A novel data mining application to detect safety signals for newly approved medications in routine care of patients with diabetes. Issue 3 (6th April 2021)

• Record Type: Journal Article
• Authors: Fralick, Michael; Kulldorff, Martin; Redelmeier, Donald; Wang, Shirley V.; Vine, Seanna; Schneeweiss, Sebastian; Patorno, Elisabetta
• Is Part Of: Endocrinology, diabetes & metabolism. Volume 4:Issue 3(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-04-06
• Subjects: data mining -- diabetes -- medication; Endocrinology -- Periodicals; Diabetes -- Periodicals; Metabolism -- Periodicals; 616.4
• DOI: 10.1002/edm2.237
• Languages: English
• ISSNs: 2398-9238

Abstract

ABSTRACT: 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 p < .05 in both cohorts. When outpatient diagnoses were also considered, signals for female and male genital infections emerged in both cohorts ( p < .05). 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. Abstract : 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 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. 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 is a useful post‐marketing safety monitoring tool for newly approved medications.