01 September 2006
Observation or laparoscopic adrenalectomy for adrenal incidentaloma? A surgical decision analysis.
Laurent Brunaud, Electron Kebebew, Frederic Sebag, Rasa Zarnegar, Orlo H. Clark, Quan-Yang DuhMed Sci Monit 2006; 12(9): CR355-362 :: ID: 455235
Abstract
BACKGROUND: The optimal strategy remains controversial for adrenal incidentaloma, 4 to 6 cm in size, nonfunctioning, and without malignant imaging characteristics. A decision analysis model was used to identify relevant variables for selecting the optimal management (observation versus adrenalectomy). MATERIAL/METHODS: Risk/benefit analysis in tertiary care center. The probabilities of each health outcome states were determined by a review of the literature from 1980 to 2002 (n=2844 patients); and from a retrospective review of experience at University of California San Francisco (UCSF). RESULTS: The baseline probabilities of morbidity after laparoscopic unilateral adrenalectomy and a new indication developing during initial observation (hypersecretion, size increase, malignancy) were 7.8% and 3.1%, respectively. We found observation to be the preferred approach when using baseline probabilities and utilities. Laparoscopic adrenalectomy becomes the preferred approach however if: (1) The morbidity rate from laparoscopic unilateral adrenalectomy is < 3.0%, 2) The probability of a new indication developing for adrenalectomy during observation is > 7.5%, 3) A patient's perspective of observation has a utility of lower than 98.6%, and (4) A patient views having a complication from adrenalectomy is not much deleterious (utility > 88.1%). CONCLUSIONS: This decision analysis model identifies the important variables for selecting the optimal management approach for adrenal incidentalomas. These results can be used to select the optimal management strategy based on individual patient preference and surgeon-specific complication rate.
Keywords: California - epidemiology, Adrenalectomy, Adrenal Gland Neoplasms - surgery, Decision Trees, Laparoscopy - methods, San Francisco - epidemiology
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