哮喘联盟

   摘要
   背景：我们使用多种临床参数开发出了一种评分算法，在有呼吸道症状的成人患者中预测哮喘的发生情况。
   方法：对566名首次因非特异性呼吸道症状就诊于Kinki大学医院门诊的成人患者进行前瞻性分析。专科医师通过症状、体征和客观工具（包括支气管扩张剂可逆性和/或评价支气管高反应性[BHR]）来诊断哮喘。使用多元logistic回归分析来对患者进行分类，确定诊断哮喘的准确性。
   结果：基于日间症状变异性（1分）、反复发作（2分）、过敏性疾病病史（1分）和喘息音（2分），开发出症状-体征评分算法。评分>3时，诊断哮喘的敏感性为35%，特异性为97%，患者存在哮喘的可能性较高（准确性90%）。评分为1或2时，哮喘诊断的可能性中等（准确性68%）。如果提供其他的数据，如第一秒用力呼气体积/用力肺活量（FEV1/FVC）比值<0.7，在该检测后诊断为哮喘的可能性增加至93%。评分为0时，诊断哮喘的可能性较低（准确性31%）。如果可逆性检测为阳性，则在该检测后诊断为哮喘的可能性增加至88%。
   结论：本研究开发的诊断算法可用于预测成人哮喘，确定患者在适当的时候需要到肺科专科医生进行咨询。
 

（刘国梁 审校）
PrimCareRespirJ.2013Jan24.pii:pcrj-2012-08-0120-R1. doi: 10.4104/pcrj.2013.00005. [Epub ahead of print]

 

A scoring algorithm for predicting the presence of adult asthma: a prospective derivation study.
 
Tomita K, Sano H, Chiba Y, Sato R, Sano A, Nishiyama O, Iwanaga T, Higashimoto Y, Haraguchi R, Tohda Y.

Source
Department of Respiratory Medicine and Allergology, Kinki University Faculty of Medicine, Osaka-Sayama, Osaka, Japan • Department of Respiratory Medicine, Yonago Medical Center, Yonago, Tottori, Japan.

Abstract 
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 (FEV1/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.

PrimCareRespirJ.2013Jan24.pii:pcrj-2012-08-0120-R1. doi: 10.4104/pcrj.2013.00005. [Epub ahead of print]