Healthcare utilization, mortality, and cardiovascular events following GLP1-RA initiation in chronic kidney disease
Folkerts, K. et al. Annual health care resource utilization and cost among type 2 diabetes patients with newly recognized chronic kidney disease within a large U.S. administrative claims database. J. Manag. Care Spec. Pharm. 26, 1506–1516 (2020).
Google Scholar
Deng, Y. et al. Global, regional, and national burden of diabetes-related chronic kidney disease from 1990 to 2019. Front. Endocrinol. 12, 672350 (2021).
Google Scholar
United States Renal Data System 2022 USRDS Annual Data Report: Epidemiology of kidney disease in the United States. National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD. (Accessed 22 December 2023).
Zhang, S. et al. Emergency department visits and hospitalizations among hemodialysis patients by day of the week and dialysis schedule in the United States. PLoS ONE 14, e0220966 (2019).
Google Scholar
Li H. L., Tai P. H., Hwang Y. T., Lin S. W. & Lan L. C. Causes of hospitalization among end-stage kidney disease cohort before and after hemodialysis. Int. J. Environ. Res. Public Health. 19, (2022).
Chu, Y. W. et al. Epidemiology and outcomes of hypoglycemia in patients with advanced diabetic kidney disease on dialysis: a national cohort study. PLoS ONE 12, e0174601 (2017).
Google Scholar
Jankowski, J., Floege, J., Fliser, D., Bohm, M. & Marx, N. Cardiovascular disease in chronic kidney disease: pathophysiological insights and therapeutic options. Circulation 143, 1157–1172 (2021).
Google Scholar
Morbidity and Mortality in Patients with CKD. United States Renal Data System 2022 USRDS Annual Data Report. /chronic-kidney-disease/3-morbidity-and-mortality-in-patients-with-ckd. Accessed 22 December 2023.
Lo, C. et al. Insulin and glucose-lowering agents for treating people with diabetes and chronic kidney disease. Cochrane Database Syst. Rev. 9, Cd011798 (2018).
Google Scholar
Kawanami, D. & Takashi, Y. GLP-1 Receptor agonists in diabetic kidney disease: from clinical outcomes to mechanisms. Front. Pharmacol. 11, 967 (2020).
Google Scholar
Michos, E. D., Bakris, G. L., Rodbard, H. W. & Tuttle, K. R. Glucagon-like peptide-1 receptor agonists in diabetic kidney disease: a review of their kidney and heart protection. Am. J. Prev. Cardiol. 14, 100502 (2023).
Google Scholar
Wexler, D. J. et al. Comparative effects of glucose-lowering medications on kidney outcomes in type 2 diabetes: the GRADE randomized clinical trial. JAMA Intern. Med. 183, 705–714 (2023).
Google Scholar
KDIGO 2020 Clinical practice guideline for diabetes management in chronic kidney disease. Kidney Int. 98, S1–s115 (2020).
American Diabetes Association. Introduction: standards of medical care in diabetes-2022. Diabetes Care. 45, S1–S2 (2022).
Yajima, T., Yajima, K., Hayashi, M., Takahashi, H. & Yasuda, K. Improved glycemic control with once-weekly dulaglutide in addition to insulin therapy in type 2 diabetes mellitus patients on hemodialysis evaluated by continuous glucose monitoring. J. Diabetes Complicat. 32, 310–315 (2018).
Google Scholar
Kondo, M., Toyoda, M., Kimura, M., Ishida, N. & Fukagawa, M. Favorable effect on blood volume control in hemodialysis patients with type 2 diabetes after switching from insulin therapy to liraglutide, a human glucagon-like peptide-1 analog–results from a pilot study in Japan. Tokai J. Exp. Clin. Med. 42, 52–57 (2017).
Google Scholar
Idorn, T. et al. Safety and efficacy of liraglutide in patients with type 2 diabetes and end-stage renal disease: an investigator-initiated, placebo-controlled, double-blind, parallel-group, randomized trial. Diabetes Care 39, 206–213 (2016).
Google Scholar
Bomholt, T. et al. The glycemic effect of liraglutide evaluated by continuous glucose monitoring in persons with type 2 diabetes receiving dialysis. Nephron. 145, 27–34 (2021).
Google Scholar
Giugliano, D. et al. GLP-1 receptor agonists and cardiorenal outcomes in type 2 diabetes: an updated meta-analysis of eight CVOTs. Cardiovasc. Diabetol. 20, 189 (2021).
Google Scholar
Kristensen, S. L. et al. Cardiovascular, mortality, and kidney outcomes with GLP-1 receptor agonists in patients with type 2 diabetes: a systematic review and meta-analysis of cardiovascular outcome trials. Lancet Diabetes Endocrinol. 7, 776–785 (2019).
Google Scholar
Sattar, N. et al. Cardiovascular, mortality, and kidney outcomes with GLP-1 receptor agonists in patients with type 2 diabetes: a systematic review and meta-analysis of randomised trials. Lancet Diabetes Endocrinol. 9, 653–662 (2021).
Google Scholar
Reifsnider, O. et al. Cost-effectiveness analysis of empagliflozin versus sitagliptin as second-line therapy for treatment in patients with type 2 diabetes in the United States. Diabetes Obes. Metab. 23, 791–799 (2021).
Google Scholar
Poonawalla, I. B., Bowe, A. T., Tindal, M. C., Meah, Y. A. & Schwab, P. A real-world comparison of cardiovascular, medical and costs outcomes in new users of SGLT2 inhibitors versus GLP-1 agonists. Diabetes Res. Clin. Pract. 175, 108800 (2021).
Google Scholar
Wilke, T. et al. Healthcare resource utilization and associated costs in new users of empagliflozin versus DPP-4 inhibitors and GLP-1 agonists: a comparative analysis based on retrospective real-world data from german sickness funds. Clinicoecon Outcomes Res. 14, 319–332 (2022).
Google Scholar
Thomsen, R. W. et al. Healthcare resource utilization and costs for empagliflozin versus glucagon-like peptide-1 receptor agonists in routine clinical care in Denmark. Diabetes Ther. 13, 1891–1906 (2022).
Google Scholar
Berg, J. K., Shenouda, S. K., Heilmann, C. R., Gray, A. L. & Holcombe, J. H. Effects of exenatide twice daily versus sitagliptin on 24-h glucose, glucoregulatory and hormonal measures: a randomized, double-blind, crossover study. Diabetes Obes. Metab. 13, 982–989 (2011).
Google Scholar
Bergenstal, R. M. et al. Efficacy and safety of exenatide once weekly versus sitagliptin or pioglitazone as an adjunct to metformin for treatment of type 2 diabetes (DURATION-2): a randomised trial. Lancet. 376, 431–439 (2010).
Google Scholar
Wysham, C. et al. DURATION-2: efficacy and safety of switching from maximum daily sitagliptin or pioglitazone to once-weekly exenatide. Diabet. Med. 28, 705–714 (2011).
Google Scholar
Pratley, R. E. et al. Liraglutide versus sitagliptin for patients with type 2 diabetes who did not have adequate glycaemic control with metformin: a 26-week, randomised, parallel-group, open-label trial. Lancet. 375, 1447–1456 (2010).
Google Scholar
Pratley, R. et al. One year of liraglutide treatment offers sustained and more effective glycaemic control and weight reduction compared with sitagliptin, both in combination with metformin, in patients with type 2 diabetes: a randomised, parallel-group, open-label trial. Int J. Clin. Pract. 65, 397–407 (2011).
Google Scholar
DeFronzo, R. A. et al. Effects of exenatide versus sitagliptin on postprandial glucose, insulin and glucagon secretion, gastric emptying, and caloric intake: a randomized, cross-over study. Curr. Med. Res. Opin. 24, 2943–2952 (2008).
Google Scholar
Patoulias, D. I. et al. Cardiovascular efficacy and safety of dipeptidyl peptidase-4 inhibitors: a meta-analysis of cardiovascular outcome trials. World J. Cardiol. 13, 585–592 (2021).
Google Scholar
White, W. B. et al. Alogliptin after acute coronary syndrome in patients with type 2 diabetes. N. Engl. J. Med. 369, 1327–1335 (2013).
Google Scholar
Scirica, B. M. et al. Saxagliptin and cardiovascular outcomes in patients with type 2 diabetes mellitus. N. Engl. J. Med. 369, 1317–1326 (2013).
Google Scholar
Green, J. B. et al. Effect of sitagliptin on cardiovascular outcomes in type 2 diabetes. N. Engl. J. Med. 373, 232–242 (2015).
Google Scholar
Rosenstock, J. et al. Effect of linagliptin vs placebo on major cardiovascular events in adults with type 2 diabetes and high cardiovascular and renal risk: the CARMELINA randomized clinical trial. JAMA 321, 69–79 (2019).
Google Scholar
Nauck, M. A., Meier, J. J., Cavender, M. A., Abd El Aziz, M. & Drucker, D. J. Cardiovascular actions and clinical outcomes with glucagon-like peptide-1 receptor agonists and dipeptidyl peptidase-4 inhibitors. Circulation 136, 849–870 (2017).
Google Scholar
Richardson, T. L. Jr. et al. Primary occurrence of cardiovascular events after adding sodium-glucose cotransporter-2 inhibitors or glucagon-like peptide-1 receptor agonists compared with dipeptidyl peptidase-4 inhibitors: a cohort study in veterans with diabetes. Ann. Intern. Med. 176, 751–760 (2023).
Google Scholar
Pfeffer, M. A. et al. Lixisenatide in patients with type 2 diabetes and acute coronary syndrome. N. Engl. J. Med. 373, 2247–2257 (2015).
Google Scholar
Husain, M. et al. Oral semaglutide and cardiovascular outcomes in patients with type 2 diabetes. N. Engl. J. Med. 381, 841–851 (2019).
Google Scholar
Kelly, M. et al. Effects of GLP-1 receptor agonists on cardiovascular outcomes in patients with type 2 diabetes and chronic kidney disease: a systematic review and meta-analysis. Pharmacotherapy 42, 921–928 (2022).
Google Scholar
Lin, Y. et al. The cardiovascular and renal effects of glucagon-like peptide 1 receptor agonists in patients with advanced diabetic kidney disease. Cardiovasc. Diabetol. 22, 60 (2023).
Google Scholar
Chen, J. J. et al. Association of glucagon-like peptide-1 receptor agonist vs dipeptidyl peptidase-4 inhibitor use with mortality among patients with type 2 diabetes and advanced chronic kidney disease. JAMA Netw. Open 5, e221169 (2022).
Google Scholar
Perkovic, V. et al. Effects of semaglutide on chronic kidney disease in patients with type 2 diabetes. N. Engl. J. Med. 391, 109–121 (2024).
Google Scholar
Alicic, R. Z., Cox, E. J., Neumiller, J. J. & Tuttle, K. R. Incretin drugs in diabetic kidney disease: biological mechanisms and clinical evidence. Nat. Rev. Nephrol. 17, 227–244 (2021).
Google Scholar
Cai, X. et al. GLP-1 treatment protects endothelial cells from oxidative stress-induced autophagy and endothelial dysfunction. Int J. Biol. Sci. 14, 1696–1708 (2018).
Google Scholar
Greco E. V. et al. GLP-1 Receptor agonists and kidney protection. Medicina. 55, 233 (2019).
Semaglutide Injection Criteria for Use July 2022 VA Pharmacy Benefits Management Services, Medical Advisory Panel, and VISN Pharmacist Executives. (Accessed 22 December 2023).
VA Informatics and Computing Infrastructure (VINCI), VA HSR RES 13-457, U.S. Department of Veterans Affairs. (2008).
Lund, J. L., Richardson, D. B. & Sturmer, T. The active comparator, new user study design in pharmacoepidemiology: historical foundations and contemporary application. Curr. Epidemiol. Rep. 2, 221–228 (2015).
Google Scholar
Stop Code References. Cost Accounting Office of the VHA. http://vaww.dss.med.va.gov/programdocs/pd_oident.aspManagerial.
Kivimäki, M. et al. Validity of cardiovascular disease event ascertainment using linkage to UK Hospital records. Epidemiology 28, 735–739 (2017).
McCormick, N., Lacaille, D., Bhole, V. & Avina-Zubieta, J. A. Validity of myocardial infarction diagnoses in administrative databases: a systematic review. PLoS ONE 9, e92286 (2014).
Google Scholar
McCormick, N., Bhole, V., Lacaille, D. & Avina-Zubieta, J. A. Validity of diagnostic codes for acute stroke in administrative databases: a systematic review. PLoS ONE 10, e0135834 (2015).
Google Scholar
Ginde, A. A., Blanc, P. G., Lieberman, R. M. & Camargo, C. A. Jr. Validation of ICD-9-CM coding algorithm for improved identification of hypoglycemia visits. BMC Endocr. Disord. 8, 4 (2008).
Google Scholar
Karter, A. J. et al. Revalidation of the hypoglycemia risk stratification tool using ICD-10 codes. Diabetes Care. 42, e58–e59 (2019).
Google Scholar
Adams, M. A., et al. Development and validation of a new ICD-10-based screening colonoscopy overuse measure in a large integrated healthcare system: a retrospective observational study. BMJ Qual Saf. (2022).
Elixhauser, A., Steiner, C., Palmer, L. Clinical Classifications Software (CCS) for ICD-9-CM. Databases and Related Tools from the Healthcare Cost and Utilization Project (HCUP) U.S. Agency for Healthcare Research and Quality; 2012:Appendix A. Accessed 01 January 2012. http://www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp.
Deyo, R. A., Cherkin, D. C. & Ciol, M. A. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J. Clin. Epidemiol. 45, 613–619 (1992).
Google Scholar
Orkaby, A. R. et al. The burden of frailty among U.S. veterans and its association with mortality, 2002-2012. J. Gerontol. A Biol. Sci. Med. Sci. 74, 1257–1264 (2019).
Google Scholar
Schneeweiss, S., Wang, P. S., Avorn, J. & Glynn, R. J. Improved comorbidity adjustment for predicting mortality in Medicare populations. Health Serv. Res. 38, 1103–1120 (2003).
Google Scholar
Quan, H. et al. Updating and validating the Charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries. Am. J. Epidemiol. 173, 676–682 (2011).
Google Scholar
D’Agostino, R. B. Sr. et al. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation 117, 743–753 (2008).
Google Scholar
Becker, S. & Ichino, A. Estimation of average treatment effects based on propensity scores. Stata J. 2, 358–377 (2002).
Google Scholar
Leuven, E. & Sianesi, B. PSMATCH2: Stata module to perform full Mahalanobis and propensity score matching, common support graphing, and covariate imbalance testing. version 4.0.5 ed. 2003.
Austin, P. C. Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies. Pharm. Stat. 10, 150–161 (2011).
Google Scholar
Staffa, S. J. & Zurakowski, D. Five steps to successfully implement and evaluate propensity score matching in clinical research studies. Anesth. Analg. 127, 1066–1073 (2018).
Google Scholar
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