Hanna, FWF, Hancock, S, George, C, Clark, A, Sim, J ORCID: https://orcid.org/0000-0002-1816-1676, Issa, BG, Powner, G, Waldron, J, Duff, CJ ORCID: https://orcid.org/0000-0002-3753-0043, Lea, SC, Golash, A, Sathiavageeswaran, M, Heald, AH and Fryer, AA ORCID: https://orcid.org/0000-0001-8678-0404 (2022) Adrenal Incidentaloma: Prevalence and Referral Patterns From Routine Practice in a Large UK University Teaching Hospital. Journal of the Endocrine Society, 6 (1). bvab180 - ?.

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Abstract

Context: Adrenal incidentalomas (AIs) are increasingly being identified during unrelated imaging. Unlike AI clinical management, data on referral patterns in routine practice are lacking. Objective: This work aimed to identify factors associated with AI referral. Methods: We linked data from imaging reports and outpatient bookings from a large UK teaching hospital. We examined (i) AI prevalence and (ii) pattern of referral to endocrinology, stratified by age, imaging modality, scan anatomical site, requesting clinical specialty, and temporal trends. Using key radiology phrases to identify scans reporting potential AI, we identified 4097 individuals from 479 945 scan reports (2015-2019). Main outcome measures included prevalence of AI and referral rates. Results: Overall, AI lesions were identified in 1.2% of scans. They were more prevalent in abdomen computed tomography and magnetic resonance imaging scans (3.0% and 0.6%, respectively). Scans performed increased 7.7% year-on-year from 2015 to 2019, with a more pronounced increase in the number with AI lesions (14.7% per year).Only 394 of 4097 patients (9.6%) had a documented endocrinology referral code within 90 days, with medical (11.8%) more likely to refer than surgical (7.2%) specialties (P < .001). Despite prevalence increasing with age, older patients were less likely to be referred (P < .001). Conclusion: While overall AI prevalence appeared low, scan numbers are large and rising; the number with identified AI are increasing still further. The poor AI referral rates, even in centers such as ours where dedicated AI multidisciplinary team meetings and digital management systems are used, highlights the need for new streamlined, clinically effective systems and processes to appropriately manage the AI workload.

Item Type: Article
Additional Information: © The Author(s) 2021. Published by Oxford University Press on behalf of the Endocrine Society. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
Subjects: L Education > L Education (General)
L Education > LB Theory and practice of education > LB2300 Higher Education
R Medicine > R Medicine (General)
R Medicine > R Medicine (General) > R735 Medical education. Medical schools. Research
R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine
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Depositing User: Symplectic
Date Deposited: 13 Jan 2022 09:33
Last Modified: 13 Jan 2022 09:33
URI: https://eprints.keele.ac.uk/id/eprint/10481

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