TY - JOUR
T1 - A Novel Automated Algorithm to Identify Lung Cancer Screening from Free Text of Radiology Orders
AU - Rustagi, Alison S.
AU - Vali, Marzieh
AU - Graham, Francis J.
AU - Lum, Emily N.
AU - Slatore, Christopher G.
AU - Keyhani, Salomeh
N1 - Publisher Copyright:
© This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2025.
PY - 2025/5
Y1 - 2025/5
N2 - 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.
AB - 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.
KW - epidemiology
KW - health services research
KW - lung cancer screening
KW - screening
KW - veterans
UR - http://www.scopus.com/inward/record.url?scp=86000026991&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=86000026991&partnerID=8YFLogxK
U2 - 10.1007/s11606-025-09429-2
DO - 10.1007/s11606-025-09429-2
M3 - Article
C2 - 40000524
AN - SCOPUS:86000026991
SN - 0884-8734
VL - 40
SP - 1306
EP - 1314
JO - Journal of general internal medicine
JF - Journal of general internal medicine
IS - 6
ER -