Sanchez Rolando, Bailey George, Kaboli Peter J, Zeliadt Steven B, Lang Julie A, Hoffman Richard M
is a Clinical Assistant Professor of Pulmonary and Critical Care Medicine; is a Professor of Internal Medicine; and is a Professor of Internal Medicine, all at the University of Iowa Carver College of Medicine in Iowa City. is a Research Data Manager; is a Registered Nurse and Research Coordinator; and Peter Kaboli is an Associate Investigator, all in the Center for Access and Delivery Research and Evaluation (CADRE) at the Iowa City VA Healthcare System. is a Research Professor of Public Health at the Seattle-Denver Center of Innovation for Veteran-Centered and Value-Driven Care, VA Puget Sound Health Care System and the University of Washington School of Public Health in Seattle.
Fed Pract. 2020 May;37(Suppl 2):S32-S37.
Chest imaging often incidentally finds indeterminate nodules that need to be monitored to ensure early detection of lung cancers. Health care systems need effective approaches for identifying these lung nodules. We compared the diagnostic performance of 2 approaches for identifying patients with lung nodules on imaging studies (chest/abdomen): (1) relying on radiologists to code imaging studies with lung nodules; and (2) applying a text search algorithm to identify references to lung nodules in radiology reports.
We assessed all radiology studies performed between January 1, 2016 and November 30, 2016 in a single Veterans Health Administration hospital. We first identified imaging reports with a diagnostic code for a pulmonary nodule. We then applied a text search algorithm to identify imaging reports with key words associated with lung nodules. We reviewed medical records for all patients with a suspicious radiology report based on either search strategy to confirm the presence of a lung nodule. We calculated the yield and the positive predictive value (PPV) of each search strategy for finding pulmonary nodules.
We identified 12,983 imaging studies with a potential lung nodule. Chart review confirmed 8,516 imaging studies with lung nodules, representing 2,912 unique patients. The text search algorithm identified all the patients with lung nodules identified by the radiology coding (n = 1,251) as well as an additional 1,661 patients. The PPV of the text search was 72% (2,912/4,071) and the PPV of the radiology code was 92% (1,251/1,363). Among the patients with nodules missed by radiology coding but identified by the text search algorithm, 130 had lung nodules > 8 mm in diameter.
The text search algorithm can identify additional patients with lung nodules compared to the radiology coding; however, this strategy requires substantial clinical review time to confirm nodules. Health care systems adopting nodule-tracking approaches should recognize that relying only on radiology coding might miss clinically important nodules.
胸部影像学检查常常会偶然发现一些性质不确定的结节，需要对其进行监测以确保早期发现肺癌。医疗保健系统需要有效的方法来识别这些肺结节。我们比较了两种在影像学检查（胸部/腹部）中识别肺结节患者的方法的诊断性能：（1）依靠放射科医生对有肺结节的影像学检查进行编码；（2）应用文本搜索算法在放射学报告中识别提及肺结节的内容。
我们评估了2016年1月1日至2016年11月30日在一家退伍军人健康管理局医院进行的所有放射学检查。我们首先识别出带有肺结节诊断编码的影像学报告。然后应用文本搜索算法识别出带有与肺结节相关关键词的影像学报告。我们基于这两种搜索策略对所有有可疑放射学报告的患者的病历进行审查，以确认肺结节的存在。我们计算了每种搜索策略发现肺结节的检出率和阳性预测值（PPV）。
我们识别出12983份可能有肺结节的影像学检查。病历审查确认了8516份有肺结节的影像学检查，涉及2912名不同患者。文本搜索算法识别出了放射学编码所识别出的所有有肺结节的患者（n = 1251）以及另外1661名患者。文本搜索的PPV为72%（2912/4071），放射学编码的PPV为92%（1251/1363）。在放射学编码遗漏但被文本搜索算法识别出的有结节的患者中，130名患者的肺结节直径大于8毫米。
与放射学编码相比，文本搜索算法可以识别出更多有肺结节的患者；然而，这种策略需要大量的临床审查时间来确认结节。采用结节跟踪方法的医疗保健系统应该认识到，仅依靠放射学编码可能会遗漏临床上重要的结节。
