Portuguese Severe Asthma Registry: Getting Answers for Severe Asthma Patients

NCT04714567 | Unknown

• 1 other identifier: 10886
• Study Type: observational
• Target: 150
• Locations: 1 country
• Sites: 1

Brief Summary

Asthma currently affects 358 million individuals worldwide, posing a substantial burden on health care systems. In particular patients with severe asthma have higher morbidity, mortality and asthma-related costs than non-severe patients. The management of severe asthma is still an unmet need and improving the disease-related knowledge is important to optimize care pathways. Registries provide an opportunity to phenotypically describe a cohort of patients in real-world settings. We hypothesize whether patient profiling based on data in the Portuguese Severe Asthma Registry (RAG - Registo de Asma Grave) may contribute to identify predictors of disease control and therapeutic response. This study aims to (Coprimary Objectives): 1) Identify multidimensional phenotypes associated with health outcomes and therapeutic responses, based on demographic characteristics, clinical features and biomarkers; 2) Explore novel composite endpoint measures of disease control and evaluate its association with the different severe asthma profiles. This is a cross-sectional, observational, multicenter, real-world study. The study population are the patients of all ages with severe asthma included in the RAG, until Dec 2021. It is estimated that 150 patients will be enrolled, in approximately 12 sites throughout Portugal, which is expected to be a representative sample of Portuguese patients with severe asthma. Eligible patients will be invited to integrate RAG by clinicians at scheduled clinic appointments. The criteria for patients' inclusion in the RAG is based on the definition of Severe Asthma by GINA guidelines, based on step of treatment, adherence and comorbidities management. An additional inclusion criterion is the patient's signed consent to have his/her data included in the registry. The main data source of this project is the data collected by RAG, an electronic Case Report Form. Descriptive and inferential statistics will be used to characterize and compare the characteristics across different sub-groups. Advanced data-driven statistical methods, such clustering analysis and latent class analysis, will be used for phenotype classification. Multivariate logistic regression modelling and Classification and Regression Tree analysis will be considered. To address the potential limitations, the RAG has database specifications concerning data definitions and parameters and data validation rules enabling collection of data in the same manner for every patient, with specific and consistent data definitions. To minimize errors related to data completeness and consistency, several validation rules have been implemented and periodic data audits are planned. To avoid unnecessary burden within the clinical workflow, data will be collected at the time of routine medical appointments by the clinician and data entry personnel will assist on this task.

Conditions

Severe Asthma

Keywords

Severe asthma; Phenotype; Disease Registry; Disease Management

Outcome Measures

Primary Outcomes (2)

• Number and characteristics of multidimensional phenotypes

  Identify multidimensional phenotypes associated with health outcomes and therapeutic responses, based on demographic characteristics, clinical features and biomarkers

  baseline, during routine medical appointment

• The proportion of asthma control measured with different measures.

  Usual control assessment measurements are symptoms, the proportion of exacerbations and increases on OCS usage. Since one of the aims of this study is to explore novel endpoints in disease control, we will further consider changes in: self-administered questionnaires (Control of Allergic Rhinitis and Asthma Test (CARAT) scores, Asthma Control Test (ACT) scores); lung function; asthma-related healthcare utilization (including changes in the number of routine primary care medical appointments, routine hospital care medical appointments, non-scheduled medical appointments, emergency department visits, hospital admissions, intensive care unit admissions, need for mechanical ventilation, school or labour absenteeism); allergy biomarkers (total serum IgE) and inflammation biomarkers (FeNO, blood eosinophils, sputum eosinophils, sputum neutrophils).

  baseline, during routine medical appointment

Study Arms (1)

Patients with severe asthma

Patients of all ages with severe asthma included in the RAG.

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients of all ages with severe asthma included in RAG, until December 2021. The sample used for data analysis will correspond to the total population (all patients in RAG).

You may qualify if:

• be under treatment on step 4 or 5 according to GINA recommendations (GINA 2018);
• have verified the optimization of treatment adherence and comorbidities management;
• provide the signed consent to have his/her data included in the registry

You may not qualify if:

• patients without given consent to participate will be excluded.

Sponsors & Collaborators

• Sociedade Portuguesa de Pneumologia: lead
• GlaxoSmithKline: collaborator

Study Sites (1)

Centro Hospitalar e Universitário de Coimbra

Coimbra, 3000-075, Portugal

RECRUITING

Related Publications (8)

• Drazen JM, Harrington D. New Biologics for Asthma. N Engl J Med. 2018 Jun 28;378(26):2533-2534. doi: 10.1056/NEJMe1806037. Epub 2018 May 21. No abstract available.

  PMID: 29782236 BACKGROUND

• Gliklich RE, Dreyer NA, Leavy MB, editors. Registries for Evaluating Patient Outcomes: A User's Guide [Internet]. 3rd edition. Rockville (MD): Agency for Healthcare Research and Quality (US); 2014 Apr. Report No.: 13(14)-EHC111. Available from http://www.ncbi.nlm.nih.gov/books/NBK208616/

  PMID: 24945055 BACKGROUND

• Kerkhof M, Tran TN, Soriano JB, Golam S, Gibson D, Hillyer EV, Price DB. Healthcare resource use and costs of severe, uncontrolled eosinophilic asthma in the UK general population. Thorax. 2018 Feb;73(2):116-124. doi: 10.1136/thoraxjnl-2017-210531. Epub 2017 Sep 16.

  PMID: 28918400 BACKGROUND

• Loureiro CC, Amaral L, Ferreira JA, Lima R, Pardal C, Fernandes I, Semedo L, Arrobas A. Omalizumab for Severe Asthma: Beyond Allergic Asthma. Biomed Res Int. 2018 Sep 17;2018:3254094. doi: 10.1155/2018/3254094. eCollection 2018.

  PMID: 30310816 BACKGROUND

• Newby C, Heaney LG, Menzies-Gow A, Niven RM, Mansur A, Bucknall C, Chaudhuri R, Thompson J, Burton P, Brightling C; British Thoracic Society Severe Refractory Asthma Network. Statistical cluster analysis of the British Thoracic Society Severe refractory Asthma Registry: clinical outcomes and phenotype stability. PLoS One. 2014 Jul 24;9(7):e102987. doi: 10.1371/journal.pone.0102987. eCollection 2014.

  PMID: 25058007 BACKGROUND

• Sa-Sousa A, Fonseca JA, Pereira AM, Ferreira A, Arrobas A, Mendes A, Drummond M, Videira W, Costa T, Farinha P, Soares J, Rocha P, Todo-Bom A, Sokolova A, Costa A, Fernandes B, Chaves Loureiro C, Longo C, Pardal C, Costa C, Cruz C, Loureiro CC, Lopes C, Mesquita D, Faria E, Magalhaes E, Menezes F, Todo-Bom F, Carvalho F, Regateiro FS, Falcao H, Fernandes I, Gaspar-Marques J, Viana J, Ferreira J, Silva JM, Simao L, Almeida L, Fernandes L, Ferreira L, van Zeller M, Quaresma M, Castanho M, Andre N, Cortesao N, Leiria-Pinto P, Pinto P, Rosa P, Carreiro-Martins P, Gerardo R, Silva R, Lucas S, Almeida T, Calvo T. The Portuguese Severe Asthma Registry: Development, Features, and Data Sharing Policies. Biomed Res Int. 2018 Nov 21;2018:1495039. doi: 10.1155/2018/1495039. eCollection 2018.

  PMID: 30584531 BACKGROUND

• Sousa AS, Pereira AM, Fonseca JA, Azevedo LF, Abreu C, Arrobas A, Calvo T, Silvestre MJ, Cunha L, Falcao H, Drummond M, Geraldes L, Loureiro C; Severe Asthma Specialist Network (Rede de Especialistas de Asma Grave REAG). Asthma control and exacerbations in patients with severe asthma treated with omalizumab in Portugal. Rev Port Pneumol (2006). 2015 Apr 16:S2173-5115(15)00080-9. doi: 10.1016/j.rppnen.2015.03.002. Online ahead of print.

  PMID: 25926263 BACKGROUND

• Tay TR, Hew M. Comorbid "treatable traits" in difficult asthma: Current evidence and clinical evaluation. Allergy. 2018 Jul;73(7):1369-1382. doi: 10.1111/all.13370. Epub 2017 Dec 15.

  PMID: 29178130 BACKGROUND

Related Links

• Global Strategy for Asthma Management and Prevention (updated 2020)
• Registo de Asma Grave Portugal
• International Severe Asthma Registry

MeSH Terms

Conditions

Asthma

Condition Hierarchy (Ancestors)

Bronchial Diseases; Respiratory Tract Diseases; Lung Diseases, Obstructive; Lung Diseases; Respiratory Hypersensitivity; Hypersensitivity, Immediate; Hypersensitivity; Immune System Diseases

Study Officials

• Cláudia Ch Loureiro, MD, PhD

  Serviço de Pneumologia, Centro Hospitalar e Universitário de Coimbra, E.P.E.

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Cláudia Ch Loureiro, MD, PhD

CONTACT

919795255; cl_loureiro@hotmail.com

Ana Sa Sousa, PhD

CONTACT

anasasousa@gmail.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: CROSS SECTIONAL
• Target Duration: 1 Day
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Study Record Dates

• First Submitted: January 8, 2021
• First Posted: January 19, 2021
• Study Start: May 1, 2021
• Primary Completion: December 1, 2022
• Study Completion: December 1, 2022
• Last Updated: November 10, 2022

Record last verified: 2022-11

Locations

PT (1)