Literature DB >> 32978779

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

Chen-Yi Yang1, Wei-Ann Lin2, Pei-Fang Su2, Lun-Jie Li1, Chun-Ting Yang1, Huang-Tz Ou1,3,4, Shihchen Kuo5,6.   

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.
© 2020 The Authors Clinical Pharmacology & Therapeutics © 2020 American Society for Clinical Pharmacology and Therapeutics.

Entities:  

Year:  2020        PMID: 32978779     DOI: 10.1002/cpt.2058

Source DB:  PubMed          Journal:  Clin Pharmacol Ther        ISSN: 0009-9236            Impact factor:   6.875


  1 in total

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

Authors:  Andreas D Meid; Lucas Wirbka; Andreas Groll; Walter E Haefeli
Journal:  Med Decis Making       Date:  2021-12-15       Impact factor: 2.749

  1 in total

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