Consistent trajectories of rhinitis control and treatment in 16,177 weeks: The <scp>MASK</scp>‐air® longitudinal study
Bernardo Sousa‐Pinto, Holger J. Schünemann, Ana Sá‐Sousa, Rafael José Vieira, Rita Amaral, Josep M. Antó, Ludger Klimek, Wienczyslawa Czarlewski, Joaquim Mullol, Oliver Pfaar, Anna Bedbrook, Luisa Brussino, Violeta Kvedarienė, Désirée Larenas‐Linnemann, Yoshitaka Okamoto, Maria Teresa Ventura, Ioana Agache, Ignacio J. Ansotegui, Karl‐Christian Bergmann, Sinthia Bosnic‐Anticevich, Giorgio Walter Canonica, Victória Cardona, Pedro Martins, Thomas B. Casale, Lorenzo Cecchi, Tomás Chivato, Derek K. Chu, Cemal Cingi, Elı́sio Costa, Álvaro A. Cruz, Stefano Del Giacco, Philippe Devillier, Patrik Eklund, Wytske J. Fokkens, Bilun Gemicioğlu, Tari Haahtela, Juan Carlos Ivancevich, Zhanat Ispayeva, Marek Jutel, Piotr Kuna, Ігор Петрович Кайдашев, Musa Khaitov, Helga Kraxner, Daniel Laune, Brian J. Lipworth, Renaud Louis, Μichael Μakris, Riccardo Monti, Mário Morais‐Almeida, Ralph Mösges, Marek Niedoszytko, Nikolaos G. Papadopoulos, Vincenzo Patella, N. Pham‐Thi, Frederico S. Regateiro, Sietze Reitsma, Philip W. Rouadi, Bolesław Samoliński, Aziz Sheikh, Milan Sova, Ana Todo‐Bom, Luís Taborda‐Barata, Sanna Toppila‐Salmi, J. Sastre, Ioanna Tsiligianni, Arūnas Valiulis, Olivier Vandenplas, Dana Wallace, Susan Waserman, Arzu Yorgancıoğlu, Mihaela Zidarn, Torsten Zuberbier, João Fonseca, Jean Bousquet
Abstract
INTRODUCTION: Data from mHealth apps can provide valuable information on rhinitis control and treatment patterns. However, in MASK-air®, these data have only been analyzed cross-sectionally, without considering the changes of symptoms over time. We analyzed data from MASK-air® longitudinally, clustering weeks according to reported rhinitis symptoms. METHODS: We analyzed MASK-air® data, assessing the weeks for which patients had answered a rhinitis daily questionnaire on all 7 days. We firstly used k-means clustering algorithms for longitudinal data to define clusters of weeks according to the trajectories of reported daily rhinitis symptoms. Clustering was applied separately for weeks when medication was reported or not. We compared obtained clusters on symptoms and rhinitis medication patterns. We then used the latent class mixture model to assess the robustness of results. RESULTS: We analyzed 113,239 days (16,177 complete weeks) from 2590 patients (mean age ± SD = 39.1 ± 13.7 years). The first clustering algorithm identified ten clusters among weeks with medication use: seven with low variability in rhinitis control during the week and three with highly-variable control. Clusters with poorly-controlled rhinitis displayed a higher frequency of rhinitis co-medication, a more frequent change of medication schemes and more pronounced seasonal patterns. Six clusters were identified in weeks when no rhinitis medication was used, displaying similar control patterns. The second clustering method provided similar results. Moreover, patients displayed consistent levels of rhinitis control, reporting several weeks with similar levels of control. CONCLUSIONS: We identified 16 patterns of weekly rhinitis control. Co-medication and medication change schemes were common in uncontrolled weeks, reinforcing the hypothesis that patients treat themselves according to their symptoms.