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

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.

An industry perspective on the use of machine learning in drug and vaccine safety

“…RWD (Nordstrom et al, 2007;Gurulingappa et al, 2012a;Cao et al, 2013;Cheetham et al, 2014;Ferrajolo et al, 2014;Yeleswarapu et al, 2014;Whalen et al, 2018;Chapman et al, 2019;Choudhury et al, 2019;Wintzell et al, 2020;Fralick et al, 2021) and social media (Jimeno-Yepes et al, 2015;Powell et al, 2016;Cocos et al, 2017;Curtis et al, 2017;Pierce et al, 2017;Comfort et al, 2018;Gupta et al, 2018;Masino et al, 2018;Gavrielov-Yusim et al, 2019;Gartland et al, 2021) were the most frequent PV functions represented and, collectively, comprised 63% (21/33) of the papers included in the review. This is not surprising, as the use of social media to supplement PV activities began around 2009, with broader use of social media by the public, and attracted interest from both industry and academia as a potential source of safety-related events in near real time.…”

Drug safety signal detection in a regional healthcare database using the tree‐based scan statistic and comparison to 3 other mining methods

“…The results of this study showed that TreeScan identified 29 positive signals from the raw data, of which 9 were true positives when compared with the standard signals. Previous studies on TreeScan have primarily been qualitative evaluations of its results 7,17–19 . For example, Kulldorff et al .…”

“…DISCUSSIONThe results of this study showed that TreeScan identified 29 positive signals from the raw data, of which 9 were true positives when compared with the standard signals. Previous studies on TreeScan have primarily been qualitative evaluations of its results 7,[17][18][19]. For example, Kulldorff et al used the TreeScan method to uncover adverse reaction signals for 2 antifungal agents (terbinafine and itraconazole) and 2 thiazolidinediones (pioglitazone and rosiglitazone), resulting in 5 of the 10 identified signals being classified as adverse reactions, including liver damage, allergic reactions, nausea and vomiting.…”

Novel methods for pregnancy drug safety surveillance in the FDA Sentinel System

“…As part of a signal identification process, TreeScan can be used to detect a wide range of potential adverse events that can be further assessed to determine clinical plausibility and followed up with targeted safety studies. TreeScan has been used successfully in applications of vaccine safety 3–8 and monitoring of medication safety in pediatric 9 and adult populations 10 …”

“…TreeScan has been used successfully in applications of vaccine safety [3][4][5][6][7][8] and monitoring of medication safety in pediatric 9 and adult populations. 10 Given the limitations of the current methods of monitoring the safety of medications used during pregnancy, TreeScan could be particularly useful in the field of perinatal pharmacoepidemiology. 11 Pregnant women are rarely included in clinical trials during drug development, resulting in gaps in knowledge of safety and effectiveness that have to be addressed in postmarketing surveillance.…”