研究背景
對於整體社會而言，第二型糖尿病會帶來非常龐大疾病和經濟負擔；其中當病患合併有心血管疾病時，相對於其他併發症，醫療支出會大幅上升，因此若是可以選擇具有心血管好處的降血糖藥品作為後續的治療，於此可以減少疾病的發生和後續花費支出。sodium glucose cotransporter 2 inhibitors（ SGLT2is ）和dipeptidyl peptidase 4 inhibitors（DPP4is）為除了metformin之外目前市面上最常處方的口服降血糖藥品，但兩藥品於心血管疾病卻具有不同的角色。目前雖有許多第二型糖尿病患者使用SGLT2is相對於DPP4is的成本效益研究，但過去文獻多以臨床試驗之結果作為參數帶入模型，且也未將第二型糖尿病患以過去心血管病史分層分析，因此若是要將過去研究結果外推至真實世界尚須謹慎。

研究目的
以健康照護體系的觀點，評估在真實世界下台灣第二型糖尿病患合併有心血管病史之SGLT2is相對於DPP4is的成本效益。

研究方法
模型與族群之假設
本研究為以馬可夫模型為基礎的成本效益分析，建立七個疾病狀態包括：無發生任何事件、心臟衰竭、心肌梗塞、中風、合併心臟衰竭和心肌梗塞、合併心臟衰竭和中風、死亡。以一年為一個循環，並模擬十年，對於療效和醫療花費皆以每年3%校正，並假設病人進入模型時皆為55歲、糖尿病持續時間為8年、並具有心血管病史。
轉移機率參數
在DPP4is分支，轉移機率藉由使用台灣健保資料庫，計算於2010年新使用DPP4is且有心血管病史的患者，觀察至2018年，第一次發生心血管事件於每一年的發生機率，和發生第一次心血管事件後，持續追蹤至發生第二次心血管事件於每一年的轉移機率；死亡轉移機率參考一個台灣第二型糖尿病死亡率的危險分數模型（危險分數的計算包含基本特徵、臨床檢驗數值、過去病史等），並根據假設的基本特徵，獲得具有5分的台灣第二型糖尿病患之第三、第五、第十年之累積死亡率，隨後利用公式轉換成第三、第五、第十年的死亡發生率，並再考量心血管病史對於死亡率的影響，得到第二型糖尿病患具有心血管病史於第三、第五、第十年的死亡率，最後再以內差法獲得其他年度的死亡率。在SGLT2is分支，以DPP4is分支的轉移機率作為參考，並考量SGLT2is相對於DPP4is於心血管事件的相對風險，進行參數轉換成SGLT2is分支的轉移機率。相對風險藉由使用台灣健保資料庫，計算於2017年新使用SGLT2is或DPP4is且有心血管病史的患者，於心血管事件的相對風險；而第二次心血管事件的轉移機率和第一次心血管事件後的死亡機率將帶入和DPP4is分支相同的參數。
醫療花費及健康效用參數
參考兩篇以台灣第二型糖尿病患為族群的醫療花費和生活品質研究，並根據不同的基本特徵和健康狀態給予校正後的醫療花費和健康效用參數。而第二型糖尿病患且具有心血管病史在無任何事件發生的疾病狀態之基本醫療花費和健康效用分別為美金624.34元和0.798，以下舉例：當一位65歲男性過去有心血管病史，並且發生了心臟衰竭，則其發生心臟衰竭當年之醫療花費為美金1,417.25元（即624.34*2.27），其健康效用為0.555（即0.798-0.002*3-0.237）。
統計分析
基礎值分析以第二型糖尿病患合併有心血管病史，分別使用SGLT2is和DPP4is並經過馬可夫模型模擬十年得到療效（即預期壽命、品質校正壽命）和花費，再進一步計算遞增成本效果比值，且後續將進行一系列的敏感度分析（即單因子敏感度分析、機率性敏感度分析、情境敏感度分析）。本研究所計算出的遞增成本效果比值會與願意支付價格（即美金30,038~90,114元）比較，而以上的所有分析將會使用TreeAge軟體進行模擬。

研究結果
基礎值分析結果顯示，SGLT2is相對於DPP4is的使用可以增加0.199年的品質校正壽命、0.205的預期壽命，但需多花美金644.25元，遞增成本效果比值分別為美金3,138.03和3,244.07元，而單因子敏感度分析、機率性敏感度分析、情境敏感度分析之結果與基礎值分析一致（除了在以第二型糖尿病且過去無心血管病史並模擬一年的情境下，其遞增成本效果比值超過最低值的願意支付價格），顯示SGLT2is相對於DPP4is之使用對於第二型糖尿病且過去有心血管病史的患者，是個高度具成本效益的選擇。

研究結論
在真實世界的情境之下，針對第二型糖尿病且過去有心血管病史之患者，鼓勵選擇SGLT2is為後續降血糖治療藥品，而非DPP4is；對於過去無心血管病史之患者，根據情境敏感度分析結果，也同樣鼓勵SGLT2is的使用，惟此族群之遞增成本效果比值變異度較大，因此未來亦須針對此族群做更深入的成本效益研究。

Despite substantial evidence on cost-effectiveness of sodium glucose cotransporter 2 inhibitors (SGLT2is) versus dipeptidyl peptidase 4 inhibitors (DPP4is) in type 2 diabetes (T2D), the generalizability of the evidence to real-world practice is limited due to the composition of study population and methodologies issues. Therefore, this study aimed to assess the cost-effectiveness of real-world use of SGLT2is versus DPP4is among Taiwanese T2D patients with CVD history from healthcare sector’s perspective. A morkov model was constructed using TreeAge software. In DPP4is arm, the transition probabilities of CVDs were estimated by the annual occurrence of CVD events among DPP4i-treated patients with CVD history from National Health Insurance Research Database in 2010, while the mortalities were calculated using an established model for all-cause death among T2D patients from a previous study. In SGLT2is arm, the probabilities in DPP4is arm and the treatment effects of SGLT2is versus DPP4is on clinical outcomes were used to estimate the SGLT2i patients’ probabilities. The model inputs of healthcare costs and health utilities regard to specifc health states were derived from previous studies of Taiwanese T2D population. In base-case analysis, T2D patients with CVD history simulated for 10 years and the result (i.e., incremental cost-effectivness ratio) was against pre-defined willingness-to-pay (i.e., one to three times of Taiwan’s gross domestic product). A series of sensitivity analyses was performed regard to parameter uncertainty. In overall analyses, SGLT2is versus DPP4is use was highly cost-effective and therefore, we encouraged SGLT2is use among T2D patients with CVD history.

1. American Diabetes Association. Economic Costs of Diabetes in the U.S. in 2017. Diabetes Care. 2018;41(5):917-928.
2. Chen HY, Kuo S, Su PF, Wu JS, Ou HT. Health Care Costs Associated With Macrovascular, Microvascular, and Metabolic Complications of Type 2 Diabetes Across Time: Estimates From a Population-Based Cohort of More Than 0.8 Million Individuals With Up to 15 Years of Follow-up. Diabetes Care. 2020;43(8):1732-1740.
3. Wilkinson S, Douglas I, Stirnadel-Farrant H, Fogarty D, Pokrajac A, Smeeth L, Tomlinson L. Changing use of antidiabetic drugs in the UK: trends in prescribing 2000-2017. BMJ Open. 2018;8(7):e022768.
4. McCoy RG, Dykhoff HJ, Sangaralingham L, Ross JS, Karaca-Mandic P, Montori VM, Shah ND. Adoption of New Glucose-Lowering Medications in the U.S.-The Case of SGLT2 Inhibitors: Nationwide Cohort Study. Diabetes Technol Ther. 2019;21(12):702-712.
5. Kim J, Park S, Kim H, Je NK. National trends in metformin-based combination therapy of oral hypoglycaemic agents for type 2 diabetes mellitus. Eur J Clin Pharmacol. 2019;75(12):1723-1730.
6. Kameda T, Kumamaru H, Nishimura S, Kohsaka S, Miyata H. Use of oral antidiabetic drugs in Japanese working-age patients with type 2 diabetes mellitus: dosing pattern for metformin initiators. Curr Med Res Opin. 2020;36(5):749-756.
7. American Diabetes Association. 10. Cardiovascular Disease and Risk Management: Standards of Medical Care in Diabetes-2021. Diabetes Care. 2021;44(Suppl 1):S125-S150.
8. American Diabetes Association. 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2021. Diabetes Care. 2021;44(Suppl 1):S15-S33.
9. Betsy B. Dokken. The Pathophysiology of Cardiovascular Disease and Diabetes: Beyond Blood Pressure and Lipids. Diabetes Spectrum. 2008;21(3):160-165.
10. Einarson TR, Acs A, Ludwig C, Panton UH. Prevalence of cardiovascular disease in type 2 diabetes: a systematic literature review of scientific evidence from across the world in 2007-2017. Cardiovasc Diabetol. 2018;17(1):83.
11. Beaudet A, Clegg J, Thuresson PO, Lloyd A, McEwan P. Review of utility values for economic modeling in type 2 diabetes. Value Health. 2014;17(4):462-470.
12. Kuo S, Yang CT, Chen HY, Ou HT. Valuing health states of people with type 2 diabetes: Analyses of the nationwide representative linked databases. J Diabetes Investig. 2021 Feb 4. doi: 10.1111/jdi.13520. Epub ahead of print. PMID: 33539655.
13. American Diabetes Association. 9. Pharmacologic Approaches to Glycemic Treatment: Standards of Medical Care in Diabetes-2021. Diabetes Care. 2021;44(Suppl 1):S111-S124.
14. Engler C, Leo M, Pfeifer B, Juchum M, Chen-Koenig D, Poelzl K, Schoenherr H, Vill D, Oberdanner J, Eisendle E, Middeldorf K, Heindl B, Gaenzer H, Bode G, Kirchmeyr K, Ladner G, Rieger L, Koellensperger U, Schwaiger A, Stoeckl F, Zangerl G, Lechleitner M, Delmarko I, Oberaigner W, Rissbacher C, Tilg H, Ebenbichler C. Long-term trends in the prescription of antidiabetic drugs: real-world evidence from the Diabetes Registry Tyrol 2012-2018. BMJ Open Diabetes Res Care. 2020;8(1):e001279.
15. White WB, Cannon CP, Heller SR, Nissen SE, Bergenstal RM, Bakris GL, Perez AT, Fleck PR, Mehta CR, Kupfer S, Wilson C, Cushman WC, Zannad F; EXAMINE Investigators. Alogliptin after acute coronary syndrome in patients with type 2 diabetes. N Engl J Med. 2013;369(14):1327-1335.
16. Scirica BM, Bhatt DL, Braunwald E, Steg PG, Davidson J, Hirshberg B, Ohman P, Frederich R, Wiviott SD, Hoffman EB, Cavender MA, Udell JA, Desai NR, Mosenzon O, McGuire DK, Ray KK, Leiter LA, Raz I; SAVOR-TIMI 53 Steering Committee and Investigators. Saxagliptin and cardiovascular outcomes in patients with type 2 diabetes mellitus. N Engl J Med. 2013;369(14):1317-1326.
17. Green JB, Bethel MA, Armstrong PW, Buse JB, Engel SS, Garg J, Josse R, Kaufman KD, Koglin J, Korn S, Lachin JM, McGuire DK, Pencina MJ, Standl E, Stein PP, Suryawanshi S, Van de Werf F, Peterson ED, Holman RR; TECOS Study Group. Effect of Sitagliptin on Cardiovascular Outcomes in Type 2 Diabetes. N Engl J Med. 2015;373(3):232-242.
18. Rosenstock J, Perkovic V, Johansen OE, Cooper ME, Kahn SE, Marx N, Alexander JH, Pencina M, Toto RD, Wanner C, Zinman B, Woerle HJ, Baanstra D, Pfarr E, Schnaidt S, Meinicke T, George JT, von Eynatten M, McGuire DK; CARMELINA Investigators. 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. 2019;321(1):69-79.
19. Rosenstock J, Kahn SE, Johansen OE, Zinman B, Espeland MA, Woerle HJ, Pfarr E, Keller A, Mattheus M, Baanstra D, Meinicke T, George JT, von Eynatten M, McGuire DK, Marx N; CAROLINA Investigators. Effect of Linagliptin vs Glimepiride on Major Adverse Cardiovascular Outcomes in Patients With Type 2 Diabetes: The CAROLINA Randomized Clinical Trial. JAMA. 2019;322(12):1155-1166.
20. Zinman B, Wanner C, Lachin JM, Fitchett D, Bluhmki E, Hantel S, Mattheus M, Devins T, Johansen OE, Woerle HJ, Broedl UC, Inzucchi SE; EMPA-REG OUTCOME Investigators. Empagliflozin, Cardiovascular Outcomes, and Mortality in Type 2 Diabetes. N Engl J Med. 2015;373(22):2117-2128.
21. Neal B, Perkovic V, Mahaffey KW, de Zeeuw D, Fulcher G, Erondu N, Shaw W, Law G, Desai M, Matthews DR; CANVAS Program Collaborative Group. Canagliflozin and Cardiovascular and Renal Events in Type 2 Diabetes. N Engl J Med. 2017;377(7):644-657.
22. Perkovic V, Jardine MJ, Neal B, Bompoint S, Heerspink HJL, Charytan DM, Edwards R, Agarwal R, Bakris G, Bull S, Cannon CP, Capuano G, Chu PL, de Zeeuw D, Greene T, Levin A, Pollock C, Wheeler DC, Yavin Y, Zhang H, Zinman B, Meininger G, Brenner BM, Mahaffey KW; CREDENCE Trial Investigators. Canagliflozin and Renal Outcomes in Type 2 Diabetes and Nephropathy. N Engl J Med. 2019;380(24):2295-2306.
23. Wiviott SD, Raz I, Bonaca MP, Mosenzon O, Kato ET, Cahn A, Silverman MG, Zelniker TA, Kuder JF, Murphy SA, Bhatt DL, Leiter LA, McGuire DK, Wilding JPH, Ruff CT, Gause-Nilsson IAM, Fredriksson M, Johansson PA, Langkilde AM, Sabatine MS; DECLARE–TIMI 58 Investigators. Dapagliflozin and Cardiovascular Outcomes in Type 2 Diabetes. N Engl J Med. 2019;380(4):347-357.
24. Sabapathy S, Neslusan C, Yoong K, Teschemaker A, Johansen P, Willis M. Cost-effectiveness of Canagliflozin versus Sitagliptin When Added to Metformin and Sulfonylurea in Type 2 Diabetes in Canada. J Popul Ther Clin Pharmacol. 2016;23(2):e151-168.
25. Charokopou M, McEwan P, Lister S, Callan L, Bergenheim K, Tolley K, Postema R, Townsend R, Roudaut M. Cost-effectiveness of dapagliflozin versus DPP-4 inhibitors as an add-on to Metformin in the Treatment of Type 2 Diabetes Mellitus from a UK Healthcare System Perspective. BMC Health Serv Res. 2015;15:496.
26. Neslusan C, Teschemaker A, Johansen P, Willis M, Valencia-Mendoza A, Puig A. Cost-Effectiveness of Canagliflozin versus Sitagliptin as Add-on to Metformin in Patients with Type 2 Diabetes Mellitus in Mexico. Value Health Reg Issues. 2015;8:8-19.
27. Tzanetakos C, Tentolouris N, Kourlaba G, Maniadakis N. Cost-Effectiveness of Dapagliflozin as Add-On to Metformin for the Treatment of Type 2 Diabetes Mellitus in Greece. Clin Drug Investig. 2016;36(8):649-659.
28. Chakravarty A, Rastogi M, Dhankhar P, Bell KF. Comparison of costs and outcomes of dapagliflozin with other glucose-lowering therapy classes added to metformin using a short-term cost-effectiveness model in the US setting. J Med Econ. 2018;21(5):497-509.
29. Ramos M, Foos V, Ustyugova A, Hau N, Gandhi P, Lamotte M. Cost-Effectiveness Analysis of Empagliflozin in Comparison to Sitagliptin and Saxagliptin Based on Cardiovascular Outcome Trials in Patients with Type 2 Diabetes and Established Cardiovascular Disease. Diabetes Ther. 2019;10(6):2153-2167.
30. Reifsnider O, Kansal A, Pimple P, Aponte-Ribero V, Brand S, Shetty S. 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. 2021;23(3):791-799.
31. Hu S, Deng X, Ma Y, Li Z, Wang Y, Wang Y. Cost-Utility Analysis of Dapagliflozin Versus Saxagliptin Treatment as Monotherapy or Combination Therapy as Add-on to Metformin for Treating Type 2 Diabetes Mellitus. Appl Health Econ Health Policy. 2021;19(1):69-79.
32. 衛生福利部中央健康保險署. 藥品給付規定. Available from https://www.nhi.gov.tw/Content_List.aspx?n=E70D4F1BD029DC37&topn=5FE8C9FEAE863B46 Accessed on July 04, 2021.
33. 中華民國行政院主計處. 消費者物價指數. Available from https://statdb.dgbas.gov.tw/pxweb/Dialog/price.asp?mp=4 Accessed in Dec, 2020.
34. Liu CS, Li CI, Wang MC, Yang SY, Li TC, Lin CC. Building clinical risk score systems for predicting the all-cause and expanded cardiovascular-specific mortality of patients with type 2 diabetes. Diabetes Obes Metab. 2021;23(2):467-479.
35. Chiang JI, Hanlon P, Li TC, Jani BD, Manski-Nankervis JA, Furler J, Lin CC, Yang SY, Nicholl BI, Thuraisingam S, Mair FS. Multimorbidity, mortality, and HbA1c in type 2 diabetes: A cohort study with UK and Taiwanese cohorts. PLoS Med. 2020;17(5):e1003094.
36. 衛生福利部中央健康保險署. 健保用藥品項查詢. Available from https://www.nhi.gov.tw/QueryN/Query1.aspx Accessed on July 04, 2021.
37. 中華民國內政部. 歷年簡易生命表. Available from https://www.moi.gov.tw/cl.aspx?n=2952 Accessed on July 04, 2021.
38. Zheng SL, Roddick AJ, Aghar-Jaffar R, Shun-Shin MJ, Francis D, Oliver N, Meeran K. Association Between Use of Sodium-Glucose Cotransporter 2 Inhibitors, Glucagon-like Peptide 1 Agonists, and Dipeptidyl Peptidase 4 Inhibitors With All-Cause Mortality in Patients With Type 2 Diabetes: A Systematic Review and Meta-analysis. JAMA. 2018;319(15):1580-1591.
39. Bertram MY, Lauer JA, De Joncheere K, Edejer T, Hutubessy R, Kieny MP, Hill SR. Cost-effectiveness thresholds: pros and cons. Bull World Health Organ. 2016;94(12):925-930.
40. 中華民國行政院主計處. 最新指標. Available from https://www.stat.gov.tw/point.asp?index=1 Accessed in Dec, 2020.
41. Yang CT, Lin WH, Li LJ, Ou HT, Kuo S. Association of Renal and Cardiovascular Safety With DPP-4 Inhibitors vs. Sulfonylureas in Patients With Type 2 Diabetes and Advanced Chronic Kidney Disease. Clin Pharmacol Ther. 2021 Apr 18. doi: 10.1002/cpt.2262.
42. Parsons L. Reducing Bias in a Propensity Score Matched-Pair Sample Using Greedy Matching Techniques. Available from https://support.sas.com/resources/papers/proceedings/proceedings/sugi26/p214-26.pdf. Accessed on 10 June 2021
43. 衛生福利部中央健康保險署. 末期腎臟病前期之病人照護與衛教計畫(Pre-ESRD). Available from https://www.nhi.gov.tw/Content_List.aspx?n=D037A6FEDF678C70&topn=5FE8C9FEAE863B46 Accessed on July 04, 2021.
44. Cheng CL, Lee CH, Chen PS, Li YH, Lin SJ, Yang YH. Validation of acute myocardial infarction cases in the national health insurance research database in taiwan. J Epidemiol. 2014;24(6):500-507.
45. Cheng CL, Kao YH, Lin SJ, Lee CH, Lai ML. Validation of the National Health Insurance Research Database with ischemic stroke cases in Taiwan. Pharmacoepidemiol Drug Saf. 2011;20(3):236-242.
46. Hsieh CY, Chen CH, Li CY, Lai ML. Validating the diagnosis of acute ischemic stroke in a National Health Insurance claims database. J Formos Med Assoc. 2015;114(3):254-259.
47. Yang D. & Dalton JE. A unified approach to measuring the effect size between two groups using SAS. SAS Global Forum 2012: Statistics and Data Analysis, Paper 335-2012. Available from https://support.sas.com/resources/papers/proceedings12/335-2012.pdf. Accessed 10 June 2021
48. Austin PC, Lee DS, Fine JP. Introduction to the analysis of survival data in the presence of competing risks. Circulation 2016;133:601-609.
49. Ethgen, O., Standaert, B. Population–versus Cohort–Based Modelling Approaches. PharmacoEconomics 2012;30:171–181.
50. Siebert U, Alagoz O, Bayoumi AM, Jahn B, Owens DK, Cohen DJ, Kuntz KM; ISPOR-SMDM Modeling Good Research Practices Task Force. State-transition modeling: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force--3. Value Health. 2012;15(6):812-820.
51. Clarke P, Gray A, Holman R. Estimating utility values for health states of type 2 diabetic patients using the EQ-5D (UKPDS 62). Med Decis Making. 2002;22(4):340-349.
52. Bagust A, Beale S. Modelling EuroQol health-related utility values for diabetic complications from CODE-2 data. Health Econ. 2005;14(3):217-30.
53. Pagano E, Petrelli A, Picariello R, Merletti F, Gnavi R, Bruno G. Is the choice of the statistical model relevant in the cost estimation of patients with chronic diseases? An empirical approach by the Piedmont Diabetes Registry. BMC Health Serv Res. 2015;15:582.
54. Arnold SV, de Lemos JA, Rosenson RS, Ballantyne CM, Liu Y, Mues KE, Alam S, Elliott-Davey M, Bhatt DL, Cannon CP, Kosiborod M; GOULD Investigators. Use of Guideline-Recommended Risk Reduction Strategies Among Patients With Diabetes and Atherosclerotic Cardiovascular Disease. Circulation. 2019;140(7):618-620.
55. Le P, Chaitoff A, Misra-Hebert AD, Ye W, Herman WH, Rothberg MB. Use of Antihyperglycemic Medications in U.S. Adults: An Analysis of the National Health and Nutrition Examination Survey. Diabetes Care. 2020;43(6):1227-1233.
56. Sutton RT, Pincock D, Baumgart DC, Sadowski DC, Fedorak RN, Kroeker KI. An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digit Med. 2020;3:17.
57. Ash JS, Sittig DF, Campbell EM, Guappone KP, Dykstra RH. Some unintended consequences of clinical decision support systems. AMIA Annu Symp Proc. 2007;2007:26-30.