# MACVIA Clinical Decision Algorithm in Allergic Rhinitis in adolescents and adults

Jean Bousquet, Holger J. Schünemann, Peter W. Hellings, Sylvie Arnavielhe, Claus Bachert, Anna Bedbrook, Karl-Christian Bergmann, Sinthia Bosnic-Anticevich, Jan Brozek, Moises Calderon, et al.

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To cite this version:

Jean Bousquet, Holger J. Schünemann, Peter W. Hellings, Sylvie Arnavielhe, Claus Bachert, et al. MACVIA Clinical Decision Algorithm in Allergic Rhinitis in adolescents and adults. Journal of Allergy and Clinical Immunology, 2016, 10.1016/j.jaci.2016.03.025. hal-01310973

HAL Id: hal-01310973

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Distributed under a Creative Commons Attribution 4.0 International License
Accepted Manuscript

MACVIA Clinical Decision Algorithm in Allergic Rhinitis in adolescents and adults

Jean Bousquet, MD, Holger J. Schu"nemann, MD, Peter W. Hellings, MD, Sylvie Arnavielhe, PhD, Claus Bachert, MD, Anna Bedbrook, BSc, Karl-Christian Bergmann, MD, Sinthia Bosnic-Anticevich, PhD, Jan Brozek, MD, Moises Calderon, MD, G. Walter Canonica, MD, Thomas B. Casale, MD, Niels H. Chavannes, MD, Linda Cox, MD, Henry Chrystyn, PhD, Alvaro A. Cruz, MD, Ronald Dahl, MD, Giuseppe De Carlo, Dr, Pascal Demoly, MD, Philippe Devillier, MD, Gérard Dray, PhD, Monica Fletcher, MSc, Wytske J. Fokkens, MD, Joao Fonseca, MD, Sandra N. Gonzalez-Diaz, MD, Lawrence Grouse, MD, Thomas Keil, MD, Piotr Kuna, MD, Désirée Larenas-Linnemann, MD, Karin C. Lodrup Carlsen, MD, Eli O. Meltzer, MD, Joaquim Mullol, MD, Antonella Muraro, MD, Robert Naclerio, MD, Susanna Palkonen, MD, Nikolaos G. Papadopoulos, MD, Giovanni Passalacqua, MD, David Price, MD, Dermot Ryan, MD, Boleslaw Samolinski, MD, Glenis K. Scadding, MD, Aziz Sheikh, MD, Arunas Valiulis, MD, Erkka Valovirta, MD, Samantha Walker, PhD, Magnus Wickman, MD, Arzu Yorgancioglu, MD, Torsten Zuberbier, MD, on behalf of the MASK study group

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PII: S0091-6749(16)30148-8

DOI: 10.1016/j.jaci.2016.03.025

Reference: YMAI 12064

To appear in: *Journal of Allergy and Clinical Immunology*

Received Date: 30 October 2015

Revised Date: 5 February 2016

Accepted Date: 15 March 2016

Please cite this article as: Bousquet J, Schu"nemann HJ, Hellings PW, Arnavielhe S, Bachert C, Bedbrook A, Bergmann K-C, Bosnic-Anticevich S, Brozek J, Calderon M, Canonica GW, Casale TB, Chavannes NH, Cox L, Chrystyn H, Cruz AA, Dahl R, De Carlo G, Demoly P, Devillier P, Dray G, Fletcher M, Fokkens WJ, Fonseca J, Gonzalez-Diaz SN, Grouse L, Keil T, Kuna P, Larenas-Linnemann D, Lodrup Carlsen KC, Meltzer EO, Mullol J, Muraro A, Naclerio R, Palkonen S, Papadopoulos NG, Passalacqua G, Price D, Ryan D, Samolinski B, Scadding GK, Sheikh A, Valiulis A, Valovirta E, Walker S, Wickman M, Yorgancioglu A, Zuberbier T, on behalf of the MASK study group, MACVIA
Clinical Decision Algorithm in Allergic Rhinitis in adolescents and adults, *Journal of Allergy and Clinical Immunology* (2016), doi: 10.1016/j.jaci.2016.03.025.

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MACVIA Clinical Decision Algorithm in Allergic Rhinitis in
adolescents and adults

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1. University Hospital, Montpellier, France

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2. MACVIA-LR, Contre les MAladies Chroniques pour un Vleillissement Actif en Languedoc-Roussillon, European Innovation Partnership on Active and Healthy Ageing Reference Site, France

3. INSERM, VIMA : Ageing and chronic diseases. Epidemiological and public health approaches, U1168, Paris, and UVSQ, UMR-S 1168, Université Versailles St-Quentin-en-Yvelines, France

4. Departments of Clinical Epidemiology and Biostatistics and Medicine, McMaster University, Hamilton, Ontario, Canada

5. Laboratory of Clinical Immunology, Department of Microbiology and Immunology, KU Leuven, Leuven, Belgium

6. Kyomed, Montpellier, France

7. Upper Airways Research Laboratory, ENT Dept, Ghent University Hospital, Ghent, Belgium

8. Allergy-Centre-Charité at the Department of Dermatology, Charité - Universitätsmedizin Berlin, Berlin, Germany; Secretary General of the Global Allergy and Asthma European Network (GA2LEN)

9. Woolcock Institute of Medical Research, University of Sydney and Sydney Local Health District, Glebe, NSW, Australia

10. Imperial College London - National Heart and Lung Institute, Royal Brompton Hospital NHS, London, UK.

11. Allergy and Respiratory Diseases Clinic, DIMI, University of Genoa, IRCCS AOU San Martino-IST, Genoa, Italy.

12. Division of Allergy/Immunology, University of South Florida, Tampa, USA

13. Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, The Netherlands

14. Department of Medicine, Nova Southeastern University, Davie, Florida, USA.

15. RiRL, 5a Coles Lane, Oakington, Cambridge, UK

16. ProAR – Nucleo de Excelencia em Asma, Federal University of Bahia, Brasil and GARD Executive Committee

17. Department of Dermatology and Allergy Centre, Odense University Hospital, Odense, Denmark

18. EFA European Federation of Allergy and Airways Diseases Patients' Associations, Brussels, Belgium

19. EPAR U707 INSERM, Paris and EPAR UMR-S UPMC, Paris VI, Paris, France

20. Department of Respiratory Diseases, Montpellier University Hospital, France

21. Laboratoire de Pharmacologie Respiratoire UPRES EA220, Hôpital Foch, Suresnes Université Versailles Saint-Quentin, France

22. Ecole des Mines, Alès, France

23. Education for Health, Warwick, UK

24. Department of Otorhinolaryngology, Academic Medical Centre, Amsterdam, Netherlands

25. Center for research in health technologies and information systems.- CINTESIS, Universidade do Porto, Porto, Portugal ; Allergy Unit, Instituto CUF Porto e Hospital CUF Porto, Porto, Portugal ; Health Information and Decision Sciences Department - CIDES, Faculdade de Medicina, Universidade do Porto, Porto, Portugal ; Faculdade de Medicina da Universidade do Porto, Rua Dr. Plácido da Costa, s/n, 4200-450 Porto, Portugal.

26. Universidad Autónoma de Nuevo León, Mexico

27. University of Washington School of Medicine, Faculty of the Department of Neurology, USA

28. Institute of Social Medicine, Epidemiology and Health Economics, Charité - Universitätsmedizin Berlin, Berlin, and Institute for Clinical Epidemiology and Biometry, University of Wuerzburg, Germany

29. Division of Internal Medicine, Asthma and Allergy, Barlicki University Hospital, Medical University of Lodz, Poland

30. Clínica de Alergia, Asma y Pediatría, Hospital Médica Sur, México

31. Oslo University Hospital, Department of Paediatrics, Oslo, and University of Oslo, Faculty of Medicine, Institute of Clinical Medicine, Oslo, Norway

32. Allergy and Asthma Medical Group and Research Center, San Diego, California, USA.

33. Unitat de Rinologia i Clínica de l'Olfacte, Servei d'ORL, Hospital Clínic, Clinical & Experimental Respiratory Immunoallergy, IDIBAPS, Barcelona, Spain.
59 34. Food Allergy Referral Centre Veneto Region, Department of Women and Child Health, Padua General University Hospital, Padua, Italy

60 35. Section of Otolaryngology-Head and Neck Surgery, The University of Chicago Medical Center and The Pritzker School of Medicine, The University of Chicago, Illinois.

61 36. Center for Pediatrics and Child Health, Institute of Human Development, Royal Manchester Children's Hospital, University of Manchester, Manchester M13 9WL, UK. Allergy Department, 2nd Pediatric Clinic, Athens General Children's Hospital "P&A Kyriakou," University of Athens, Athens 11527, Greece.

62 37. Academic Centre of Primary Care, University of Aberdeen, Aberdeen; Research in Real-Life, Cambridge, UK.

63 38. Honorary Clinical Research Fellow, Allergy and Respiratory Research Group, The University of Edinburgh, Edinburgh, UK

64 39. Department of Prevention of Environmental Hazards and Allergology, Medical University of Warsaw, Poland

65 40. The Royal National TNE Hospital, University College London, UK

66 41. Allergy and Respiratory Research Group, Centre for Population Health Sciences, The University of Edinburgh, Medical School, UK

67 42. Vilnius University Clinic of Children's Diseases, Vilnius, Lithuania

68 43. Dept. of Lung Diseases and Clinical Allergology, University of Turku, Finland.

69 44. Asthma UK, Mansell street, London, UK

70 45. Sachs' Children's Hospital, Stockholm; Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden

71 46. Celal Bayar University Department of Pulmonology, Manisa, Turkey and GARD Executive Committee

72 73 42. Vilnius University Clinic of Children's Diseases, Vilnius, Lithuania

74 43. Dept. of Lung Diseases and Clinical Allergology, University of Turku, Finland.
75 44. Asthma UK, Mansell street, London, UK
76 45. Sachs' Children's Hospital, Stockholm; Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
77 46. Celal Bayar University Department of Pulmonology, Manisa, Turkey and GARD Executive Committee

78 79 70 40. The Royal National TNE Hospital, University College London, UK
80 81 81 71 41. Allergy and Respiratory Research Group, Centre for Population Health Sciences, The University of Edinburgh, Medical School, UK
82 82 72 73 42. Vilnius University Clinic of Children's Diseases, Vilnius, Lithuania
83 83 74 43. Dept. of Lung Diseases and Clinical Allergology, University of Turku, Finland.
84 84 75 44. Asthma UK, Mansell street, London, UK
85 85 76 45. Sachs' Children's Hospital, Stockholm; Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
86 86 76 45. Sachs' Children's Hospital, Stockholm; Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
87 87 78 79 71 41. Allergy and Respiratory Research Group, Centre for Population Health Sciences, The University of Edinburgh, Medical School, UK
88 88 72 73 42 34295 Montpellier Cedex 5, France Tel +33 611 42 88 47 jean.bousquet@orange.fr

Authors with degrees :

Bousquet Jean, MD
Schünemann Holger J, MD
Hellings Peter W, MD
Arnavielhe Sylvie, PhD
Bachert Claus, MD
Bedbrook Anna, BSc
Bergmann Karl-Christian, MD
Bosnic-Anticevitch Sinthia, PhD
Brozek Jan, MD
Calderon Moises, MD
Canonica G. Walter, MD
Casale Thomas B, MD
Chavannes Niels H, MD
Cox Linda, MD
Chrystyn Henry, PhD
Cruz Alvaro A., MD
Dahl Ronald, MD
De Carlo Giuseppe, Dr
Demoly Pascal, MD
Devillier Philippe, MD
Dray Gérard, PhD
Fletcher Monica, MSc
Fokkens Wytske J, MD
Fonseca Joao, MD
Gonzalez-Diaz Sandra N., MD
Grouse Lawrence, MD
Keil Thomas, MD
Kuna Piotr, MD
Larenas-Linnemann Désirée, MD
Lodrup Carlsen Karin C., MD
Meltzer Eli O., MD
Mullol Joaquim, MD
Muraro Antonella, MD
Naclerio Robert, MD
Palkonen Susanna, MD
Papadopoulos Nikolaos G, MD
Passalacqua Giovanni, MD
Price David, MD
Ryan Dermot, MD
Samolinski Boleslaw, MD
Scadding Glenis K, MD
Sheikh Aziz, MD
Valiulis Arunas, MD
Valovirta Erkka, MD
Walker Samantha, PhD
Wickman Magnus, MD
Yorgancioglu Arzu, MD
Zuberbier Torsten, MD

Working group members:

90 Aberer Werner, MD
91 Adachi Mitsuru, MD
92 Agache Ioana, MD
93 Akdis Cezmi A., MD
94 Akdis Mubeccel, MD
95 Annesi-Maesano Isabella, MD
96 Ansotegui Ignacio J, MD
97 Anto Josep M., MD
98 Arshad S. Hasan, MD
99 Baiardini Ilaria, MD
100 Baigenzhin Abay K, MD
101 Barbara Cristina, MD
102 Bateman Eric D., MD
103 Beghé Bianca, MD
104 Bel Elisabeth H., MD
105 Ben Kheder Ali, MD
106 Bennoor Kazi S., MD
107 Benson Michael, MD
108 Bernstein David, MD
109 Bewick Michael, MD
110 Bieber Thomas, MD
111 Bindslev-Jensen Carsten, MD
112 Bjermer Leif, MD
113 Blain Hubert, MD
114 Boner Attilio, MD
115 Bonini Matteo, MD
116 Bonini Sergio, MD
117 Bosse Isabelle, MD
118 Bouchard Jacques, MD
119 Boulet Louis-Philippe, MD
120 Bourret Rodolphe A., PhD
121 Bousquet Philippe J., MD
122 Braido Fulvio, MD
123 Briggs Andrew H., PhD
124 Brightling Christopher E., MD
125 Buhl Roland, MD
126 Burney Peter, MD
127 Bush Andrew, MD
128 Caballero-Fonseca Fernando, MD
129 Caimmi Davide P., MD
130 Camargos Paulo, MD
131 Camuzat Thierry, Mr
132 Carlsen Kai-Hakon, MD

133 Carr Warner, MD

134 Casale Thomas B., Md

135 Cepeda Sarabia Alfonso M

136 Chatzi Ledra, PhD

137 Chen Yuzhi, MD

138 Chiron Raphaël, MD

139 Chkhartishvili Ekaterine, MD

140 Chuchalin Alexander, MD

141 Ciprandi Georgio, MD

142 Cirule Ieva

143 Correia de Sousa Jaime, MD

144 Costa David, MD

145 Crooks George, MD

146 Custovic Adnan, MD

147 Dahlen Sven-Erik, MD

148 Darsow Ulf, MD

149 De Blay Frédéric, MD

150 De Manuel Keenoy Esteban, MD

151 Dedeu Tony, MD

152 Deleanu Diana, MD
Denburg Judah, MD
Didier Alain, MD
Dinh-Xuan Anh-Tuan, MD
Dokic Dejan, Md
Douagui Habib B., MD
Dubakiene Ruta, MD
Durham Stephen, MD
Dykewicz Mark, MD
El-Gamal Yehia, MD
Emuzyte Regina, MD
Fink Wagner Antje, PhD
Fiocchi Alessandro, MD
Forastiere Francesco, MD
Gamkrelidze Amiran, MD
Gemicioğlu Bilun, MD
Gereda Jose E., MD
Gerth van Wijk Roy, MD
Gotua Maia, MD
Grisle Ineta, MD
Guzmán M. Antonieta, MD
Haahtela Tari, Md
Heinrich Joachim, PhD
Hellquist-Dahl Birthe, PhD
Horak Friedrich, MD
Howarth Peter H., MD
Humbert Marc, MD
Hyland Michael, PhD
Ivancevich Juan-Carlos, MD
Jares Edgardo J., MD
Johnston Sebastian L, MD
Jonquet Olivier, MD
Joos Guy, MD
Jung Ki-Suck, MD
Just Jocelyne, MD
Jutel Marek, MD
Kaidashev Igor P., MD
Kaitov Musa, MD
Kalayci Omer, MD
Kalyoncu Fuat, Md
Keith Paul, Md
Khaltaev Nikolai, MD
Kleine-Tebbe Jorg, MD
Klimek Ludger, MD
Koffi N'Goran Bernard, MD
Kolek Vitezlav, MD
Koppelman Gerard H., MD
Kowalski Marek, MD
Kull Inger, PhD
Kvedariene Violeta, MD
Lambrecht Bart, MD
Lau Susanne, MD
Laune Daniel, PhD
Le Thi Tuyet Lan, MD
Li Jing, MD
Lieberman Philippe, MD
Lipworth Brian J., MD
Louis Renaud, MD
Magard Yves, MD
Magnan Antoine, MD
Mahboub Bassam, MD
Majer Ivan, MD
Makela Mika, MD
Manning Peter J., MD
Masjedi Mohamad, R., MD
Maurer Marcus, MD
Mavale-Manuel Sandra, MD
Melén Erik, MD
Melo-Gomes Elisabete, MD
Mercier Jacques, MD
Merk Hans, MD
Miculinic Neven, MD
Mihaltan Florin, MD
Milenkovic Branislava, MD
Mohammad Yousser, MD
Molimard Mathieu, MD
Momas Isabelle, PhD
Montilla-Santana Anna, MD
Morais-Almeida Mario, MD
Mösges Ralph, MD
Nadif Rachel, PhD
Namazova-Baranova Leyla, MD
Neffen Hugo, MD
Nekam Kristof, MD
Neou Angelos, MD
Niggemann Bodo, MD
Nyembue Dieudonné, MD
O'Hehir Robyn, MD
Ohta Ken, MD
Okamoto Yoshitaka, MD
Okubo Kim, MD
Ouedraogo Solange, MD
Paggiaro Pier-Luigi, MD
Pali-Schöll Isabella, MD
Palmer Stephen, MSc
Panzner Petr, Md
Papi Alberto, MD
Park Hae-Sim, MD
Pavord Ian, MD
Pawankar Ruby, MD
Pfaar Oliver, MD
Picard Robert, PhD
Pigearias Bernard, MD
Pin Isabelle, MD
Plavec Davor, MD
Pohl Wofgang, MD
Popov Todor, MD
Postma Dirkje S., MD
Potter Paul, MD
Poulsen Lars K., PhD
Rabe Klaus, F., MD
Raciborski Filip, PhD
Radier Pontal Françoise, MD
Reitamo Sakari, MD
Repka-Ramirez Maria-Susana, MD
Robalo-Cordeiro Carlos, MD
Roberts Graham, MD
Rodenas Francisco, PhD
Rolland Christine, MD
Roman Rodriguez Miguel, MD
Romano Antonino, MD
Rosado-Pinto José, MD
Rosario Nelson A., MD
Rosenwasser Larry, MD
Rottem Menachem, MD
Sanchez-Borges Mario, MD
Sastre-Dominguez Joaquim, MD
Schmid-Grendelmeier Peter, MD
Serrano Eli, MD
Simons F. Estelle R., MD
Sisul Juan-Carlos, MD
Skrindo Ingebjorg, MD
Smit Henriette A., PhD
Solé Dirceu, MD
Sooronbaev Talant, MD
Spranger Otto, Mr
Stelmach Rafael, MD
Strandberg Timo, MD
Sunyer Jordi, MD
Thijs Carel, MD
Todo-Bom Ana-Maria, MD
Triggiani Massimo, MD
Valenta Rudolf, MD
Valero Antonio L., MD
van Hage Marianne, MD
Vandenplas Olivier, MD
Vezzani Giorgio, MD
Vichyanond Pakit, MD
Viegi Giovanni, MD
Wagenmann Martin, MD
Wahn Ulrich, MD
Wang De Yun, MD
Williams Dennis, PhD
Wright John, MD
Yawn Barbara P., MD
Yiallouros Panayiotis, MD
Yusuf Osman M., MD
Zar Heather J., MD
Zernotti Mario, MD
Zhang Luo, MD
Zhong Nanshan, Md
Zidarn Mihaela, MD
155 **Key words:** allergic rhinitis, conjunctivitis, ARIA, MACVIA-LR, ICT, clinical decision support

156 system

157

## Abbreviations

159

160 AHA: Active and Healthy Ageing

161

161 AIRWAYS ICPs: Integrated Care Pathways for Airway diseases

162

162 AIT: Allergen immunotherapy

163

163 AR: Allergic rhinitis

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164 ARIA: Allergic Rhinitis and its Impact on Asthma

165

165 CDSS: Clinical decision support system

166

166 EIP: European Innovation Partnership

167

167 ICP: Integrated care pathway

168

168 MACVIA-LR: Contre les MAladies Chroniques pour un VIeillissement Actif en Languedoc-Roussillon

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169 MASK: MACVIA-ARIA Sentinel networK

170

170 QOL: Quality of life

171

171 SCUAD: Severe chronic Upper Airway Disease

172

172 VAS: Visual analogue scale

173

## Summary

174

175

176

176 The selection of pharmacotherapy for patients with allergic rhinitis depends on several factors, including age, prominent symptoms, symptom severity, control of allergic rhinitis, patient preferences and cost. Allergen exposure and resulting symptoms vary and treatment adjustment is required.

177

178

179

179 Clinical decision support systems (CDSS) may be beneficial for the assessment of disease control.

180

180 Clinical decision support systems should be based on the best evidence and algorithms to aid patients

181

181 and health care professionals to jointly determine the treatment and its step-up or step-down strategy

182

182 depending on AR control. MACVIA-LR (Fighting chronic diseases for active and healthy ageing) one

183

183 of the reference sites of the European Innovation Partnership on Active and Healthy Ageing, has

184

184 initiated an allergy sentinel network (MASK: MACVIA-ARIA Sentinel networK). A clinical decision

185

185 support system is currently being developed to optimize allergic rhinitis control. An algorithm

186

186 developed by consensus is presented in this paper. This algorithm should be confirmed by appropriate

187

187 trials.
188 **Introduction**

189 The selection of pharmacotherapy for patients with allergic rhinitis (AR) depends on several factors
190 such as age, prominent symptoms, symptom severity, control of AR, patient preferences, availability
191 of treatment and cost (1). Allergen exposure and resulting symptoms varying daily, AR patients would
192 benefit from regular monitoring of their symptoms to facilitate treatment adjustment. Clinical decision
193 support systems (CDSS) may be beneficial for the accomplishment of this task by assessing disease
194 control, for example in response to treatment (2). A CDSS is a health information technology system
195 designed to assist health care professionals and patients with clinical decision-making tasks.
196 Knowledge-based CDSSs consist of three parts: the knowledge base, an inference engine, and a
197 mechanism to communicate (3, 4). The knowledge base contains the rules and associations of
198 compiled data. The inference engine combines the rules from the knowledge base with the patient's
199 data. The communication mechanism allows the system to show the results to the user as well as have
200 input into the system. CDSS should be based on the best evidence and algorithms to aid patients and
201 health care professionals to jointly determine the treatment and its step-up or step-down strategy
202 depending on AR control (1). Thus, CDSS should help to optimize treatment.

203 MACVIA-LR (Fighting chronic diseases for active and healthy ageing, http://macvia.cr-
204 languedocroussillon.fr) is one of the reference sites of the European Innovation Partnership on Active
205 and Healthy Ageing (7). It initiated the project AIRWAYS ICPs (integrated care pathways for airway
206 diseases) (8) and the allergy sentinel network MASK (MACVIA-ARIA Sentinel NetworK) (2). A
207 knowledge-based CDSS is currently being developed to optimize AR control. The communication
208 mechanism of MASK uses interconnected tablets and cell phones (5, 6). The proposed algorithm of
209 the MACVIA-CDSS is presented in this paper.

210 **Control of allergic rhinitis and rhino-conjunctivitis**

211 In asthma, the treatment strategy is based on disease control and current treatment (9-11). The
212 variability in symptom control is challenging, and necessitates careful monitoring as well as the step
213 up / step down of individualized therapeutic regimens over time. Both long- and short-term
214 maintenance and reliever approaches have been proposed (12) including the combination of inhaled
215 corticosteroid and fast-onset long-acting β-agonist inhaler as maintenance and reliever therapy (13).

216 The symptoms of AR can cause considerable morbidity in physical and emotional comfort as well as
217 in functional capacity and quality-of-life (QOL). The control and severity of AR have been defined in
218 a similar manner to asthma (2, 14, 15). Measures of AR control include symptom scores, patients'
219 self-administered visual analogue scales (VAS), objective measures of nasal obstruction, a recent
220 modification of the ARIA severity classification, and patients' reported outcomes such as QOL or
221 scores with several items (16, 17). However, the challenges of managing AR are increased by the fact
222 that patients do not often recognise their AR symptoms or confuse them with those of asthma (18).
223 Therefore it is important for patients to be able to use an AR symptom scoring system that is simple to
224 use and rapidly responsive to change.

225 As is the case for asthma, the best control of AR should be achieved as early as possible in order to: (i)
226 improve patient satisfaction and concordance to treatment, and (ii) reduce the consequences of AR
227 including symptoms, reduced QOL, and school and work absenteeism. Untreated AR can impair
228 driving ability and put patients at risk (19). The ultimate goal of AR control is to reduce the costs
229 incurred by AR (20-23).

230 A step-up/step-down approach to AR pharmacotherapy, based on patient response, may hold the
231 potential for optimal AR control and cost of treatment (1). MASK has proposed that electronic daily
232 monitoring using VAS may help patients to achieve optimal control of AR symptoms (2). Well-
233 controlled AR is defined as VAS score ≤ 2 out of 10. VAS cut-off values to step up or down treatment
234 were proposed by comparison to pain VAS scores and step-up schemes or from literature in the field
235 of allergy (Online supplement 1) (24-26).

236 **Recommendations for the treatment of allergic rhinitis and rhino-**
237 **conjunctivitis**

238 The treatment of AR also requires the consideration of (i) the type (rhinitis, conjunctivitis and/or
239 asthma) and severity of symptoms, (ii) the relative efficacy of the treatment, (iii) speed of onset of
240 action of treatment, (iv) current treatment, (v) historic response to treatment, (vi) patient preference,
241 (vi) interest to self-manage and (viii) resource use. Guidelines (27) and various statements by experts
242 for AR pharmacotherapy usually propose the approach summarized in Box 1.

243 **Box 1: Summary of recommendations for the treatment of allergic rhinitis and conjunctivitis used**
244 **in the algorithm**

245 • Oral or intra-nasal H1-anti-histamines are less effective than intra-nasal corticosteroids for the control of all rhinitis symptoms (28-33).

246 • Leukotriene receptor antagonists are usually considered to be less effective than oral H1-anti-histamines (30, 34, 35).

247 • Comparisons between oral and intra-nasal H1-anti-histamines differ between recommendations, thus no definite conclusions have yet been reached.

248 • The combined intranasal fluticasone propionate and azelastine hydrochloride in a single device is more effective than monotherapy and is indicated for patients when monotherapy with either intra-nasal H1-antihistamine or glucocorticoid is considered inadequate (1, 34-37).

249 • Intra-nasal anti-histamines and intra-nasal corticosteroids are effective for ocular symptoms with no significant difference between them (38, 39). However, the combination of azelastine and fluticasone propionate was more effective than fluticasone propionate alone (36, 37).

250 • In most studies, combinations of oral anti-histamines or leukotriene receptor antagonists and intra-nasal corticosteroids are in general not more effective than monotherapy with intra-nasal corticosteroids (40, 41).

251 • Intra-ocular H1 anti-histamines or cromones are effective for ocular symptoms (42). The importance of decongestants is debatable (30). However, efficacy of treatment varies with individual patient response.

252 • In clinical practice, intra-nasal corticosteroids need a few days to be fully effective, whereas intra-nasal H1 anti-histamines or combined intra-nasal fluticasone and azelastine are rapidly effective (43).

253 • All recommended medications are considered to be safe at the usual dosage. First-generation oral H1-antihistamines are sedating and should be avoided (44).

254 • Oral or nebulized corticosteroids may be helpful in severe patients uncontrolled by other treatment, although studies are lacking in AR (45).

255 • Further studies are needed in pre-school children to make more firm recommendations possible, although recent studies show the efficacy of oral H1 anti-histamines (46).

269 Allergen immunotherapy appears to be as effective as pharmacotherapy (47, 48) but is also regarded as a disease modifier intervention with the potential of altering the natural history of allergic diseases (49, 50).

270 Non-pharmacologic interventions such as nasal filters (51) or saline have been found to be effective.

271 **Patients' views**

275 Many patients with AR are not satisfied with their current treatment (52-54), and this results in frequent non-adherence to therapy (55, 56). In some studies, most patients were satisfied with their treatment but full control was rarely achieved (54, 57-59). Despite the vast availability of treatment options, most patients are “very interested” in finding a new medication (56, 60) and around 25% are “constantly” trying different medications to find one that “works” (56). Patients want more effective
280 treatments that can control all their symptoms, including ocular ones (61, 62), and a more rapid onset
281 of action (63).

282 Some patients feel that their healthcare provider does not understand their allergy treatment needs or
283 does not take their allergy symptoms seriously (52). Many patients self-medicate using over-the-
284 counter (OTC) drugs for a long period of time and usually only consult a physician when their
285 treatment is ineffective (58). In one study, patients chose a step down therapy to speed up the control
286 of symptoms (64).

287 Patients' individual preference for an oral or an intra-nasal route treatment needs to be considered (52, 64, 65). In addition, health care professionals need to inform the patient of the relative benefits and harms of each prescribed treatment in order to support their decision making.

290 Algorithm decision aid

291 A step-up/step-down individualized approach to AR pharmacotherapy may hold the potential for
292 optimal control of AR symptoms while minimizing side effects and costs (1). However,

293 • As in asthma, treated and untreated patients should be considered differently (Figures 1 and 2).

294 • Most patients have received a previous treatment that should guide health care professionals with regards to the current prescription.

295 • Patterns of use of medication in previously-treated patients should be evaluated when future treatment is initiated.

298 The step-up or step-down strategy should be discussed with the patient and should consider:

299 • Efficacy of previous treatments.

300 • Adherence to treatment

301 • The patient's preference (route of administration, fear of side effects and experience of the patient regarding the treatment).

302 • Possible side effects or harms.

304 • Costs.

305 Step-up approach:

306 • Step 1, for mild symptoms, intranasal or oral non-sedating H1-antihistamine.

307 • Step 2, for moderate-severe symptoms and/or persistent AR, intranasal corticosteroids. The dose of some intra-nasal corticosteroids can be increased according to the package insert.

308 • Step 3, for patients with uncontrolled symptoms at step 2 (current or historical), combination of intra-nasal corticosteroids and intra-nasal H1-antihistamines. However, depending on the physicians's experience, other therapeutic strategies may be used.

309 • Step 4: It is possible that an additional short course of oral steroids may help to establish control and continue control by Step 3. Intra-ocular cromones or H1-anti-histamines may be added to improve the control of ocular symptoms.

310 • Treatment should be re-assessed quickly (e.g. 1 to 7 days) to confirm control using a step-up approach.

311 • Patients uncontrolled at Step 3 should be considered as having severe chronic upper airway disease (SCUAD) (66, 67) and may benefit from specialist referral and assessment for allergy workup and nasal examination (68). For example, specialist referral should be considered if there is failure to reduce VAS <5/10 after 10-14 days assuming the patient is adherent to therapy.

312 • At all times, patient adherence and intranasal device technique mastery should be regarded as potential for lack of treatment effect.

313 Alternatively, a step-down approach may be used and Step 3 treatment should be considered as the first option in patients with a previous treatment failure or resistance to monotherapy. After a few days of achieving complete control, consideration could be given to treatment reduction. However, the step down approach is based on consensus and more data are needed.

314 The duration of treatment is determined by the type of rhinitis (intermittent or persistent). In the patient with intermittent rhinitis, treatment should be continued daily for two weeks or for the duration
329 of the pollen season or other specific allergen exposure. In the patient with persistent rhinitis, a longer
330 course of treatment is often needed. It is of course important to assess concordance with agreed
331 regimens, as treatment failure may be a result of poor patient concordance.

332 **Conclusion**

333 We propose a simple algorithm to step up or step down AR treatment globally. However, its use varies
334 depending on the availability of medications in the different countries and on resources. These issues
335 have not been approached in the present paper due to their variability between countries. Algorithms,
336 inherently, are a combination of individual decision nodes that represent separate recommendations.
337 They require testing as a complete algorithm and comparison to alternative strategies to explore
338 whether the combination of these separate recommendations leads to more benefit than harm when
339 applied in practice. Thus, this algorithm, as with other algorithms, requires testing in large scale trials
340 to provide the necessary certainty in the available evidence. The current algorithm is being developed
341 by MASK (2) for a CDSS that will be available on Apple and Android and that will provide
342 opportunities for evaluation.

343
344
345
346 **Figure 1: Step-up algorithm in untreated patients using visual analogue scale (adolescents and adults)**

347 *The proposed algorithm considers the treatment steps and patient's preference*

348 VAS levels in ratio

350 If remaining ocular symptoms, add intra-ocular treatment

351

352

353

354 **Figure 2: Step-up algorithm in treated patients using visual analogue scale (adolescents and adults)**

355 *The proposed algorithm considers the treatment steps and patient's preference*

356 VAS levels in ratio

358 If remaining ocular symptoms, add intra-ocular treatment

359
360 **References**

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542
543
Assessment of control in untreated symptomatic patient

<figure><img src="image_4.png" /></figure>

<footnote>*: consider INCS+AZE if previous treatment ineffective (historical)</footnote>
Assessment of control in treated symptomatic patient

<figure><img src="image_5.png" /></figure>
1 **ONLINE SUPPLEMENT**

2

3 **Rationale for using VAS in the algorithm**

4

Certain differences between groups in their VAS scores or changes in score may have no clinical relevance, even if they achieve statistical significance. A wide range of Minimally Clinically Important Differences (MCID) in change scores on the pain VAS have been reported (69) using different methods. MCDI ranged from nine to 30 mm (out of 100 mm) in emergency departments (70-74). In other settings changes of 33% (75) and 31 mm (76) have been shown as clinically meaningful. In endometriosis pain MCID was set at 10 mm (77). The MCID for fatigue VAS was around 10 mm in a large rheumatoid arthritis clinical practice and similar to that seen in clinical trials (78). The MCID in VAS pain score does not differ with gender, age and cause-of-pain groups (71) or with the severity of pain being experienced (79). However, the linearity of the pain VAS is found in some (80) but not all studies (69, 81, 82). Pain VAS measurement error has been reported up to 20 mm (83, 84). Consequently, change scores and the calculations of aspects such as MCID may be carefully considered by the potential lack of interval scaling of the VAS, and further compromised by the magnitude of measurement error. Repeated pain VAS data meets the strict requirements of the Rasch model, including unidimensionality, and that it is internally valid (69). However, pain VAS does not behave linearly and the MCID may under- or overestimate true change during repeated pain VAS (85).

In allergic rhinitis, there is to our knowledge, a single study that has estimated MCDI in VAS during treatment (25). Using receiver operating characteristic (ROC) curve analysis, an appropriate method for the estimation of MCDI, the established cut-off variation of 23 mm for VAS was associated with a cut-off variation of 0.5 for RQLQ. Sensitivity analysis with RQLQ and TSS6 scales confirmed the aptitude of the cut-off value (23 mm) to discriminate changes in symptoms and quality-of-life. The MCID was the same whatever the baseline VAS level (25). A level of over 23 mm appears to be a relevant cutoff. VAS changes appear to encompass both symptoms and disease-specific QOL (25, 86). Another study, CARAT (Control of Allergic Rhinitis and Asthma Test (87, 88)), approximated the VAS-MCDI. In CARAT, the MCID is 4 (range 0-30) (89). The real life study of Demoly et al in primary care (25) used the same methods as a cluster randomized trial carried out in specialist practices (24). Both studies, carried out in France in large populations, showed a very similar change in VAS levels during treatment depending on total symptom scores and RQLQ. These studies suggest that the cutoff of 23 mm (25) is appropriate to find a clinically significant difference.

VAS levels appear to be similar in different countries in severe intermittent or persistent rhinitis. VAS can be used in all age groups including preschool children (guardian evaluation) (90) and the elderly (91). Furthermore, it can be used in a wide variety of languages (91-98). VAS levels vary with the ARIA classification in many languages (94, 99, 100, 101). A VAS level of 50 (over 100 mm) is suggestive of moderate-severe AR (62, 102, 103) although in some studies the cutoff was of over 60 mm (95). VAS was used to define SCUAD (24). Thus, the MCDI found in two large French populations may be generalized to other countries with different languages and cultures across the life cycle. However, future studies should refine this cutoff level.

41

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