Do additional imaging studies change operative management in patients undergoing adrenalectomy?

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

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.

Adrenal Referral Pattern: Management of Patients With an Adrenal Incidentaloma

INTRODUCTION

Adrenal incidentalomas (AIs) are found in 3%-4% of abdominal computed tomography scans. Timely evaluation of their functional status and malignant potential is necessary to guide nonoperative surveillance or surgery. This study aims to evaluate the adherence of referring service patterns to the American Association of Endocrine Surgeons and American Association of Clinical Endocrinologists guidelines for the biochemical workup of AIs at a tertiary surgical clinic.

METHODS

We conducted a retrospective study of 125 patients evaluated for AIs at the endocrine surgery clinic between 2017 and 2022. Information on patient demographics, referral source, and reasons for referral was collected. The appropriateness of the biochemical workup for AIs by referring physicians was assessed. Statistical analyses included chi-square and Kruskal-Wallis tests.

RESULTS

Referrals came from endocrinologists (44.8%), other subspecialists (31.2%), and primary care physicians (PCPs) (19.2%). Among 125 patients, diagnoses included benign adrenal masses (52.8%), aldosteronomas (10.4%), cortisol-secreting tumors (15.2%), pheochromocytomas (12.8%), and metastatic masses (4.0%). Endocrinologists were more likely to conduct a complete biochemical workup compared to other subspecialties and PCPs (P < 0.001). Eighty-three (66.4%) patients underwent adrenalectomy, with those referred by endocrinologists more likely to undergo surgery than those referred by other subspecialties and PCPs (P < 0.001). There was no significant difference in the time from the initial clinic visit to surgery by referral source (P > 0.05).

CONCLUSIONS

Over half of AIs referrals to the endocrine surgery clinic came from subspecialists and PCPs rather than endocrinologists. Familiarizing all referring physicians with American Association of Endocrine Surgeons/Association of Clinical Endocrinologists guidelines may reduce undiagnosed functional AI cases and facilitate timely surgical management.

Characterizing and quantifying low-value diagnostic imaging internationally: a scoping review

BACKGROUND

Inappropriate and wasteful use of health care resources is a common problem, constituting 10-34% of health services spending in the western world. Even though diagnostic imaging is vital for identifying correct diagnoses and administrating the right treatment, low-value imaging-in which the diagnostic test confers little to no clinical benefit-is common and contributes to inappropriate and wasteful use of health care resources. There is a lack of knowledge on the types and extent of low-value imaging. Accordingly, the objective of this study was to identify, characterize, and quantify the extent of low-value diagnostic imaging examinations for adults and children.

METHODS

A scoping review of the published literature was performed. Medline-Ovid, Embase-Ovid, Scopus, and Cochrane Library were searched for studies published from 2010 to September 2020. The search strategy was built from medical subject headings (Mesh) for Diagnostic imaging/Radiology OR Health service misuse/Medical overuse OR Procedures and Techniques Utilization/Facilities and Services Utilization. Articles in English, German, Dutch, Swedish, Danish, or Norwegian were included.

RESULTS

A total of 39,986 records were identified and, of these, 370 studies were included in the final synthesis. Eighty-four low-value imaging examinations were identified. Imaging of atraumatic pain, routine imaging in minor head injury, trauma, thrombosis, urolithiasis, after thoracic interventions, fracture follow-up and cancer staging/follow-up were the most frequently identified low-value imaging examinations. The proportion of low-value imaging varied between 2 and 100% inappropriate or unnecessary examinations.

CONCLUSIONS

A comprehensive list of identified low-value radiological examinations for both adults and children are presented. Future research should focus on reasons for low-value imaging utilization and interventions to reduce the use of low-value imaging internationally.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO: CRD42020208072.

Association between Tumor Size and Malignancy Risk in Hormonally Inactive Adrenal Incidentalomas

Introduction

Adrenal incidentalomas (AI) are adrenal masses that are discovered during radiological examinations conducted for other reasons. In this study, we focused on the pathological and radiological properties of nonfunctional AI(NFAI) and the association with malignancy risk in our clinical series.

Methods

A total of 186 patients underwent adrenalectomy between 2010 and 2017; of these, 76 (40.8%) patients with non-functional AI were included in the current study. The radiological and pathologic characteristics of these AIs were retrospectively analyzed to determine the malignancy rate.

Results

There were 22 (28.9%) male and 54 (71.1%) female patients with nonfunctional AI included in this study. The median age was 55 (range: 24-85) years. Of the patients included, 37 (48.6%) had AI on the left and 39 (51.3%) had AI on the right adrenal gland. Sixty-one (80.2%) cases were treated laparoscopically, four (5.3%) required conversion to open surgery due to intraoperative difficulties such as bleeding and adhesions, and 11 (14.4%) were managed with open adrenalectomy. The rate of malignancy in the tumors with diameters of <4 cm, 4-6 cm, and >6 cm was found to be 0%, 2.9%, and 13.6%, respectively.

Conclusions

Determining the ideal cutoff value for surgical indication in an NFAI is challenging. Besides the malignancy risk, the rate of silent pheochromacytomas must be taken into account in the surgical decision.