Characterization of Liver Metastasis Risk and Timing in Pancreatic Cancer Patients Using Electronic Health Records
talukdar, n.; Yu, Z.; Zeng, Z.; Zhang, X.; Lu, Y.; Joseph, D. F.; Leshchiner, D.; Wang, H.; Chen, B.
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BackgroundLiver metastasis is a frequent and serious complication of pancreatic cancer, contributing to its high mortality rate. Identifying risk factors and understanding the timing of liver metastasis may improve early detection and support more effective treatment planning. MethodA cohort of 12,955 incident pancreatic cancer patients was assembled from the Truveta platform. A subgroup of cases and controls that met the inclusion/exclusion criteria was analyzed using logistic regression and was reported as the primary analysis for this study. Effects on time to liver metastasis were also analyzed using univariate and multivariate Cox regression models. The primary outcome was the occurrence of liver metastasis within 1 year of diagnosis. Subjects were categorized as having baseline metastasis ([≤] 30 days from pancreatic cancer diagnosis) or post-baseline metastasis (>30 days). Demographic characteristics and comorbidities were evaluated for their potential role as risk factors. ResultAmong 7,858 patients in the case-control cohort, 2,920 (37%) developed liver metastasis within one year, while 2,066 (70%) subjects were diagnosed with metastasis at baseline. Male sex, older age, and a history of Type 2 diabetes mellitus, depression, obesity, anemia, abdominal pain, and distant metastasis were significantly associated with a higher risk of liver metastasis. Lower odds of liver metastasis were observed among Black or African American and Hispanic or Latino subjects. In the subgroup analysis after removing baseline metastasis, surgery and radiotherapy were protective, while tumors located in the head of the pancreas showed a higher risk for metastasis in this cohort. ConclusionsThis study identified key clinical and demographic risk factors for liver metastasis in pancreatic cancer, emphasizing the importance of using real-world data, analyzing the timing of disease progression, and highlighting opportunities for earlier intervention and personalized care.
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