Discovering Associations of Adverse Events with Pharmacotherapy in Patients with Non-Small Cell Lung Cancer Using Modified Apriori Algorithm

Abstract

Aim. To explore the associations between adverse events and pharmacotherapy in patients with non-small cell lung cancer. Methods. 16,527 patients with non-small cell lung cancer admitted to the Cancer Hospital, Chinese Academy of Medical Sciences, between January 1, 2010, and December 31, 2016, were included in the study. Their medication and laboratory examinations data were extracted from the medical records. Common Terminology Criteria for Adverse Events Version 4.03 were utilized for adverse events reporting. A new association algorithm was developed based on Apriori algorithm and used to investigate the associations between drugs and adverse events. In addition, a statistical comparison was conducted to compare the modified Apriori algorithm with the conventional Apriori algorithm. Results. Different types and levels of adverse events were identified from the abnormal laboratory findings. The three most common adverse events were hypocalcemia, elevated creatine phosphokinase, and hypertriglyceridemia. In addition, using the modified Apriori algorithm, 380 association rules were found between adverse events and chemotherapy. Moreover, the statistical comparison of the two methods demonstrated that the modified Apriori algorithm was more advantageous in analyzing the correlation between drugs and adverse events than the conventional Apriori algorithm. Conclusions. The modified Apriori algorithm can be used to more efficiently associate pharmacotherapy with adverse events. Based on the modified Apriori algorithm, meaningful association rules between drugs and adverse events were found, demonstrating a promising way to reveal the risk factors of adverse events during cancer treatment.