Skip to main content
. Author manuscript; available in PMC: 2024 Jul 1.
Published in final edited form as: Am J Kidney Dis. 2023 Jan 23;82(1):53–62.e1. doi: 10.1053/j.ajkd.2022.11.017

Figure 2. Factors associated with SGLT2i prescription.

Figure 2.

A Forest plot displays the multivariable logistic regression analyses for the odds of SGLT2i prescription based on patient demographic and clinical characteristics among patients with CKD, T2DM, and ASCVD. The model is adjusted for age; sex; race; hypertension; systolic heart failure; ischemic heart disease; use of statins, high-intensity statins, sulfonylurea, insulin, biguanide, thiazolidinedione, dipeptidyl peptidase 4 (DPP-4) inhibitors, glucagon-like peptide-1 (GLP-1) agonist, angiotensin converting enzyme inhibitor (ACEi) or angiotensin receptor blocker (ARB), or beta blocker; body mass index (BMI); estimated glomerular filtration rate (eGFR); hemoglobin A1c; whether the primary care provider (PCP) was a physician or advanced practice provider; whether care was received at a teaching facility; and the number of visits in the 12 months preceding the index visit with PCP, cardiology, endocrinology, and nephrology.

*Square root transformation was applied to address the right-skewed distribution of the number of visits within the 12 months prior to the index visit.