Patients' perception of asthma severity.

OBJECTIVES To identify variables patients use to determine the severity of their asthma, the perceived severity (PS), using a fuzzy decision-making analysis (FDMA). To compare these variables with those involved in the assessment of asthma severity according to the global initiative for asthma (GINA) guidelines, the objective severity (OS). PATIENTS Outpatients (51 men, 62 women), aged (m+/-SD) 42.9+/-16.3 years with (% patients) mild intermittent (6.2), mild persistent (15.9), moderate (65.5) and severe (12.4) asthma. DESIGN Cross sectional, observational study. METHODS Both OS (rated by doctors) and PS (rated by patients) were rated as mild intermittent, mild persistent, moderate, or severe. Variables involved in OS assessment, variables self-assessed by patients (dyspnea, perceived treatment efficacy, asthma-related quality of life questionnaire [AQLQ]), patients' sociodemographic characteristics, and asthma characteristics, were evaluated with questionnaires. These variables were pooled, and considered as potential variables patients might use to determine their PS. They were tested against the PS measurement using FDMA. This identified variables patients actually used to determine PS. RESULTS On the day of consultation, 68.1% of patients classed their asthma as mild intermittent or mild persistent, 23.9% as moderate persistent, and 8.0% as severe persistent. There was a significant discrepancy (p<0.01) between PS and OS with a clear patient tendency to underestimate asthma severity as compared to OS. Patients determined PS level according to variables assessing their asthma perception, i.e., AQLQ measures and dyspnea, but not variables involved in OS assessment, such as symptom frequency or knowledge of their peak flow rates. Duration of asthma and treatment characteristics were also involved. CONCLUSION FDMA identified variables patients used to determine PS. It highlighted a discrepancy between patients' and doctors' perceptions of asthma severity, suggesting that assessment of asthma severity should consider both patients' and doctors' perceptions of the disease and includes an AQLQ measure.

[1]  R. Hill,et al.  Description and evaluation , 1976 .

[2]  G. Colice.  The seduction of asthma severity categorization. , 2003, Chest.

[3]  H. Boushey,et al.  Severity, control, and responsiveness in asthma. , 2006, The Journal of allergy and clinical immunology.

[4]  Jason H T Bates,et al.  Applying fuzzy logic to medical decision making in the intensive care unit. , 2003, American journal of respiratory and critical care medicine.

[5]  Claude E. Shannon,et al.  The Mathematical Theory of Communication , 1950 .

[6]  Petra Perner.  Intelligent data analysis in medicine - Recent advances , 2006, Artif. Intell. Medicine.

[7]  E. Juniper,et al.  Relationship between quality of life and clinical status in asthma: a factor analysis , 2004, European Respiratory Journal.

[8]  M. Moy,et al.  Clinical predictors of health-related quality of life depend on asthma severity. , 2001, American journal of respiratory and critical care medicine.

[9]  Cheung-Hwa Hsu,et al.  Fuzzy logic automatic control of the Phoenix-7 total artificial heart , 2004, Journal of Artificial Organs.

[10]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[11]  Alfons Schuster,et al.  Fuzzy Logic in a Decision Support System in the Domain of Coronary Heart Disease Risk Assessment , 2002 .

[12]  R. Pauwels,et al.  GLOBAL STRATEGY FOR ASTHMA MANAGEMENT AND PREVENTION , 1996 .

[13]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[14]  J. G. Douglas,et al.  Patient weighting of importance of asthma symptoms , 2001, Thorax.

[15]  G. Colice.  Categorizing asthma severity: an overview of national guidelines. , 2004, Clinical medicine & research.

[16]  R. Zuwallack,et al.  Common measures of asthma severity lack association for describing its clinical course. , 1994, The Journal of allergy and clinical immunology.

[17]  L. Boulet,et al.  Canadian Asthma Consensus Report, 1999. Canadian Asthma Consensus Group. , 1999, CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne.

[18]  D. Brand,et al.  Classifying asthma: disagreement among specialists. , 2003, Chest.

[19]  Pierre Ernst,et al.  Canadian asthma consensus report, 1999 , 1999 .

[20]  P. Thompson,et al.  Categorizing asthma severity. , 1999, American journal of respiratory and critical care medicine.

[21]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[22]  A. Buist,et al.  Characteristics of patients with asthma within a large HMO: A comparison by age and gender , 1998 .

[23]  G H Guyatt,et al.  Development and validation of a questionnaire to measure asthma control. , 1999, The European respiratory journal.

[24]  Lei Li,et al.  Using classification trees to assess low birth weight outcomes , 2006, Artif. Intell. Medicine.

[25]  B. Yawn,et al.  Asthma management: are GINA guidelines appropriate for daily clinical practice? , 2005, Primary care respiratory journal : journal of the General Practice Airways Group.

[26]  Metin Akay,et al.  Nonlinear Biomedical Signal Processing: Fuzzy Logic, Neural Networks, and New Algorithms , 2000 .

[27]  Knowledge, Self-Management, Compliance and Quality of Life in Asthma: A Cross-Sectional Study of the French Version of the Asthma Quality of Life Questionnaire , 1998, Quality of Life Research.

[28]  G. Guyatt,et al.  Measuring quality of life in asthma. , 1993, The American review of respiratory disease.

[29]  B. Yawn,et al.  Measuring asthma quality in primary care: can we develop better measures? , 2006, Respiratory medicine.

[30]  K. Torén,et al.  Self-reported asthma was biased in relation to disease severity while reported year of asthma onset was accurate. , 2006, Journal of clinical epidemiology.

[31]  G. Nocea,et al.  Asthma-related resource use and cost by GINA classification of severity in three European countries. , 2006, Respiratory medicine.

[32]  P. Jones,et al.  A comparison of global questions versus health status questionnaires as measures of the severity and impact of asthma. , 1999, The European respiratory journal.

[33]  Charles F. Hockett,et al.  A mathematical theory of communication , 1948, MOCO.

[34]  Xu-Sheng Zhang,et al.  Depth of Anesthesia Control with Fuzzy Logic , 2002 .

[35]  B. Penninx,et al.  Determinants of different dimensions of disease severity in asthma and COPD : pulmonary function and health-related quality of life. , 2001, Chest.

[36]  E. Bleecker,et al.  Relationship between airway obstruction and respiratory symptoms in adult asthmatics. , 1998, Chest.

[37]  S. Weiss,et al.  The burden of asthma in the United States: level and distribution are dependent on interpretation of the national asthma education and prevention program guidelines. , 2002, American journal of respiratory and critical care medicine.

[38]  R. Zeiger,et al.  Relationships between duration of asthma and asthma severity among children in the Childhood Asthma Management Program (CAMP) , 1999, The Journal of allergy and clinical immunology.

[39]  J H Bates,et al.  Fuzzy logic and mechanical ventilation. , 2001, Respiratory care clinics of North America.

[40]  C. Hanson,et al.  Artificial intelligence applications in the intensive care unit , 2001, Critical care medicine.

[41]  S. Barro,et al.  Fuzzy Logic in Medicine , 2002 .

[42]  Kevin B Weiss,et al.  Worldwide severity and control of asthma in children and adults: the global asthma insights and reality surveys. , 2004, The Journal of allergy and clinical immunology.

[43]  A. Woolcock,et al.  An evaluation of an asthma quality of life questionnaire as a measure of change in adults with asthma. , 1993, Journal of clinical epidemiology.

[44]  G. Guyatt,et al.  Quality of life in asthma clinical trials: comparison of salmeterol and salbutamol. , 1995, American journal of respiratory and critical care medicine.

[45]  M. Aubier,et al.  Airway structural alterations selectively associated with severe asthma. , 2003, American journal of respiratory and critical care medicine.

[46]  Uwe Hampel,et al.  Volume image reconstruction for diffuse optical tomography. , 2002, Applied optics.

[47]  C Combescure,et al.  Assessment of variations in control of asthma over time , 2003, European Respiratory Journal.

[48]  K. Clancy.  British guidelines on the management of asthma. , 2004, Thorax.

[49]  C. Marsala.  Fuzzy partitioning methods , 2001 .

[50]  B Wallaert,et al.  Assessment of the severity of asthma by an expert system. Description and evaluation. , 1995, American journal of respiratory and critical care medicine.

[51]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[52]  E. Cook,et al.  Relationships among quality of life, severity, and control measures in asthma: an evaluation using factor analysis. , 2005, The Journal of allergy and clinical immunology.

[53]  C M Higgs,et al.  Accuracy of perception of severity of asthma: patients treated in general practice. , 1993, BMJ.

[54]  Charles A. Johnson,et al.  Severity assessment in asthma: An evolving concept. , 2005, The Journal of allergy and clinical immunology.

[55]  B. Bouchon-Meunier,et al.  Learning Fuzzy Decision Rules , 1999 .

[56]  B. Yawn,et al.  Asthma Severity: The Patient's Perspective , 2004, The Journal of asthma : official journal of the Association for the Care of Asthma.