Identification by cluster analysis of patients with asthma and nasal symptoms using the MASK-air® mHealth app

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Identification by cluster analysis of patients with asthma and nasal symptoms using the MASK-air® mHealth app. / Bousquet, J.; Sousa-Pinto, B.; Anto, J.M.; Amaral, R.; Brussino, L.; Canonica, G W; Cruz, A. A.; Gemicioglu, B.; Haahtela, T.; Kupczyk, M.; Kvedariene, V; Larenas-Linnemann, D E; Louis, R.; Pham-Thi, N.; Puggioni, F.; Regateiro, F. S.; Romantowski, J.; Sastre, J.; Scichilone, N; Taborda-Barata, L.; Ventura, M. T.; Agache, I.; Bedbrook, A.; Bergmann, K C; Bosnic-Anticevich, S.; Bonini, M.; Boulet, L P; Brusselle, G.; Buhl, R.; Cecchi, L.; Charpin, D.; Chaves-Loureiro, C.; Czarlewski, W.; de Blay, F.; Devillier, P.; Joos, G.; Jutel, M.; Klimek, L.; Kuna, P; Laune, D.; Pech, J. L.; Makela, M.; Morais-Almeida, M.; Nadif, R.; Niedoszytko, M.; Ohta, K.; Papadopoulos, N. G.; Papi, A.; Yeverino, D. R.; Roche, N.; Sá-Sousa, A.; Samolinski, B.; Shamji, M H; Sheikh, A; Suppli Ulrik, C.; Usmani, O. S.; Valiulis, A.; Vandenplas, O; Yorgancioglu, A; Zuberbier, T; Fonseca, J. A.

In: Pulmonology, Vol. 29, No. 4, 2023, p. 292-305.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Bousquet, J, Sousa-Pinto, B, Anto, JM, Amaral, R, Brussino, L, Canonica, GW, Cruz, AA, Gemicioglu, B, Haahtela, T, Kupczyk, M, Kvedariene, V, Larenas-Linnemann, DE, Louis, R, Pham-Thi, N, Puggioni, F, Regateiro, FS, Romantowski, J, Sastre, J, Scichilone, N, Taborda-Barata, L, Ventura, MT, Agache, I, Bedbrook, A, Bergmann, KC, Bosnic-Anticevich, S, Bonini, M, Boulet, LP, Brusselle, G, Buhl, R, Cecchi, L, Charpin, D, Chaves-Loureiro, C, Czarlewski, W, de Blay, F, Devillier, P, Joos, G, Jutel, M, Klimek, L, Kuna, P, Laune, D, Pech, JL, Makela, M, Morais-Almeida, M, Nadif, R, Niedoszytko, M, Ohta, K, Papadopoulos, NG, Papi, A, Yeverino, DR, Roche, N, Sá-Sousa, A, Samolinski, B, Shamji, MH, Sheikh, A, Suppli Ulrik, C, Usmani, OS, Valiulis, A, Vandenplas, O, Yorgancioglu, A, Zuberbier, T & Fonseca, JA 2023, 'Identification by cluster analysis of patients with asthma and nasal symptoms using the MASK-air® mHealth app', Pulmonology, vol. 29, no. 4, pp. 292-305. https://doi.org/10.1016/j.pulmoe.2022.10.005

APA

Bousquet, J., Sousa-Pinto, B., Anto, J. M., Amaral, R., Brussino, L., Canonica, G. W., Cruz, A. A., Gemicioglu, B., Haahtela, T., Kupczyk, M., Kvedariene, V., Larenas-Linnemann, D. E., Louis, R., Pham-Thi, N., Puggioni, F., Regateiro, F. S., Romantowski, J., Sastre, J., Scichilone, N., ... Fonseca, J. A. (2023). Identification by cluster analysis of patients with asthma and nasal symptoms using the MASK-air® mHealth app. Pulmonology, 29(4), 292-305. https://doi.org/10.1016/j.pulmoe.2022.10.005

Vancouver

Bousquet J, Sousa-Pinto B, Anto JM, Amaral R, Brussino L, Canonica GW et al. Identification by cluster analysis of patients with asthma and nasal symptoms using the MASK-air® mHealth app. Pulmonology. 2023;29(4):292-305. https://doi.org/10.1016/j.pulmoe.2022.10.005

Author

Bousquet, J. ; Sousa-Pinto, B. ; Anto, J.M. ; Amaral, R. ; Brussino, L. ; Canonica, G W ; Cruz, A. A. ; Gemicioglu, B. ; Haahtela, T. ; Kupczyk, M. ; Kvedariene, V ; Larenas-Linnemann, D E ; Louis, R. ; Pham-Thi, N. ; Puggioni, F. ; Regateiro, F. S. ; Romantowski, J. ; Sastre, J. ; Scichilone, N ; Taborda-Barata, L. ; Ventura, M. T. ; Agache, I. ; Bedbrook, A. ; Bergmann, K C ; Bosnic-Anticevich, S. ; Bonini, M. ; Boulet, L P ; Brusselle, G. ; Buhl, R. ; Cecchi, L. ; Charpin, D. ; Chaves-Loureiro, C. ; Czarlewski, W. ; de Blay, F. ; Devillier, P. ; Joos, G. ; Jutel, M. ; Klimek, L. ; Kuna, P ; Laune, D. ; Pech, J. L. ; Makela, M. ; Morais-Almeida, M. ; Nadif, R. ; Niedoszytko, M. ; Ohta, K. ; Papadopoulos, N. G. ; Papi, A. ; Yeverino, D. R. ; Roche, N. ; Sá-Sousa, A. ; Samolinski, B. ; Shamji, M H ; Sheikh, A ; Suppli Ulrik, C. ; Usmani, O. S. ; Valiulis, A. ; Vandenplas, O ; Yorgancioglu, A ; Zuberbier, T ; Fonseca, J. A. / Identification by cluster analysis of patients with asthma and nasal symptoms using the MASK-air® mHealth app. In: Pulmonology. 2023 ; Vol. 29, No. 4. pp. 292-305.

Bibtex

@article{734bddf78ee54d8e8e02f4f33b2dc31c,

title = "Identification by cluster analysis of patients with asthma and nasal symptoms using the MASK-air{\textregistered} mHealth app",

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{\textregistered} 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{\textregistered} 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{\textregistered} 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.",

keywords = "Asthma, Cluster analysis, Control, Rhinitis, Treatment",

author = "J. Bousquet and B. Sousa-Pinto and J.M. Anto and R. Amaral and L. Brussino and Canonica, {G W} and Cruz, {A. A.} and B. Gemicioglu and T. Haahtela and M. Kupczyk and V Kvedariene and Larenas-Linnemann, {D E} and R. Louis and N. Pham-Thi and F. Puggioni and Regateiro, {F. S.} and J. Romantowski and J. Sastre and N Scichilone and L. Taborda-Barata and Ventura, {M. T.} and I. Agache and A. Bedbrook and Bergmann, {K C} and S. Bosnic-Anticevich and M. Bonini and Boulet, {L P} and G. Brusselle and R. Buhl and L. Cecchi and D. Charpin and C. Chaves-Loureiro and W. Czarlewski and {de Blay}, F. and P. Devillier and G. Joos and M. Jutel and L. Klimek and P Kuna and D. Laune and Pech, {J. L.} and M. Makela and M. Morais-Almeida and R. Nadif and M. Niedoszytko and K. Ohta and Papadopoulos, {N. G.} and A. Papi and Yeverino, {D. R.} and N. Roche and A. S{\'a}-Sousa and B. Samolinski and Shamji, {M H} and A Sheikh and {Suppli Ulrik}, C. and Usmani, {O. S.} and A. Valiulis and O Vandenplas and A Yorgancioglu and T Zuberbier and Fonseca, {J. A.}",

note = "Publisher Copyright: {\textcopyright} 2022 Sociedade Portuguesa de Pneumologia",

year = "2023",

doi = "10.1016/j.pulmoe.2022.10.005",

language = "English",

volume = "29",

pages = "292--305",

journal = "Pulmonology",

issn = "2531-0429",

publisher = "Elsevier Espana",

number = "4",

}

RIS

TY - JOUR

T1 - Identification by cluster analysis of patients with asthma and nasal symptoms using the MASK-air® mHealth app

AU - Bousquet, J.

AU - Sousa-Pinto, B.

AU - Anto, J.M.

AU - Amaral, R.

AU - Brussino, L.

AU - Canonica, G W

AU - Cruz, A. A.

AU - Gemicioglu, B.

AU - Haahtela, T.

AU - Kupczyk, M.

AU - Kvedariene, V

AU - Larenas-Linnemann, D E

AU - Louis, R.

AU - Pham-Thi, N.

AU - Puggioni, F.

AU - Regateiro, F. S.

AU - Romantowski, J.

AU - Sastre, J.

AU - Scichilone, N

AU - Taborda-Barata, L.

AU - Ventura, M. T.

AU - Agache, I.

AU - Bedbrook, A.

AU - Bergmann, K C

AU - Bosnic-Anticevich, S.

AU - Bonini, M.

AU - Boulet, L P

AU - Brusselle, G.

AU - Buhl, R.

AU - Cecchi, L.

AU - Charpin, D.

AU - Chaves-Loureiro, C.

AU - Czarlewski, W.

AU - de Blay, F.

AU - Devillier, P.

AU - Joos, G.

AU - Jutel, M.

AU - Klimek, L.

AU - Kuna, P

AU - Laune, D.

AU - Pech, J. L.

AU - Makela, M.

AU - Morais-Almeida, M.

AU - Nadif, R.

AU - Niedoszytko, M.

AU - Ohta, K.

AU - Papadopoulos, N. G.

AU - Papi, A.

AU - Yeverino, D. R.

AU - Roche, N.

AU - Sá-Sousa, A.

AU - Samolinski, B.

AU - Shamji, M H

AU - Sheikh, A

AU - Suppli Ulrik, C.

AU - Usmani, O. S.

AU - Valiulis, A.

AU - Vandenplas, O

AU - Yorgancioglu, A

AU - Zuberbier, T

AU - Fonseca, J. A.

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - Asthma

KW - Cluster analysis

KW - Control

KW - Rhinitis

KW - Treatment

U2 - 10.1016/j.pulmoe.2022.10.005

DO - 10.1016/j.pulmoe.2022.10.005

M3 - Journal article

C2 - 36428213

AN - SCOPUS:85142526120

VL - 29

SP - 292

EP - 305

JO - Pulmonology

JF - Pulmonology

SN - 2531-0429

IS - 4

ER -

ID: 335024131