Heterogeneous Treatment Effects on Cardiovascular Diseases With Dipeptidyl Peptidase‐4 Inhibitors Versus Sulfonylureas in Type 2 Diabetes Patients

Abstract

This study explored heterogeneous treatment effects (HTEs) of the real‐world use of dipeptidyl peptidase‐4 inhibitors (DPP‐4is) vs. sulfonylureas (SUs) on cardiovascular diseases (CVDs) and mortality in patients with type 2 diabetes. Utilizing Taiwan’s National Health Insurance Research Database, 19,853 propensity score‐matched pairs of DPP‐4i and SU stable users were identified. Classification and regression tree analyses and Cox models were applied to explore HTEs, according to various patient characteristics, on the composite CVDs, three‐point major adverse cardiovascular event (MACE), and all‐cause mortality. The absolute risk difference (ARD), hazard ratio (HR), and 95% confidence interval (CI) were estimated for comparing treatment effects. CVD history, ischemic stroke, or transient ischemic attack (IS/TIA) history, and age at treatment initiation were significant treatment effect modifiers. Patients with prior IS/TIA but without any other prior CVDs benefited most in reduced risks of composite CVDs from using DPP‐4i vs. SU (ARD −4.31%, 95% CI −7.48% to −1.14%, HR 0.81, 95% CI 0.69 ~ 0.95), followed by those without prior IS/TIA and CVDs and initiated with DPP‐4i at age < 69.3 years (ARD −0.90%, 95% CI −1.47% to −0.32%, HR 0.86, 95% CI 0.77 ~ 0.97). Patients with prior IS/TIA benefited most in reduced risks of three‐point MACE from using DPP‐4i vs. SU (ARD −4.22%, 95% CV −6.66% to −1.78%, HR 0.80, 95% CI 0.69 ~ 0.93), followed by those without prior IS/TIA and initiated with DPP‐4i at age < 69.3 years (ARD −0.68%, 95% CI −1.08% to −0.29%, HR 0.81, 95% CI 0.70 ~ 0.93). Consideration of CVD and IS/TIA histories and age could facilitate individualized diabetes management of using DPP‐4i vs. SU. Future studies are warranted given the hypothesis‐generating nature in this exploratory research.

Adaptation of risk prediction equations for cardiovascular outcomes among patients with type 2 diabetes in real-world settings: a cross-institutional study using common data model approach

“…When the data of risk predictors were unavailable in the EHRs, some assumptions were made. First, since patients’ diabetes onsets could not be ascertained in EHRs (e.g., patients might have been diagnosed with type 2 diabetes before they came to the two medical centers in this study), we assumed a 5-year diabetes duration for study patients based on the literature, which shows an average diabetes duration of 5 to 8 years among the Taiwanese type 2 diabetes populations treated with glucose-lowering agents [ 19 , 20 ]. Additionally, due to the lack of smoking history and hemoglobin data in the NTUH’s EHRs, the NTUH population was assumed to be non-smokers and have a hemoglobin level of 12.5 g/dL, which is aligned with the average hemoglobin level of the NCKUH population.…”

Can Machine Learning from Real-World Data Support Drug Treatment Decisions? A Prediction Modeling Case for Direct Oral Anticoagulants

“…The current literature shows promising examples from RCT reanalyses to infer ITEs by machine learning 9 or from observational studies to elucidate effect modifiers that modulated the (individual) effectiveness of different classes of antidiabetics. 10 ITEs within a pharmacological class such as DOACs have not been investigated yet. Until recently, it was generally assumed in clinical practice that DOACs were largely equivalent.…”