Predicting the risk of pancreatic cancer in women with new‐onset diabetes mellitus

医学 糖尿病 胰腺癌 内科学 癌症 人口 回顾性队列研究 2型糖尿病 内分泌学 环境卫生
作者
Sitwat Ali,Michael Coory,Peter Donovan,Renhua Na,Nirmala Pandeya,Sallie‐Anne Pearson,Katrina Spilsbury,Karen Tuesley,Susan J. Jordan,Rachel Ε. Neale
出处
期刊:Journal of Gastroenterology and Hepatology [Wiley]
卷期号:39 (6): 1057-1064 被引量:3
标识
DOI:10.1111/jgh.16503
摘要

Abstract Background and Aim People with new‐onset diabetes mellitus (diabetes) could be a possible target population for pancreatic cancer surveillance. However, distinguishing diabetes caused by pancreatic cancer from type 2 diabetes remains challenging. We aimed to develop and validate a model to predict pancreatic cancer among women with new‐onset diabetes. Methods We conducted a retrospective cohort study among Australian women newly diagnosed with diabetes, using first prescription of anti‐diabetic medications, sourced from administrative data, as a surrogate for the diagnosis of diabetes. The outcome was a diagnosis of pancreatic cancer within 3 years of diabetes diagnosis. We used prescription medications, severity of diabetes (i.e., change/addition of medication within 2 months after first medication), and age at diabetes diagnosis as potential predictors of pancreatic cancer. Results Among 99 687 women aged ≥ 50 years with new‐onset diabetes, 602 (0.6%) were diagnosed with pancreatic cancer within 3 years. The area under the receiver operating curve for the risk prediction model was 0.73. Age and diabetes severity were the two most influential predictors followed by beta‐blockers, acid disorder drugs, and lipid‐modifying agents. Using a risk threshold of 50%, sensitivity and specificity were 69% and the positive predictive value (PPV) was 1.3%. Conclusions Our model doubled the PPV of pancreatic cancer in women with new‐onset diabetes from 0.6% to 1.3%. Age and rapid progression of diabetes were important risk factors, and pancreatic cancer occurred more commonly in women without typical risk factors for type 2 diabetes. This model could prove valuable as an initial screening tool, especially as new biomarkers emerge.

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