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Citation counts are frequently used for assessing the scientific impact of articles. Current approaches for forecasting future citations counts have important limitations. This study aims to analyse and predict the trajectories of citation counts of systematic reviews (SR) based on their citation profiles in the previous years and predict quantiles of future citation counts. We included all SR published between 2010 and 2012 in medical journals indexed in the Web of Science. A longitudinal k-means (KML) clustering approach was applied to identify trajectories of citations counts 10 years after publication, according to the yearly citation count, the proportion of all cites attained in a specific year and the annual variation in citation counts. Finally, we built multinomial logistic regression models aiming to predict in what tercile or quartile of citation counts a SR would be 10 years after publication. Using clustering approaches, we obtained 24 groups of SR. Two groups (7.9% of the articles) had an average of > 200 citations, while two other groups (10.4% of the articles) presented an average of < 10 citations. The model predicting terciles of citation counts attained an accuracy of 72.8% (95%CI = 71.1–74.3%) and a kappa coefficient of 0.59 (95%CI = 0.57–0.62). Prediction of citation quartiles (combining the second and third quartiles into a single group) attained a accuracy of 76.2% (95%CI = 74.7–77.8%) and a kappa coefficient of 0.62 (95%CI = 0.59–0.64). This study provides an approach for predicting of future citations of SR based exclusively on citation counts from the previous years, with the models developed displaying an encouraging accuracy and agreement.

Fixed airflow obstruction (FAO) can complicate asthma. Inflammation is a proposed underlying mechanism. Our aim in this cross-sectional investigation was to evaluate the blood leucocyte pattern and level of exhaled nitric oxide in asthmatics and non-asthmatics with or without FAO. A total of 11,579 individuals aged ≥20 years from the US National Health and Nutrition Examination Survey were included. They were grouped as: controls without asthma and FAO (n = 9,935), asthmatics without FAO (n = 674), asthmatics with FAO (n = 180) and non-asthmatics with FAO (n = 790). FAO was defined as post-bronchodilator FEV1/FVC < lower limit of normal. Exhaled nitric oxide ≥ 25ppb, blood eosinophil levels ≥300 cells/μL, and blood neutrophil levels ≥5100 cells/μL were defined as elevated. Stratified analyses for smoking and smoking history were performed. Elevated blood eosinophil levels were more common in all groups compared to the controls, with the highest prevalence in the group with asthma and fixed airflow obstruction (p<0.01). In a multiple logistic regression model adjusted for potential confounders including smoking, the asthma groups had significantly higher odds ratios for elevated B-Eos levels compared to the control group (odds ratio 1.4, (confidence interval: 1.1-1.7) for the asthma group without fixed airflow obstruction and 2.5 (1.4-4.2) for the asthma group with fixed airflow obstruction). The group with fixed airflow obstruction without asthma had higher odds ratio for elevated blood neutrophil levels compared to the controls: 1.4 (1.1-1.8). Smoking and a history of smoking were associated to elevated B-Neu levels. Fixed airflow obstruction in asthma was associated with elevated blood eosinophil levels, whereas fixed airflow obstruction without asthma was associated with elevated blood neutrophil levels.

ObjectiveThe A2 score is an eight-question patient-reported outcome measure that has been validated for ruling in (score ≥4) and ruling out (score 0–1) asthma. However, this screening tool has been validated in a cohort similar to the derivation cohort used. This study aims to validate the predictive accuracy of the A2 score in a primary care population against general practitioner (GP) clinical assessment and to determine whether the proposed cut-offs are the most appropriate.DesignThis accuracy study is a secondary analysis of the EPI-ASTHMA population-based study.SettingPrimary care centres in Portugal.ParticipantsRandom adult participants answered the A2 score by phone interview.OutcomesThose with an A2 score ≥1 (plus 5% with an A2 score of 0) were invited to a diagnostic visit carried out by a GP to confirm or not a diagnosis of asthma. Diagnostic accuracy was assessed using receiver operating characteristic (ROC) curves.ResultsA total of 1283 participants (median 54 (p25–p75 43–66) years; 60% women) were analysed. The A2 score showed high discriminatory power in identifying asthma, with an area under the ROC curve of 82.9% (95% CI 80.4% to 85.4%). The proposed cut-off ≥4 was the most appropriate to rule in asthma (specificity 83.1%, positive predictive value 62.4%, accuracy 78%). Similarly, the proposed cut-off<2 was the most suitable for excluding asthma (sensitivity 92.7%, negative predictive value 93.7%, accuracy 60.5%).ConclusionsThe A2 score is a useful tool to identify patients with asthma in a primary care population.Trial registration number NCT0516961.

ObjectivesHigh-quality data are crucial for guiding decision-making and practising evidence-based healthcare, especially if previous knowledge is lacking. Nevertheless, data quality frailties have been exposed worldwide during the current COVID-19 pandemic. Focusing on a major Portuguese epidemiological surveillance dataset, our study aims to assess COVID-19 data quality issues and suggest possible solutions.SettingsOn 27 April 2020, the Portuguese Directorate-General of Health (DGS) made available a dataset (DGSApril) for researchers, upon request. On 4 August, an updated dataset (DGSAugust) was also obtained.ParticipantsAll COVID-19-confirmed cases notified through the medical component of National System for Epidemiological Surveillance until end of June.Primary and secondary outcome measuresData completeness and consistency.ResultsDGSAugust has not followed the data format and variables as DGSApril and a significant number of missing data and inconsistencies were found (eg, 4075 cases from the DGSApril were apparently not included in DGSAugust). Several variables also showed a low degree of completeness and/or changed their values from one dataset to another (eg, the variable ‘underlying conditions’ had more than half of cases showing different information between datasets). There were also significant inconsistencies between the number of cases and deaths due to COVID-19 shown in DGSAugust and by the DGS reports publicly provided daily.ConclusionsImportant quality issues of the Portuguese COVID-19 surveillance datasets were described. These issues can limit surveillance data usability to inform good decisions and perform useful research. Major improvements in surveillance datasets are therefore urgently needed—for example, simplification of data entry processes, constant monitoring of data, and increased training and awareness of healthcare providers—as low data quality may lead to a deficient pandemic control.

IntroductionIn Portugal as in other countries, data on the epidemiology of asthma are mainly grounded in questionnaire studies. Additionally, the detailed characterisation of asthma in terms of disease severity, control and phenotypes remain scarce. Studies assessing the prevalence of asthma and its subgroups using accurate methods are needed. This study aims to determine the prevalence of asthma, difficult-to-treat asthma and severe asthma, and to evaluate sociodemographic and clinical characteristics of those patients, in mainland Portugal.Methods and analysisA population-based nationwide study with a multicentre stepwise approach will be conducted between 2021 and 2023 in 38 primary care centres of the Portuguese National Health Service. The stepwise approach will comprise four stages: Stage 0—telephone call invitation to adult subjects (≥18 years) randomly selected (n~15 000); stage 1—telephone screening interview assessing the participants’ respiratory symptoms (n~7500); stage 2—diagnostic visit, including physical examination, diagnostic tests (eg, spirometry, fraction of exhaled nitric oxide, blood eosinophil count) and patient-reported outcome measures for diagnostic confirmation of those identified with possible asthma at stage 1 (n~1800); stage 3—further evaluation of patients with asthma and of patients with difficult-to-treat asthma and severe asthma, after 3 months (n~460). At stage 3, data will be collected from a review of the patient’s electronic health records, a follow-up telephone call and the CARATm (Caracteristicas Auto-reportadas de Asma em Tecnologias Móveis) app database. The prevalence of asthma, difficult-to-treat asthma and severe asthma will be determined as the percentage of patients with asthma confirmed from the overall population (stage 1). For the analysis of factors associated with asthma, difficult-to-treat asthma and severe asthma, logistic regression models will be explored.Ethics and disseminationEthical approvals for the study were obtained from the ethics committee of the local health unit of Matosinhos, Porto (38/CES/JAS), Alto Minho (38/2021/CES) and the regional health administration of Lisbon-Vale do Tejo (035/CES/INV/2021). Results will be published in peer-reviewed journals.Trial registration numberNCT05169619.

IntroductionIntranasal antihistamines and corticosteroids are some of the most frequently used drug classes in the treatment of allergic rhinitis. However, there is uncertainty as to whether effectiveness differences may exist among different intranasal specific medications. This systematic review aims to analyse and synthesise all evidence from randomised controlled trials (RCTs) on the effectiveness of intranasal antihistamines and corticosteroids in rhinitis nasal and ocular symptoms and in rhinoconjunctivitis-related quality-of-life.Methods and analysisWe will search four electronic bibliographic databases and three clinical trials databases for RCTs (1) assessing patients ≥12 years old with seasonal or perennial allergic rhinitis and (2) comparing the use of intranasal antihistamines or corticosteroids versus placebo. Assessed outcomes will include the Total Nasal Symptom Score (TNSS), the Total Ocular Symptom Score (TOSS) and the Rhinoconjunctivitis Quality-of-Life Questionnaire (RQLQ). We will assess the methodological quality of included primary studies by using the Cochrane risk-of-bias tool. Certainty in the body of evidence for the analysed outcomes will be assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. We will perform a random-effects meta-analysis for each assessed medication and outcome, presenting results as pooled mean differences and standardised mean differences. Heterogeneity will be explored by sensitivity and subgroup analyses, considering (1) the risk of bias, (2) the follow-up period and (3) the drug dose.Ethics and disseminationEthical considerations will not be required. Results will be disseminated in a peer-review journal.PROSPERO registration numberCRD42023416573.

Background Blood eosinophil (B‐Eos) count is an emerging biomarker in the management of respiratory disease but determinants of B‐Eos count besides respiratory disease are poorly described. Therefore, we aimed to evaluate the influence of non‐respiratory diseases on B‐Eos count, in comparison to the effect on two other biomarkers: fraction of exhaled nitric oxide (FeNO) and C‐reactive protein (CRP), and to identify individual characteristics associated with B‐Eos count in healthy controls. Methods Children/adolescents (<18 years) and adults with complete B‐Eos data from the US National Health and Nutritional Examination Surveys 2005–2016 were included, and they were divided into having respiratory diseases (n = 3333 and n = 7,894, respectively) or not having respiratory disease (n = 8944 and n = 15,010, respectively). After excluding any respiratory disease, the association between B‐Eos count, FeNO or CRP, and non‐respiratory diseases was analyzed in multivariate models and multicollinearity was tested. After excluding also non‐respiratory diseases independently associated with B‐Eos count (giving healthy controls; 8944 children/adolescents and 5667 adults), the independent association between individual characteristics and B‐Eos count was analyzed. Results In adults, metabolic syndrome, heart disease or stroke was independently associated with higher B‐Eos count (12%, 13%, and 15%, respectively), whereas no associations were found with FeNO or CRP. In healthy controls, male sex or being obese was associated with higher B‐Eos counts, both in children/adolescents (15% and 3% higher, respectively) and adults (14% and 19% higher, respectively) (p < 0.01 all). A significant influence of race/ethnicity was also noted, and current smokers had 17% higher B‐Eos count than never smokers (p < 0.001). Conclusions Non‐respiratory diseases influence B‐Eos count but not FeNO or CRP. Male sex, obesity, certain races/ethnicities, and current smoking are individual characteristics or exposures that are associated with higher B‐Eos counts. All these factors should be considered when using B‐Eos count in the management of respiratory disease.

Introduction Patient and public involvement (PPI) initiatives involving patients with chronic respiratory disease (CRD) are rare. Therefore, this study aimed to explore the perspectives of patients with CRD, carers and interested citizens regarding the relevance and need for a PPI network and suggestions for its implementation. Methods A qualitative study based on focus groups was conducted. Recruitment occurred through invitations on social media platforms and to patients who have participated in previous asthma studies of the team. Three focus groups were conducted, via video conference, using a semi‐structured guide. Thematic analysis was performed by two independent researchers and discussed with the extended team. Results Fifteen patients with CRD, one carer and one interested citizen (13 females, median 36 (range: 18–72) years) participated. All participants acknowledged the importance of implementing a collaborative network and demonstrated interest in being integrated. Participants acknowledged the importance of their involvement in several phases of the research cycle. The main aim identified for this network was to facilitate communication between patients and researchers. Participants regarded the integration of patients, carers, researchers and healthcare professionals from different scientific areas as relevant. The use of digital platforms to attract members and support the work, together with group dynamics and regular meetings, were some of the most relevant practical considerations for implementing the network. The identified facilitators for their engagement were sharing experiences, researchers' and healthcare professionals' support and feedback and schedule flexibility. The identified barriers included the amount of time dedicated, low health/digital literacy and the potential detachment of nondiagnosed patients or those with low symptom impact in daily life. Conclusion Patients, carers and citizens acknowledged the relevance of implementing a collaborative network and demonstrated interest in active participation in every stage of the health research cycle. A deeper knowledge of the barriers and facilitators identified in this study could support implementing these initiatives in Portugal. Patient or Public Contribution This study was designed by a research team that included one patient with asthma and one carer. They were specifically involved in building the study protocol and the interview guide. They also gave feedback regarding the electronic consent form and the short sociodemographic questionnaire created, namely by removing noncontributing words or phrases and rewording expressions. The lay summary was written by another patient with asthma. All participants of this study were invited to implement and integrate the ConectAR network—a collaborative network of research in respiratory health. Public Summary In Portugal, chronic respiratory patients do not have an active role as ‘coinvestigators’. This study aimed to acknowledge if patients and citizens considered a patient and public involvement network useful, whose main purpose would be to facilitate communication between patients and researchers. A study based on online group interviews was carried out with patients with chronic respiratory diseases and interested citizens, both recruited on social media platforms. Participants considered that bringing together patients, carers, researchers and healthcare professionals is valuable because sharing different experiences and perspectives may help patients to improve their daily lives and increase research quality. In conclusion, patients agree that implementing a collaborative network with researchers and healthcare professionals and participating in the health research cycle is quite preponderant. Acknowledging what can help and deter this network may be beneficial to implementing this type of initiative in Portugal.

Classification of asthma phenotypes has a potentially relevant impact on the clinical management of the disease. Methods for statistical classification without a priori assumptions (data-driven approaches) may contribute to developing a better comprehension of trait heterogeneity in disease phenotyping. This study aimed to summarize and characterize asthma phenotypes derived by data-driven methods. We performed a systematic review using three scientific databases, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria. We included studies reporting adult asthma phenotypes derived by data-driven methods using easily accessible variables in clinical practice. Two independent reviewers assessed studies. The methodological quality of included primary studies was assessed using the ROBINS-I tool. We retrieved 7446 results and included 68 studies of which 65% (n = 44) used data from specialized centers and 53% (n = 36) evaluated the consistency of phenotypes. The most frequent data-driven method was hierarchical cluster analysis (n = 19). Three major asthma-related domains of easily measurable clinical variables used for phenotyping were identified: personal (n = 49), functional (n = 48) and clinical (n = 47). The identified asthma phenotypes varied according to the sample’s characteristics, variables included in the model, and data availability. Overall, the most frequent phenotypes were related to atopy, gender, and severe disease. This review shows a large variability of asthma phenotypes derived from data-driven methods. Further research should include more population-based samples and assess longitudinal consistency of data-driven phenotypes.

This review analyzes the state and recent progress in the field of information support for pollen allergy sufferers. For decades, information available for the patients and allergologists consisted of pollen counts, which are vital but insufficient. New technology paves the way to substantial increase in amount and diversity of the data. This paper reviews old and newly suggested methods to predict pollen and air pollutant concentrations in the air and proposes an allergy risk concept, which combines the pollen and pollution information and transforms it into a qualitative risk index. This new index is available in an app (Mobile Airways Sentinel NetworK-air) that was developed in the frame of the European Union grant Impact of Air POLLution on sleep, Asthma and Rhinitis (a project of European Institute of Innovation and Technology-Health). On-going transformation of the pollen allergy information support is based on new technological solutions for pollen and air quality monitoring and predictions. The new information-technology and artificial-intelligence-based solutions help to convert this information into easy-to-use services for both medical practitioners and allergy sufferers.