• Chengyi Zheng, Brian Z Huang, Andranik A Agazaryan, Beth Creekmur, Thearis A Osuj, and Michael K Gould.
• Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, CA. Electronic address: Chengyi.X.Zheng@kp.org.
• Chest. 2021 Nov 1; 160 (5): 1902-1914.

BackgroundThere is an urgent need for population-based studies on managing patients with pulmonary nodules.Research QuestionIs it possible to identify pulmonary nodules and associated characteristics using an automated method?Study Design And MethodsWe revised and refined an existing natural language processing (NLP) algorithm to identify radiology transcripts with pulmonary nodules and greatly expanded its functionality to identify the characteristics of the largest nodule, when present, including size, lobe, laterality, attenuation, calcification, and edge. We compared NLP results with a reference standard of manual transcript review in a random test sample of 200 radiology transcripts. We applied the final automated method to a larger cohort of patients who underwent chest CT scan in an integrated health care system from 2006 to 2016, and described their demographic and clinical characteristics.ResultsIn the test sample, the NLP algorithm had very high sensitivity (98.6%; 95% CI, 95.0%-99.8%) and specificity (100%; 95% CI, 93.9%-100%) for identifying pulmonary nodules. For attenuation, edge, and calcification, the NLP algorithm achieved similar accuracies, and it correctly identified the diameter of the largest nodule in 135 of 141 cases (95.7%; 95% CI, 91.0%-98.4%). In the larger cohort, the NLP found 217,771 reports with nodules among 717,304 chest CT reports (30.4%). From 2006 to 2016, the number of reports with nodules increased by 150%, and the mean size of the largest nodule gradually decreased from 11 to 8.9 mm. Radiologists documented the laterality and lobe (90%-95%) more often than the attenuation, calcification, and edge characteristics (11%-14%).InterpretationThe NLP algorithm identified pulmonary nodules and associated characteristics with high accuracy. In our community practice settings, the documentation of nodule characteristics is incomplete. Our results call for better documentation of nodule findings. The NLP algorithm can be used in population-based studies to identify pulmonary nodules, avoiding labor-intensive chart review.Copyright © 2021 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.

36 keywords...

hide…