Development and Validation of an Attitudinal-Profiling Tool for Patients With Asthma.
OBJECTIVE
To develop a profiling tool which accurately assigns a patient to the appropriate attitudinal cluster for the management of asthma.


METHODS
Attitudinal data from an online survey of 2,467 patients with asthma from 8 Asian countries/region, aged 18-50 years, having had ≥2 prescriptions in the previous 2 years and access to social media was used in a discriminant function analysis to identify a minimal set of questions for the Profiling Tool. A split-sample procedure based on 100 sets of randomly selected estimation and validation sub-samples from the original sample was used to cross-validate the Tool and assess the robustness of its predictive accuracy.


RESULTS
Our Profiling Tool contained 10 attitudinal questions for the patient and 1 GINA-based level of asthma control question for the physician. It achieved a predictive accuracy of 76.2%. The estimation and validation sub-sample accuracies of 76.7% and 75.3%, respectively, were consistent with the tool's predictive accuracy at 95% confidence level; and their 1.4 percentage-points difference set upper-bound estimate for the degree of over-fitting.


CONCLUSIONS
The Profiling Tool is highly predictive (>75%) of the attitudinal clusters that best describe patients with asthma in the Asian population. By identifying the attitudinal profile of the patient, the physician can make the appropriate asthma management decisions in practice. The challenge is to integrate its use into the consultation workflow and apply to areas where Internet resources are not available or patients who are not comfortable with the use of such technology.
Section of Pulmonary Medicine, University of the Philippines-Philippine General Hospital, Manila, Philippines. asdavidwang1@up.edu.ph.
Aileen
A
David-Wang
Centre of Academic Primary Care, University of Aberdeen, Aberdeen, UK.|Observational & Pragmatic Research Institute Pte Ltd, Singapore.
David
D
Price
College of Medicine, Seoul National University, Seoul, Korea.
Sang Heon
SH
Cho
Department of Medicine, University of Hong Kong, Hong Kong Special Administrative Region, China.
James Chung Man
JC
Ho
Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia.
Chong Kin
CK
Liam
Medical Affairs Department, Mundipharma Pte Ltd, Singapore.
Glenn
G
Neira
Research Partnership Pte Ltd, Singapore.
Pei Li
PL
Teh
Asia; Asthma; discriminant analysis; disease management