Real-world Management Research Articles

Outcomes of Broader Genomic Profiling in Metastatic Colorectal Cancer: A Portuguese Cohort Study

Background: Colorectal cancer (CRC) is the third most diagnosed cancer globally and the second leading cause of cancer-related deaths. Despite advancements, metastatic CRC (mCRC) has a five-year survival rate below 20%. Next-generation sequencing (NGS) is recommended nowadays to guide mCRC treatment; however, its clinical utility when compared with traditional molecular testing in mCRC is debated due to limited survival improvement and cost-effectiveness concerns. Methods: This retrospective study included mCRC patients (≥18 years) treated at a single oncology centre who underwent NGS during treatment planning. Tumour samples were analysed using either a 52-gene Oncomine™ Focus Assay or a 500+-gene Oncomine™ Comprehensive Assay Plus. Variants were classified by clinical significance (ESMO ESCAT) and potential benefit (ESMO-MCBS and OncoKBTM). The Mann–Whitney and Chi square tests were used to compare characteristics of different groups, with significance at p < 0.05. Results: Eighty-six metastatic colorectal cancer (mCRC) patients were analysed, all being MMR proficient. Most cases (73.3%) underwent sequencing at diagnosis of metastatic disease, using primary tumour samples (74.4%) and a focused NGS assay (75.6%). A total of 206 somatic variants were detected in 86.0% of patients, 31.1% of which were classified as clinically significant, predominantly KRAS mutations (76.6%), with G12D and G12V variants as the most frequent. Among 33.7% RAS/BRAF wild-type patients, 65.5% received anti-EGFR therapies. Eleven patients (12.8%) had other actionable variants which were ESCAT level I-II, including four identified as TMB-high, four KRAS G12C, two BRAF V600E, and one HER2 amplification. Four received therapies classified as OncoKbTM level 1–2 and ESMO-MCBS score 4, leading to disease control in three cases. Conclusions: NGS enables the detection of rare variants, supports personalised treatments, and expands therapeutic options. As new drugs emerge and genomic data integration improves, NGS is poised to enhance real-world mCRC management.

Integrating intuitionistic fuzzy and MCDM methods for sustainable energy management in smart factories.

Improving energy efficiency is crucial for smart factories that want to meet sustainability goals and operational excellence. This study introduces a novel decision-making framework to optimize energy efficiency in smart manufacturing environments, integrating Intuitionistic Fuzzy Sets (IFS) with Multi-Criteria Decision-Making (MCDM) techniques. The proposed approach addresses key challenges, including reducing carbon footprints, managing operating costs, and adhering to stringent environmental standards. Eight essential criteria are identified, such as the use of renewable energy, the efficiency of production, and the health and safety of workers, to evaluate energy performance. Using the entropy method for criterion weighting and the CRADIS technique for alternative ranking, we prioritize a range of energy-efficient solutions. The novelty of our approach lies in its comprehensive assessment of complex real-world energy management scenarios within smart factories, offering a robust and adaptable decision-support tool. Our empirical results, validated through sensitivity analysis, show that alternative 5 delivers the most significant improvement in energy efficiency. This study provides valuable information for industry practitioners seeking to transition to more sustainable production methods and supports the broader sustainability agenda.

Adherence to guidelines for incidental pulmonary nodules: insights from a Nordic survey.

There is limited data on the real-world management of incidental pulmonary nodules (IPN). In this article, we review current practices and adherence to international guidelines in the Nordic countries. This non-interventional, observational survey study based on an online survey consisting of 13 questions. In total, 32 hospitals responded to the survey, with 11 from Denmark, 10 from Sweden, 7 from Norway, and 4 from Finland, resulting in an overall response rate of 86% (32/37). These institutions reported following a median of 20 new lung nodules monthly (5-400 IPN cases per month). In Denmark and Sweden, 100% of respondents indicated the presence of national guidelines. In Norway, this rate was 86%, and in Finland 80%. Among the primary guidelines followed, 70% of respondents reported using national guidelines, 20% used international guidelines, and only 10% reported relying on local/institutional guidelines as their first choice. Most sites used a combination of international and national guidelines (75%, 24/32). Available international guidelines were equally represented, with 35% using the Fleischner Criteria, 30% using British Thoracic Society guidelines, and 35% using others (e.g. European Society for Medical Oncology, National Comprehensive Cancer Network). There was variation in which department held primary responsibility for IPN follow-up. The article also demonstrated differences in suggested follow-up cases from the survey. The study reveals strong adherence to guidelines among Nordic hospitals, with a notable preference for hybrid approaches that combine different guidelines. We need continued efforts to harmonize and update guidelines.

Demographic Features, Diagnoses and Real-World Clinical Management of Uveitis in Japan

ABSTRACT Purpose This study aimed to investigate demographic features, diagnoses of uveitis (intraocular inflammation), and real-world clinical practice in the use of local and systemic therapies for patients with uveitis in Tokyo, Japan. Methods Clinical records of 1,174 consecutive new patients (480 males, 694 females) referred to the Kyorin Eye Center, Kyorin University Hospital between January 2011 and December 2018 were retrospectively reviewed. Results Mean age at presentation was 52.6 years (range 4–94 years). By anatomic location, 439 patients (37.4%) had anterior uveitis, 18 (1.5%) had intermediate uveitis, 214 (18.2%) had posterior uveitis and 503 (42.8%) had panuveitis. The 3 most common diagnoses were sarcoidosis (9.1%), Vogt-Koyanagi-Harada (VKH) disease (8.3%), and acute anterior uveitis (5.7%). Compared to our previous study, rates of herpetic anterior uveitis and cytomegalovirus (CMV) retinitis increased while tuberculosis-related uveitis decreased. Unclassified uveitis remained the most common diagnosis (44.9%). Systemic corticosteroids and/or immunomodulatory agents were used in only 18.3% of patients. Immunomodulatory drugs including biologic agents were utilized in 4.9% of patients. Conclusions The most common uveitis anatomic type was panuveitis due mainly to high rates of sarcoidosis and VKH disease. Diagnoses of herpetic anterior uveitis and CMV retinitis increased, while tuberculosis-related uveitis decreased. Less than one-fifth of uveitis patients required systemic treatment.

Factors Influencing the Prescription of Add-on Long-Acting Muscarinic Antagonists in Real-World Asthma Management: Insights from a National Registry

Current guidelines recommend adding long-acting muscarinic antagonists (LAMAs) in patients with uncontrolled asthma, despite the use of moderate to high doses of inhaled steroid-long-acting beta agonists (ICS/LABA). This study aims to analyze the factors related to the prescription of add-on LAMA in clinical practice for asthma patients, shedding light on physicians’ preferences.This study included adult asthma patients on add-on LAMA and ICS/LABA monitored for at least one year in a national registry comprising 2053 asthmatics. Patients’ characteristics and disease profiles were analyzed to identify factors associated with the prescription of add-on LAMA across the entire cohort. A comparative analysis was performed among three groups: MART (ICS/formoterol as a maintenance and reliever therapy) plus LAMA, Conventional (ICS/LABA as a maintenance and short-acting beta agonist as reliever) plus LAMA and Triple (ICS/LABA/LAMA single inhaler).LAMAs were added to ICS/LABA in 11.7% of patients in the national registry. Logistic regression analysis revealed that older age, low FEV1 (%), Asthma Control Test (ACT) scores less than 20, and severe exacerbation were the main factors influencing the initiation of LAMA in our registry. However, demographic characteristics of asthma, control status, pulmonary function test results were similar among the three groups of LAMA users (p>0.05). Physicians used LAMAs without phenotyping based on allergic status or eosinophil levels (p>0.05). Mepolizumab was added after LAMA in all patients, while omalizumab was initiated before LAMA in 16.9% of the patients receiving LAMA along with biologics.Add-on LAMAs were predominantly prescribed for older, uncontrolled, and exacerbated asthma patients with low FEV1.

Prognostic factors and impact of management strategies for status epilepticus: The STEPPER study in the Emilia-Romagna region, Italy.

The STEPPER (Status Epilepticus in Emilia-Romagna) study aimed to investigate the clinical characteristics, prognostic factors, and treatment approaches of status epilepticus (SE) in adults of the Emilia-Romagna region (ERR), Northern Italy. STEPPER, an observational, prospective, multicentric cohort study, was conducted across neurology units, emergency departments, and intensive care units of the ERR over 24 months (October 2019-October 2021), encompassing incident cases of SE. Patients were followed up for 30 days. A total of 578 cases were recruited (56% female, mean age = 70 years, 32% with previous diagnosis of epilepsy, 43% with in-hospital onset, 35% stuporous/comatose, 46% with nonconvulsive SE). Etiology was known in 87% (acute 43%, remote 24%, progressive 17%, definite epileptic syndrome 3%). The mean pre-SE Rankin Scale score was 2, the Status Epilepticus Severity Score was ≥4 in 33%, the Epidemiology-Based Mortality Score in Status Epilepticus score was ≥64 in 61%, and 34% were refractory. The sequence of treatments followed current clinical practice guidelines in 63%. Benzodiazepines (BDZs) were underused as first-line therapy (71%), especially in in-hospital onset cases; 15% were treated with continuous intravenous anesthetic drugs. Mortality was 24%; 63% of survivors had functional worsening. At the two-step multivariable analysis, incorrect versus correct treatment sequence with correct BDZ dose was the strongest predictor of failure to resolve SE in the in-hospital group (odds ratio [OR] = 4.42, 95% confidence interval [CI] = 1.86-10.5), with a similar trend in the out-of-hospital group (OR = 2.22, 95% CI = .98-5.02). In turn, failure to resolve was the strongest predictor of 30-day mortality (OR = 11.3, 95% CI = 4.16-30.9, out-of-hospital SE; OR = 6.42, 95% CI = 2.79-14.8, in-hospital SE) and functional worsening (OR = 5.83, 95% CI = 2.05-16.6, out-of-hospital SE; OR = 9.30, 95% CI 2.22-32.3, in-hospital SE). The STEPPER study offers insights into real-world SE management, highlighting its significant morbidity and functional decline implications. Although nonmodifiable clinical factors contribute to SE severity, modifiable factors such as optimized first-line therapies and adherence to guidelines can potentially influence prognosis.

Atrial Fibrillation in Patients Receiving Mavacamten for Obstructive Hypertrophic Cardiomyopathy: Real-World Incidence, Management, and Outcomes.

Atrial Fibrillation in Patients Receiving Mavacamten for Obstructive Hypertrophic Cardiomyopathy: Real-World Incidence, Management, and Outcomes.

Committee machine learning: A breakthrough in the precise prediction of CO2 storage mass and oil production volumes in unconventional reservoirs

Accurate prediction of CO2 storage mass and cumulative oil production is critical in the context of combining subsurface carbon capture with enhanced oil recovery (CCS-EOR). This study introduces a novel committee machine-learning Gaussian Process Regression (CML-GPR) model, which integrates three well-established machine-learning algorithms—Random Forest (RF), Multi-Layer Extreme Learning Machine (MELM), and Generalized Regression Neural Network (GRNN). The combination of these models leverages the strengths of each algorithm: RF captures nonlinear relationships, MELM enhances computational efficiency, and GRNN provides smooth, generalized predictions. By integrating these complementary techniques, the CML-GPR model demonstrates significant improvements in predictive accuracy over individual models, addressing limitations in their performance. The model predicts CO2 storage mass and cumulative oil production based on nine key reservoir input variables, including depth, porosity, and CO2 injection rate, among others. Utilizing a large dataset of 21,193 data points from reservoir simulations, a Mahalanobis distance-based outlier detection method further refines the input data quality. The CML-GPR model achieves root mean square error (RMSE) values of 0.49 million metric tons for CO2 storage mass and 13.68 million barrels for cumulative oil production, significantly outperforming individual models. The CML-GPR model provides a robust tool for optimizing CO2 storage capacity and oil recovery, with practical implications for real-world reservoir management, ensuring more efficient and reliable CCS-EOR operations. This study represents a pioneering advancement in predictive modeling, offering valuable insights for optimizing both CO2 storage and enhanced oil recovery in complex reservoirs.

Exploring the real-world management of catheter-associated urinary tract infections by Swiss general practitioners and urologists: insights from an online survey.

To assess and compare the real-world management of catheters and catheter-associated urinary tract infections (CAUTI) among Swiss general practitioners and urologists, encompassing diagnosis, treatment and prophylaxis. An anonymised online questionnaire was distributed among Swiss general practitioners and urologists between January and October 2023 via the networks of Sentinella and the Swiss Association of Urology. The questionnaire consisted of questions on catheter management, including diagnosis, treatment and prophylaxis of CAUTI. Analysis was performed by discipline. Fisher's exact test was applied for comparisons (statistical significance with p <0.05). Out of 175 participating physicians, the majority were involved in catheter management. Urologists exhibited significantly higher levels of competence as compared to general practitioners (67.1% vs 20.9%). Although no significant differences were observed regarding diagnostic approaches between disciplines, unrecommended diagnostic methods were frequently applied. general practitioners reported that they treated non-febrile CAUTI for longer durations, while urologists indicated that they treated febrile CAUTI longer. Additionally, the use of fluoroquinolones was more prevalent among general practitioners compared to urologists, while prophylactic measures were more frequently applied by urologists. Catheter and CAUTI management entail significant uncertainty for general practitioners. CAUTI management varied notably between general practitioners and urologists in terms of treatment and prophylaxis. The use of non-recommended diagnostic approaches and drugs was common. This trend, along with inappropriate diagnostic methods and prophylaxis, may increase antimicrobial resistance and CAUTI morbidity. The study emphasises the necessity for diagnostic and antimicrobial stewardship interventions, and proper training in CAUTI management for general practitioners and urologists.

Real-World Management Patterns of Acute Toxicity Following Bispecific Antibody Therapy for Patients with B-Cell Lymphoma - the Geltamo (Spanish Lymphoma Group) Experience

Real-World Management Patterns of Acute Toxicity Following Bispecific Antibody Therapy for Patients with B-Cell Lymphoma - the Geltamo (Spanish Lymphoma Group) Experience

Clinicians' Preferences for Sphingosine-1-Phosphate Receptor Modulators in Multiple Sclerosis Based on Clinical Management Considerations: A Choice Experiment.

Four sphingosine-1-phosphate receptor (S1PR) modulators are currently available in the USA for treating relapsing forms of multiple sclerosis (MS). These S1PR modulators have similar efficacy. Clinicians may therefore consider other factors, such as clinical management considerations, when distinguishing among treatments. This study estimated which S1PR modulator clinicians would choose on the basis of a treatment's clinical management and quantified how individual aspects of clinical management might drive this choice. A multi-criteria decision analysis (MCDA) was conducted on the basis of clinical management preferences elicited in a discrete choice experiment (DCE) and real-world clinical management profiles of the S1PR modulators currently available to treat relapsing forms of MS (fingolimod, ozanimod, ponesimod, siponimod). The DCE was completed by neurologists in the USA experienced in treating MS and included eight clinical management attributes: first-dose observations, genotyping, liver function tests, eye exams, drug-drug interactions, interactions with antidepressants, interactions with foods high in tyramine, and immune system recovery time. Attribute levels were selected on the basis of S1PR modulator product labels. In the MCDA, partial MCDA scores were created for each attribute and summed to produce an overall MCDA score for each S1PR modulator. The DCE was completed by 200 neurologists. The overall MCDA score was highest for ponesimod (4.78 points), followed by siponimod (4.10 points), fingolimod (3.61 points), and ozanimod (2.38 points). Having fewer drug-drug interactions contributed most to the overall scores (up to 1.56 points), followed by having no first-dose observations (0.95 points), the shortest immune system recovery time (0.94 points), and not interacting with foods high in tyramine (0.86 points). When considering clinical management convenience, the average US-based neurologist treating MS is likely to choose ponesimod over siponimod, fingolimod, or ozanimod. The strongest driver of preferences was the number of drug-drug interactions. This information can help inform recommendations for the treatment of MS and facilitate shared decision-making between clinicians and patients.

Real-world experience with glucagon-like peptide 2 analogues in patients with short bowel syndrome and chronic intestinal failure: Results from an international survey in expert intestinal failure centers

Background and AimsGlucagon-like peptide 2 (GLP-2) analogues are the first available disease-modifying treatments for patients with intestinal failure (IF) due to short bowel syndrome (SBS). Efficacy in terms of reduction of parenteral support (PS) has been demonstrated in multiple studies and real-world reports. However, it remains unclear how many patients are eligible to receive the treatment, when treatment is started after intestinal resection, how treatment efficacy is assessed outside of clinical trials, and how the treatment is modified in case of non-response or adverse events. The aim of this study was to investigate the real-world management of patients treated with GLP-2 analogues in expert centers around the world. MethodsA survey questionnaire was developed by a multidisciplinary working group consisting of 52 questions related to various aspects of multidisciplinary care of SBS-IF patients. The 17 questions related to the use of GLP-2 analogues in clinical practice were analyzed for this study. The online survey was sent to 33 participating centers in a phase 3 study of a long-acting GLP-2 analogue. Only responses from countries with access to commercially available GLP-2 analogues were included in the study. A descriptive analysis was performed for each question. Results are presented as median (interquartile range). ResultsThe responses from the 19 expert IF centers with access to GLP-2 analogues indicated that 10 (10-20) % of patients with SBS-IF were treated with a GLP-2 analogue, which was less than the number of eligible patients (30 (25-40) %). In most centers (10 centers, 53%), GLP-2 therapy was started 6-12 months after the last intestinal resection, with 5 centers (26%) starting later (12-24 months). Multiple parameters were used in combination to determine the response to GLP-2 analogues of which the three most common were >20% decrease in PS (95%), at least 1 day of PS reduction per week (84%) and increased urinary output (68%). In non-responders GLP-2 therapy was stopped within the first year by 67% of the centers. Finally, strategies in case of significant adverse events include stopping the GLP-2 analogue (used by 79% of experts), dose reduction (67%) and temporary treatment interruption (62%). ConclusionThe results of this survey completed by expert IF centers show the real-life use of GLP-2 analogues in clinical practice. Key learning points identified include the accounting for a period of intestinal adaptation before starting GLP-2 analogues and not stopping the treatment too early in case of non-response. The best strategy in case of adverse effects should be studied further.

Current Radiotherapy Management of Extensive-Stage Small-Cell Lung Cancer in the Immunotherapy Era: An Italian National Survey on Behalf of the Italian Association of Radiotherapy and Clinical Oncology (AIRO).

Extensive-stage small-cell lung cancer (ES-SCLC) treatment has recently been revolutionized by the advent of immune checkpoint inhibitors. This survey was conducted to evaluate the current pattern of care among Italian clinicians, in particular about the integration with radiation therapy (RT). In June 2023, 225 Italian cancer care professionals were invited to complete a 21-question web-based survey about ES-SCLC management through personal contacts and the Italian Association for Radiotherapy and Clinical Oncology (AIRO) network. We received 90 responses; the majority were radiation oncologists (89%) with more than 10 years of experience (51%). The preferred management of ES-SCLC in patients with a good performance status was concomitant chemo-immunotherapy (84%). Almost all respondents recommended prophylactic cranial irradiation (PCI) (85%), taking into account age and thoracic response; PCI was performed mainly between the end of chemotherapy and before starting immunotherapy (37%), with a three-dimensional conformal technique (46%). Furthermore, 83% of respondents choose to deliver thoracic RT in the case of both an intrathoracic and extrathoracic response, with an RT schedule of 30 Gy/10 fractions. Stereotactic RT is increasingly being used in oligoprogressions. Our analysis showed the variability of real-world management of ES-SCLC. Future clinical trials and developments are needed to improve the multidisciplinary treatment of these patients.

Deep learning applications for real-time and early detection of fall armyworm, African armyworm, and maize stem borer

The application of artificial intelligence for identifying Fall armyworm (Spodoptera frugiperda), African armyworm (Spodoptera exempta), and Maize stem borer (Busseola fusca) is critical due to the threats they pose to global food production. This study aims to evaluate and identify the most accurate and robust DL models in detecting and classifying these three significant agricultural pests. Seven traditional DL models: Convolutional Neural Network, Visual Geometry Group (VGG16), Residual Networks (ResNet50), MobileNetV2, InceptionV3, Deep Neural Network (DNN), and InceptionResNetV2 and the advanced You Look Only Once (YOLOv8) model were trained and tested using pest image datasets. The results showed that all traditional models except DNN had high accuracies ranging from 93.17% (InceptionResNetV2) to 99.43% (MobileNet) in training and testing, with losses ranging from 1.71% (MobileNetV2) to 24.99% (InceptionResNetV2). DNN had a slightly lower accuracy range of 55.27% to 56.39% and a loss range of 85.02% to 89.96% in training and testing. YOLOv8 emerged as the best and most robust model in the pest detection and classification tasks, achieving Precision and Recall scores ranging from 98.4% to 100% on single-class and multi-class classifications, making it highly suitable for real-world pest management applications. This research pioneers the use of DL for the classification and detection of maize stem borer, African armyworm and Fall armyworm, unique and separately addressing a critical gap in agricultural pest management in corn. With early and accurate pest identification, crop protection measures can be implemented efficiently. The findings lead to reduced crop damage and enhanced food security.

Characterizing and communicating uncertainty: lessons from NASA’s Carbon Monitoring System

Abstract Navigating uncertainty is a critical challenge in all fields of science, especially when translating knowledge into real-world policies or management decisions. However, the wide variance in concepts and definitions of uncertainty across scientific fields hinders effective communication. As a microcosm of diverse fields within Earth Science, NASA’s Carbon Monitoring System (CMS) provides a useful crucible in which to identify cross-cutting concepts of uncertainty. The CMS convened the Uncertainty Working Group (UWG), a group of specialists across disciplines to evaluate and synthesize efforts to characterize uncertainty in CMS projects. This paper represents efforts by the UWG to build a heuristic framework designed to evaluate data products and communicate uncertainty to both scientific and non-scientific end users. We consider four pillars of uncertainty: addressing its origins, severity, stochasticity versus incomplete knowledge, and spatial and temporal autocorrelation. Using a common vocabulary and a generalized workflow, the framework introduces a graphical heuristic accompanied by a narrative, exemplified through contrasting case studies. Envisioned as a versatile tool, this framework provides clarity in reporting uncertainty, guiding users and tempering expectations. Beyond CMS, it stands as a simple yet powerful means to communicate uncertainty across diverse scientific communities.

JobFormer: Skill-Aware Job Recommendation with Semantic-Enhanced Transformer

Job recommendation aims to provide potential talents with suitable job descriptions (JDs) consistent with their career trajectory, which plays an essential role in proactive talent recruitment. In real-world management scenarios, the available JD-user records always consist of JDs, user profiles, and click data, in which the user profiles are typically summarized as the user's skill distribution for privacy reasons. Although existing sophisticated recommendation methods can be directly employed, effective recommendation still has challenges considering the information deficit of JD itself and the natural heterogeneous gap between JD and user profile. To address these challenges, we proposed a novel skill-aware recommendation model based on the designed semantic-enhanced Transformer to parse JDs and complete personalized job recommendation. Specifically, we first model the relative items of each JD and then adopt an encoder with the local-global attention mechanism to better mine the intra-job and inter-job dependencies from JD tuples. Moreover, we adopt a two-stage learning strategy for skill-aware recommendation, in which we utilize the skill distribution to guide JD representation learning in the recall stage, and then combine the user profiles for final prediction in the ranking stage. Consequently, we can embed rich contextual semantic representations for learning JDs, while skill-aware recommendation provides effective JD-user joint representation for click-through rate (CTR) prediction. To validate the superior performance of our method for job recommendation, we present a thorough empirical analysis of large-scale real-world and public datasets to demonstrate its effectiveness and interpretability.

Artificial intelligence and surgical radiology - how it is shaping real-world management.

Artificial intelligence and surgical radiology - how it is shaping real-world management.

Neurointerventional surveys between 2000 and 2023: a systematic review

BackgroundSurveys are increasingly used in neurointervention to gauge physicians’ and patients’ attitudes, practice patterns, and ‘real-world’ treatment strategies, particularly in conditions for which few, or no evidence-based, recommendations exist. While...

Data-driven inventory control for large product portfolios: A practical application of prescriptive analytics

Motivated by the real-world inventory management problem of a large network of pharmacies, this paper proposes and studies a practically relevant Prescriptive Analytics approach for data-driven dynamic inventory control of large portfolios of interrelated products. We extend existing research on weighted Sample Average Approximation by integrating a ‘global learning’ model that effectively exploits cross-learning opportunities within the product portfolio. The results of an extensive numerical evaluation on real-world data suggest that our approach outperforms relevant benchmarks—in particular, models that rely on ‘local learning’ strategies where weight functions are trained separately for each product. The numerical results also allow us to derive important practical and structural insights regarding the value of contextual information in our global learning framework.

Allergic rhinitis management: a survey on Italian primary care pediatricians.

Background. Allergic rhinitis (AR) is a widespread condition. The Italian Society of Pediatric Allergology and Immunology (SIAIP) promoted an initiative to update the knowledge on AR in children and adolescents. The present survey directly addressed primary care pediatricians, thus reflecting the real-world management of AR in children and adolescents. The aim was to investigate common practice in managing AR children. Methods. A panel of experts drafted a series of questions concerning the practical management of children with AR in clinical practice. The questionnaire was administered to a large sample of primary care pediatricians (864). Results. 864 primary care pediatricians participated to the survey. Each pediatrician on average follows 94 children with AR; globally 81,231 children. More than 70% of participants follow ARIA guidelines. Accordingly, 42% of children have mild AR and 58% moderate/severe. Asthma, conjunctivitis and adenoid hypertrophy are the most common comorbidity. Most pediatricians autonomously follow their patients. The intensity of treatment (use of medication) is directly proportional to the symptom severity. Intranasal corticosteroids are the most common medication used followed by oral antihistamines and nasal lavages (with hypertonic or isotonic solution). Up to 20% of participants prescribe the fixed association topical corticosteroids plus antihistamine. Conclusions. The present survey demonstrated that Italian primary care pediatricians accomplish ARIA guidelines and adapt treatment on the basis of the intensity of symptoms. Corticosteroids and antihistamines are the most common prescribed medications. Nasal lavages are also popular.