A natural language processing-informed adrenal gland incidentaloma clinic improves guideline-based care.

Abstract

INTRODUCTION Adrenal gland incidentalomas (AGIs) are found in up to 5% of cross-sectional images. However, rates of guideline-based workup for AGIs are notoriously low. We sought to determine if a natural language processing (NLP)-informed AGI clinic could improve the rates of indicated biochemical evaluation and adrenal-specific imaging. METHODS An NLP algorithm was created to detect clinically significant adrenal nodules from radiology reports of cross-sectional images at an academic institution. The NLP algorithm was applied to scans occurring between June 2020 and July 2021 to form a baseline cohort. The NLP algorithm was re-applied to scans from August 2021 to February 2023 and identified patients were invited to join an outpatient clinic dedicated to AGIs. Patients evaluated in the clinic from March 2022 to February 2023 were included in the intervention cohort. Statistical analysis utilized chi-square, t-test, and a multivariable logistic regression. RESULTS The baseline and intervention cohorts included 1784 and 322 unique patients, respectively. Patients in the intervention cohort were more likely to be female (59% vs. 51%, p = 0.01), be younger (60 ± 13.1 vs. 64 ± 13.2 years, p < 0.001), have smaller nodules (1.7 cm, IQR 1.4-2.1 vs. 1.8 cm, IQR 1.4-2.5 cm, p = 0.017), have had biochemical workup (99% vs. 13%, p < 0.001), and have had adrenal-specific imaging (40% vs. 11%, p < 0.001). In a multivariable analysis, intervention cohort patients were significantly more likely to have had biochemical workup (odds ratio ,OR 1209, confidence interval ,CI 434-5117, p < 0.001) and adrenal-specific imaging (OR 8.89, CI 6.42-12.4, p < 0.001). CONCLUSION The implementation of an NLP-informed AGI clinic was associated with a seven-fold increase in biochemical workup and a three-fold increase in adrenal-specific imaging in participating patients.