of 83 years. NIV was used as a primary mode of respiratory support in 697 participants (35.1%). ICU admission due to COVID-19 was associated with an increased 30-day mortality (65.5% vs. 36.5%, HR 2.18, 95% CI 1.71 to 2.77), more frequent intubation (36.9% vs. 17.5%, OR 2.63, 95% CI 1.74 to 3.99) and NIV failure (76.2% vs. 45.3%, OR 4.21, 95% CI 2.84 to 6.34) compared to non-COVID causes of respiratory failure. Sensitivity analysis after exclusion of patients in whom life supporting treatment limitation was introduced during primary NIV confirmed higher 30-day mortality in patients with COVID-19 (52.5% vs. 23.4%, HR 2.64, 95% CI 1.83 to 3.80). Conclusion The outcomes of patients aged ≥80 years treated with NIV during COVID-19 pandemic were worse compared then those treated with NIV in the pre-pandemic era.