Identification by cluster analysis of patients with asthma and nasal symptoms using the MASK-air® mHealth app
Jean Bousquet, Bernardo Sousa‐Pinto, Josep M. Antó, Rita Amaral, Luisa Brussino, Giorgio Walter Canonica, Álvaro A. Cruz, Bilun Gemicioğlu, Tari Haahtela, Maciej Kupczyk, Violeta Kvedarienė, Désirée Larenas‐Linnemann, Renaud Louis, N. Pham‐Thi, Francesca Puggioni, Frederico S. Regateiro, Jan Romantowski, J. Sastre, Nicola Scichilone, Luís Taborda‐Barata, Maria Teresa Ventura, Ioana Agache, Anna Bedbrook, Karl‐Christian Bergmann, Sinthia Bosnic‐Anticevich, Matteo Bonini, Louis‐Philippe Boulet, Guy Brusselle, Roland Buhl, Lorenzo Cecchi, D. Charpin, C. Chaves-Loureiro, W. Czarlewski, F. de Blay, Philippe Devillier, Guy Joos, Marek Jutel, Ludger Klimek, Piotr Kuna, D. Laune, Jorge Luna Pech, Mika J. Mäkelä, Mário Morais‐Almeida, Rachel Nadif, Marek Niedoszytko, Ken Ohta, Nikolaos G. Papadopoulos, Alberto Papi, D.R. Yeverino, Nicolás Roche, Ana Sá‐Sousa, Bolesław Samoliński, Mohamed H. Shamji, Aziz Sheikh, Charlotte Suppli Ulrik, Omar S. Usmani, Arūnas Valiulis, Olivier Vandenplas, Arzu Yorgancıoğlu, Torsten Zuberbier, João Fonseca
Abstract
BACKGROUND: The self-reporting of asthma frequently leads to patient misidentification in epidemiological studies. Strategies combining the triangulation of data sources may help to improve the identification of people with asthma. We aimed to combine information from the self-reporting of asthma, medication use and symptoms to identify asthma patterns in the users of an mHealth app. METHODS: We studied MASK-air® users who reported their daily asthma symptoms (assessed by a 0-100 visual analogue scale - "VAS Asthma") at least three times (either in three different months or in any period). K-means cluster analysis methods were applied to identify asthma patterns based on: (i) whether the user self-reported asthma; (ii) whether the user reported asthma medication use and (iii) VAS asthma. Clusters were compared by the number of medications used, VAS asthma levels and Control of Asthma and Allergic Rhinitis Test (CARAT) levels. FINDINGS: We assessed a total of 8,075 MASK-air® users. The main clustering approach resulted in the identification of seven groups. These groups were interpreted as probable: (i) severe/uncontrolled asthma despite treatment (11.9-16.1% of MASK-air® users); (ii) treated and partly-controlled asthma (6.3-9.7%); (iii) treated and controlled asthma (4.6-5.5%); (iv) untreated uncontrolled asthma (18.2-20.5%); (v) untreated partly-controlled asthma (10.1-10.7%); (vi) untreated controlled asthma (6.7-8.5%) and (vii) no evidence of asthma (33.0-40.2%). This classification was validated in a study of 192 patients enrolled by physicians. INTERPRETATION: We identified seven profiles based on the probability of having asthma and on its level of control. mHealth tools are hypothesis-generating and complement classical epidemiological approaches in identifying patients with asthma.