| dc.contributor.author | Sousa-Pinto, B; Azevedo, LF; Jutel, M; Agache, I; Canonica, GW; Czarlewski, W; Papadopoulos, NG; Bergmann, KC; Devillier, P; Laune, D; Klimek, L; Anto, A; Anto, JM; Eklund, P; Almeida, R; Bedbrook, A; Bosnic-Anticevich, S; Brough, HA; Brussino, L; Cardona, V; Casale, T; Cecchi, L; Charpin, D; Chivato, T; Costa, EM; Cruz, AA; Dramburg, S; Durham, SR; De Feo, G; van Wijk, RG; Fokkens, WJ; Gemicioglu, B; Haahtela, T; Illario, M; Ivancevich, JC; Kvedariene, V; Kuna, P; Larenas-Linnemann, DE; Makris, M; Mathieu-Dupas, E; Melen, E; Morais-Almeida, M; Mosges, R; Mullol, J; Nadeau, KC; Nhan, PH; O'Hehir, R; Regateiro, FS; Reitsma, S; Samolinski, B; Sheikh, A; Stellato, C; Todo-Bom, A; Tomazic, PV; Toppila-Salmi, S; Valero, A; Valiulis, A; Ventura, MT; Wallace, D; Waserman, S; Yorgancioglu, A; Vries, G; Eerd, M; Zieglmayer, P; Zuberbier, T; Pfaar, O; Fonseca, JA; Bousquet, J | |
| dc.description.abstract | Background Validated combined symptom-medication scores (CSMSs) are needed to investigate the effects of allergic rhinitis treatments. This study aimed to use real-life data from the MASK-air(R) app to generate and validate hypothesis- and data-driven CSMSs. Methods We used MASK-air(R) data to assess the concurrent validity, test-retest reliability and responsiveness of one hypothesis-driven CSMS (modified CSMS: mCSMS), one mixed hypothesis- and data-driven score (mixed score), and several data-driven CSMSs. The latter were generated with MASK-air(R) data following cluster analysis and regression models or factor analysis. These CSMSs were compared with scales measuring (i) the impact of rhinitis on work productivity (visual analogue scale [VAS] of work of MASK-air(R), and Work Productivity and Activity Impairment: Allergy Specific [WPAI-AS]), (ii) quality-of-life (EQ-5D VAS) and (iii) control of allergic diseases (Control of Allergic Rhinitis and Asthma Test [CARAT]). Results We assessed 317,176 days of MASK-air(R) use from 17,780 users aged 16-90 years, in 25 countries. The mCSMS and the factor analyses-based CSMSs displayed poorer validity and responsiveness compared to the remaining CSMSs. The latter displayed moderate-to-strong correlations with the tested comparators, high test-retest reliability and moderate-to-large responsiveness. Among data-driven CSMSs, a better performance was observed for cluster analyses-based CSMSs. High accuracy (capacity of discriminating different levels of rhinitis control) was observed for the latter (AUC-ROC = 0.904) and for the mixed CSMS (AUC-ROC = 0.820). Conclusion The mixed CSMS and the cluster-based CSMSs presented medium-high validity, reliability and accuracy, rendering them as candidates for primary endpoints in future rhinitis trials. | |
