Discriminating severe seasonal allergic rhinitis. Results from a large nation-wide database.

Allergic rhinitis (AR) is a chronic disease affecting a large amount of the population. To optimize treatment and disease management, it is crucial to detect patients suffering from severe forms. Several tools have been used to classify patients according to severity: standardized questionnaires, vi...
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Autor*in:	Davide Caimmi [verfasserIn] Nour Baiz [verfasserIn] Shreosi Sanyal [verfasserIn] Soutrik Banerjee [verfasserIn] Pascal Demoly [verfasserIn] Isabella Annesi-Maesano [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Übergeordnetes Werk:	In: PLoS ONE - Public Library of Science (PLoS), 2007, 13(2018), 11, p e0207290
Übergeordnetes Werk:	volume:13 ; year:2018 ; number:11, p e0207290

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1371/journal.pone.0207290

Katalog-ID:	DOAJ005714389

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allfields	10.1371/journal.pone.0207290 doi (DE-627)DOAJ005714389 (DE-599)DOAJ1f23915ce356434abb94ab509b79546a DE-627 ger DE-627 rakwb eng Davide Caimmi verfasserin aut Discriminating severe seasonal allergic rhinitis. Results from a large nation-wide database. 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Allergic rhinitis (AR) is a chronic disease affecting a large amount of the population. To optimize treatment and disease management, it is crucial to detect patients suffering from severe forms. Several tools have been used to classify patients according to severity: standardized questionnaires, visual analogue scales (VAS) and cluster analysis. The aim of this study was to evaluate the best method to stratify patients suffering from seasonal AR and to propose cut-offs to identify severe forms of the disease. In a multicenter French study (PollinAir), patients suffering from seasonal AR were assessed by a physician that completed a 17 items questionnaire and answered a self-assessment VAS. Five methods were evaluated to stratify patients according to AR severity: k-means clustering, agglomerative hierarchical clustering, Allergic Rhinitis Physician Score (ARPhyS), total symptoms score (TSS-17), and VAS. Fisher linear, quadratic discriminant analysis, non-parametric kernel density estimation methods were used to evaluate miss-classification of the patients and cross-validation was used to assess the validity of each scale. 28,109 patients were categorized into "mild", "moderate", and "severe", through the 5 different methods. The best discrimination was offered by the ARPhyS scale. With the ARPhyS scale, cut-offs at a score of 8-9 for mild to moderate and of 11-12 for moderate to severe symptoms were found. Score reliability was also acceptable (Cronbach's α coefficient: 0.626) for the ARPhyS scale, and excellent for the TSS-17 (0.864). The ARPhyS scale seems the best method to target patients with severe seasonal AR. In the present study, we highlighted optimal discrimination cut-offs. This tool could be implemented in daily practice to identify severe patients that need a specialized intervention. Medicine R Science Q Nour Baiz verfasserin aut Shreosi Sanyal verfasserin aut Soutrik Banerjee verfasserin aut Pascal Demoly verfasserin aut Isabella Annesi-Maesano verfasserin aut In PLoS ONE Public Library of Science (PLoS), 2007 13(2018), 11, p e0207290 (DE-627)523574592 (DE-600)2267670-3 19326203 nnns volume:13 year:2018 number:11, p e0207290 https://doi.org/10.1371/journal.pone.0207290 kostenfrei https://doaj.org/article/1f23915ce356434abb94ab509b79546a kostenfrei https://doi.org/10.1371/journal.pone.0207290 kostenfrei https://doaj.org/toc/1932-6203 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_34 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_235 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2018 11, p e0207290
spelling	10.1371/journal.pone.0207290 doi (DE-627)DOAJ005714389 (DE-599)DOAJ1f23915ce356434abb94ab509b79546a DE-627 ger DE-627 rakwb eng Davide Caimmi verfasserin aut Discriminating severe seasonal allergic rhinitis. Results from a large nation-wide database. 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Allergic rhinitis (AR) is a chronic disease affecting a large amount of the population. To optimize treatment and disease management, it is crucial to detect patients suffering from severe forms. Several tools have been used to classify patients according to severity: standardized questionnaires, visual analogue scales (VAS) and cluster analysis. The aim of this study was to evaluate the best method to stratify patients suffering from seasonal AR and to propose cut-offs to identify severe forms of the disease. In a multicenter French study (PollinAir), patients suffering from seasonal AR were assessed by a physician that completed a 17 items questionnaire and answered a self-assessment VAS. Five methods were evaluated to stratify patients according to AR severity: k-means clustering, agglomerative hierarchical clustering, Allergic Rhinitis Physician Score (ARPhyS), total symptoms score (TSS-17), and VAS. Fisher linear, quadratic discriminant analysis, non-parametric kernel density estimation methods were used to evaluate miss-classification of the patients and cross-validation was used to assess the validity of each scale. 28,109 patients were categorized into "mild", "moderate", and "severe", through the 5 different methods. The best discrimination was offered by the ARPhyS scale. With the ARPhyS scale, cut-offs at a score of 8-9 for mild to moderate and of 11-12 for moderate to severe symptoms were found. Score reliability was also acceptable (Cronbach's α coefficient: 0.626) for the ARPhyS scale, and excellent for the TSS-17 (0.864). The ARPhyS scale seems the best method to target patients with severe seasonal AR. In the present study, we highlighted optimal discrimination cut-offs. This tool could be implemented in daily practice to identify severe patients that need a specialized intervention. Medicine R Science Q Nour Baiz verfasserin aut Shreosi Sanyal verfasserin aut Soutrik Banerjee verfasserin aut Pascal Demoly verfasserin aut Isabella Annesi-Maesano verfasserin aut In PLoS ONE Public Library of Science (PLoS), 2007 13(2018), 11, p e0207290 (DE-627)523574592 (DE-600)2267670-3 19326203 nnns volume:13 year:2018 number:11, p e0207290 https://doi.org/10.1371/journal.pone.0207290 kostenfrei https://doaj.org/article/1f23915ce356434abb94ab509b79546a kostenfrei https://doi.org/10.1371/journal.pone.0207290 kostenfrei https://doaj.org/toc/1932-6203 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_34 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_235 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2018 11, p e0207290
allfields_unstemmed	10.1371/journal.pone.0207290 doi (DE-627)DOAJ005714389 (DE-599)DOAJ1f23915ce356434abb94ab509b79546a DE-627 ger DE-627 rakwb eng Davide Caimmi verfasserin aut Discriminating severe seasonal allergic rhinitis. Results from a large nation-wide database. 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Allergic rhinitis (AR) is a chronic disease affecting a large amount of the population. To optimize treatment and disease management, it is crucial to detect patients suffering from severe forms. Several tools have been used to classify patients according to severity: standardized questionnaires, visual analogue scales (VAS) and cluster analysis. The aim of this study was to evaluate the best method to stratify patients suffering from seasonal AR and to propose cut-offs to identify severe forms of the disease. In a multicenter French study (PollinAir), patients suffering from seasonal AR were assessed by a physician that completed a 17 items questionnaire and answered a self-assessment VAS. Five methods were evaluated to stratify patients according to AR severity: k-means clustering, agglomerative hierarchical clustering, Allergic Rhinitis Physician Score (ARPhyS), total symptoms score (TSS-17), and VAS. Fisher linear, quadratic discriminant analysis, non-parametric kernel density estimation methods were used to evaluate miss-classification of the patients and cross-validation was used to assess the validity of each scale. 28,109 patients were categorized into "mild", "moderate", and "severe", through the 5 different methods. The best discrimination was offered by the ARPhyS scale. With the ARPhyS scale, cut-offs at a score of 8-9 for mild to moderate and of 11-12 for moderate to severe symptoms were found. Score reliability was also acceptable (Cronbach's α coefficient: 0.626) for the ARPhyS scale, and excellent for the TSS-17 (0.864). The ARPhyS scale seems the best method to target patients with severe seasonal AR. In the present study, we highlighted optimal discrimination cut-offs. This tool could be implemented in daily practice to identify severe patients that need a specialized intervention. Medicine R Science Q Nour Baiz verfasserin aut Shreosi Sanyal verfasserin aut Soutrik Banerjee verfasserin aut Pascal Demoly verfasserin aut Isabella Annesi-Maesano verfasserin aut In PLoS ONE Public Library of Science (PLoS), 2007 13(2018), 11, p e0207290 (DE-627)523574592 (DE-600)2267670-3 19326203 nnns volume:13 year:2018 number:11, p e0207290 https://doi.org/10.1371/journal.pone.0207290 kostenfrei https://doaj.org/article/1f23915ce356434abb94ab509b79546a kostenfrei https://doi.org/10.1371/journal.pone.0207290 kostenfrei https://doaj.org/toc/1932-6203 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_34 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_235 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2018 11, p e0207290
allfieldsGer	10.1371/journal.pone.0207290 doi (DE-627)DOAJ005714389 (DE-599)DOAJ1f23915ce356434abb94ab509b79546a DE-627 ger DE-627 rakwb eng Davide Caimmi verfasserin aut Discriminating severe seasonal allergic rhinitis. Results from a large nation-wide database. 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Allergic rhinitis (AR) is a chronic disease affecting a large amount of the population. To optimize treatment and disease management, it is crucial to detect patients suffering from severe forms. Several tools have been used to classify patients according to severity: standardized questionnaires, visual analogue scales (VAS) and cluster analysis. The aim of this study was to evaluate the best method to stratify patients suffering from seasonal AR and to propose cut-offs to identify severe forms of the disease. In a multicenter French study (PollinAir), patients suffering from seasonal AR were assessed by a physician that completed a 17 items questionnaire and answered a self-assessment VAS. Five methods were evaluated to stratify patients according to AR severity: k-means clustering, agglomerative hierarchical clustering, Allergic Rhinitis Physician Score (ARPhyS), total symptoms score (TSS-17), and VAS. Fisher linear, quadratic discriminant analysis, non-parametric kernel density estimation methods were used to evaluate miss-classification of the patients and cross-validation was used to assess the validity of each scale. 28,109 patients were categorized into "mild", "moderate", and "severe", through the 5 different methods. The best discrimination was offered by the ARPhyS scale. With the ARPhyS scale, cut-offs at a score of 8-9 for mild to moderate and of 11-12 for moderate to severe symptoms were found. Score reliability was also acceptable (Cronbach's α coefficient: 0.626) for the ARPhyS scale, and excellent for the TSS-17 (0.864). The ARPhyS scale seems the best method to target patients with severe seasonal AR. In the present study, we highlighted optimal discrimination cut-offs. This tool could be implemented in daily practice to identify severe patients that need a specialized intervention. Medicine R Science Q Nour Baiz verfasserin aut Shreosi Sanyal verfasserin aut Soutrik Banerjee verfasserin aut Pascal Demoly verfasserin aut Isabella Annesi-Maesano verfasserin aut In PLoS ONE Public Library of Science (PLoS), 2007 13(2018), 11, p e0207290 (DE-627)523574592 (DE-600)2267670-3 19326203 nnns volume:13 year:2018 number:11, p e0207290 https://doi.org/10.1371/journal.pone.0207290 kostenfrei https://doaj.org/article/1f23915ce356434abb94ab509b79546a kostenfrei https://doi.org/10.1371/journal.pone.0207290 kostenfrei https://doaj.org/toc/1932-6203 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_34 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_235 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2018 11, p e0207290
allfieldsSound	10.1371/journal.pone.0207290 doi (DE-627)DOAJ005714389 (DE-599)DOAJ1f23915ce356434abb94ab509b79546a DE-627 ger DE-627 rakwb eng Davide Caimmi verfasserin aut Discriminating severe seasonal allergic rhinitis. Results from a large nation-wide database. 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Allergic rhinitis (AR) is a chronic disease affecting a large amount of the population. To optimize treatment and disease management, it is crucial to detect patients suffering from severe forms. Several tools have been used to classify patients according to severity: standardized questionnaires, visual analogue scales (VAS) and cluster analysis. The aim of this study was to evaluate the best method to stratify patients suffering from seasonal AR and to propose cut-offs to identify severe forms of the disease. In a multicenter French study (PollinAir), patients suffering from seasonal AR were assessed by a physician that completed a 17 items questionnaire and answered a self-assessment VAS. Five methods were evaluated to stratify patients according to AR severity: k-means clustering, agglomerative hierarchical clustering, Allergic Rhinitis Physician Score (ARPhyS), total symptoms score (TSS-17), and VAS. Fisher linear, quadratic discriminant analysis, non-parametric kernel density estimation methods were used to evaluate miss-classification of the patients and cross-validation was used to assess the validity of each scale. 28,109 patients were categorized into "mild", "moderate", and "severe", through the 5 different methods. The best discrimination was offered by the ARPhyS scale. With the ARPhyS scale, cut-offs at a score of 8-9 for mild to moderate and of 11-12 for moderate to severe symptoms were found. Score reliability was also acceptable (Cronbach's α coefficient: 0.626) for the ARPhyS scale, and excellent for the TSS-17 (0.864). The ARPhyS scale seems the best method to target patients with severe seasonal AR. In the present study, we highlighted optimal discrimination cut-offs. This tool could be implemented in daily practice to identify severe patients that need a specialized intervention. Medicine R Science Q Nour Baiz verfasserin aut Shreosi Sanyal verfasserin aut Soutrik Banerjee verfasserin aut Pascal Demoly verfasserin aut Isabella Annesi-Maesano verfasserin aut In PLoS ONE Public Library of Science (PLoS), 2007 13(2018), 11, p e0207290 (DE-627)523574592 (DE-600)2267670-3 19326203 nnns volume:13 year:2018 number:11, p e0207290 https://doi.org/10.1371/journal.pone.0207290 kostenfrei https://doaj.org/article/1f23915ce356434abb94ab509b79546a kostenfrei https://doi.org/10.1371/journal.pone.0207290 kostenfrei https://doaj.org/toc/1932-6203 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_34 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_235 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2018 11, p e0207290
language	English
source	In PLoS ONE 13(2018), 11, p e0207290 volume:13 year:2018 number:11, p e0207290
sourceStr	In PLoS ONE 13(2018), 11, p e0207290 volume:13 year:2018 number:11, p e0207290
format_phy_str_mv	Article
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topic_facet	Medicine R Science Q
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container_title	PLoS ONE
authorswithroles_txt_mv	Davide Caimmi @@aut@@ Nour Baiz @@aut@@ Shreosi Sanyal @@aut@@ Soutrik Banerjee @@aut@@ Pascal Demoly @@aut@@ Isabella Annesi-Maesano @@aut@@
publishDateDaySort_date	2018-01-01T00:00:00Z
hierarchy_top_id	523574592
id	DOAJ005714389
language_de	englisch
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author	Davide Caimmi
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topic_title	Discriminating severe seasonal allergic rhinitis. Results from a large nation-wide database
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title	Discriminating severe seasonal allergic rhinitis. Results from a large nation-wide database.
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title_full	Discriminating severe seasonal allergic rhinitis. Results from a large nation-wide database
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title_sort	discriminating severe seasonal allergic rhinitis. results from a large nation-wide database
title_auth	Discriminating severe seasonal allergic rhinitis. Results from a large nation-wide database.
abstract	Allergic rhinitis (AR) is a chronic disease affecting a large amount of the population. To optimize treatment and disease management, it is crucial to detect patients suffering from severe forms. Several tools have been used to classify patients according to severity: standardized questionnaires, visual analogue scales (VAS) and cluster analysis. The aim of this study was to evaluate the best method to stratify patients suffering from seasonal AR and to propose cut-offs to identify severe forms of the disease. In a multicenter French study (PollinAir), patients suffering from seasonal AR were assessed by a physician that completed a 17 items questionnaire and answered a self-assessment VAS. Five methods were evaluated to stratify patients according to AR severity: k-means clustering, agglomerative hierarchical clustering, Allergic Rhinitis Physician Score (ARPhyS), total symptoms score (TSS-17), and VAS. Fisher linear, quadratic discriminant analysis, non-parametric kernel density estimation methods were used to evaluate miss-classification of the patients and cross-validation was used to assess the validity of each scale. 28,109 patients were categorized into "mild", "moderate", and "severe", through the 5 different methods. The best discrimination was offered by the ARPhyS scale. With the ARPhyS scale, cut-offs at a score of 8-9 for mild to moderate and of 11-12 for moderate to severe symptoms were found. Score reliability was also acceptable (Cronbach's α coefficient: 0.626) for the ARPhyS scale, and excellent for the TSS-17 (0.864). The ARPhyS scale seems the best method to target patients with severe seasonal AR. In the present study, we highlighted optimal discrimination cut-offs. This tool could be implemented in daily practice to identify severe patients that need a specialized intervention.
abstractGer	Allergic rhinitis (AR) is a chronic disease affecting a large amount of the population. To optimize treatment and disease management, it is crucial to detect patients suffering from severe forms. Several tools have been used to classify patients according to severity: standardized questionnaires, visual analogue scales (VAS) and cluster analysis. The aim of this study was to evaluate the best method to stratify patients suffering from seasonal AR and to propose cut-offs to identify severe forms of the disease. In a multicenter French study (PollinAir), patients suffering from seasonal AR were assessed by a physician that completed a 17 items questionnaire and answered a self-assessment VAS. Five methods were evaluated to stratify patients according to AR severity: k-means clustering, agglomerative hierarchical clustering, Allergic Rhinitis Physician Score (ARPhyS), total symptoms score (TSS-17), and VAS. Fisher linear, quadratic discriminant analysis, non-parametric kernel density estimation methods were used to evaluate miss-classification of the patients and cross-validation was used to assess the validity of each scale. 28,109 patients were categorized into "mild", "moderate", and "severe", through the 5 different methods. The best discrimination was offered by the ARPhyS scale. With the ARPhyS scale, cut-offs at a score of 8-9 for mild to moderate and of 11-12 for moderate to severe symptoms were found. Score reliability was also acceptable (Cronbach's α coefficient: 0.626) for the ARPhyS scale, and excellent for the TSS-17 (0.864). The ARPhyS scale seems the best method to target patients with severe seasonal AR. In the present study, we highlighted optimal discrimination cut-offs. This tool could be implemented in daily practice to identify severe patients that need a specialized intervention.
abstract_unstemmed	Allergic rhinitis (AR) is a chronic disease affecting a large amount of the population. To optimize treatment and disease management, it is crucial to detect patients suffering from severe forms. Several tools have been used to classify patients according to severity: standardized questionnaires, visual analogue scales (VAS) and cluster analysis. The aim of this study was to evaluate the best method to stratify patients suffering from seasonal AR and to propose cut-offs to identify severe forms of the disease. In a multicenter French study (PollinAir), patients suffering from seasonal AR were assessed by a physician that completed a 17 items questionnaire and answered a self-assessment VAS. Five methods were evaluated to stratify patients according to AR severity: k-means clustering, agglomerative hierarchical clustering, Allergic Rhinitis Physician Score (ARPhyS), total symptoms score (TSS-17), and VAS. Fisher linear, quadratic discriminant analysis, non-parametric kernel density estimation methods were used to evaluate miss-classification of the patients and cross-validation was used to assess the validity of each scale. 28,109 patients were categorized into "mild", "moderate", and "severe", through the 5 different methods. The best discrimination was offered by the ARPhyS scale. With the ARPhyS scale, cut-offs at a score of 8-9 for mild to moderate and of 11-12 for moderate to severe symptoms were found. Score reliability was also acceptable (Cronbach's α coefficient: 0.626) for the ARPhyS scale, and excellent for the TSS-17 (0.864). The ARPhyS scale seems the best method to target patients with severe seasonal AR. In the present study, we highlighted optimal discrimination cut-offs. This tool could be implemented in daily practice to identify severe patients that need a specialized intervention.
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Discriminating severe seasonal allergic rhinitis. Results from a large nation-wide database.

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