Department of Respiratory Epidemiology, Occupational Medicine and Public Health, Imperial College London, London SW3 6LR, UK,
Respiratory Unit, Navarra Hospital Complex, Pamplona 31008, Spain.
Occup Med (Lond). 2016 Jan;66(1):50-3. doi: 10.1093/occmed/kqv127. Epub 2015 Oct 13.
Spirometry is often included in workplace-based respiratory surveillance programmes but its performance in the identification of restrictive lung disease is poor, especially when the prevalence of this condition is low in the tested population.
To improve the specificity (Sp) and positive predictive value (PPV) of current spirometry-based algorithms in the diagnosis of restrictive pulmonary impairment in the workplace and to reduce the proportion of false positives findings and, as a result, unnecessary referrals for lung volume measurements.
We re-analysed two studies of hospital patients, respectively used to derive and validate a recommended spirometry-based algorithm [forced vital capacity (FVC) < 85% predicted and forced expiratory volume in 1 s (FEV1)/FVC > 55%] for the recognition of restrictive pulmonary impairment. We used true lung restrictive cases as a reference standard in 2×2 contingency tables to estimate sensitivity (Sn), Sp and PPV and negative predictive values for each diagnostic cut-off. We simulated a working population aged <65 years and with a disease prevalence ranging 1-10% and compared our best algorithm with those previously reported using receiver operating characteristic curves.
There were 376 patients available from the two studies for inclusion. Our best algorithm (FVC < 70% predicted and FEV1/FVC ≥ 70%) achieved the highest Sp (96%) and PPV (67 and 15% for a disease prevalence of 10 and 1%, respectively) with the lowest proportion of false positives (4%); its high Sn (71%) predicted the highest proportion of correctly classified restrictive cases (91%).
Our new spirometry-based algorithm may be adopted to accurately exclude pulmonary restriction and to possibly reduce unnecessary lung volume testing in an occupational health setting.
肺量测定法常用于基于工作场所的呼吸监测计划，但在识别限制性肺病方面其性能不佳，尤其是在测试人群中该疾病的患病率较低时。
提高当前基于肺量测定法的算法在工作场所诊断限制性肺损伤的特异性（Sp）和阳性预测值（PPV），减少假阳性结果的比例，并相应减少不必要的肺量测量转诊。
我们重新分析了两项分别用于推导和验证推荐的基于肺量测定法的算法（用力肺活量（FVC）<85%预计值且 1 秒用力呼气量（FEV1）/FVC>55%）的医院患者研究。我们使用真实的肺部限制性病例作为 2×2列联表中的参考标准，以估计每个诊断截止值的敏感性（Sn）、Sp 和 PPV 和阴性预测值。我们模拟了年龄<65 岁且疾病患病率为 1%-10%的工作人群，并使用受试者工作特征曲线比较了我们的最佳算法与先前报道的算法。
两项研究共有 376 例患者可供纳入。我们的最佳算法（FVC<70%预计值且 FEV1/FVC≥70%）具有最高的 Sp（96%）和 PPV（患病率分别为 10%和 1%时，PPV 分别为 67%和 15%）和最低的假阳性比例（4%）；其高 Sn（71%）预测正确分类的限制性病例比例最高（91%）。
我们的新基于肺量测定法的算法可能被采用以准确排除肺部限制，并可能减少职业健康环境中不必要的肺量测试。
