A novel data mining application to detect safety signals for newly approved medications in routine care of patients with diabetes.

BACKGROUND

Clinical trials are often underpowered to detect serious but rare adverse events of a new medication. We applied a novel data mining tool to detect potential adverse events of canagliflozin, the first sodium glucose co-transporter 2 (SGLT2 inhibitor) in the United States, using real-world data from shortly after its market entry and before public awareness of its potential safety concerns.

METHODS

In a U. S. commercial claims dataset (29 March 2013-30 Sept 2015), two pairwise cohorts of patients over 18 years of age with type 2 diabetes (T2D) who were newly dispensed canagliflozin or an active comparator, that is a dipeptidyl peptidase 4 inhibitor (DPP4) or a glucagon-like peptide 1 receptor agonist (GLP1), were identified and propensity score-matched. We used variable ratio matching with up to four people receiving a DPP4 or GLP1 for each person receiving canagliflozin. We identified potential safety signals using a hierarchical tree-based scan statistic data mining method with the hierarchical outcome tree constructed based on international classification of disease coding. We screened for incident adverse events where there were more outcomes observed among canagliflozin vs. comparator initiators than expected by chance, after adjusting for multiple testing.

RESULTS

We identified two pairwise propensity score variable ratio matched cohorts of 44,733 canagliflozin vs. 99,458 DPP4 initiators, and 55,974 canagliflozin vs. 74,727 GLP1 initiators. When we screened inpatient and emergency room diagnoses, diabetic ketoacidosis was the only severe adverse event associated with canagliflozin initiation with

CONCLUSIONS AND RELEVANCE

In a large population-based study, we identified known but no other adverse events associated with canagliflozin, providing reassurance on its safety among adult patients with T2D and suggesting the tree-based scan statistic method is a useful post-marketing safety monitoring tool for newly approved medications.

Informations de copyright

© 2021 The Authors. Endocrinology, Diabetes & Metabolism published by John Wiley & Sons Ltd.

Déclaration de conflit d'intérêts

Dr. Patorno is co‐investigator of an investigator‐initiated grant to the Brigham and Women's Hospital from Boehringer‐Ingelheim, not directly related to the topic of the submitted work. Dr. Donald Redelmeier has received funding from a Canada Research Chair in Medical Decision Sciences, the Canadian Institutes of Health Research and the BrightFocus Foundation. Dr. Schneeweiss is consultant to WHISCON, LLC and to Aetion, Inc., a software manufacturer of which he also owns equity. He is principal investigator of investigator‐initiated grants to the Brigham and Women's Hospital from Genentech, Bayer and Boehringer Ingelheim not directly related to the topic of this manuscript. Dr. Kulldorff was supported by National Institute of General Medical Sciences Grant RO1GM108999. Ms. Vine worked for a consulting company where some of her clients were pharmaceutical companies and her projects involved diabetes drugs, but is no longer employed there and none of her projects there relate to this paper. Dr. Wang received salary support from investigator‐initiated grants to the Brigham and Women's Hospital from Boehringer‐Ingelheim, Novartis Pharmaceuticals and Johnson & Johnson, unrelated to this work.