Introduction CytoSorb® is a cartridge for the adsorption of inflammatory mediators, bilirubin, myoglobin and other xenobiotics, directly from the blood stream. Clinical experience is widely documented in adults, whereas, in the paediatric settings, it is currently limited to single case reports or monocentric studies. In order to be able to collect evidence in larger paediatric populations, an Italian multicentre network (CYTOPED study group) was founded. Methods Italian multicentric observational registry on the use of CytoSorb® in critically ill paediatric patients. Prospective enrolment by Italian Children’s Hospitals has been ongoing since February 2021 with a retrospective analysis conducted from February 2018 to February 2021. Results 62 patients have been enrolled. Median Paediatric Logistic Organ Dysfunction 2 (PELOD-2) score on Paediatric Intensive Care Unit (PICU) admission was 7 (IQR 4;10). The primary clinical indications for haemoadsorption (HA) were sepsis or septic shock (n = 36), followed by liver failure, rhabdomyolysis, cardiac surgery. CytoSorb® has been applied in 87% of cases integrated in a continuous renal replacement therapy (CRRT) circuit. The median time of HA was 48 h (IQR 26;72) and the median number of cartridges used was 2 (IQR 1;3). Anticoagulation in the extracorporeal circuit has been managed with heparin (76%) and regional citrate anticoagulation (24%). Adverse events were recorded in 12 patients. Conclusion Our data provide some insights into safety and feasibility of CytoSorb® therapy in children. The advancement of the study and the prospective arm of CYTOPED registry will allow further investigation into this therapy, including dosage, timing and use of antibiotics in conjunction with extracorporeal blood purification techniques.

Background: Acute pain in children is frequently under-recognized and undertreated despite validated assessment tools and effective therapies. This systematic review aimed to synthesize recent evidence on assessment and management of acute pediatric pain. Methods: The review followed PRISMA 2020 guidelines. Clinical questions were developed using the PICO framework, and evidence certainty was assessed with GRADE. PubMed and Embase were searched for studies published from 2016 to 2025. Italian pediatric scientific societies and specialists involved in acute pediatric care contributed to the process. Results: A total of 13 studies were included. Validated pain scales showed high reliability and were associated with reduced pain scores after analgesic administration. Paracetamol and ibuprofen showed comparable efficacy for mild-to-moderate pain; evidence suggesting a modest late advantage of ibuprofen was inconsistent. Combination therapy reduced rescue analgesia in individual studies. Non-pharmacological interventions, including virtual reality, distraction, and hospital play, reduced anxiety and procedural distress, although certainty of evidence was low. Evidence on opioids was limited and did not show clear superiority over non-opioid strategies. Intranasal fentanyl appeared comparable to intravenous morphine and more feasible in emergency settings. Overall, evidence for several clinical questions remains limited, and GRADE recommendations were only possible for selected PICOs. Conclusions: Acute pediatric pain management should rely on structured assessment and multimodal strategies. Validated pain scales should be used routinely. Paracetamol and ibuprofen remain first-line treatments for mild-to-moderate pain, with possible context-specific benefits of ibuprofen or combination therapy. Non-pharmacological interventions may be useful adjuncts, especially for anxiety and procedural distress. Further high-quality studies are needed on opioids, intranasal administration, alternating regimens, and inhaled versus topical analgesia.

Sleep-disordered breathing and obstructive sleep apnea syndrome are two diseases of relevant clinical and research interest, especially in the pediatric field. However, there are gaps in knowledge regarding these diseases. We performed a survey that was administered electronically, via the SurveyMonkey platform, to 15,000 Italian anesthesiologists registered on the SIAARTI mailing list for a period of 4 months (April–July 2021). A total of 223 anesthesiologists completed the questionnaire (1.48

Burnout (BO) is a serious issue affecting professionals across various sectors, leading to adverse psychological and occupational consequences, even in anesthesiologists. Machine learning, particularly neural networks, can offer effective data-driven approaches to identifying BO risk more accurately. This study aims to develop and evaluate different artificial dense neural network (DNN)-based models to predict BO based on occupational, psychological, and behavioral factors. A dataset (300 Italian anesthesiologists) comprising workplace stressors, psychological well-being indicators, and demographic variables was used to train DNN models. Model performance was measured using standard evaluation metrics, including accuracy, precision, recall, and F1 score. Statistical tests were adopted to assess differences in prediction across the DNNs. The best neural architecture achieved a predictive accuracy of 0.68, with key contributors to BO including workload, emotional exhaustion, job dissatisfaction, and lack of work-life balance. Despite substantial differences among the six implemented algorithms, no significant variation in prediction performance was observed. Psychological distress scores are significantly higher in the high-risk BO group, suggesting greater anxiety, depression, and overall distress in this category. While challenges remain, continued advancements in artificial intelligence and data science promise more effective and personalized mental health care solutions. Not applicable.

OBJECTIVE:This study was undertaken to describe a cohort of pediatric patients with status epilepticus (SE) in Italy over the past decade, focusing on the variability of treatment protocols among centers, adherence to guidelines, and potential predictors of refractoriness. METHODS:This is a multicenter retrospective observational cohort study including patients aged 1 month to 18 years who experienced convulsive SE (CSE) between January 2010 and June 2022. Variables analyzed included age at CSE onset, etiology, and treatment. RESULTS:We included 1374 CSE episodes in 1071 patients (median age = 3.3 years); 46% occurred in the first 3 years of life. The prominent etiology was remote symptomatic (32%). Resolution was obtained only with benzodiazepine administration in 19.2% of SE episodes. Phenytoin, phenobarbital, and midazolam by infusion were the drugs most frequently used. Maximum therapeutic response occurred with low-dose (<.2 mg/kg/h) midazolam infusion administered at an early stage, following a single dose of benzodiazepine or an antiseizure medication (ASM; 59%). Midazolam effectiveness decreased to 37% when it was used after multiple ASMs, even at high doses. CSE was refractory in 39% of cases. Predictors of refractoriness included nonadherence to current guidelines, type of CSE, and etiology. SIGNIFICANCE:This study emphasizes that low-dose midazolam infusion, not requiring endotracheal intubation and administered at an early phase, appears to be effective in permanently stopping seizure and preventing the evolution toward a refractory CSE. Given its proven efficacy and widespread use in many hospitals, early midazolam infusion could be considered in the management of pediatric CSE. Adherence to treatment protocols, specific etiologies, and type of CSE are correlated with refractoriness; thus, when facing SE in infants, these factors should guide treatment protocol selection, including medication choice and timing.

Background: : Prolonged hospital stays after pediatric surgeries, such as tonsillectomy and adenoidectomy, pose significant fi cant concerns regarding cost and patient care. Dissecting the determinants of extended hospitalization is crucial for optimizing postoperative care and resource allocation. Objective: : This study aims to utilize machine learning (ML) techniques to predict post-surgery discharge times in pediatric patients and identify key variables influencing fluencing hospital stays. Methods: : The study analyzed data from 423 children who underwent tonsillectomy and/or adenoidectomy at the IRCCS Istituto Giannina Gaslini, Genoa, Italy. Variables included demographic factors, anesthesia-related details, and postoperative events. Preprocessing involved handling missing values, detecting outliers, and converting categorical variables to numerical classes. Univariate statistical analyses identified fied features correlated with discharge time. Four ML algorithms-Random Forest (RF), Logistic Regression, RUSBoost, and AdaBoost-were trained and evaluated using stratified fi ed 10-fold cross-validation. Results: : Significant fi cant predictors of delayed discharge included postoperative nausea and vomiting (PONV), continuous infusion of dexmedetomidine, fentanyl use, pain during discharge, and extubation time. The best-performing model, AdaBoost, demonstrated high accuracy and reliable prediction capabilities, with strong performance metrics across all evaluation criteria. Conclusion: : ML models can effectively predict discharge times and highlight critical factors impacting prolonged hospitalization. These insights can enhance postoperative care strategies and resource management in pediatric surgical settings. Future research should explore integrating these predictive models into clinical practice for real-time decision support.

AIM:In the pediatric surgical population, Emergence Delirium (ED) poses a significant challenge. This study aims to develop and validate machine learning (ML) models to identify key features associated with ED and predict its occurrence in children undergoing tonsillectomy or adenotonsillectomy. METHODS:The analysis involved data cleaning, exploratory data analysis (EDA), supervised predictive modeling, and unsupervised learning on a medical dataset (n = 423). After preliminary data cleaning, EDA encompassed plotting histograms, boxplots, pairplots, and correlation heatmaps to understand variable distributions and relationships. Four predictive models were trained including logistic regression (LR), random forest (RF), Support Vector Machine (SVM), and Gradient Boosting (XGBoost). The models were evaluated and compared using Receiver Operating Characteristic (ROC) Area Under the Curve (AUC), precision, recall, and feature importance. The RF model showed better performance and was used for the test (AUC-ROC 0.96, precision 1.00, and recall 0.92 on the validation set). K-means clustering was applied to find groups within the data. Elbow method and silhouette scores were used to determine the optimal number of clusters. The formed clusters were analyzed by aggregating features to understand the characteristics of each cluster. RESULTS:EDA revealed significant positive correlations between age, weight, American Society of Anesthesiologists (ASA) health score, and surgery duration with the risk of developing ED. Among the ML models, RF achieved the highest performance. Key predictive variables, based on the model's feature importance, included delirium screening scales, extubation time, and time to regain consciousness. Unsupervised K-means clustering identified 2-3 optimal clusters, which represented distinct patient subgroups: younger, healthier, low-risk individuals (cluster 0), and older patients with increasing chronic disease burden, higher delirium screening scores, and consequently higher post-operative delirium risk (clusters 1 and 2). CONCLUSIONS:ML techniques are valuable tools for extracting insights and making accurate predictions from healthcare data. High-performing algorithm-based models can be implemented for clinical decision support systems, facilitating early identification and intervention for ED in pediatric patients. By investigating various variables, it is possible to assess risk and implement preventive measures effectively. Furthermore, unsupervised clustering reveals distinct patient subgroups, enabling personalized perioperative management strategies and enhancing overall patient care.

In recent years, the field of anesthesiology has seen remarkable advancements in patient safety, comfort, and outcomes [...]

Introduction: Effective pain management is crucial for patient care, impacting comfort, recovery, and overall well-being. Traditional subjective pain assessment methods can be challenging, particularly in specific patient populations. This research explores an alternative approach using computer vision (CV) to detect pain through facial expressions. Methods: The study implements the YOLOv8 real-time object detection model to analyze facial expressions indicative of pain. Given four pain datasets, a dataset of pain-expressing faces was compiled, and each image was carefully labeled based on the presence of pain-associated Action Units (AUs). The labeling distinguished between two classes: pain and no pain. The pain category included specific AUs (AU4, AU6, AU7, AU9, AU10, and AU43) following the Prkachin and Solomon Pain Intensity (PSPI) scoring method. Images showing these AUs with a PSPI score above 2 were labeled as expressing pain. The manual labeling process utilized an opensource tool, makesense.ai, to ensure precise annotation. The dataset was then split into training and testing subsets, each containing a mix of pain and no-pain images. The YOLOv8 model underwent iterative training over 10 epochs. The model's performance was validated using precision, recall, and mean Average Precision (mAP) metrics, and F1 score. Results: When considering all classes collectively, our model attained a mAP of 0.893 at a threshold of 0.5. The precision for "pain" and "nopain" detection was 0.868 and 0.919, respectively. F1 scores for the classes "pain", "nopain", and "all classes" reached a peak value of 0.80. Finally, the model was tested on the Delaware dataset and in a real-world scenario. Discussion: Despite limitations, this study highlights the promise of using real-time computer vision models for pain detection, with potential applications in clinical settings. Future research will focus on evaluating the model's generalizability across diverse clinical scenarios and its integration into clinical workflows to improve patient care.

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

BACKGROUND Pediatric patients affected by oncologic disease have a significant risk of clinical deterioration that requires admission to the intensive care unit. This study reported the results of a national survey describing the characteristics of Italian onco-hematological units (OHUs) and pediatric intensive care units (PICUs) that admit pediatric patients, focusing on the high-complexity treatments available before PICU admission, and evaluating the approach to the end-of-life (EOL) when cared in a PICU setting. METHODS A web-based electronic survey has been performed in April 2021, involving all Italian PICUs admitting pediatric patients with cancer participating in the study. RESULTS Eighteen PICUs participated, with a median number of admissions per year of 350 (IQR 248-495). Availability of Extracorporeal Membrane Oxygenation therapy and the presence of intermediate care unit are the only statistically different characteristics between large or small PICUs. Different high-level treatments and protocols are performed in OHUs, non depending on the volume of PICU. Palliative sedation is mainly performed in the OHUs (78%), however, in 72% it is also performed in the PICU. In most centers protocols that address EOL comfort care and treatment algorithms are missing, non depending on PICU or OHU volume. CONCLUSIONS A non-homogeneous availability of high-level treatments and in OHUs is described. Moreover, protocols addressing EOL comfort care and treatment algorithms in palliative care are lacking in many centers.

ObjectiveThe purpose of this study was to determine whether the use of a humanoid robot (Estrabot) could reduce preoperative anxiety levels in children.MethodsAn experimental study was conducted at Azienda Ospedaliero Universitaria delle Marche Hospital, involving the Pediatric Surgery ward and the Operating Room (OR). Patients aged between 2 and 14 years who underwent minor surgery were included. The Instruments used were the Children's Emotional Manifestation Scale to evaluate anxiety levels, and Estrabot, a humanoid robot that interacts with people. Medical records between April and May 2023 were analyzed and the data was anonymous. The level of anxiety is extrapolated in Pediatric Surgery during the administration of oral pre-medication, and in the Operating Room, during the induction of anesthesia. Patients were divided into an intervention group treated with Estrabot, and a control group without a robot.ResultsThe population consists of 60 patients (86.7% male) with a median (IQR) age of 6 (4–8) years. The median (IQR) anxiety score during premedication was 7 (5–11), while the median (IQR) anxiety score during anesthesia was 6 (5–10). A significantly lower level of anxiety was reported in the Estrabot group. Patients in the Estrabot group had significantly lower anxiety levels in different age groups.ConclusionA humanoid robot can reduce preoperative anxiety levels in children during premedication and the induction of anesthesia.

Surgical site infections (SSIs) represent a potential complication of surgical procedures, with a significant impact on mortality, morbidity, and healthcare costs. Patients undergoing cardiac surgery and thoracic surgery are often considered patients at high risk of developing SSIs. This consensus document aims to provide information on the management of peri-operative antibiotic prophylaxis for the pediatric and neonatal population undergoing cardiac and non-cardiac thoracic surgery. The following scenarios were considered: (1) cardiac surgery for the correction of congenital heart disease and/or valve surgery; (2) cardiac catheterization without the placement of prosthetic material; (3) cardiac catheterization with the placement of prosthetic material; (4) implantable cardiac defibrillator or epicardial pacemaker placement; (5) patients undergoing ExtraCorporal Membrane Oxygenation; (6) cardiac tumors and heart transplantation; (7) non-cardiac thoracic surgery with thoracotomy; (8) non-cardiac thoracic surgery using video-assisted thoracoscopy; (9) elective chest drain placement in the pediatric patient; (10) elective chest drain placement in the newborn; (11) thoracic drain placement in the trauma setting. This consensus provides clear and shared indications, representing the most complete and up-to-date collection of practice recommendations in pediatric cardiac and thoracic surgery, in order to guide physicians in the management of the patient, standardizing approaches and avoiding the abuse and misuse of antibiotics.

A surgical site infection (SSI) is an infection that occurs in the incision created by an invasive surgical procedure. Although most infections are treatable with antibiotics, SSIs remain a significant cause of morbidity and mortality after surgery and have a significant economic impact on health systems. Preventive measures are essential to decrease the incidence of SSIs and antibiotic abuse, but data in the literature regarding risk factors for SSIs in the pediatric age group are scarce, and current guidelines for the prevention of the risk of developing SSIs are mainly focused on the adult population. This document describes the current knowledge on risk factors for SSIs in neonates and children undergoing surgery and has the purpose of providing guidance to health care professionals for the prevention of SSIs in this population. Our aim is to consider the possible non-pharmacological measures that can be adopted to prevent SSIs. To our knowledge, this is the first study to provide recommendations based on a careful review of the available scientific evidence for the non-pharmacological prevention of SSIs in neonates and children. The specific scenarios developed are intended to guide the healthcare professional in practice to ensure standardized management of the neonatal and pediatric patients, decrease the incidence of SSIs and reduce antibiotic abuse.

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

Ocular surgery encompasses a wide range of procedures, including surgery of the tear ducts, eyelid, cornea and conjunctiva, lens, ocular muscle, and vitreoretinal and iris surgery. Operations are also performed for the removal of tumors, repairs of ocular trauma and, finally, corneal transplantation. Antibiotic prophylaxis for the prevention of surgical site infections (SSIs) in ocular surgery is a complex field in which shared lines of action are absent. In light of the scarcity of shared evidence in the use of ocular antimicrobial prophylaxis for the pediatric population, this consensus document aims to provide clinicians with a series of recommendations on antimicrobial prophylaxis for patients of neonatal and pediatric age undergoing eye surgery. The following scenarios are considered: (1) intraocular surgery; (2) extraocular surgery; (3) ocular trauma; (4) ocular neoplasm; (5) ocular surface transplantations; (6) corneal grafts. This work has been made possible by the multidisciplinary contribution of experts belonging to the most important Italian scientific societies and represents, in our opinion, the most complete and up-to-date collection of recommendations regarding clinical actions in the peri-operative environment in eye surgery. The application of uniform and shared protocols aims to improve surgical practice, through the standardization of procedures, with a consequent reduction of SSIs, also limiting the phenomenon of antimicrobial resistance.

Purpose To analyze the mechanisms involved in the fetal heart rate (FHR) abnormalities after the epidural analgesia in labor. Methods A prospective unblinded single-center observational study on 55 term singleton pregnant women with spontaneous labor. All women recruited underwent serial bedside measurements of the main hemodynamic parameters using a non-invasive ultrasound system (USCOM-1A). Total vascular resistances (TVR), heart rate (HR), stroke volume (SV), cardiac output (CO) and arterial blood pressure were measured before epidural administration (T0), after 5 min 5 (T1) from epidural bolus and at the end of the first stage of labor (T2). FHR was continuously recorded through computerized cardiotocography before and after the procedure. Results The starting CO was significantly higher in a subgroup of women with low TVR than in women with high-TVR group. After the bolus of epidural analgesia in the low-TVR group there was a significant reduction in CO and then increased again at the end of the first stage, in the high-TVR group the CO increased insignificantly after the anesthesia bolus, while it increased significantly in the remaining part of the first stage of labor. On the other hand, CO was inversely correlated with the number of decelerations detected on cCTG in the 1 hour after the epidural bolus while the short-term variation was significantly lower in the group with high-TVR. Conclusion Maternal hemodynamic status at the onset of labor can make a difference in fetal response to the administration of epidural analgesia.

Although ketamine is primarily used for induction and maintenance of general anesthesia, it also presents sedative, amnestic, anesthetics, analgesic, antihyperalgesia, neuroprotective, anti-inflammatory, immunomodulant, and antidepressant effects. Its unique pharmacodynamics and pharmacokinetic properties allow the use of ketamine in various clinical settings including sedation, ambulatory anesthesia, and intensive care practices. It has also adopted to manage acute and chronic pain management. Clinically, ketamine produces dissociative sedation, analgesia, and amnesia while maintaining laryngeal reflexes, with respiratory and cardiovascular stability. Notably, it does not cause respiratory depression, maintaining both the hypercapnic reflex and the residual functional capacity with a moderate bronchodilation effect. In the pediatric population, ketamine can be administered through practically all routes, making it an advantageous drug for the sedation required setting such as placement of difficult vascular access and in uncooperative and oppositional children. Consequently, ketamine is indicated in prehospital induction of anesthesia, induction of anesthesia in potentially hemodynamic unstable patients, and in patients at risk of bronchospasm. Even more, ketamine does not increase intracranial pressure, and it can be safely used also in patients with traumatic brain injuries. This article is aimed to provide a brief and practical summary of the role of ketamine in the pediatric field.