Rustagi Alison S, Vali Marzieh, Graham Francis J, Lum Emily N, Slatore Christopher G, Keyhani Salomeh
Center for Data to Discovery and Delivery Innovation (3DI), San Francisco VA Health Care System, San Francisco, CA, USA.
Division of General Internal Medicine, University of California, San Francisco, San Francisco, CA, USA.
J Gen Intern Med. 2025 May;40(6):1306-1314. doi: 10.1007/s11606-025-09429-2. Epub 2025 Feb 25.
Lung cancer screening (LCS) is recommended for asymptomatic patients. Administrative codes for LCS may capture tests prompted by signs/symptoms.
To validate an automated algorithm that identifies LCS among asymptomatic patients.
In this cross-sectional study, an algorithm was iteratively developed to identify outpatient low-dose chest CT scans via Current Procedural Terminology (CPT) codes, search free text of radiology orders for screening terms and signs/symptoms (e.g., cough), and classify scans as screening or not.
National population-based sample of 4503 adults ages 65-80 in Veterans Health Affairs primary care, with detailed smoking history to identify LCS-eligible individuals (30 + pack-years, current tobacco use, or quit < 15 years prior).
Algorithm specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV) relative to manual chart review (gold standard) on 100% of screening scans and > 10% random sample of non-screening scans.
Chart review was conducted on n = 335 scans. The final algorithm could not classify 22% of scans, of which 73% were non-screening; these were excluded from primary analyses. Among 842 LCS-eligible individuals, the algorithm demonstrated 97% sensitivity (95%CI 91-99%) and 79% specificity (58-93%). Only 69% (61-77%) of scans classified as LCS via administrative codes were truly screening, compared to 95% of those classified as screening via the algorithm (p < 0.001). Algorithm performance was similar regardless of LCS eligibility, with 90% PPV (84-94%) and 93% NPV (86-97%) in the overall population regardless of tobacco cigarette history.
An automated algorithm can accurately identify screening versus diagnostic chest imaging, a necessary step to unbiased analyses of LCS in non-randomized settings. Studies should assess the accuracy of administrative codes for LCS in other health systems.
肺癌筛查(LCS)推荐用于无症状患者。LCS的管理代码可能会涵盖由体征/症状引发的检查。
验证一种能在无症状患者中识别LCS的自动化算法。
在这项横断面研究中,通过当前操作术语(CPT)代码迭代开发了一种算法,以识别门诊低剂量胸部CT扫描,在放射检查单的自由文本中搜索筛查术语和体征/症状(如咳嗽),并将扫描分类为筛查或非筛查。
退伍军人健康管理局初级保健中4503名年龄在65 - 80岁的全国性基于人群的样本,有详细吸烟史以确定符合LCS条件的个体(30包年以上、当前吸烟或在15年内戒烟)。
相对于手动病历审查(金标准),算法在100%的筛查扫描和超过10%的非筛查扫描随机样本上的特异性、敏感性、阳性预测值(PPV)和阴性预测值(NPV)。
对n = 335次扫描进行了病历审查。最终算法无法对22%的扫描进行分类,其中73%为非筛查;这些被排除在主要分析之外。在842名符合LCS条件的个体中,该算法显示出97%的敏感性(95%CI 91 - 99%)和79%的特异性(58 - 93%)。通过管理代码分类为LCS的扫描中,只有69%(61 - 77%)是真正的筛查,而通过算法分类为筛查的扫描中这一比例为95%(p < 0.001)。无论是否符合LCS条件,算法性能相似,总体人群中无论吸烟史如何,PPV为90%(84 - 94%),NPV为93%(86 - 97%)。
一种自动化算法能够准确识别筛查性与诊断性胸部成像,这是在非随机环境中对LCS进行无偏分析的必要步骤。研究应评估其他卫生系统中LCS管理代码的准确性。