An Algorithm Approach to Diagnosing Bilateral Parotid Enlargement. Issue 5 (4th February 2013)

• Record Type: Journal Article
• Authors: Chen, Si; Paul, Benjamin C.; Myssiorek, David
• Is Part Of: Otolaryngology--head and neck surgery. Volume 148:Issue 5(2013)
• Page Start: 732
• Page End: 739
• Publication Date: 2013-02-04
• Subjects: bilateral parotid enlargement -- algorithm; Head -- Surgery -- Periodicals; Neck -- Surgery -- Periodicals; Otolaryngology -- Periodicals; 617.51
• DOI: 10.1177/0194599813476669
• Languages: English
• ISSNs: 0194-5998

Abstract

Abstract : Objective: This contemporary review aims to categorize the disease entities that cause bilateral parotid enlargement and to develop a question‐based algorithm to improve diagnosis of bilateral parotid masses. Data Sources: A PubMed search for bilateral and parotid showed 818 results. Of these, 68 relevant studies were reviewed to compile a list of disease processes that can cause bilateral parotid enlargement. Review Methods: A total of 22 diseases entities were reviewed. The disease processes were initially grouped into 6 categories based on etiology: sialadenosis, infection, neoplasm, autoimmune, iatrogenic, and miscellaneous. For each lesion, the incidence, history, and physical examination were compiled in a matrix. Conclusion: After reviewing the matrix, it was clear that grouping diseases based on specific history and physical findings limits the differential diagnosis. The most important factors included disease incidence, timing of onset, nodular or diffuse, pain, and overlying skin changes. With this algorithm, the differential diagnosis can be limited from 28 to 7 or fewer likely diagnoses for a given presentation. Implications for Practice: Bilateral parotid disease has a wide differential diagnosis with an expanding number of available tests. An algorithm, based solely on data obtained from the history and physical examination in the first patient encounter, may reduce the differential and aid the clinician in deciding on further workup and treatment. Following the algorithm presented here should allow the clinician to arrive at a diagnosis rapidly without ordering unnecessary tests and wasting resources.