1 Department of Medicine and.
2 Department of Radiology and.
Ann Am Thorac Soc. 2017 Jun;14(6):880-887. doi: 10.1513/AnnalsATS.201610-764OC.
Population-based studies of idiopathic pulmonary fibrosis (IPF) in the United States have been limited by reliance on diagnostic code-based algorithms that lack clinical validation.
To validate a well-accepted International Classification of Diseases, Ninth Revision, code-based algorithm for IPF using patient-level information and to develop a modified algorithm for IPF with enhanced predictive value.
The traditional IPF algorithm was used to identify potential cases of IPF in the Kaiser Permanente Northern California adult population from 2000 to 2014. Incidence and prevalence were determined overall and by age, sex, and race/ethnicity. A validation subset of cases (n = 150) underwent expert medical record and chest computed tomography review. A modified IPF algorithm was then derived and validated to optimize positive predictive value.
From 2000 to 2014, the traditional IPF algorithm identified 2,608 cases among 5,389,627 at-risk adults in the Kaiser Permanente Northern California population. Annual incidence was 6.8/100,000 person-years (95% confidence interval [CI], 6.1-7.7) and was higher in patients with older age, male sex, and white race. The positive predictive value of the IPF algorithm was only 42.2% (95% CI, 30.6 to 54.6%); sensitivity was 55.6% (95% CI, 21.2 to 86.3%). The corrected incidence was estimated at 5.6/100,000 person-years (95% CI, 2.6-10.3). A modified IPF algorithm had improved positive predictive value but reduced sensitivity compared with the traditional algorithm.
A well-accepted International Classification of Diseases, Ninth Revision, code-based IPF algorithm performs poorly, falsely classifying many non-IPF cases as IPF and missing a substantial proportion of IPF cases. A modification of the IPF algorithm may be useful for future population-based studies of IPF.
美国基于人群的特发性肺纤维化（IPF）研究受到仅依赖缺乏临床验证的诊断代码算法的限制。
使用患者水平信息验证一种广泛接受的国际疾病分类，第九修订版（ICD-9）IPF 代码算法，并开发一种具有增强预测值的改良 IPF 算法。
该研究使用传统的 IPF 算法在 2000 年至 2014 年期间从 Kaiser Permanente 北加利福尼亚州成年人群中确定潜在的 IPF 病例。总体上以及按年龄、性别和种族/族裔确定发病率和患病率。然后对 150 例病例进行专家病历和胸部计算机断层扫描回顾。然后得出并验证改良的 IPF 算法，以优化阳性预测值。
2000 年至 2014 年，传统的 IPF 算法在 Kaiser Permanente 北加利福尼亚州的高危成人中识别出 2608 例病例。发病率为 6.8/100000 人年（95%置信区间[CI]，6.1-7.7），年龄较大、男性和白人患者发病率更高。IPF 算法的阳性预测值仅为 42.2%（95%CI，30.6-54.6%）；敏感性为 55.6%（95%CI，21.2-86.3%）。校正后的发病率估计为 5.6/100000 人年（95%CI，2.6-10.3）。与传统算法相比，改良的 IPF 算法具有更高的阳性预测值，但敏感性降低。
一种广泛接受的 ICD-9 代码 IPF 算法性能不佳，错误地将许多非 IPF 病例归类为 IPF，并且漏诊了相当比例的 IPF 病例。IPF 算法的修改可能对未来的 IPF 基于人群的研究有用。
