A scoring algorithm for predicting the presence of adult asthma: a prospective derivation study.
BACKGROUND
To predict the presence of asthma in adult patients with respiratory symptoms, we developed a scoring algorithm using clinical parameters.


METHODS
We prospectively analysed 566 adult outpatients who visited Kinki University Hospital for the first time with complaints of nonspecific respiratory symptoms. Asthma was comprehensively diagnosed by specialists using symptoms, signs, and objective tools including bronchodilator reversibility and/or the assessment of bronchial hyperresponsiveness (BHR). Multiple logistic regression analysis was performed to categorise patients and determine the accuracy of diagnosing asthma.


RESULTS
A scoring algorithm using the symptom-sign score was developed, based on diurnal variation of symptoms (1 point), recurrent episodes (2 points), medical history of allergic diseases (1 point), and wheeze sound (2 points). A score of >3 had 35% sensitivity and 97% specificity for discriminating between patients with and without asthma and assigned a high probability of having asthma (accuracy 90%). A score of 1 or 2 points assigned intermediate probability (accuracy 68%). After providing additional data of forced expiratory volume in 1 second/forced vital capacity (FEV(1)/FVC) ratio <0.7, the post-test probability of having asthma was increased to 93%. A score of 0 points assigned low probability (accuracy 31%). After providing additional data of positive reversibility, the post-test probability of having asthma was increased to 88%.


CONCLUSIONS
This pragmatic diagnostic algorithm is useful for predicting the presence of adult asthma and for determining the appropriate time for consultation with a pulmonologist.
Department of Respiratory Medicine and Allergology, Kinki University Faculty of Medicine, Osaka-Sayama, Osaka, Japan. tomita-katsuyuki@nho-yonago.jp
Katsuyuki
K
Tomita
Hiroyuki
H
Sano
Yasutaka
Y
Chiba
Ryuji
R
Sato
Akiko
A
Sano
Osamu
O
Nishiyama
Takashi
T
Iwanaga
Yuji
Y
Higashimoto
Ryuta
R
Haraguchi
Yuji
Y
Tohda
D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001249:Asthma / Q000175:diagnosis*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D011446:Prospective Studies; D055815:Young Adult