Clinical trials are underpowered to detect serious but rare adverse events (AEs). We applied a novel data mining approach to detect potential AEs for canagliflozin (CANA), the first marketed SGLT2 inhibitor, prior to public awareness of its potential safety concerns. In a U.S. commercial claims dataset (3/2013-9/2015), two pair-wise cohorts of patients with T2DM initiating CANA or a comparator, i.e., a DPP-4i or a GLP-1RA, were identified and propensity score matched. We used variable ratio matching with up to 4 comparators for each CANA initiator (44,733 CANA vs. 99,458 DPP-4i; and 55,974 CANA vs. 74,727 GLP-1RA). Using a tree-based scan statistic data mining method, we assessed thousands of incident outcomes in CANA vs. comparator initiators, scanning for statistical AE alerts after adjusting for multiple testing. Incident outcomes were defined by hierarchical groupings of clinically related ICD-9 codes. For inpatient and emergency room diagnoses, diabetic ketoacidosis was the only severe AE associated with CANA (p<0.05 in both cohorts) (Table). When outpatient diagnoses were also considered, alerts for female and male genital infections emerged in both cohorts (p<0.05). The identification of known but no other AEs provides reassurance for the safety of CANA in adults and suggests that the tree-based scan statistic is a useful post-market safety monitoring tool for new diabetes drugs.

Disclosure

M. Fralick: None. M. Kulldorff: None. S. Wang: Research Support; Self; Boehringer Ingelheim International GmbH, Johnson & Johnson, Novartis Pharmaceuticals Corporation. S. Schneeweiss: None. D. Redelmeier: None. E. Patorno: Other Relationship; Self; Boehringer Ingelheim International GmbH.

Funding

National Institute on Aging (K08AG055670)

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