Multinational patterns of second line antihyperglycaemic drug initiation across cardiovascular risk groups: federated pharmacoepidemiological evaluation in LEGEND-T2DM

doi:10.1136/bmjmed-2023-000651

. 2023 Oct 6;2(1):e000651.

doi: 10.1136/bmjmed-2023-000651. eCollection 2023.

Multinational patterns of second line antihyperglycaemic drug initiation across cardiovascular risk groups: federated pharmacoepidemiological evaluation in LEGEND-T2DM

Rohan Khera^{1

2

3}, Lovedeep Singh Dhingra¹, Arya Aminorroaya¹, Kelly Li⁴, Jin J Zhou^{4

5}, Faaizah Arshad⁴, Clair Blacketer⁶, Mary G Bowring⁷, Fan Bu⁴, Michael Cook⁷, David A Dorr⁸, Talita Duarte-Salles⁹, Scott L DuVall^{10

11}, Thomas Falconer¹², Tina E French^{13

14}, Elizabeth E Hanchrow^{13

14}, Scott Horban¹⁵, Wallis Cy Lau^{16

17

18

19}, Jing Li²⁰, Yuntian Liu^{1

2}, Yuan Lu¹, Kenneth Kc Man^{16

17

18

19}, Michael E Matheny^{13

14}, Nestoras Mathioudakis²¹, Michael F McLemore^{13

14}, Evan Minty²², Daniel R Morales¹⁵, Paul Nagy^{23

24}, Akihiko Nishimura²⁵, Anna Ostropolets²⁶, Andrea Pistillo⁹, Jose D Posada²⁷, Nicole Pratt²⁸, Carlen Reyes⁹, Joseph S Ross^{2

29

30}, Sarah Seager²⁰, Nigam Shah^{31

32

33}, Katherine Simon^{13

14}, Eric Yf Wan^{18

19

34}, Jianxiao Yang³⁵, Can Yin²⁰, Seng Chan You^{36

37}, Martijn J Schuemie³⁸, Patrick B Ryan²⁶, George Hripcsak²⁶, Harlan Krumholz^{1

2

30}, Marc A Suchard^{4

10

39

40}

Affiliations

¹ Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, USA.
² Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT, USA.
³ Section of Health Informatics, Department of Biostatistics, Yale University School of Public Health, New Haven, CT, USA.
⁴ Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, USA.
⁵ Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
⁶ Observational Health Data Analytics, Janssen Research and Development, Titusville, NJ, USA.
⁷ Johns Hopkins University School of Medicine, Baltimore, MD, USA.
⁸ Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University School of Medicine, Portland, OR, USA.
⁹ Real-World Epidemiology Research Group, Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain.
¹⁰ Veterans Affairs Informatics and Computing Infrastructure, United States Department of Veterans Affairs, Salt Lake City, UT, USA.
¹¹ The University of Utah School of Medicine, Salt Lake City, UT, USA.
¹² Department of Biomedical Informatics, Columbia University, New York, NY, USA.
¹³ Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA.
¹⁴ Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
¹⁵ Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, UK.
¹⁶ Research Department of Practice and Policy, School of Pharmacy, University College London, London, UK.
¹⁷ Centre for Medicines Optimisation Research and Education, University College London Hospitals NHS Foundation Trust, London, UK.
¹⁸ Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China.
¹⁹ Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong, China.
²⁰ Data Transformation, Analytics, and Artificial Intelligence, Real World Solutions, IQVIA Inc, Durham, NC, USA.
²¹ Division of Endocrinology, Diabetes, and Metabolism, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
²² Faculty of Medicine, O'Brien Institute for Public Health, University of Calgary, Calgary, AB, Canada.
²³ Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
²⁴ Division of Health Science Informatics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA.
²⁵ Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA.
²⁶ Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA.
²⁷ Systems Engineering and Computing, School of Engineering, Universidad del Norte, Barranquilla, Colombia.
²⁸ Quality Use of Medicines and Pharmacy Research Centre, UniSA Clinical and Health Sciences, University of South Australia, Adelaide, SA, Australia.
²⁹ Section of General Medicine and National Clinician Scholars Program, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
³⁰ Department of Health Policy and Management, Yale University School of Public Health, New Haven, CT, USA.
³¹ Center for Biomedical Informatics Research, Stanford University School of Medicine, Stanford, CA, USA.
³² Clinical Excellence Research Center, Stanford University School of Medicine, Stanford, CA, USA.
³³ Technology and Digital Solutions, Stanford Health Care, Stanford, CA, USA.
³⁴ Department of Family Medicine and Primary Care, School of Clinical Medicine, University of Hong Kong, Hong Kong, China.
³⁵ Department of Computational Medicine, University of California Los Angeles David Geffen School of Medicine, Los Angeles, CA, USA.
³⁶ Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea (aka South Korea).
³⁷ Institute for Innovation in Digital Healthcare, Yonsei University, Seoul, Republic of Korea (aka South Korea).
³⁸ Epidemiology, Office of the Chief Medical Officer, Johnson & Johnson, Titusville, NJ, USA.
³⁹ Department of Biomathematics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
⁴⁰ Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.

PMID: 37829182
PMCID: PMC10565313
DOI: 10.1136/bmjmed-2023-000651

Multinational patterns of second line antihyperglycaemic drug initiation across cardiovascular risk groups: federated pharmacoepidemiological evaluation in LEGEND-T2DM

Rohan Khera et al. BMJ Med. 2023.

. 2023 Oct 6;2(1):e000651.

doi: 10.1136/bmjmed-2023-000651. eCollection 2023.

Authors

Affiliations

¹ Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, USA.
² Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT, USA.
³ Section of Health Informatics, Department of Biostatistics, Yale University School of Public Health, New Haven, CT, USA.
⁴ Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, USA.
⁵ Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
⁶ Observational Health Data Analytics, Janssen Research and Development, Titusville, NJ, USA.
⁷ Johns Hopkins University School of Medicine, Baltimore, MD, USA.
⁸ Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University School of Medicine, Portland, OR, USA.
⁹ Real-World Epidemiology Research Group, Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain.
¹⁰ Veterans Affairs Informatics and Computing Infrastructure, United States Department of Veterans Affairs, Salt Lake City, UT, USA.
¹¹ The University of Utah School of Medicine, Salt Lake City, UT, USA.
¹² Department of Biomedical Informatics, Columbia University, New York, NY, USA.
¹³ Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA.
¹⁴ Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
¹⁵ Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, UK.
¹⁶ Research Department of Practice and Policy, School of Pharmacy, University College London, London, UK.
¹⁷ Centre for Medicines Optimisation Research and Education, University College London Hospitals NHS Foundation Trust, London, UK.
¹⁸ Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China.
¹⁹ Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong, China.
²⁰ Data Transformation, Analytics, and Artificial Intelligence, Real World Solutions, IQVIA Inc, Durham, NC, USA.
²¹ Division of Endocrinology, Diabetes, and Metabolism, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
²² Faculty of Medicine, O'Brien Institute for Public Health, University of Calgary, Calgary, AB, Canada.
²³ Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
²⁴ Division of Health Science Informatics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA.
²⁵ Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA.
²⁶ Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA.
²⁷ Systems Engineering and Computing, School of Engineering, Universidad del Norte, Barranquilla, Colombia.
²⁸ Quality Use of Medicines and Pharmacy Research Centre, UniSA Clinical and Health Sciences, University of South Australia, Adelaide, SA, Australia.
²⁹ Section of General Medicine and National Clinician Scholars Program, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
³⁰ Department of Health Policy and Management, Yale University School of Public Health, New Haven, CT, USA.
³¹ Center for Biomedical Informatics Research, Stanford University School of Medicine, Stanford, CA, USA.
³² Clinical Excellence Research Center, Stanford University School of Medicine, Stanford, CA, USA.
³³ Technology and Digital Solutions, Stanford Health Care, Stanford, CA, USA.
³⁴ Department of Family Medicine and Primary Care, School of Clinical Medicine, University of Hong Kong, Hong Kong, China.
³⁵ Department of Computational Medicine, University of California Los Angeles David Geffen School of Medicine, Los Angeles, CA, USA.
³⁶ Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea (aka South Korea).
³⁷ Institute for Innovation in Digital Healthcare, Yonsei University, Seoul, Republic of Korea (aka South Korea).
³⁸ Epidemiology, Office of the Chief Medical Officer, Johnson & Johnson, Titusville, NJ, USA.
³⁹ Department of Biomathematics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
⁴⁰ Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.

PMID: 37829182
PMCID: PMC10565313
DOI: 10.1136/bmjmed-2023-000651

Abstract

Objective: To assess the uptake of second line antihyperglycaemic drugs among patients with type 2 diabetes mellitus who are receiving metformin.

Design: Federated pharmacoepidemiological evaluation in LEGEND-T2DM.

Setting: 10 US and seven non-US electronic health record and administrative claims databases in the Observational Health Data Sciences and Informatics network in eight countries from 2011 to the end of 2021.

Participants: 4.8 million patients (≥18 years) across US and non-US based databases with type 2 diabetes mellitus who had received metformin monotherapy and had initiated second line treatments.

Exposure: The exposure used to evaluate each database was calendar year trends, with the years in the study that were specific to each cohort.

Main outcomes measures: The outcome was the incidence of second line antihyperglycaemic drug use (ie, glucagon-like peptide-1 receptor agonists, sodium-glucose cotransporter-2 inhibitors, dipeptidyl peptidase-4 inhibitors, and sulfonylureas) among individuals who were already receiving treatment with metformin. The relative drug class level uptake across cardiovascular risk groups was also evaluated.

Results: 4.6 million patients were identified in US databases, 61 382 from Spain, 32 442 from Germany, 25 173 from the UK, 13 270 from France, 5580 from Scotland, 4614 from Hong Kong, and 2322 from Australia. During 2011-21, the combined proportional initiation of the cardioprotective antihyperglycaemic drugs (glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors) increased across all data sources, with the combined initiation of these drugs as second line drugs in 2021 ranging from 35.2% to 68.2% in the US databases, 15.4% in France, 34.7% in Spain, 50.1% in Germany, and 54.8% in Scotland. From 2016 to 2021, in some US and non-US databases, uptake of glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors increased more significantly among populations with no cardiovascular disease compared with patients with established cardiovascular disease. No data source provided evidence of a greater increase in the uptake of these two drug classes in populations with cardiovascular disease compared with no cardiovascular disease.

Conclusions: Despite the increase in overall uptake of cardioprotective antihyperglycaemic drugs as second line treatments for type 2 diabetes mellitus, their uptake was lower in patients with cardiovascular disease than in people with no cardiovascular disease over the past decade. A strategy is needed to ensure that medication use is concordant with guideline recommendations to improve outcomes of patients with type 2 diabetes mellitus.

Keywords: Cardiology; Diabetes mellitus; Epidemiology; Pharmacology, clinical; Quality of health care.

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Conflict of interest statement

Competing interests: This study is undertaken within Observational Health Data Sciences and Informatics, an open collaboration. RK received support from the National Heart, Lung, and Blood Institute of the National Institutes of Health (under award K23HL153775) and the Doris Duke Charitable Foundation (under award, 2022060). He is an Associate Editor of JAMA. He also receives research support, through Yale, from Bristol-Myers Squibb. He is a coinventor of US Provisional Patent Applications 63/177,117 and 63/346,610, unrelated to current work. He is also a founder of Evidence2Health, a precision health platform to improve evidence-based cardiovascular care. TD-S acknowledges receiving financial support from the Instituto de Salud Carlos III (ISCIII; Miguel Servet 2021: CP21/00023). KKCM reports grants from C W Maplethorpe Fellowship, grants from National Institute of Health Research, UK, grants from European Commission Framwork Horizon 2020, grants from Hong Kong Research Grant Council, grants from Innovation and Technology Commission of the Hong Kong Special Administration Region Government, and personal fees from IQVIA outside of the submitted work. DRM was supported by a Wellcome Trust Clinical Research Fellowship (214588/Z/18/Z). MJS is an employee and shareholder of Johnson & Johnson. HK received expenses and/or personal fees from UnitedHealth, Element Science, Aetna, Reality Labs, Tesseract/4Catalyst, F-Prime, the Siegfried and Jensen Law Firm, Arnold and Porter Law Firm, and Martin/Baughman Law Firm. He is a co-founder of Refactor Health and HugoHealth, and is associated with contracts, through Yale New Haven Hospital, from the Centers for Medicare and Medicaid Services and through Yale University from Johnson & Johnson. MAS receives contracts and grants from the National Institutes of Health, the US Department of Veterans Affairs, the US Food and Drug Administration and Janssen Research and Development, the latter two of which are outside the scope of this work. Other authors declare no competing interests.

Figures

**Figure 2**
Proportional incident use of second line antihyperglycaemic drugs in United States national databases, United States health system. CCAE=IBM MarketScan Commercial Claims and Encounters Data; CUIMC=Columbia University Irving Medical Centre; DPP-4i=dipeptidyl peptidase-4 inhibitors; FLPD=France Longitudinal Patient Database; GDA=Germany Disease Analyser; GLP-1 RA=glucagon-like peptide-1 receptor agonist; HIC=Health Informatics Centre at the University of Dundee; JHM=Johns Hopkins Medicine; MDCR=IBM Health MarketScan Medicare Supplemental and Coordination of Benefits Database; OCEDM=Optum Clinformatics Extended Data Mart-Date of Death; OEHR=Optum de-identified Electronic Health Record Dataset; SGLT2i=sodium-glucose cotransporter 2 inhibitor; SIDIAP=Information System for Research in Primary Care; STARR=Stanford Medicine; SU=sulfonylurea; USOC=United States Open Claims; VA=Department of Veterans Affairs Healthcare System

**Figure 3**
Proportional first incident use of glucagon-like peptide-1 receptor agonists as second line therapy after metformin in patients with established cardiovascular disease, and patients without established cardiovascular disease. ALPD=Australia Longitudinal Patient Database Practice Profile; CCAE=IBM MarketScan Commercial Claims and Encounters Data; CUIMC=Columbia University Irving Medical Center; FLPD=France Longitudinal Patient Database; GDA=Germany Disease Analyser; GLP-1 RA=glucagon-like peptide-1 receptor agonist; HIC=Health Informatics Centre at the University of Dundee; HKHA=Hong Kong Hospital Authority; IMRD=UK-IQVIA Medical Research Data; JHM=Johns Hopkins Medicine; MDCD=IBM Health MarketScan Multi-State Medicaid Database; MDCR=IBM Health MarketScan Medicare Supplemental and Coordination of Benefits Database; OCEDM=Optum Clinformatics Extended Data Mart - Date of Death; OEHR=Optum de-identified Electronic Health Record Dataset; SIDIAP=Information System for Research in Primary Care; STARR=Stanford Medicine; USOC=United States Open Claims; VA=Department of Veterans Affairs Healthcare System

**Figure 4**
Proportional first incident use of sodium-glucose Cctransporter 2 inhibitors as second line therapy after metformin in (A) patients with established cardiovascular disease, and (B) patients without established cardiovascular disease. ALPD=Australia Longitudinal Patient Database Practice Profile; CCAE=IBM MarketScan Commercial Claims and Encounters Data; CUIMC=Columbia University Irving Medical Centre; FLPD=France Longitudinal Patient Database; GDA=Germany Disease Analyser; HIC=Health Informatics Centre at the University of Dundee; HKHA=Hong Kong Hospital Authority; IMRD=UK-IQVIA Medical Research Data; JHM=Johns Hopkins Medicine; MDCD=IBM Health MarketScan Multi-State Medicaid Database; MDCR=IBM Health MarketScan Medicare Supplemental and Coordination of Benefits Database; OCEDM=Optum Clinformatics Extended Data Mart–Date of Death; OEHR=Optum de-identified Electronic Health Record Dataset; SGLT2i=sodium-glucose cotransporter 2 inhibitor; SIDIAP=Information System for Research in Primary Care; STARR=Stanford Medicine; USOC=United States Open Claims; VA=Department of Veterans Affairs Healthcare System

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[1] Zinman B, Wanner C, Lachin JM, et al. . Empagliflozin, cardiovascular outcomes, and mortality in type 2 diabetes. N Engl J Med 2015;373:2117–28. 10.1056/NEJMoa1504720 - DOI - PubMed

[2] Zinman B, Wanner C, Lachin JM, et al. . Empagliflozin, cardiovascular outcomes, and mortality in type 2 diabetes. N Engl J Med 2015;373:2117–28. 10.1056/NEJMoa1504720 - DOI - PubMed

[3] Wiviott SD, Raz I, Sabatine MS, et al. . Dapagliflozin and cardiovascular outcomes in type 2 diabetes. reply. N Engl J Med 2019;380:1881–2. 10.1056/NEJMc1902837 - DOI - PubMed

[4] Wiviott SD, Raz I, Sabatine MS, et al. . Dapagliflozin and cardiovascular outcomes in type 2 diabetes. reply. N Engl J Med 2019;380:1881–2. 10.1056/NEJMc1902837 - DOI - PubMed

[5] Neal B, Perkovic V, Matthews DR, et al. . Canagliflozin and cardiovascular and renal events in type 2 diabetes. N Engl J Med 2017;377:2099. 10.1056/NEJMc1712572 - DOI - PubMed

[6] Neal B, Perkovic V, Matthews DR, et al. . Canagliflozin and cardiovascular and renal events in type 2 diabetes. N Engl J Med 2017;377:2099. 10.1056/NEJMc1712572 - DOI - PubMed

[7] Marso SP, Bain SC, Consoli A, et al. . Semaglutide and cardiovascular outcomes in patients with type 2 diabetes. N Engl J Med 2016;375:1834–44. 10.1056/NEJMoa1607141 - DOI - PubMed

[8] Marso SP, Bain SC, Consoli A, et al. . Semaglutide and cardiovascular outcomes in patients with type 2 diabetes. N Engl J Med 2016;375:1834–44. 10.1056/NEJMoa1607141 - DOI - PubMed

[9] Marso SP, Daniels GH, Brown-Frandsen K, et al. . Liraglutide and cardiovascular outcomes in type 2 diabetes. N Engl J Med 2016;375:311–22. 10.1056/NEJMoa1603827 - DOI - PMC - PubMed

[10] Marso SP, Daniels GH, Brown-Frandsen K, et al. . Liraglutide and cardiovascular outcomes in type 2 diabetes. N Engl J Med 2016;375:311–22. 10.1056/NEJMoa1603827 - DOI - PMC - PubMed

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Multinational patterns of second line antihyperglycaemic drug initiation across cardiovascular risk groups: federated pharmacoepidemiological evaluation in LEGEND-T2DM

Affiliations

Multinational patterns of second line antihyperglycaemic drug initiation across cardiovascular risk groups: federated pharmacoepidemiological evaluation in LEGEND-T2DM

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