(A224) Identifying Potential Risk Factors for Sialolithiasis Using Machine Learning Algorithm

Introduction: The exact pathogenesis of sialolithiasis remains poorly understood, though it is generally attributed to salivary stasis, decreased salivary flow, or inflammatory processes. Given this limited understanding, we aimed to investigate potential risk factors associated with the development of sialolithiasis.

Methods: The All of Us database was queried for participants with sialolithiasis. These participants were matched to controls by age, race, and gender. Demographics and medical history data related to conditions such as obesity, hypertension, tonsillitis, gout, and other conditions associated with salivary stones were extracted and compared among participants. A logistic regression model using optimized parameters was used to identify variables associated with sialolithiasis development.

Results: In total, 1080 participants were included in the analysis, with 540 diagnosed with sialolithiasis and 540 matched controls. Sjögren’s syndrome (OR=3.3, p=0.003), dehydration (OR=1.5, p=0.037), and cholelithiasis without obstruction (OR=2.0, p=0.011) significantly contributed to a high risk of sialolithiasis, while essential hypertension (OR=0.7, p=0.0003) was inversely associated. Conditions such as alcohol abuse, hypercalcemia, gout, hyperparathyroidism, systemic lupus erythematosus, and bipolar disorder were not found to correlate with sialolithiasis.

Conclusions: This study, using a large-scale database, aimed to identify potential risk factors associated with sialolithiasis, thereby improving our understanding of its pathogenesis and paving the way for early diagnosis and intervention. The commonality among the identified risk factors appears to be their association with reduced salivary flow or alterations in fluid dynamics, which may contribute to the formation of salivary stones.