Use of Lipid-, Blood Pressure–, and Glucose-Lowering Pharmacotherapy in Patients With Type 2 Diabetes and Atherosclerotic Cardiovascular Disease

Adam J Nelson; Emily C O’Brien; Lisa A Kaltenbach; Jennifer B Green; Renato D Lopes; Caryn G Morse; Hussein R Al-Khalidi; Vanita R Aroda; Matthew A Cavender; Tanya Gaynor; Julienne K Kirk; Ildiko Lingvay; Melissa L Magwire; Darren K McGuire; Jonathan Pak; Rodica Pop-Busui; Caroline R Richardson; Cagri Senyucel; Michelle D Kelsey; Neha J Pagidipati; Christopher B Granger

doi:10.1001/jamanetworkopen.2021.48030

. 2022 Feb 17;5(2):e2148030. doi: 10.1001/jamanetworkopen.2021.48030

Use of Lipid-, Blood Pressure–, and Glucose-Lowering Pharmacotherapy in Patients With Type 2 Diabetes and Atherosclerotic Cardiovascular Disease

Adam J Nelson ¹, Emily C O’Brien ¹, Lisa A Kaltenbach ¹, Jennifer B Green ¹, Renato D Lopes ¹, Caryn G Morse ², Hussein R Al-Khalidi ¹, Vanita R Aroda ³, Matthew A Cavender ⁴, Tanya Gaynor ⁵, Julienne K Kirk ², Ildiko Lingvay ⁶, Melissa L Magwire ⁷, Darren K McGuire ^6,⁸, Jonathan Pak ⁵, Rodica Pop-Busui ⁹, Caroline R Richardson ¹⁰, Cagri Senyucel ¹¹, Michelle D Kelsey ¹, Neha J Pagidipati ¹, Christopher B Granger ^1,^✉

¹Duke Clinical Research Institute, Durham, North Carolina

²Wake Forest School of Medicine, Winston-Salem, North Carolina

³Brigham and Women’s Hospital, Boston, Massachusetts

⁴University of North Carolina at Chapel Hill, Chapel Hill, North Carolina

⁵Boehringer Ingelheim Pharmaceuticals, Ridgefield, Connecticut

⁶University of Texas Southwestern Medical Center, Dallas

⁷St Luke’s Health System, Kansas City, Missouri

⁸Parkland Health and Hospital System, Dallas, Texas

⁹University of Michigan, Ann Arbor

¹⁰University of Michigan Medical School, Ann Arbor

¹¹Eli Lilly and Company, Indianapolis, Indiana

Accepted for Publication: December 19, 2021.

Published: February 17, 2022. doi:10.1001/jamanetworkopen.2021.48030

^✉

Corresponding Author: Christopher B. Granger, MD, Duke Clinical Research Institute, 200 Morris St, Durham, NC 27701 (Christopher.granger@duke.edu).

Author Contributions: Drs Al-Khalidi and Kaltenbach had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Nelson, O’Brien, Kaltenbach, Lopes, Cavender, Gaynor, Kirk, Magwire, McGuire, Pak, Pop-Busui, Richardson, Senyucel, Pagidipati, Granger.

Acquisition, analysis, or interpretation of data: Nelson, Kaltenbach, Green, Morse, Al-Khalidi, Aroda, Lingvay, Pop-Busui, Kelsey, Pagidipati, Granger.

Drafting of the manuscript: Nelson, Kaltenbach.

Critical revision of the manuscript for important intellectual content: O’Brien, Green, Lopes, Morse, Al-Khalidi, Aroda, Cavender, Gaynor, Kirk, Lingvay, Magwire, McGuire, Pak, Pop-Busui, Richardson, Senyucel, Kelsey, Pagidipati, Granger.

Statistical analysis: Nelson, Kaltenbach, Al-Khalidi.

Obtained funding: Nelson, Gaynor, Pak, Pagidipati, Granger.

Administrative, technical, or material support: Morse, Cavender, Gaynor, Pak.

Supervision: O’Brien, Green, Lopes, Cavender, Richardson, Pagidipati.

Conflict of Interest Disclosures: Dr Nelson reported receiving grants from Diabetes Australia and the Royal Australasian College of Physicians. Dr O’Brien reported receiving grants from Novartis, Glaxo Smith Kline, and Bristol Myer Squib outside the submitted work. Dr Green reported receiving grants and personal fees from Boehringer Ingelheim/Lilly Alliance, Sanofi/Lexicon, Glaxo Smith Kline, and AstraZeneca; personal fees from Novo Nordisk, Hawthorne Effect, Pfizer, Regeneron Pharmaceuticals, and Bayer; and grants from Merck, GlaxoSmithKline, and Roche outside the submitted work. Dr Lopes reported receiving personal fees from Bayer, Boehringer Ingelheim, Bristol Myers Squibb, Daiichi Sankyo, Merck, Portola; grants from Amgen; and grants and personal fees from Bristol-Myers Squibb, GlaxoSmithKline, Medtronic, Pfizer, and Sanofi outside the submitted work. Dr Aroda reported receiving grants from Applied Therapeutics, Novo Nordisk, Sanofi, Eli Lilly and Company, and Fractyl; personal fees from Duke University, Liberum, Novo Nordisk, and Pfizer; and that her spouse is employed by Janssen and Merck outside the submitted work. Dr Cavender reported receiving grants and personal fees from Amgen, Boehringer Ingelheim, and Novo Nordisk; grants from AstraZeneca and Novartis; and personal fees from Merck and Edwards Lifesciences outside the submitted work. Dr Lingvay reported receiving personal fees from Duke Clinical Research Institute during the conduct of the study and personal fees from Novo Nordisk, Eli Lilly and Company, Merck, Janssen, Sanofi, Boehringer Ingelheim, Intarcia, Bayer, AstraZeneca, Target RWE, Mannkind, Valerita, AstraZeneca, and DataRevive and grants from Novo Nordisk, Sanofi, Merck, Pfizer, and Mylan outside the submitted work. Dr Magwire reported receiving personal fees from Novo Nordisk and Boehringer Ingelheim outside the submitted work. Dr McGuire reported receiving personal fees from Boehringer Ingelheim, Janssen, Sanofi, AstraZeneca, Merck, Pfizer, Novo Nordisk, Esperion, Lilly, Lexicon Pharmaceuticals, CSL Behring, Applied Therapeutics, Metavant, Afimmune, GlaxoSmithKline, Eisai, and Bayer outside the submitted work. Dr Pop-Busui reported receiving personal fees from Boehringer Ingelheim, Novo Nordisk, Bayer, and Averitas, grants from AstraZeneca and the National Institutes of Health, and serving as an associated editor for Diabetes outside the submitted work. Dr Senyucel reported owning stock in Eli Lilly and Company outside the submitted work. Dr Kelsey reported receiving grants from the National Institutes of Health during the conduct of the study. Dr Pagidipati reported receiving personal fees and grants from Boehringer Ingelheim, Eli Lilly and Company, and AstraZeneca and grants from Amgen, Novo Nordisk, Novartis, Regeneron, Sanofi, and Verily Life Sciences outside the submitted work. Dr Granger reported receiving grants and personal fees from Boehringer Ingelheim, Bristol Myer Squib, Janssen, Pfizer, and Medtronic; grants from Akros Pharma, Apple, AstraZeneca, Daichi-Sankyo, and Novartis; and personal fees from AbbVie, Bayer, Boston Scientific, CeleCor, Correvio, Espero, Merck, Novo Nordisk, Rhoshan Pharmaceuticals, and Roche Diagnostics outside the submitted work. No other disclosures were reported.

Funding/Support: This study was funded by Boehringer Ingelheim and Eli Lilly and Company. The research reported in this publication was conducted using the National Patient-Centered Clinical Research Network, developed with funding from the Patient-Centered Outcomes Research Institute.

Role of the Funder/Sponsor: The funders were involved in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, and approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: The views presented in this publication are solely the responsibility of the authors and do not necessarily represent the views of organizations participating in, collaborating with, or funding National Patient-Centered Clinical Research Network or of the Patient-Centered Outcomes Research Institute.

Additional Contributions: Gretchen Sanders, MSN, provided operational support; Mary Williams, BSc, and Stephanie Poley, PhD, assisted in design and data collection; ; Yinghong Zhang, BA, assisted in design, data collection, and interpretation; and Vladimir Demyanenko, MS, assisted in data collection and interpretation. They are employees at Duke Clinical Research Institute and were not compensated outside of their normal salaries.

^✉

Corresponding author.

PMCID: PMC8855234 PMID: 35175345

Key Points

Question

What is the contemporary pattern of evidence-based pharmacotherapy use among a real-world population of US patients with type 2 diabetes and atherosclerotic cardiovascular disease?

Findings

In this cohort study of 324 706 patients from health systems across the US, 58.6% of patients were receiving a statin (and a total of 26.8% of patients were receiving a high-intensity formation), 45.5% of patients were receiving an angiotensin-converting enzyme inhibitor or angiotensin-receptor blocker, 3.9% of patients were prescribed a glucagon-like peptide-1 receptor agonist, and 2.8% of patients were receiving a sodium glucose cotransporter-2 inhibitors.

Meaning

These findings suggest that multifaceted interventions are needed to overcome the large gaps in evidence-based pharmacotherapy use among this increasing population of patients at high risk of adverse outcomes.

This cohort study examines the prevalence of evidence-based cardiovascular prescribing among patients with type 2 diabetes and atherosclerotic cardiovascular disease in the US.

Abstract

Importance

Based on contemporary estimates in the US, evidence-based therapies for cardiovascular risk reduction are generally underused among patients with type 2 diabetes and atherosclerotic cardiovascular disease (ASCVD).

Objective

To determine the use of evidence-based cardiovascular preventive therapies in a broad US population with diabetes and ASCVD.

Design, Setting, and Participants

This multicenter cohort study used health system–level aggregated data within the National Patient-Centered Clinical Research Network, including 12 health systems. Participants included patients with diabetes and established ASCVD (ie, coronary artery disease, cerebrovascular disease, and peripheral artery disease) between January 1 and December 31, 2018. Data were analyzed from September 2020 until January 2021.

Exposures

One or more health care encounters in 2018.

Main Outcomes and Measures

Patient characteristics by prescription of any of the following key evidence-based therapies: high-intensity statin, angiotensin-converting enzyme inhibitor (ACEI) or angiotensin-receptor blocker (ARB) and sodium glucose cotransporter-2 inhibitors (SGLT2I) or glucagon-like peptide-1 receptor agonist (GLP-1RA).

Results

The overall cohort included 324 706 patients, with a mean (SD) age of 68.1 (12.2) years and 144 169 (44.4%) women and 180 537 (55.6%) men. A total of 59 124 patients (18.2% ) were Black, and 41 470 patients (12.8%) were Latinx. Among 205 885 patients with specialized visit data from the prior year, 17 971 patients (8.7%) visited an endocrinologist, 54 330 patients (26.4%) visited a cardiologist, and 154 078 patients (74.8%) visited a primary care physician. Overall, 190 277 patients (58.6%) were prescribed a statin, but only 88 426 patients (26.8%) were prescribed a high-intensity statin; 147 762 patients (45.5%) were prescribed an ACEI or ARB, 12 724 patients (3.9%) were prescribed a GLP-1RA, and 8989 patients (2.8%) were prescribed an SGLT2I. Overall, 14 918 patients (4.6%) were prescribed all 3 classes of therapies, and 138 173 patients (42.6%) were prescribed none. Patients who were prescribed a high-intensity statin were more likely to be men (59.9% [95% CI, 59.6%-60.3%] of patients vs 55.6% [95% CI, 55.4%-55.8%] of patients), have coronary atherosclerotic disease (79.9% [95% CI, 79.7%-80.2%] of patients vs 73.0% [95% CI, 72.8%-73.3%] of patients) and more likely to have seen a cardiologist (40.0% [95% CI, 39.6%-40.4%] of patients vs 26.4% [95% CI, 26.2%-26.6%] of patients).

Conclusions and Relevance

In this large cohort of US patients with diabetes and ASCVD, fewer than 1 in 20 patients were prescribed all 3 evidence-based therapies, defined as a high-intensity statin, either an ACEI or ARB, and either an SGLT2I and/or a GLP-1RA. These findings suggest that multifaceted interventions are needed to overcome barriers to the implementation of evidence-based therapies and facilitate their optimal use.

Introduction

Up to two-thirds of individuals with type 2 diabetes will develop atherosclerotic cardiovascular disease (ASCVD) in their lifetimes.^1,2,3 In individuals with diabetes, ASCVD is more extensive, less amenable to treatment, and associated with worse outcomes compared with the general population.⁴ Fortunately, a number of secondary prevention therapies have been shown to reduce the morbidity and mortality of ASCVD in individuals with diabetes; yet, for a variety of reasons, they are underused in clinical practice.⁵

Estimates from studies evaluating individuals with diabetes and ASCVD demonstrate a wide variation in the use of key preventive pharmacotherapies, namely high-intensity statins (from 24.7%-45.4%), angiotensin-converting enzyme inhibitors (ACEIs) or angiotensin-II receptor blockers (ARBs) (from 53.1%-72.0%), and antihyperglycemic agents with proven cardiovascular benefit, such as sodium glucose cotransporter-2 inhibitors (SGLT2I) and glucagon-like peptide-1 receptor agonists (GLP-1RA) (from 2.5% to 17.6%).^{6,7,8,9,10,11,12,13} While these data consistently demonstrate concerning gaps in the use of evidence-based therapy, there is significant variation in the magnitude of these estimates owing to differences in data source (eg, registries vs trials vs single-site studies), evolving prescribing trends, and selected patient characteristics. Thus, there is considerable uncertainty as to how representative these findings are for most individuals with diabetes and ASCVD, and we hypothesize that a real-world estimate of evidence-based therapy use will be considerably lower.

The objective of this study was to describe the contemporary use of lipid-, blood pressure–, and glucose-lowering pharmacotherapy among a large, national and representative cohort of individuals with diabetes and ASCVD in the US. Accurately determining the patterns and gaps in evidence-based therapy in this high-risk and increasing population will more precisely inform ongoing implementation programs aimed to increase adoption and improve outcomes.

Methods

For this cohort study, participating sites obtained formal determination from their local institutional review board that this study was not human participants research and thus waiver for informed consent was provided. Because of specifications in the data-use agreements that prohibit release of health system–level data, it is not possible to provide data generated from sites participating in this study. This study is reported following the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.

Data Source and Study Design

This was a multicenter cohort study performed using data from the US National Patient-Centered Clinical Research Network (PCORnet). PCORnet is composed of multiple clinical data research networks (CDRNs), which in turn comprise 1 or more datamarts (eTable 1 in the Supplement). Datamarts are collections of data that participating health systems generate and store using the PCORnet Common Data Model (CDM) version 5.1 and include demographics, vital signs, diagnoses, encounters, clinician types, procedures, prescription orders, and laboratory results.¹⁴ These datamarts enable multisite research by housing individual patient-level data that can be queried and analyzed by site and then returned in aggregate and standardized form.

Cohort Identification

A study period from January 1, 2018, until December 31, 2018, was defined, with an eligible patient’s most recent inpatient or outpatient encounter during this period considered their index date. As previously described,¹⁵ International Classification of Diseases, Ninth Revision (ICD-9), International Statistical Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM), and Current Procedural Terminology codes were used to select patients aged 18 years and older with evidence of diabetes and ASCVD during the 5-year period preceding the index date (ie, January 1, 2014, to December 31, 2018). All code lists are available in the eTable 2 in the Supplement. Briefly, ASCVD was defined as patients with coronary artery disease (eg, obstructive coronary atherosclerosis, prior myocardial infarction, prior percutaneous coronary intervention, or coronary artery bypass grafts), peripheral arterial disease (eg, vascular claudication, prior peripheral percutaneous or open revascularization, or amputation from poor circulation), or cerebrovascular disease (eg, carotid atherosclerosis, ischemic stroke, or prior percutaneous or open cerebrovascular revascularization).

Variables

Demographic information was obtained from the CDM demographic and location tables. We used existing information on race and ethnicity, which was documented in the medical record based on self-report and/or clinician observation. Race and ethnicity data were included in this analysis because of prior work suggesting associations between race and ethnicity and clinical care patterns. Comorbid conditions defined by ICD-9 and ICD-10-CM codes were extracted from the CDM diagnosis table, active smoking status from the CDM vital table, and laboratory results (ie, lipid profile, estimated glomerular filtration rate [eGFR] and hemoglobin A_1c [HbA_1c]) from the investigations table. Lower- and upper-bound truncation points for biologically plausible measurement ranges were based on a test query to improve data quality prior to aggregation. Medication prescriptions were defined using RxNorm concept unique identifiers from the CDM prescribing table. Patients were considered to be using a medication of interest if it was listed in the prescribing table at any time in the 12 months prior to their index date. Health care resource utilization was obtained from the CDM encounter and diagnosis tables over a 12-month period prior to index date. Clinicians of interest were endocrinologists, cardiologists, and primary care physicians.

Evidence-Based Therapy Composite Score

Evidence-based therapy was defined as the use of a high-intensity statin (atorvastatin 40-80 mg or rosuvastatin 20-40 mg), an ACEI or ARB (or angiotensin-II receptor/neprilysin inhibitor [ARNI]), and either an SGLT2I and/or GLP-1RA. Although simvastatin 80 mg is considered a high-intensity formulation, it is not recommended by the US Food and Drug Administration and does not appear in the American College of Cardiology guidelines; thus, it was not considered an evidence-based therapy. Patients in this cohort were considered to have indications for all 3 components and given a composite score of 0 to 3 reflecting the number of evidence-based therapies prescribed. Patients with HbA_1c less than 7% (to convert to proportion of total hemoglobin, multiply by 0.01) with or without metformin were ascribed 1 point for the SGLT2I and/or GLP-1RA domain in the 3-point composite score. Of note, it is now recognized that metformin monotherapy is no longer adequate for these patients with high risk, and while this is reflected in the current guidelines, it was not contemporary guidance during the study period. Although some heterogeneity with regard to effects on specific cardiovascular (CV) and kidney outcomes has been found within the SGLT2I and GLP-1RA classes, for the purposes of the analyses and their interpretation, these medications were considered to exhibit class effects.^16,17

Statistical Analysis

Site-level aggregate data were summarized using weighted summary measures to account for sample size from each site. Categorical variables are presented as frequencies (percentages) by summing numerators and denominators for each site. Missing data for categorical variables are presented for each variable as frequencies (percentages) of the expected column total. Continuous variables are presented as pooled means; variances across sites were tested for homogeneity according to Hartley test,¹⁸ and pooled SDs are presented. To understand patient and health care characteristics associated with the prescription of the evidence-based therapies of interest, the cohort was dichotomized into low (ie, use of 0 or 1 evidence-based therapy) and high (ie, use of 2 or 3 evidence-based therapies) score groups. Statistical comparison between treatment groups prescribed each medication of interest (ie, high-intensity statin, ACEI or ARB, and SGLT2I and/or GLP-1RA) was not possible, given the lack of mutual exclusivity; however, descriptive comparisons were made on the basis of clinically relevant differences and narrow 95% CIs, generated using pooled SDs for continuous variables and assumed binomial proportions for categorical variables.

All analyses were performed using SAS statistical software version 9.1 (SAS Institute). Data were analyzed from September 2020 to January 2021.

Results

Cohort Assembly

Twelve geographically diverse health systems contributing to 5 CDRNs and 16 datamarts responded within the required timeframe and were able to distribute the query (eTable 1 in the Supplement). Within these participating datamarts, there were 561 259 eligible patients, of whom 324 706 patients (57.9%) had complete medication tables, and among these, 205 885 patients (63.4%) had data on encounters by clinician type, 188 662 patients (58.1%) had laboratory results, and 161 874 patients (49.9%) reported insurance status (Figure 1).

Study Participants

Among 324 706 patients included in analysis, the overall mean (SD) age was 68.1 (12.2) years, 144 169 (44.4%) were women and 180 537 (55.6%) were men. A total of 9282 patients (2.8%) were Asian, 59 124 patients (18.2%) were Black, 41 470 patients (12.3%) were Latinx, and 207 846 patients (64.0%) were White. Coronary artery disease was present in 237 012 patients (73.0%), 60 125 patients (18.5%) had cerebrovascular disease, and 151 709 patients (46.7%) had peripheral arterial disease. Baseline characteristics are presented overall and by evidence-based therapy in Table 1.

Table 1. Patient Characteristics by Individual Evidence-Based Therapy.

Patient characteristics	Patients, % (95% CI)
	Overall (N = 324 706)	Evidence-based therapy prescription
	Overall (N = 324 706)	High-intensity statin (n = 88 426)	ACEI/ARB (n = 147 762)	SGLT2I (n = 8989)	GLP-1RA (n = 12 724)
Age, mean (SD) [95% CI], y	68.1 (12.2) [68.0-68.1]	67.0 (10.9) [67.0-67.1]	68.1 (11.4) [68.0-68.1]	63.2 (9.9) [63.0-63.4]	62.9 (10.3) [62.7-63.1]
Sex
Women	44.4 (44.2-44.6)	40.1 (39.7-40.4)	44.2 (44.0-44.5)	36.5 (35.5-37.5)	46.7 (45.8-47.5)
Men	55.6 (55.4-55.8)	59.9 (59.6-60.3)	55.8 (55.5-56.0)	63.5 (62.5-64.5)	53.3 (52.5-54.2)
Race
Asian	2.9 (2.8-2.9)	3.0 (2.9-3.1)	2.8 (2.7-2.9)	3.6 (3.2-4.0)	2.0 (1.8-2.3)
Black	18.2 (18.1-18.3)	20.5 (20.2-20.7)	19.6 (19.4-19.8)	13.4 (12.7-14.1)	17.5 (16.8-18.1)
White	64.0 (63.9-64.2)	61.0 (60.7-61.3)	63.4 (63.2-63.7)	66.2 (65.2-67.2)	64.8 (64.0-65.6)
Other^a	14.9 (14.8-15.0)	15.6 (15.3-15.8)	14.2 (14.0-14.3)	16.9 (16.1-17.7)	15.7 (15.1-16.4)
Latinx ethnicity	12.8 (12.7-12.9)	14.0 (13.8-14.2)	13.6 (13.4-13.8)	13.4 (12.7-14.1)	13.5 (12.9-14.1)
ASCVD
Coronary artery disease	73.0 (72.8-73.2)	79.9 (79.7-80.2)	72.3 (72.1-72.6)	72.2 (71.3-73.2)	69.5 (68.7-70.3)
MI	24.0 (23.8-24.1)	33.9 (33.6-34.2)	25.5 (25.2-25.7)	20.6 (19.8-21.4)	19.9 (19.2-20.6)
PCI	21.4 (21.2-21.5)	30.5 (30.2-30.8)	22.2 (22.0-22.4)	21.4 (20.5-22.2)	19.6 (18.9-20.3)
Cerebrovascular disease	18.5 (18.4-18.7)	23.5 (23.2-23.8)	19.6 (19.4-19.8)	16.1 (15.3-16.9)	16.4 (15.8-17.1)
Stroke	12.4 (12.3-12.5)	16.6 (16.4-16.8)	13.2 (13.0-13.4)	9.7 (9.1-10.3)	10.5 (10.0-11.1)
Peripheral arterial disease	46.7 (46.6-46.9)	47.3 (47.0-47.6)	50.1 (49.9-50.4)	47.1 (46.1-48.2)	51.2 (50.4-52.1)
Comorbidities
Heart failure	32.1 (31.9-32.3)	38.8 (38.5-39.2)	34.6 (34.3-34.8)	21.3 (20.5-22.2)	26.3 (25.5-27.1)
Hypertension	92.1 (92.0-92.2)	94.8 (94.6-94.9)	96.9 (96.8-97.0)	92.1 (91.5-92.6)	93.5 (93.0-93.9)
Dyslipidemia	82.9 (82.8-83.1)	90.3 (90.1-90.5)	87.6 (87.5-87.8)	89.7 (89.0-90.3)	89.6 (89.1-90.1)
Smoking	11.6 (11.5-11.7)	15.1 (14.8-15.3)	12.8 (12.7-13.)	9.8 (9.2-10.4)	10.4 (9.9-10.9)
Charlson Comorbidity Index score, mean (SD) [95% CI]	4.1 (2.8) [4.1-4.1]	4.5 (2.9) [4.5-4.5]	4.3 (2.8) [4.2-4.3]	3.3 (2.4) [3.2-3.3]	3.8 (2.6) [3.8-3.9]
Diabetes complications
Diabetic ketoacidosis	1.7 (1.7-1.8)	2.4 (2.3-2.5)	2.0 (1.9-2.0)	1.6 (1.3-1.9)	2.4 (2.1-2.7)
Retinopathy	9.4 (9.3-9.5)	12.8 (12.6-13.0)	11.4 (11.2-11.6)	11.6 (11.0-12.3)	17.0 (16.4-17.7)
Neuropathy	26.3 (26.2-26.5)	30.4 (30.1-30.7)	29.6 (29.4-29.9)	31.0 (30.1-32.0)	40.1 (39.3-41.0)
Diabetic foot	5.1 (5.0-5.2)	6.0 (5.8-6.2)	5.3 (5.2-5.5)	3.8 (3.4-4.2)	6.1 (5.7-6.5)
SBP, mean (SD) [95% CI], mm Hg	132 (20) [132-132]	132 (21) [131-132]	133 (21) [133-133]	129 (18) [128-129]	131 (18) [130-131]
BMI, mean (SD) [95% CI]	31.3 (7.3) [31.3-31.4]	31.5 (7.1) [31.5-31.6]	31.9 (7.3) [31.8-31.9]	32.7 (6.8) [32.6-32.9]	35.2 (7.6) [35.1-35.3]
Laboratory values, mean (SD) [95% CI]^b
Cholesterol, mg/dL
LDL (n = 159 903)	83.3 (35.6) [83.2-83.5]	79.3 (37.4) [79.1-79.8]	82.7 (35.5) [82.4-82.9]	81.0 (34.6) [80.1-81.9]	79.5 (34.6) [78.7-80.3]
HDL (n = 142 453)	46.3 (15.2) [46.2-46.4]	44.0 (14.1) [43.9-44.1]	45.7 (14.7) [45.6-45.8]	43.5 (12.9) [43.2-43.9]	43.2 (13.3) [42.9-43.4]
Triglycerides, mg/dL (n = 159 493)	158.0 (119.4) [157.4-158.6]	162.5 (129.7) [161.4-163.6]	160.8 (120.8) [160.1-161.6]	186.2 (161.2) [182.0-190.5]	185.5 (137.0) [182.5-188.6]
HbA_1c, % (n = 188 662)	7.3 (2.3) [7.2-7.3]	7.4 (2.0) [7.4-7.5]	7.3 (1.9) [7.3-7.4]	8.0 (1.6) [7.9-8.0]	8.1 (1.8) [8.0-8.1]
eGFR, mL/min/1.73 m² (n = 177 224)
<15	5.2 (5.1-5.3)	5.8 (5.6-6.0)	3.8 (3.7-3.9)	0.7 (0.5-1.0)	2.2 (1.9-2.5)
15-29	6.5 (6.4-6.6)	7.2 (7.1-7.4)	5.3 (5.1-5.4)	1.1 (0.8-1.4)	4.3(3.9-4.8)
30-59	34.4 (34.2-34.7)	35.5 (35.1-35.8)	35.7 (35.4-36.0)	26.1 (25.0-27.3)	32.9 (31.9-33.9)
≥60	53.6 (53.4-53.8)	51.3 (51.0-51.7)	55.0 (54.7-55.3)	71.7 (70.5-72.9)	60.3 (59.3-61.4)
Missing	0.2 (0.19-0.23)	0.2 (0.1-0.2)	0.2 (0.2-0.2)	0.4 (0.2-0.5)	0.2 (0.1-0.3)
Geographic region (n = 191 376)^b
Northeast	46.1 (45.9-46.3)	43.1 (42.7-43.5)	41.6 (41.3-41.9)	50.1 (48.9-51.4)	46.1 (45.1-47.2)
South	41.2 (41.0-41.5)	43.6 (43.2-44.0)	43.7 (43.4-44.0)	38.6 (37.4-39.8)	42.2 (41.1-43.3)
West	7.3 (7.2-7.4)	8.4 (8.2-8.6)	10.2 (10.0-10.4)	5.7 (5.1-6.2)	6.5 (6.0-7.0)
Other	0.2 (0.2-0.2)	0.3 (0.2-0.3)	0.2 (0.2-0.3)	0.2 (0.1-0.3)	0.2 (0.1-0.3)
Missing	4.0 (4.0-4.1)	2.9 (2.8-3.0)	2.9 (2.8-3.0)	4.5 (4.0-5.0)	3.4 (3.0-3.8)
Insurance coverage (n = 161 874)^b
Medicare	46.5 (46.2-46.7)	39.3 (38.8-39.7)	40.7 (40.3-41.1)	23.9 (22.7-25.2)	32.4 (31.2-33.7)
Medicaid	3.4 (3.3-3.5)	5.2 (5.0-5.5)	4.2 (4.0-4.4)	2.0 (1.6-2.4)	2.8 (2.4-3.2)
Military health care	1.3 (1.3-1.4)	1.8 (1.7-1.9)	1.3 (1.3-1.4)	2.0 (1.6-2.4)	1.8 (1.5-2.2)
Private	12.0 (11.9-12.2)	8.8 (8.4-9.0)	9.5 (9.3-9.7)	17.1 (16.0-18.2)	15.5 (14.5-16.5)
State-specific	0.2 (0.2-0.3)	0.4 (0.3-0.4)	0.3 (0.25-0.3)	0.4 (0.2-0.5)	0.4 (0.2-0.6)
Self-pay	1.0 (1.0-1.1)	1.8 (1.6-1.9)	1.5 (1.4-1.6)	0.7 (0.4-0.9)	0.6 (0.4-0.9)
Other	11.9 (11.8-12.1)	16.9 (15.8-16. 6)	15.4 (15.1-15.7)	17.0 (15.9-18.1)	19.9 (18.8-21.0)
Missing	23.6 (23.4-23.8)	26.7 (26.2-27.1)	27.1 (26.8-27.4)	37.0 (35.7-38.5)	26.5 (25.4-27.7)
Physicians (n = 205 885)^b
Endocrinologist	8.7 (8.6-8.9)	9.8 (9.5-10.1)	10.0 (9.8-10.1)	28.4 (27.2-29.7)	30.5 (29.5-31.6)
Cardiologist	26.4 (26.2-26.6)	40.0 (39.6-40.4)	34.1 (33.8-34.4)	32.1 (30.8-33.4)	32.5 (31.4-33.5)
Primary care	74.8 (74.7-75.0)	81.0 (80.6-81.3)	82.1 (81.9-82.4)	76.5 (75.3-77.7)	79.7 (78.8-80.6)

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Abbreviations: ASCVD, atherosclerotic cardiovascular disease; BMI, body mass index (calculated as weight in kilograms divided by height in meters squared); eGFR, estimated glomerular filtration rate; HbA_1c, glycosylated hemoglobin A_1c; HDL, high-density lipoprotein; LDL, low-density lipoprotein; MI, myocardial infarction; PCI, percutaneous coronary intervention; SBP, systolic blood pressure.

SI conversion factors: To convert cholesterol to millimoles per liter, multiply by 0.029; HbA_1c to proportion of total hemoglobin, multiply by 0.01; triglycerides to millimoles per liter, multiply by 0.0113.

^{^a}

Other race refers to American Indian, Alaskan Native, Native Hawaiian or other Pacific Islander, or some other nonlisted race that is not Asian, Black, or White.

^{^b}

Completeness varied for specific data elements; therefore the analytic population size is presented where the full cohort was smaller.

Lipid-, Blood Pressure–, and Glucose-Lowering Therapies

Use of lipid-, blood pressure–, and glucose-lowering therapies are presented in Figure 2. Overall, 190 346 patients (58.6%) were prescribed a statin, but only 87 160 patients (26.8%) were prescribed a high-intensity statin. Use of nonstatin low-density cholesterol–lowering therapies was low, with 8161 patients (2.5%) prescribed ezetimibe and 1055 patients (0.3%) prescribed a PCSK9 inhibitor. ACEIs or ARBs were prescribed in 147 762 patients (45.5%). Of the antihyperglycemic medications, metformin was prescribed in 120 821 patients (37.2%), sulfonylureas in 42 027 patients (12.9%), and insulin in 118 508 patients (36.5%). Use of glucose-lowering drugs with proven CV benefit was low, with 12 724 patients (3.9%) of patients prescribed a GLP-1RA and 8989 patients (2.8%) prescribed an SGLT2I.

Receipt of Evidence-Based Therapy

Compared with the overall cohort, patients prescribed a high-intensity statin were more likely to be men (59.9% [95% CI, 59.6%-60.3%] of patients vs 55.6% [95% CI, 55.4%-55.8%] of patients), more likely to have coronary (79.9% [95% CI, 79.7%-80.2%] of patients vs 73.0% [95% CI, 72.8%-73.3%] of patients) or cerebrovascular (23.5% [95% CI, 23.2%-23.8%] of patients vs 18.5% [95% CI, 18.4%-18.7%] of patients) disease, and more likely to have seen a cardiologist (40.0% [95% CI, 39.6%-40.4%] of patients vs 26.4% [95% CI, 26.2%-26.6%] of patients). Patients prescribed a high-intensity statin, compared with the overall cohort, had a greater burden of heart failure (38.8% [95% CI, 38.5%-39.2%] of patients vs 32.1% [95% CI, 31.9%-32.3%] of patients), cigarette smoking (15.1% [95% CI, 14.8%-15.3%] of patients vs 11.6% [95% CI, 11.5%-11.7%] of patients) and dyslipidemia (90.3% [95% CI, 90.1%-90.5%] of patients vs 82.9% [95% CI, 82.8%-83.1%] of patients).

The demographics of participants prescribed an ACEI or ARB did not differ significantly from the overall cohort. However, patients receiving an ACEI or ARB, compared with the overall cohort, were more likely to have peripheral artery disease (50.1% [95% CI, 49.9%-50.4%] of patients vs 46.7% [95% CI, 46.6%-46.9%] of patients), hypertension (96.9% [95% CI, 96.8%-97.0%] of patients vs 92.1% [95% CI, 92.0%-92.2%] of patients), and dyslipidemia (87.6% [95% CI, 87.5%-87.8%] of patients vs 82.9% [95% CI, 82.8%-83.1%] of patients). Those prescribed an ACEI or ARB were also more likely to have seen a primary care physician (82.1% [95% CI, 81.9%-82.4%] of patients vs 74.8% [95% CI, 74.7%-75.0%] of patients) or a cardiologist (34.1% [95% CI, 33.8%-34.4%] of patients vs 26.4% [95% CI, 26.6%-26.6%] of patients) in the prior 12 months (Table 1).

Patients prescribed an SGLT2I or GLP-1RA, compared with the overall cohort, were younger (mean age: SGLT2I, 63.2 [95% CI, 63.0-63.4] years; GLP-1RA: 62.9 [95% CI, 62.7-63.1] years; overall: 68.1 [95% CI, 68.0-68.1] years), were more likely to have private insurance (SGLT2I: 17.1% [95% CI, 16.0%-18.2%] of patients; GLP-1RA: 15.5% [95% CI, 14.5%-16.5%] of patients; overall: 12.0% [95% CI, 11.9%-12.2%] of patients), and had fewer medical comorbidities (mean Charlson comorbidity index score: SGLT2I: 3.3 [95% CI, 3.2-3.3]; GLP-1RA: 3.8 [95% CI, 3.8-3.9]; overall: 4.1 [95% CI, 4.1-4.1]), and lower prevalence of heart failure (SGLT2I: 21.3% [95% CI, 20.5%-22.2%] of patients; GLP-1RA: 26.3% [95% CI, 25.5%-27.1%] of patients; overall: 32.1% [95% CI, 31.9%-32.3%] of patients) and atrial fibrillation (SGLT2I: 13.8% [95% CI, 13.1%-14.5%] of patients; GLP-1RA: 14.8% [95% CI, 14.2%-15.4%] of patients; overall: 21.3% [95% CI, 21.2%-21.5%] of patients). Patients prescribed either an SGLT2I or a GLP-1RA had similar rates of end-organ diabetes complications yet were more likely to have visited an endocrinologist in the prior 12 months compared with the overall population (SGLT2I: 28.4% [95% CI, 27.2%-29.7%] of patients; GLP-1RA: 30.5% [95% CI, 29.5%-31.6%] of patients; overall: 8.7% [95% CI, 8.6%-8.9%] of patients). Patients prescribed an SGLT2I, compared with those prescribed a GLP-1RA, were less likely to be women (36.5% [95% CI, 35.5%-37.5%] of patients vs 46.7% [95% CI, 45.8%-47.5%] of patients) and less likely to be Black (13.4% [95% CI, 12.7%-14.1%] of patients vs 17.5% [95% CI, 16.8%-18.1%] of patients). Patients prescribed an SGLT2I had fewer diabetes end-organ complications compared with those prescribed a GLP-1RA (neuropathy: 31.0% [95% CI, 30.1%-32.0%] of patients vs 40.1% [95% CI, 39.3%-41.0%] of patients; retinopathy: 11.6% [95% CI, 11.2%-11.6%] of patients vs 17.0% [95% CI, 16.4%-17.7%] of patients; DKA: 1.6% [95% CI, 1.3%-1.9%] of patients vs 2.4% [95% CI, 2.1%-2.7%] of patients; diabetic foot: 3.8% [95% CI, 3.4%-4.2%] of patients vs 6.1% [95% CI, 5.7%-6.5%] of patients). Of note, heart failure (26.3% [95% CI, 25.5%-27.1%] of patients vs 21.3% [95% CI, 20.5%-22.2%] of patients) and mild kidney dysfunction (eGFR 30-59 mL/min/m²: 32.9% [95% CI, 31.9%-33.9%] of patients vs 26.1% [95% CI, 25.0%-27.3%] of patients) were more common among those prescribed a GLP-1RA than those prescribed an SGLT2I.

Extent of Evidence-Based Therapy Use

Overall, 138 173 patients (42.6%) were prescribed no evidence-based CV-risk mitigating medications, 103 420 patients (31.9%) were prescribed 1 medication, 68 195 patients (21.0%) were prescribed 2 medications, and only 14 918 patients (4.6%) were prescribed 3 evidence-based medications.

Groups of patients with low (0 or 1 points) and high (2 or 3 points) composite medication scores are presented in Table 2. Patients with high evidence-based therapy scores, compared with those with low scores, had similar racial and ethnic characteristics but were less likely to be women (41.5% [95% CI, 41.2%-41.8%] of patients vs 45.4% [95% CI, 45.2%-45.6%] of patients). Patients with high composite scores, compared with those with low scores, had a higher burden of hypertension (96.4% [95% CI, 96.2%-96.5%] of patients vs 90.7% [95% CI, 90.6%-90.8%] of patients) and dyslipidemia (90.6% [95% CI, 90.4%-90.8%] of patients vs 80.3% [95% CI, 80.2%-80.3%] of patients) and a higher prevalence of end-organ diabetes complications (neuropathy: 31.3% [95% CI, 30.9%-31.6%] of patients vs 24.6% [95% CI, 24.4%-24.8%] of patients; retinopathy: 12.4% [95% CI, 12.2%-12.6%] of patients vs 8.3% [95% CI, 8.2%-8.5%] of patients; DKA: 2.1% [95% CI, 2.1%-2.2%] of patients vs 1.6% [95% CI, 1.5%-1.6%] of patients). Regional and payer variation in care was observed, with lower scores more common than high scores among patients in the Northeast (48.3% [95% CI, 48.1%-48.6%] of patients vs 40.7% [95% CI, 40.3%-41.1%] of patients) and patients with Medicare coverage (49.5% [95% CI, 49.2%-49.8%] of patients vs 35.7% [95% CI, 35.2%-36.2%] of patients). Patients with high scores, compared with those with low scores, were more likely to have seen a cardiologist (39.2% [95% CI, 38.8%-39.6%] of patients vs 22.3% [95% CI, 22.1%-22.5%] of patients) or primary care physician (83.4% [95% CI, 83.1%-83.7%] of patients vs 72.1% [95% CI, 71.9%-72.3%] of patients) in the prior 12 months.

Table 2. Patient Characteristic by Overall Evidence-Based Composite Score.

Patient characteristics	Evidence-based therapy score, % (95% CI)
Patient characteristics	0-1 (n = 241 593)	2-3 (n = 83 113)
Age, mean (SD) [95% CI], y	68.5 (12.6) [68.5-68.6]	66.7 (10.7) [66.7-66.8]
Sex
Women	45.4 (45.2-45.6)	41.5 (41.2-41.8)
Men	54.6 (54.4-54.8)	58.5 (58.2-58.9)
Race
Asian	2.8 (2.8-2.9)	2.9 (2.8-3.1)
Black	17.6 (17.5-17.8)	19.9 (19.7-20.2)
White	64.5 (64.4-64.7)	62.5 (62.1-62.8)
Other^a	15.0 (14.9-15.2)	14.7 (14.4-14.9)
Latinx ethnicity	12.3 (12.2-12.5)	14.0 (13.8-14.3)
ASCVD
Coronary artery disease	72.1 (71.9-72.2)	75.7 (75.4-76.0)
MI	22.0 (21.8-22.1)	29.8 (29.5-30.2)
PCI	19.6 (19.4-19.7)	26.7 (26.37-26.97)
Cerebrovascular disease	17.6 (17.4-17.7)	21.2 (20.96-21.51)
Stroke	11.6 (11.5-11.8)	14.7 (14.46-14.94)
Peripheral arterial disease	45.9 (45.7-46.1)	49.2 (48.90-49.58)
Comorbidities
Heart failure	31.0 (30.8-31.2)	35.3 (35.02-35.67)
Atrial fibrillation	21.7 (21.6-21.9)	20.3 (20.0-20.5)
Hypertension	90.7 (90.6-90.8)	96.4 (96.2-96.5)
Dyslipidemia	80.3 (80.2-80.5)	90.6 (90.4-90.8)
Smoking	10.6 (10.4-10.7)	14.6 (14.4-14.9)
Charlson Comorbidity Index score, mean (SD) [95% CI]	4.1 (2.8) [4.1-4.1]	4.3 (2.8) [4.2-4.3]
Diabetes complications
Diabetic ketoacidosis	1.6 (1.5-1.6)	2.1 (2.1-2.2)
Retinopathy	8.3 (8.2-8.5)	12.4 (12.2-12.6)
Neuropathy	24.6 (24.4-24.8)	31.3 (30.9-31.6)
Diabetic foot	5.0 (4.9-5.1)	5.4 (5.3-5.6)
SBP, mm Hg (n = 303 859)^b	132 (20) [132-132]	132 (20) [132-132]
BMI (n = 295 725)^b	31.0 (7.2) [31.0-31.1]	32.1 (7.3) [32.1-32.2]
Laboratory values, mean (SD) [95% CI]^b
Cholesterol, mg/dL
LDL (n = 159 903)	85.0 (35.3) [84.8-85.2]	80.1 (35.7) [79.8-80.4]
HDL (n = 142 453)	47.3 (15.7) [47.2-47.4]	44.6 (13.9) [44.4-44.7]
Triglycerides, mg/dL (n = 159 493)	155.2 (116.1) [154.5-155.9]	163.3 (125.2) [162.3-164.4]
HbA_1c, % (n = 188 662)	7.2 (1.7) [7.2-7.2]	7.3 (1.7) [7.2-7.3]
eGFR, mL/min/1.73m² (n = 177 224)
<15	6.0 (5.9-6.1)	3.7 (3.6-3.9)
15-29	7.5 (7.3-7.6)	4.7 (4.5-4.8)
30-59	34.6 (34.3-34.9)	34.1 (33.7-34.5)
≥60	51.7 (51.4-52.0)	57.3 (56.9-57.7)
Missing	0.2 (0.2-0.3)	0.2 (0.1-0.2)
Geographic region (n = 191 376)^b
Northeast	48.3 (48.1-48.6)	40.7 (40.3-41.1)
South	40.0 (39.8-40.3)	44.2 (43.8-44.6)
West	6.0 (5.9-6.2)	10.4 (10.2-10.7)
Other	0.2 (0.1-0.2)	0.3 (0.2-0.3)
Missing	4.7 (4.6-4.8)	2.5 (2.4-2.6)
Insurance coverage (n = 161 874)^b
Medicare	49.5 (49.2-49.8)	35.7 (35.2-36.2)
Medicaid	2.9 (2.8-3.0)	5.1 (4.9-5.4)
Military health care	1.2 (1.2-1.3)	1.6 (1.4-1.7)
Private	12.9 (12.7-13.1)	9.0 (8.7-9.3)
State-specific	0.2 (0.2-0.2)	0.4 (0.3-0.4)
Self-pay	0.8 (0.8-0.9)	1.8 (1.7-2.0)
Other	10.3 (10.1-10.4)	17.9 (17.5-18.3)
Missing	22.2 (22.0-22.4)	28.5 (28.1-29.0)
Physicians (n = 205 885)^b
Endocrinologist	8.0 (7.9-8.1)	11.0 (10.7-11.3)
Cardiologist	22.3 (22.1-22.5)	39.2 (38.8-39.6)
Primary care	72.1 (71.9-72.3)	83.4 (83.1-83.71)

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^{^a}

Other race refers to American Indian, Alaskan Native, Native Hawaiian or other Pacific Islander, or some other nonlisted race that is not Asian, Black, or White.

^{^b}

Completeness varied for specific data elements; therefore the analytic population size is presented where the full cohort was smaller.

Discussion

This cohort study including more than 300 000 patients across multiple health systems represents a large contemporary landscape evaluation of the real-world cardiometabolic care patterns of patients in the US with both diabetes and ASCVD. The study has a number of important findings. First, more than one-third of patients were receiving none of the 3 key evidence-based therapies associated with significant CV benefit, and fewer than 1 in 20 patients were receiving all 3. Second, more than one-quarter of patients were prescribed a guideline-recommended dose of statin, less than half were prescribed an ACEI or ARB, and fewer than 1 in 15 patients were prescribed an antihyperglycemic agent with CV benefit. Third, while endocrinologist encounters were more common among those receiving either an SGLT2I or GLP-1RA, they were infrequent care episodes and reinforce the need for other physicians, such as cardiologists and primary care physicians, to assist with adoption of these agents.

These data suggest that previously described gaps in the use of evidence-based therapies for individuals with diabetes and ASCVD in selected environments extend to this large, distributed network of health systems across the US. The finding that only 58.6% of patients in this study were prescribed a statin is considerably lower than a recently published estimate of 74.6% from a database of commercially insured patients in the US.⁹ Notably, the rate of overall statin use in this study was similar to findings from a comparable Medical Expenditure Panel Survey population from 2013, which reported that only 52.7% of patients with diabetes and ASCVD were receiving a statin.¹⁹ In this context, these new data raise concerns that despite strengthening of guideline recommendations in the years prior to our study window,²⁰ there has been minimal progress in increasing the use of these widely available, cost-effective, safe, and proven medications in the general population.

A plethora of data support the role of ACEIs and ARBs in diabetes with and without chronic kidney disease,^21,22,23,24 ASCVD with or without diabetes^25,26,27 and as first-line agents in hypertension with or without diabetes.²⁸ Thus, our cohort had at least 1 indication for either an ACEI or an ARB, and yet only 45% of patients were prescribed one. This estimate is lower than other current national estimates from survey (55%²⁹) and health system (IQR, 51%-69%³⁰) data evaluating similar populations and lower than that reported in contemporary registry (72%⁶) and clinical trial (80%³¹) cohorts. While higher use may be observed among registry and trial cohorts owing to their enrichment for patients whose medical histories are less complex and who are more adherent with medical instruction, there were no significant differences in the prevalence of chronic kidney disease, CV and non-CV comorbidity, or diabetes complications among patients receiving ACEIs or ARBs vs those not receiving either drug in this cohort, making the risk-treatment paradox a less obvious explanation in this study. Given their potential role in mitigating the increasing prevalence of chronic kidney disease³² among patients with diabetes and that treatment benefits from established and emerging diabetes therapies (eg, SGLT2I,³³ GLP-1RA,³⁴ fineronone³⁵) were observed at high levels of background ACEI or ARB therapy, further enquiry into the barriers preventing the use of these inexpensive and well-tolerated medications is urgently required.

More than one-third of patients were receiving none of the evidence-based therapies, with just one-quarter achieving a higher composite score of 2 or more evidence-based therapies. While interpretation is limited by univariable comparison, there was no obvious evidence of risk-treatment paradox,^36,37 with patients with higher composite evidence-based therapy scores having similar comorbidity scores and risk profiles. Furthermore while other studies have described marked disparity in preventive care patterns by race and sex,^38,39,40,41 these were less apparent in our cohort, with only a modest difference in high-intensity statin use favoring men compared with women. Given that our cohort was restricted to those with a recent encounter, patients with less access to care would have been more likely to be excluded; as Black and Latinx patients continue to suffer from inequitable health system access,^42,43 disparate patterns among these groups may have been attenuated and deserve further, dedicated enquiry.

Only 6.7% of patients in the cohort were prescribed either an SGLT2I or GLP-1RA, which is considerably lower than other contemporary estimates of 9.9% from an insured population⁹ and 17% from a diabetes registry.⁶ Some potential barriers to the optimal use of SGLT2I and GLP-1RA are cost and insurance formulary preferences, as evidenced by the greater proportion of patients receiving these drugs in this study having private insurance. Since the acquisition of these data, a number of consensus documents and guidelines have emerged calling on cardiologists to embrace these agents as key tenets of cardiovascular risk reduction^{44,45,46,47,48,49}; the impact of these publications on adoption of SGLT2I and GLP-1RA remains to be seen.

Limitations

There are several limitations to this study. The use of aggregate data meant that multivariable analyses for factors associated with individual therapy prescription or high vs low composite score were not possible. Furthermore, a granular understanding of the contributors to missingness of relevant data are also not possible with an aggregated data set. In this context, there was an obligate loss in sample size, as data completeness in the CDM varied among datamarts, particularly with respect to insurance status and laboratory values. Medication use was discerned from prescribing information available in PCORnet, and actual use or dispensing was not observed. A more nuanced analysis comparing medication prescription, dispensing, and actual use is not currently possible in the PCORnet environment and would require linkage with individual pharmacy dispensing records and claims data. Prescriptions administered outside of the PCORnet health system are also not captured. In contrast, patients who abandon their prescription or discontinue without informing their physician would be considered as prescribed and thus our estimate may overall be optimistic. While these functions of the data set contribute to variations, definitions, and estimates of clinical use (ie, a composite of prescription, dispensing, adherence, and continuation) the presence of a medication in the electronic health record represents a real-world assessment of treatment status. Our assessment of evidence-based therapy prescription must only be considered an estimate, as access to patient-level data was not available and thus it was not possible to consider the impact of relative or absolute contraindications. The intent was to generate an inclusive cohort of patients without removing any from the denominator; thus, while every patient had a potential evidence-based therapy score of 3, this is a broad generalization, since a number of patients could never be prescribed all 3 medications owing to contraindications, allergies, or intolerance (eg, dialysis, prior rhabdomyolysis). However, there are significant strengths of this type of analysis; namely, the large and unselected nature of this data set encompasses not only geographic variation but also patient, physician, and practice diversity, which strengthen the generalizability of the present findings.

Conclusions

In this cohort study of more than 300 000 patients with diabetes and ASCVD in contemporary clinical practice from the US, more than one-third of patients were not receiving any guideline-directed, evidence-based, CV risk–mitigating therapies (or doses), and fewer than 1 in 20 patients were receiving all 3 therapies. It is particularly concerning that only one-quarter were prescribed a high-intensity statin and less than half an ACE or ARB, treatments that are inexpensive and well tolerated. These estimates of evidence-based therapy prescription are considerably lower than those observed in other recent analyses in selected populations. These findings amplify the need to close these critical gaps between evidence generation and clinical practice for most patients in the US with diabetes and ASCVD.

Supplement.

eTable 1. Datamarts and Respective Health System Participants

eTable 2. Code Lists for Comorbidities and Qualifying ASCVD

Click here for additional data file.^{(192.5KB, pdf)}

References

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement.

eTable 1. Datamarts and Respective Health System Participants

eTable 2. Code Lists for Comorbidities and Qualifying ASCVD

Click here for additional data file.^{(192.5KB, pdf)}

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[zoi211321r9] 9.Nelson AJ, Ardissino M, Haynes K, et al. Gaps in evidence-based therapy use in insured patients in the United States with type 2 diabetes mellitus and atherosclerotic cardiovascular disease. J Am Heart Assoc. 2021;10(2):e016835. doi: 10.1161/JAHA.120.016835 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi211321r10] 10.Pantalone KM, Misra-Hebert AD, Hobbs TM, et al. Antidiabetic treatment patterns and specialty care utilization among patients with type 2 diabetes and cardiovascular disease. Cardiovasc Diabetol. 2018;17(1):54. doi: 10.1186/s12933-018-0699-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi211321r11] 11.Sumarsono A, Buckley LF, Machado SR, et al. Medicaid expansion and utilization of antihyperglycemic therapies. Diabetes Care. 2020;43(11):2684-2690. doi: 10.2337/dc20-0735 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi211321r12] 12.Vaduganathan M, Sathiyakumar V, Singh A, et al. Prescriber patterns of SGLT2i after expansions of U.S. Food and Drug Administration labeling. J Am Coll Cardiol. 2018;72(25):3370-3372. doi: 10.1016/j.jacc.2018.08.2202 [DOI] [PubMed] [Google Scholar]

[zoi211321r13] 13.Weng W, Tian Y, Kong SX, et al. The prevalence of cardiovascular disease and antidiabetes treatment characteristics among a large type 2 diabetes population in the United States. Endocrinol Diabetes Metab. 2019;2(3):e00076. doi: 10.1002/edm2.76 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi211321r14] 14.Fleurence RL, Curtis LH, Califf RM, Platt R, Selby JV, Brown JS. Launching PCORnet, a national patient-centered clinical research network. J Am Med Inform Assoc. 2014;21(4):578-582. doi: 10.1136/amiajnl-2014-002747 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi211321r15] 15.Wiese AD, Roumie CL, Buse JB, et al. Performance of a computable phenotype for identification of patients with diabetes within PCORnet: the Patient-Centered Clinical Research Network. Pharmacoepidemiol Drug Saf. 2019;28(5):632-639. doi: 10.1002/pds.4718 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi211321r16] 16.Aroda VR. A review of GLP-1 receptor agonists: evolution and advancement, through the lens of randomised controlled trials. Diabetes Obes Metab. 2018;20(suppl 1):22-33. doi: 10.1111/dom.13162 [DOI] [PubMed] [Google Scholar]

[zoi211321r17] 17.Caruso I, Cignarelli A, Giorgino F. Heterogeneity and similarities in GLP-1 receptor agonist cardiovascular outcomes trials. Trends Endocrinol Metab. 2019;30(9):578-589. doi: 10.1016/j.tem.2019.07.004 [DOI] [PubMed] [Google Scholar]

[zoi211321r18] 18.Frey J. Testing for equivalence of variances using Hartley’s ratio. Can J Stat. 2010;38:647-664. doi: 10.1002/cjs.10069 [DOI] [Google Scholar]

[zoi211321r19] 19.Salami JA, Warraich HJ, Valero-Elizondo J, et al. National trends in nonstatin use and expenditures among the US adult population from 2002 to 2013: insights from Medical Expenditure Panel Survey. J Am Heart Assoc. 2018;7(2):7. doi: 10.1161/JAHA.117.007132 [DOI] [PMC free article] [PubMed] [Google Scholar]

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[zoi211321r21] 21.Brenner BM, Cooper ME, de Zeeuw D, et al. ; RENAAL Study Investigators . Effects of losartan on renal and cardiovascular outcomes in patients with type 2 diabetes and nephropathy. N Engl J Med. 2001;345(12):861-869. doi: 10.1056/NEJMoa011161 [DOI] [PubMed] [Google Scholar]

[zoi211321r22] 22.Heart Outcomes Prevention Evaluation Study Investigators; Yusuf S, Sleight P, Pogue J, Bosch J, Davies R, Dagenais G. Effects of an angiotensin-converting-enzyme inhibitor, ramipril, on cardiovascular events in high-risk patients. N Engl J Med. 2000;342(3):145-153. doi: 10.1056/NEJM200001203420301 [DOI] [PubMed] [Google Scholar]

[zoi211321r23] 23.Lewis EJ, Hunsicker LG, Clarke WR, et al. ; Collaborative Study Group . Renoprotective effect of the angiotensin-receptor antagonist irbesartan in patients with nephropathy due to type 2 diabetes. N Engl J Med. 2001;345(12):851-860. doi: 10.1056/NEJMoa011303 [DOI] [PubMed] [Google Scholar]

[zoi211321r24] 24.Parving HH, Lehnert H, Bröchner-Mortensen J, Gomis R, Andersen S, Arner P; Irbesartan in Patients with Type 2 Diabetes and Microalbuminuria Study Group . The effect of irbesartan on the development of diabetic nephropathy in patients with type 2 diabetes. N Engl J Med. 2001;345(12):870-878. doi: 10.1056/NEJMoa011489 [DOI] [PubMed] [Google Scholar]

[zoi211321r25] 25.Dagenais GR, Pogue J, Fox K, Simoons ML, Yusuf S. Angiotensin-converting-enzyme inhibitors in stable vascular disease without left ventricular systolic dysfunction or heart failure: a combined analysis of three trials. Lancet. 2006;368(9535):581-588. doi: 10.1016/S0140-6736(06)69201-5 [DOI] [PubMed] [Google Scholar]

[zoi211321r26] 26.Danchin N, Cucherat M, Thuillez C, Durand E, Kadri Z, Steg PG. Angiotensin-converting enzyme inhibitors in patients with coronary artery disease and absence of heart failure or left ventricular systolic dysfunction: an overview of long-term randomized controlled trials. Arch Intern Med. 2006;166(7):787-796. doi: 10.1001/archinte.166.7.787 [DOI] [PubMed] [Google Scholar]

[zoi211321r27] 27.Evans M, Carrero JJ, Szummer K, et al. Angiotensin-converting enzyme inhibitors and angiotensin receptor blockers in myocardial infarction patients with renal dysfunction. J Am Coll Cardiol. 2016;67(14):1687-1697. doi: 10.1016/j.jacc.2016.01.050 [DOI] [PubMed] [Google Scholar]

[zoi211321r28] 28.Palmer AJ, Valentine WJ, Chen R, et al. A health economic analysis of screening and optimal treatment of nephropathy in patients with type 2 diabetes and hypertension in the USA. Nephrol Dial Transplant. 2008;23(4):1216-1223. doi: 10.1093/ndt/gfn082 [DOI] [PubMed] [Google Scholar]

[zoi211321r29] 29.Chu CD, Powe NR, McCulloch CE, et al. ; Centers for Disease Control and Prevention Chronic Kidney Disease Surveillance Team . Angiotensin-converting enzyme inhibitor or angiotensin receptor blocker use among hypertensive US adults with albuminuria. Hypertension. 2021;77(1):94-102. doi: 10.1161/HYPERTENSIONAHA.120.16281 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi211321r30] 30.Arnold SV, Goyal A, Inzucchi SE, et al. Quality of Care of the initial patient cohort of the Diabetes Collaborative Registry. J Am Heart Assoc. 2017;6(8):6. doi: 10.1161/JAHA.117.005999 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi211321r31] 31.Zinman B, Wanner C, Lachin JM, et al. ; EMPA-REG OUTCOME Investigators . Empagliflozin, cardiovascular outcomes, and mortality in type 2 diabetes. N Engl J Med. 2015;373(22):2117-2128. doi: 10.1056/NEJMoa1504720 [DOI] [PubMed] [Google Scholar]

[zoi211321r32] 32.Collaboration GBDCKD; GBD Chronic Kidney Disease Collaboration . Global, regional, and national burden of chronic kidney disease, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2020;395(10225):709-733. doi: 10.1016/S0140-6736(20)30045-3 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi211321r33] 33.Cannon CP, Pratley R, Dagogo-Jack S, et al. ; VERTIS CV Investigators . Cardiovascular outcomes with ertugliflozin in type 2 diabetes. N Engl J Med. 2020;383(15):1425-1435. doi: 10.1056/NEJMoa2004967 [DOI] [PubMed] [Google Scholar]

[zoi211321r34] 34.Hernandez AF, Green JB, Janmohamed S, et al. ; Harmony Outcomes committees and investigators . Albiglutide and cardiovascular outcomes in patients with type 2 diabetes and cardiovascular disease (Harmony Outcomes): a double-blind, randomised placebo-controlled trial. Lancet. 2018;392(10157):1519-1529. doi: 10.1016/S0140-6736(18)32261-X [DOI] [PubMed] [Google Scholar]

[zoi211321r35] 35.Bakris GL, Agarwal R, Anker SD, et al. ; FIDELIO-DKD Investigators . Effect of finerenone on chronic kidney disease outcomes in type 2 diabetes. N Engl J Med. 2020;383(23):2219-2229. doi: 10.1056/NEJMoa2025845 [DOI] [PubMed] [Google Scholar]

[zoi211321r36] 36.Hall M, Bebb OJ, Dondo TB, et al. Guideline-indicated treatments and diagnostics, GRACE risk score, and survival for non-ST elevation myocardial infarction. Eur Heart J. 2018;39(42):3798-3806. doi: 10.1093/eurheartj/ehy517 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi211321r37] 37.Redelmeier DA, Tan SH, Booth GL. The treatment of unrelated disorders in patients with chronic medical diseases. N Engl J Med. 1998;338(21):1516-1520. doi: 10.1056/NEJM199805213382106 [DOI] [PubMed] [Google Scholar]

[zoi211321r38] 38.Gamboa CM, Colantonio LD, Brown TM, Carson AP, Safford MM. Race-sex differences in statin use and low-density lipoprotein cholesterol control among people with diabetes mellitus in the Reasons for Geographic and Racial Differences in Stroke Study. J Am Heart Assoc. 2017;6(5):6. doi: 10.1161/JAHA.116.004264 [DOI] [PMC free article] [PubMed] [Google Scholar]

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PERMALINK

Use of Lipid-, Blood Pressure–, and Glucose-Lowering Pharmacotherapy in Patients With Type 2 Diabetes and Atherosclerotic Cardiovascular Disease

Adam J Nelson, MBBS, MBA, MPH, PhD

Emily C O’Brien, PhD

Lisa A Kaltenbach, MS

Jennifer B Green, MD

Renato D Lopes, MD, PhD

Caryn G Morse, MD, MPH

Hussein R Al-Khalidi, PhD

Vanita R Aroda, MD

Matthew A Cavender, MD, MPH

Tanya Gaynor, MPAS

Julienne K Kirk, PharmD

Ildiko Lingvay, MD, MPH, MSCS

Melissa L Magwire, MSN, RN

Darren K McGuire, MD, MHSc

Jonathan Pak, PharmD, MBA

Rodica Pop-Busui, MD, PhD

Caroline R Richardson, MD

Cagri Senyucel, MD, PhD

Michelle D Kelsey, MD

Neha J Pagidipati, MD, MPH

Christopher B Granger, MD

Key Points

Question

Findings

Meaning

Abstract

Importance

Objective

Design, Setting, and Participants

Exposures

Main Outcomes and Measures

Results

Conclusions and Relevance

Introduction

Methods

Data Source and Study Design

Cohort Identification

Variables

Evidence-Based Therapy Composite Score

Statistical Analysis

Results

Cohort Assembly

Figure 1. Cohort Inclusion Flowchart.

Study Participants

Table 1. Patient Characteristics by Individual Evidence-Based Therapy.

Lipid-, Blood Pressure–, and Glucose-Lowering Therapies

Figure 2. Proportions of Patients Receiving Evidence-Based Therapies for Diabetes and Atherosclerotic Cardiovascular Disease.

Receipt of Evidence-Based Therapy

Extent of Evidence-Based Therapy Use

Table 2. Patient Characteristic by Overall Evidence-Based Composite Score.

Discussion

Limitations

Conclusions

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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Links to NCBI Databases