Myocardial infarction in type 2 diabetes using sodium–glucose co-transporter-2 inhibitors, dipeptidyl peptidase-4 inhibitors or glucagon-like peptide-1 receptor agonists: proportional hazards analysis by deep neural network based machine learning. (3rd March 2020)

• Record Type: Journal Article
• Authors: Yamada, Tomohide; Iwasaki, Kosuke; Maedera, Shotaro; Ito, Katsuya; Takeshima, Tomomi; Noma, Hisashi; Shojima, Nobuhiro
• Is Part Of: Current medical research and opinion. Volume 36:Number 3(2020)
• Page Start: 403
• Page End: 409
• Publication Date: 2020-03-03
• Subjects: Cardiovascular disease -- machine learning -- myocardial infarction -- oral antidiabetic drugs -- type 2 diabetes; Clinical medicine -- Periodicals; Therapeutics -- Periodicals; 615.5
• DOI: 10.1080/03007995.2019.1706043
• Languages: English
• ISSNs: 0300-7995

Abstract

Abstract: Aims: Some hypoglycemic therapies are associated with lower risk of cardiovascular outcomes. We investigated the incidence of cardiovascular disease among patients with type 2 diabetes using antidiabetic drugs from three classes, which were sodium–glucose co-transporter-2 inhibitors (SGLT-2is), glucagon-like peptide-1 receptor agonists (GLP-1RAs) and dipeptidyl peptidase-4 inhibitors (DPP-4is). Materials and methods: We compared the risk of myocardial infarction (MI) among these drugs and developed a machine learning model for predicting MI in patients without prior heart disease. We analyzed US health plan data for patients without prior MI or insulin therapy who were aged ≥40 years at initial prescription and had not received oral antidiabetic drugs for ≥6 months previously. After developing a machine learning model to predict MI, proportional hazards analysis of MI incidence was conducted using the risk obtained with this model and the drug classes as explanatory variables. Results: We analyzed 199, 116 patients (mean age: years), comprising 110, 278 (58.6) prescribed DPP-4is, 43, 538 (55.1) prescribed GLP-1RAs and 45, 300 (55.3) prescribed SGLT-2is. Receiver operating characteristics analysis showed higher precision of machine learning over logistic regression analysis. Proportional hazards analysis by machine learning revealed a significantly lower risk of MI with SGLT-2is or GLP-1RAs than DPP-4is (hazard ratio: 0.81, 95% confidence interval: 0.72–0.91, p = .0004 vs. 0.63, 0.56–0.72, p < .0001). MI risk was also significantly lower with GLP-1RAs than SGLT-2is (0.77, 0.66–0.90, p = .001). Limitations: All patients analyzed were covered by US commercial health plans, so information on patients aged ≥65 years was limited and the socioeconomic background may have been biased. Also, the observation period differed among the three classes of drugs due to differing release dates. Conclusions: Machine learning analysis suggested the risk of MI was 37% lower for type 2 diabetes patients without prior MI using GLP-1RAs versus DPP-4is, while the risk was 19% lower for SGLT-2is versus DPP-4is.