4Controle da asma com Ceinal isolado ou combinado com LABA… SF 500F 500SF 250F 250SF 100F 10020804060%Sem CEinal 500 µg beclo> < 1000 µg becloBateman. Am J Respir Crit Care Med 2004; 170: 836–844ver 1.444
5Não controle da asma com Ceinal isolado ou combinado com LABA… %10080604020Sem CEinal 500 µg beclo> < 1000 µg becloBateman. Am J Respir Crit Care Med 2004; 170: 836–844ver 1.455
6Quantos pacientes que são tratados de acordo com os consensos participariam dos estudos que definiram as condutas?Travers. Thorax 2007;62:219–223
7Quantos pacientes que são tratados de acordo com os consensos participariam dos estudos que definiram as condutas?Mediana 6% (0-43%)Travers. Thorax 2007;62:219–223
8Publicações contendo ‘phenotype’, ‘asthma or wheeze’ e ‘paediatric or child’ no Pubmed Spycher. Clin Exp Allergy 2010, 40:1130–1141
9Haldar P. Am J Respir Crit Care Med 2008; 178: 218. wider approach including clinical, physiologic, and pathologicparameters in a k-means cluster analysis has been performedby Haldar et al. (3). The authors studied two distinct asthmapopulations: a group from primary care with mild to moderateasthma and a group from secondary care with refractoryasthma. In the mild asthma group, three clusters could beidentified: (i) early-onset atopic asthma (airway dysfunction,asthma symptoms, and eosinophilic airway inflammation, frequentexacerbations requiring oral corticosteroids), (ii) obesenoneosinophilic asthma (preponderance of female subjects,asthma symptoms, absence of eosinophilic airway inflammation),and (iii) benign asthma (middle-aged, little evidenceof asthma symptoms, airway inflammation, airway hyperresponsiveness,and exacerbations). In the refractory asthmapopulation, four clusters were identified: (i) early-onset atopicasthma (see earlier text), (ii) obese, noneosinophilic asthma(see earlier text), (iii) early-onset, symptom-predominantasthma (minimal eosinophilic disease), and (iv) eosinophilicinflammation-predominant asthma (few symptoms, late-onsetdisease, greater proportion of male subjects). The first twoclusters were concordant; disease severity differed in cluster 1,which seemed to be strongly associated with a lack of patientcompliance. Interestingly, degrees of eosinophilic inflammationand symptoms were discordant in clusters 2 and 3 of the refractory asthma group. This may give rise to overtreatmentand treatment failure with inhaled corticosteroids in thesegroups.Haldar P. Am J Respir Crit Care Med 2008; 178: 218.
10Moore WC. Am J Respir Crit Care Med 2010;181:315–23 Clusters no SARPIn a study aimed at phenotype identificationby clinical features alone, data from 726 subjects from a persistentasthma cohort were analyzed using an unsupervisedhierarchical cluster analysis of 34 clinical variables, includingage at onset, gender, body weight, degree of airflow limitation,reversibility of airflow limitation, and frequency of asthmaexacerbation (39). The authors showed that the resulting fivepatientcluster could be correctly characterized on the basis ofonlymerely three clinical parameters: pre- and postbronchodilatorpercentage of predicted forced expiratory volume in 1 sand age of onset of asthma. However, this cluster did not correlatewith the terms “severe asthma” and “treatment refractoryasthma.”Moore WC. Am J Respir Crit Care Med 2010;181:315–23
15Não atópico Precoce Não eosinofílico Obstrução fixa Carvalho Pinto, RM. Resp Med 2012; 106:47
16MiscelâneaCarvalho Pinto, RM. Resp Med 2012; 106:47
17Asma neutrofílica – pior resposta a CE inalatório VEF1 Escore de sintomas-0,10,10,2-20-15-10-5 Neu Eo-0,2Green. Thorax 2002;57:875–879ver 1.417
18Asma neutrofílica –resposta a macrolídeos BasalEm tratamentoPós tratamentoSimpson. Am J Respir Crit Care Med 2008; 177: 148–155
19Tiotrópio em asma não controlada com beta 2 longa + CEinal Kerstjens. NEJM 2012
20Fenótipo asma grave e obesidade Mais corticoide inalatório, cursos de corticoide oral, beta 2 de curtaMais refluxo e utilização de inibidores de bombaMenor CVF e capacidade de difusãoMaior IMC, menor nível de IgEMais eczema, menos pólipos nasaisGibeon D. Chest 2013;143:406
21Influência de perda de peso sobre controle de asma e função pulmonar PrePostp<0.001p=0.974> 10%≤ 10%> 10%≤ 10%Submetido para publicação
23Project ObjectivesThe airways diseases asthma and chronic obstructive pulmonary disease affect over 400 millionpeople world-wide and cause considerable morbidity and mortality. Airways disease costs theEuropean Union in excess of 56 billion per annum. Current therapies are inadequate and we donot have sufficient tools to predict disease progression or response to current or futuretherapies. Our consortium, Airway Disease PRedicting Outcomes through Patient SpecificComputational Modelling (AirPROM), brings together the existing clinical consortia (EvA FP7,U-BIOPRED IMI and BTS Severe Asthma), and expertise in physiology, radiology, imageanalysis, bioengineering, data harmonization, data security and ethics, computational modellingand systems biology. We shall develop an integrated multi-scale model building upon existingmodels.This airway model will be comprised of an integrated micro-scale and macro-scale airway modelinformed and validated by omic data and ex vivo models at thegenome-transcriptome-cell-tissue scale and by CT and functional MRI imaging coupled todetailed physiology at the tissue-organ scale utilising Europe's largest airway disease cohort.Validation will be undertaken cross-sectionally, following interventions and after longitudinalfollow-up to incorporate both spatial and temporal dimensions.AirPROM has a comprehensive data management platform and a well-developed ethico-legalframework. Critically, AirPROM has an extensive exploitation plan, involving at its inception andthroughout its evolution those that will develop and use the technologies emerging from thisproject. AirPROM therefore will bridge the critical gaps in our clinical management of airwaysdisease, by providing validated models to predict disease progression and response totreatment and the platform to translate these patient-specific tools, so as to pave the way toimproved, personalised management of airways disease.
24Segurança de dados e ética Modelagem computacional AIRPROM : Airway Disease PRedicting Outcomes through Patient Specific Computational ModellingFisiologiaRadiologiaAnálise de imagensBioengenhariaHarmonização de dadosSegurança de dados e éticaModelagem computacionalBiologia de sistemas.
25Pediatric Research. online publication 27 February 2013. doi:10 Pediatric Research. online publication 27 February doi: /pr
26Interface Focus 2013 3, 20120057, published 21 February 2013
27Interface Focus 2013 3, 20120057, published 21 February 2013
28Pediatric Research. online publication 27 February 2013. doi:10 Pediatric Research. online publication 27 February doi: /pr
29Medicina dos 4 P Personalizada Genoma pessoal Preditiva Avaliação riscos a partir da personalizaçãoPreventivaA partir da predição pode-se prevenirParticipativaO indivíduo tem de participar para resolver o que for detectado