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Machine Learning Prediction of Pancreatitis Risk With Antithyroid Drugs: A Nationwide Retrospective Observational
Muhammed Kızılgül1,2, Talip Yigit3, Naim Ata4
1Department of Endocrinology and Metabolism, Etlik City Hospital, 06170 Ankara, Türkiye.
Background:
In recent years, there has been increasing data showing that the risk of acute pancreatitis (AP) is increased in patients using methimazole (MMI). The aim of this population-based study was to investigate the association between drugs used in the treatment of hyperthyroidism (MMI, propylthiouracil, propranolol) and the diagnosis of AP.
Material And Methods:
The database consisted of more than 85 million citizen records between January 1, 2017, and December 31, 2022. Within the scope of the study, the data were examined of patients diagnosed with hyperthyroidism and the individuals who developed acute pancreatitis after being diagnosed with hyperthyroidism, which was accomplished using the International Classification of Diseases, Tenth Revision codes. Analyses were carried out using R Studio 4.2.0 software. The test-train technique as a machine learning method was used in Cox regression models.
Results:
A total of 1 329 934 individuals diagnosed with hyperthyroidism were examined. Of these, 10 815 (0.81%) patients developed AP after being diagnosed with hyperthyroidism. The use of propylthiouracil was determined to increase the probability of experiencing AP diagnosis 0.10-fold (10%) (P .0009), and the use of propranolol decreased the probability of experiencing AP 0.5-fold (5%) (P .032), with a 52.2% predictive success rate. There is no any statistically significant association between MMI use and the probability of experiencing AP according to the model we built.
Conclusion:
Although the risk of experiencing AP was not increased by MMI use, propylthiouracil use increased and propranolol use decreased the risk of experiencing AP in patients with hyperthyroidism. Although drug-induced AP is relatively rare, a firm understanding of the drugs associated with the condition should alert the clinician to appropriately diagnose and treat patients.
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