哮喘临床实践中一种检测小气道功能障碍的工具研发
2022/12/21
摘要
背景：支气管哮喘患者的小气道功能障碍（SAD）很难定量评估，也缺乏相关金标准。
目的：本研究旨在开发一种简单的工具，包括小气道功能障碍工具（SADT）问卷、患者基本特征和呼吸测试，以预测哮喘患者的小气道功能障碍程度。
方法：本研究基于跨国ATLANTIS（哮喘小气道评估）研究的数据，包括早期开发的SADT问卷。关键SADT题目和临床信息目前已应用于建立逻辑回归模型，以预测SAD分组（罹患SAD可能性的大小）。模型的诊断能力表示为受试者操作特征曲线下面积（AUC）和正似然比（LR+）。
结果：SADT题目8（“安静坐着或躺着时，我有时会喘”）、患者特征年龄、哮喘诊断年龄和BMI可以合理地评估SAD（AUC：0.74，LR+：2.3）。联合肺功能测定（FEV1pp；AUC:0.87，LR+：5.0）和脉冲强迫震荡（R5-R20和AX；AUC:9.96，LR+∶12.8），诊断能力提高。
结论：如果呼吸测试的机会有限（如在许多国家的初级保健机构），通过询问休息时的喘息情况和一些患者特征，可以很好地识别出SAD患者。在（高级）医院环境中，使用肺活量测定和脉冲强迫震荡可以更准确地识别出SAD患者。
Development of a tool to detect small airways dysfunction in asthma clinical practice.
Kocks J, van der Molen T, Voorham J, Baldi S, van den Berge M, Brightling C, Fabbri LM, Kraft M, Nicolini G, Papi A, Rabe KF, Siddiqui S, Singh D, Vonk J, Leving M, Flokstra-de Blok B.
Abstract
BACKGROUND:Small airways dysfunction (SAD) in asthma is difficult to measure and a gold standard is lacking.
OBJECTIVES: The aim of this study was to develop a simple tool including items of the small airways dysfunction tool (SADT) questionnaire, basic patient characteristics and respiratory tests available depending on clinical setting, to predict SAD in asthma.
METHODS:This study was based on the data of the multinational ATLANTIS (Assessment of Small Airways Involvement in Asthma) study including the earlier developed SADT questionnaire. Key SADT-items together with clinical information was now used to build logistic regression models to predict SAD group (less likely or more likely to have SAD). Diagnostic ability of the models was expressed as area under the receiver operating characteristic curve (AUC) and positive likelihood ratios (LR+).
RESULTS:SADT-item 8, "I sometimes wheeze when I am sitting or lying quietly", and the patient characteristics age, age at asthma diagnosis and BMI could reasonably well detect SAD (AUC:0.74, LR+:2.3). The diagnostic ability increased by adding spirometry (FEV1pp; AUC:0.87, LR+:5.0) and oscillometry (R5-R20 and AX; AUC:0.96, LR+:12.8).
CONCLUSIONS:If access to respiratory tests is limited (e.g. primary care in many countries), patients with SAD could reasonably well be identified by asking about wheezing at rest and a few patient characteristics. In (advanced) hospital settings patients with SAD could be identified with considerably higher accuracy using spirometry and oscillometry.
背景：支气管哮喘患者的小气道功能障碍（SAD）很难定量评估，也缺乏相关金标准。
目的：本研究旨在开发一种简单的工具，包括小气道功能障碍工具（SADT）问卷、患者基本特征和呼吸测试，以预测哮喘患者的小气道功能障碍程度。
方法：本研究基于跨国ATLANTIS（哮喘小气道评估）研究的数据，包括早期开发的SADT问卷。关键SADT题目和临床信息目前已应用于建立逻辑回归模型，以预测SAD分组（罹患SAD可能性的大小）。模型的诊断能力表示为受试者操作特征曲线下面积（AUC）和正似然比（LR+）。
结果：SADT题目8（“安静坐着或躺着时，我有时会喘”）、患者特征年龄、哮喘诊断年龄和BMI可以合理地评估SAD（AUC：0.74，LR+：2.3）。联合肺功能测定（FEV1pp；AUC:0.87，LR+：5.0）和脉冲强迫震荡（R5-R20和AX；AUC:9.96，LR+∶12.8），诊断能力提高。
结论：如果呼吸测试的机会有限（如在许多国家的初级保健机构），通过询问休息时的喘息情况和一些患者特征，可以很好地识别出SAD患者。在（高级）医院环境中，使用肺活量测定和脉冲强迫震荡可以更准确地识别出SAD患者。
(中日友好医院呼吸与危重症医学科 张婧媛 摘译 林江涛 审校)
(Eur Respir J. 2022 Dec 14:2200558. doi: 10.1183/13993003.00558-2022.)
(Eur Respir J. 2022 Dec 14:2200558. doi: 10.1183/13993003.00558-2022.)
Development of a tool to detect small airways dysfunction in asthma clinical practice.
Kocks J, van der Molen T, Voorham J, Baldi S, van den Berge M, Brightling C, Fabbri LM, Kraft M, Nicolini G, Papi A, Rabe KF, Siddiqui S, Singh D, Vonk J, Leving M, Flokstra-de Blok B.
Abstract
BACKGROUND:Small airways dysfunction (SAD) in asthma is difficult to measure and a gold standard is lacking.
OBJECTIVES: The aim of this study was to develop a simple tool including items of the small airways dysfunction tool (SADT) questionnaire, basic patient characteristics and respiratory tests available depending on clinical setting, to predict SAD in asthma.
METHODS:This study was based on the data of the multinational ATLANTIS (Assessment of Small Airways Involvement in Asthma) study including the earlier developed SADT questionnaire. Key SADT-items together with clinical information was now used to build logistic regression models to predict SAD group (less likely or more likely to have SAD). Diagnostic ability of the models was expressed as area under the receiver operating characteristic curve (AUC) and positive likelihood ratios (LR+).
RESULTS:SADT-item 8, "I sometimes wheeze when I am sitting or lying quietly", and the patient characteristics age, age at asthma diagnosis and BMI could reasonably well detect SAD (AUC:0.74, LR+:2.3). The diagnostic ability increased by adding spirometry (FEV1pp; AUC:0.87, LR+:5.0) and oscillometry (R5-R20 and AX; AUC:0.96, LR+:12.8).
CONCLUSIONS:If access to respiratory tests is limited (e.g. primary care in many countries), patients with SAD could reasonably well be identified by asking about wheezing at rest and a few patient characteristics. In (advanced) hospital settings patients with SAD could be identified with considerably higher accuracy using spirometry and oscillometry.
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