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Background <p>Lung cancer screening (LCS) is recommended for asymptomatic patients. Administrative codes for LCS may capture tests prompted by signs/symptoms.</p> Objective <p>To validate an automated algorithm that identifies LCS among asymptomatic patients.</p> Design <p>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.</p> Participants <p>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 &lt; 15&#xa0;years prior).</p> Main Measures <p>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 &gt; 10% random sample of non-screening scans.</p> Key Results <p>Chart review was conducted on <i>n</i> = 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 (<i>p</i> &lt; 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.</p> Conclusions <p>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.</p>

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A Novel Automated Algorithm to Identify Lung Cancer Screening from Free Text of Radiology Orders

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月25日

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  • Alison S. Rustagi,
  • Marzieh Vali,
  • Francis J. Graham,
  • Emily N. Lum,
  • Christopher G. Slatore,
  • Salomeh Keyhani

摘要

Background

Lung cancer screening (LCS) is recommended for asymptomatic patients. Administrative codes for LCS may capture tests prompted by signs/symptoms.

Objective

To validate an automated algorithm that identifies LCS among asymptomatic patients.

Design

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.

Participants

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).

Main Measures

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.

Key Results

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.

Conclusions

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.