farmaecFARMAKOEKONOMIKA. Modern Pharmacoeconomics and PharmacoepidemiologyФАРМАКОЭКОНОМИКА. Современная фармакоэкономика и фармакоэпидемиология2070-49092070-4933IRBIS LLC10.17749/2070-4909.2017.10.3.047-058farmaec-208Research ArticleReview articlesНаучные обзорыDecision modelling for the evaluation of diabetes outcomesОсобенности симуляционного моделирования при оценке клинико-экономических исходов сахарного диабета 1-го и 2-го типовМосикянА. А.MosikianA. A.

Чжао Вэньлун – аспирант кафедры клинической фармакологии и доказательной медицины.

Адрес: ул. Льва Толстого, д. 6-8, Санкт-Петербург, Россия, 197022. 

Mosikian Anna Albertovna – Medical Resident at the Dpt. of Internal diseases and Endocrinology.

Address: ul. Akkuratova, 2, St. Petersburg, Russia, 197341.

mosikian.anna@gmail.comЧжаоВ.ZhaoW.

Zhao Wenlong – PhD student at the Dpt. of Clinical pharmacology and evidence-based medicine

Address: ul. L’va Tolstogo, 6-8, St. Petersburg, Russia, 197022.

travall@163.comГаланкинА. С.GalankinT. L.

Галанкин Тимофей Леонидович – кандидат медицинских наук, заведующий лабораторией фармакоэпидемиологии и фармакокинетики Института фармакологии им. А. В. Вальдмана.

Адрес: ул. Льва Толстого, д. 6-8, Санкт-Петербург, Россия, 197022. 

Pavlov First Saint Petersburg State Medical University

galankint@gmail.comКолбинА. С.KolbinA. S.

Колбин Алексей Сергеевич – доктор медицинских наук, профессор, заведующий кафедрой клинической фармакологии и доказательной медицины, Первый Санкт-Петербургский государственный медицинский университет им. академика И. П. Павлова; профессор кафедры фармакологии медицинского факультета Санкт-Петербургского государственного университета.

Адрес: 21-я линия, д. 8, В.О., Санкт-Петербург, Россия, 199106. 

Kolbin Aleksei Sergeevich – MD, Professor, Head of the Dpt. of Clinical pharmacology and evidence-based medicine, First St. Petersburg State Medical University; Professor at the Dpt. of Pharmacology, St. Petersburg State University.

Address: 21-Line, 8, VO, St. Petersburg, Russia, 199106. 

alex.kolbin@mail.comФедеральное государственное бюджетное учреждение «Национальный медицинский исследовательский центр имени В. А. Алмазова» Министерства здравоохранения Российской Федерации,РоссияFederal Almazov North West Medical Research CentreRussian FederationФедеральное государственное бюджетное образовательное учреждение высшего образования «Первый Санкт-Петербургский государственный медицинский университет имени акад. И. П. Павлова» Минздрава РоссииРоссияPavlov First Saint Petersburg State Medical UniversityRussian FederationФедеральное государственное бюджетное образовательное учреждение высшего образования «Первый Санкт-Петербургский государственный медицинский университет имени акад. И. П. Павлова» Минздрава РоссииРоссияФедеральное государственное бюджетное образовательное учреждение высшего образования «Первый Санкт-Петербургский государственный медицинский университет имени акад. И. П. Павлова» Минздрава России; Федеральное государственное бюджетное образовательное учреждение высшего образования «Санкт-Петербургский государственный университет»Pavlov First Saint Petersburg State Medical University: Saint Petersburg State University2017241120171034758Copyright © Mosikian A.A., Zhao W., Galankin T.L., Kolbin A.S., 20172017Мосикян А.А., Чжао В., Галанкин А.С., Колбин А.С.Mosikian A.A., Zhao W., Galankin T.L., Kolbin A.S.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.pharmacoeconomics.ru/jour/article/view/208

The process of decision modelling in diabetes mellitus (DM) is often complicated by comorbidity among diabetic patients, complexity of endpoint selection, and unclear time horizons. Aim. To review the available recommendations, relevant methods and mathematical approaches to decision modelling in DM. Materials and Methods. We searched through the PubMed database using the ResearchGate and Mendeley networks; we also collected data from the websites of the key opinion leaders in the field of pharmacoeconomics and decision modelling. Results. This review contains up-to-date information on the validity of the most common DM decision models and on the validity of extrapolating the type 2 DM models to patients with type 1 DM. We also provide some clinically relevant comments on the American Diabetes Association’s requirements concerning the decision models in DM. The review incorporates data on the current mathematical approaches to modelling the changes in glycated hemoglobin levels, the body mass index and the quality-adjusted life expectancy – for both type 1 and type 2 DM. Conclusion. Despite recent successes in DM decision modelling, the existing approaches are not always relevant to some groups of DM patients or to some aspects of the disease. Thus, the use of the novel anti-diabetic drugs (liraglutide, semaglutide, empagliflozin) capable of significantly reducing cardiovascular risks in DM patients, require new approaches to decision modelling in diabetes mellitus. 

При симуляционном моделировании течения сахарного диабета (СД) исследователь нередко сталкивается с рядом сложностей, обусловленных коморбидностью в популяции, выбором моделируемых конечных точек и обоснованного горизонта моделирования. Цель исследования – провести обзор современных подходов и математических методов, применяемых при построении симуляционных моделей в диабетологии. Материалы и методы. Поиск релевантных источников по текстовой базе данных PubMed с помощью встроенных поисковых систем сетей ResearchGate и Mendeley, а также на электронных ресурсах ключевых регуляторов в области фармакоэкономических исследований и математического моделирования в медицине. Результаты. В настоящем обзоре представлены обновленные сведения о валидности широко известных моделей, о возможности экстраполяции результатов моделей для СД 2-го типа на популяцию СД 1-го типа, а также приведены комментарии к требованиям Американской диабетологической ассоциации к моделям СД с акцентом на клиническую релевантность моделей. В обзоре также приведены существующие математические подходы к моделированию изменения уровня гликированного гемоглобина, индекса массы тела, количества ожидаемых лет качественной жизни – для СД 1-го и 2-го типов. Заключение. Несмотря на значительные успехи в математическом моделировании СД, существующие подходы к симуляционному моделированию не всегда релевантны для СД в целом или для решения конкретных задач в частности, а выход на рынок новых гипогликемических препаратов, улучшающих сердечно-сосудистые прогнозы, требует создания принципиально новых моделей. 

diabetes mellitushealth economic evaluationdecision modelingReferencesПеречень зарегистрированных на территории РФ лекарственных препаратов. URL: http://grls.rosminzdrav.ru/grls.aspx. Дата обращения: 05.07.2017.List of medicines registered on the territory of the Russian Federation [Perechen’ zaregistrirovannykh na territorii RF lekarstvennykh preparatov (in Russian)]. URL: http://grls.rosminzdrav. ru/grls.aspx. Accessed: 05.07.2017.Fitchett D., Cheng A., Connelly K. et al. A Practical Guide to the Use of Glucose-Lowering Agents With Cardiovascular Benefit or Proven Safety. Can J Cardiol. 2017; 33 (7): 940-942.Fitchett D., Cheng A., Connelly K. et al. A Practical Guide to the Use of Glucose-Lowering Agents With Cardiovascular Benefit or Proven Safety. Can J Cardiol. 2017; 33 (7): 940-942.Standl E., Schnell O., McGuire D.K., et al. Integration of recent evidence into management of patients with atherosclerotic cardiovascular disease and type 2 diabetes. Lancet Diabetes Endocrinol. 2017; 5 (5): 391-402.Standl E., Schnell O., McGuire D.K., et al. Integration of recent evidence into management of patients with atherosclerotic cardiovascular disease and type 2 diabetes. Lancet Diabetes Endocrinol. 2017; 5 (5): 391-402.Мосикян А. А., Чжао В., Галанкин Т. Л., Колбин А. С. Исследования EMPA-REG OUTCOME, LEADER и SUSTAIN-6: возможные механизмы снижения сердечно-сосудистых рисков новыми сахароснижающими препаратами. Клиническая фармакология и терапия. 2017; 26 (2): 77-82.Mosikyan A. A., Chzhao V., Galankin T. L., Kolbin A. S. Issledovaniya EMPA-REG OUTCOME, LEADER i SUSTAIN-6: vozmozhnye mekhanizmy snizheniya serdechno-sosudistykh riskov novymi sakharosnizhayushchimi preparatami. Klinicheskaya farmakologiya i terapiya (in Russian). 2017; 26 (2): 77-82.Колбин А. С., Курылев А. А., Проскурин М. А., Балыкина Ю. Е. Моделирование медицинских и экономических исходов сахарного диабета. Анализ применяемых в мире математических моделей. Клиническая фармакология и терапия. 2012;21 (5): 9196.Kolbin A. S., Kurylev A. A., Proskurin M. A., Balykina Yu. E. Modelirovanie meditsinskikh i ekonomicheskikh iskhodov sakharnogo diabeta. Analiz primenyaemykh v mire matematicheskikh modelei. Klinicheskaya farmakologiya i terapiya (in Russian). 2012; 21 (5): 91-96.International Society for Pharmacoeconomics and Outcomes Research (ISPOR). URL: www.ispor.org. Дата обращения: 15.08.2017.International Society for Pharmacoeconomics and Outcomes Research (ISPOR). URL: www.ispor.org. Accessed: 15.08.2017.American Diabetes Association (ADA). URL: www.diabetes.org. Дата обращения: 15.08.2017.American Diabetes Association (ADA). URL: www.diabetes.org. Accessed: 15.08.2017.Briggs A., Claxton C., Sculpher M. Decision modelling for health economic evaluation. Oxford University Press, 2006.Briggs A., Claxton C., Sculpher M. Decision modelling for health economic evaluation. Oxford University Press, 2006.Колбин А. С., Зырянов С. К., Белоусов Д. Ю. Основные понятия в оценке медицинских технологий: метод. пособие. Под общ. ред. А. С. Колбина, С. К. Зырянова, Д. Ю. Белоусова. М. 2013; 42 с.Kolbin A. S., Zyryanov S. K., Belousov D. Yu. Basic concepts in the evaluation of medical technologies: a method. allowance. Under the Society. Ed. A. S. Kolbin, S. K. Zyryanov, D. Yu. Belousov. [Osnovnye ponyatiya v otsenke meditsinskikh tekhnologii: metod. posobie. Pod obshch. red. A. S. Kolbina, S. K. Zyryanova, D. Yu. Belousova (in Russian)]. Moscow. 2013; 42 s.Вентцель Е. С. Теория вероятностей. 6-е изд. стер. М. 1999; 576 c.Venttsel’ E. S. Probability theory. 6 th ed. sr. [Teoriya veroyatnostei. 6-e izd. ster. (in Russian)]. Moscow. 1999; 576 c.Henriksson M., Jindal R., Sternhufvud C. et al. A Systematic Review of Cost-Effectiveness Models in Type 1 Diabetes Mellitus. Pharmacoeconomics. 2016; 34 (6): 569-85.Henriksson M., Jindal R., Sternhufvud C. et al. A Systematic Review of Cost-Effectiveness Models in Type 1 Diabetes Mellitus. Pharmacoeconomics (in Russian). 2016; 34 (6): 569-85.Willis M., Asseburg C., He J. Validation of economic and health outcomes simulation model of type 2 diabetes mellitus (ECHO-T2DM). J Med Econ 2013; 16 (8): 1007-21.Willis M., Asseburg C., He J. Validation of economic and health outcomes simulation model of type 2 diabetes mellitus (ECHO-T2DM). J Med Econ. 2013; 16 (8): 1007-21.Palmer A. J., Roze S., Valentine W. J. et al. The CORE Diabetes Model: Projecting long-term clinical outcomes, costs and costeffectiveness of interventions in diabetes mellitus (types 1 and 2) to support clinical and reimbursement decision-making. CurrMed Res Opin 2004; 20 (1): 5-26.Palmer A. J., Roze S., Valentine W. J. et al. The CORE Diabetes Model: Projecting long-term clinical outcomes, costs and costeffectiveness of interventions in diabetes mellitus (types 1 and 2) to support clinical and reimbursement decision-making. CurrMed Res Opin. 2004; 20 (1): 5-26.Palmer A. J., Roze S., Valentine W. J. et al: Validation of the CORE Diabetes Model against epidemiological and clinical studies. Curr Med Res Opin. 2004; 20 (1): 27-40.Palmer A. J., Roze S., Valentine W. J. et al: Validation of the CORE Diabetes Model against epidemiological and clinical studies. Curr Med Res Opin. 2004; 20 (1): 27-40.Hayes A. J., Leal J., Gray A. M., et al. UKPDS Outcomes Model 2: a new version of a model to simulate lifetime health outcomes of patients with type 2 diabetes mellitus using data from the 30 year United Kingdom Prospective Diabetes Study: UKPDS 82. Diabetologia 2013; 56: 1925-33.Hayes A. J., Leal J., Gray A. M., et al. UKPDS Outcomes Model 2: a new version of a model to simulate lifetime health outcomes of patients with type 2 diabetes mellitus using data from the 30 year United Kingdom Prospective Diabetes Study: UKPDS 82. Diabetologia (in Russian). 2013; 56: 1925-33.Chen J., Alemao E., Yin D., Cook J. Development of a diabetes treatment simulation model: with application to assessing alternative treatment intensification strategies on survival and diabetes-related complications. Diabetes Obes Metab 2008; 10 (1): 33-42.Chen J., Alemao E., Yin D., Cook J. Development of a diabetes treatment simulation model: with application to assessing alternative treatment intensification strategies on survival and diabetes-related complications. Diabetes Obes Metab. 2008; 10 (1): 33-42.Mueller E., Maxion-Bergemann S., Gultyaev D. et al. Development and validation of the Economic Assessment of Glycemic Control and Long-Term Effects of diabetes (EAGLE) model. Diabetes Technol Ther. 2006; 8: 219-36.Mueller E., Maxion-Bergemann S., Gultyaev D. et al. Development and validation of the Economic Assessment of Glycemic Control and Long-Term Effects of diabetes (EAGLE) model. Diabetes Technol Ther. 2006; 8: 219-36.Govan L., Wu O., Lindsay R., Briggs A. How Do Diabetes Models Measure Up? A Review Of Diabetes Economic Models and ADA Guidelines. JHEOR. 2015; 3 (2): 132-52.Govan L., Wu O., Lindsay R., Briggs A. How Do Diabetes Models Measure Up? A Review Of Diabetes Economic Models and ADA Guidelines. JHEOR. 2015; 3 (2): 132-52.Колбин А. С., Хмельницкий О. К., Курылев А. А., Балыкина Ю. Е., Проскурин М. А., Колпак Е. П., Буре М. В. Первый в России опыт построения симуляционной модели исходов сахарного диабета 2 типа с дискретированным моделированием событий. Клинико-экономическая экспертиза. ФАРМАКОЭКОНОМИКА. Современная фармакоэкономика и фармакоэпидемиология. 2013; 6 (2): 24-31.Kolbin A. S., Khmel’nitskii O.K., Kurylev A. A., Balykina Yu. E., Proskurin M. A., Kolpak E. P., Bure M. V. Pervyi v Rossii opyt postroeniya simulyatsionnoi modeli iskhodov sakharnogo diabeta 2 tipa s diskretirovannym modelirovaniem sobytii. Klinikoekonomicheskaya ekspertiza. FARMAKOEKONOMIKA. Sovremennaya farmakoekonomika i farmakoepidemiologiya / PHARMACOECONOMICS. Modern pharmacoeconomics and pharmacoepidemiology (in Russian). 2013; 6 (2): 24-31.Claudius Ptolemaeus. System Design, Modelling, and Simulation using Ptolemy II. Chapter 9: Continious Time Models. 2014. URL: https://ptolemy.eecs.berkeley.edu/books/Systems/chapters/ ContinuousTimeModels.pdf. Дата обращения: 13.08.2017.Claudius Ptolemaeus. System Design, Modelling, and Simulation using Ptolemy II. Chapter 9: Continious Time Models. 2014. URL: https://ptolemy.eecs.berkeley.edu/books/Systems/chapters/ ContinuousTimeModels.pdf. Accessed: 13.08.2017.Eddy D. M., Schlessinger L. Archimedes: a trial-validated model of diabetes. Diabetes Care. 2003; 26: 3093-101.Eddy D. M., Schlessinger L. Archimedes: a trial-validated model of diabetes. Diabetes Care. 2003; 26: 3093-101.Модель UKPDS-OM2. URL: www.dtu.ox.ac.uk/outcomesmodel/. Дата обращения: 30.06.17.Model’ UKPDS-OM2. URL: www.dtu.ox.ac.uk/outcomesmodel/. Accessed: 30.06.17.Marso S. P., Daniels G. H., Brown-Frandsen K. et al. Liraglutide and Cardiovascular Outcomes in Type 2 Diabetes. N Engl J Med. 2016; 375 (4): 311-22.Marso S. P., Daniels G. H., Brown-Frandsen K. et al. Liraglutide and Cardiovascular Outcomes in Type 2 Diabetes. N Engl J Med. 2016; 375 (4): 311-22.Marso S. P., Bain S. C., Consoli A. et al. Semaglutide and Cardiovascular Outcomes in Patients with Type 2 Diabetes. N Engl J Med. 2016; 375 (19): 1834-1844.Marso S. P., Bain S. C., Consoli A. et al. Semaglutide and Cardiovascular Outcomes in Patients with Type 2 Diabetes. N Engl J Med. 2016; 375 (19): 1834-1844.Zinman B., Wanner C., Lachin J. M. et al. Empagliflozin, cardiovascular outcomes, and mortality in type 2 diabetes. N Engl J Med. 2015; 373: 2117-2128.Zinman B., Wanner C., Lachin J. M. et al. Empagliflozin, cardiovascular outcomes, and mortality in type 2 diabetes. N Engl J Med. 2015; 373: 2117-2128.American Diabetes Association: Guidelines for computer modeling of diabetes and its complications. Diabetes Care. 2004; 27: 2262-5.American Diabetes Association: Guidelines for computer modeling of diabetes and its complications. Diabetes Care. 2004; 27: 2262-5.Eddy D. M., Hollingworth W., Caro J. J. et al. Model transparency and validation: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force-7. Med Decis Making. 2012; 32: 733-43.Eddy D. M., Hollingworth W., Caro J. J. et al. Model transparency and validation: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force-7. Med Decis Making. 2012; 32: 733-43.Методические рекомендации по оценке сравнительной клинической эффективности и безопасности лекарственного препарата. ФГБУ «ЦЭККМП» Минздрава России. 2016.Methodological recommendations for the evaluation of comparative clinical efficacy and safety of the drug. FGBU “CECMPS” of the Ministry of Health of Russia [Metodicheskie rekomendatsii po otsenke sravnitel’noi klinicheskoi effektivnosti i bezopasnosti lekarstvennogo preparata. FGBU “TsEKKMP” Minzdrava Rossii (in Russian)]. 2016.McEwan P., Bennett H., Qin L., et al. An alternative approach to modelling HbA1c trajectories in patients with type 2 diabetes mellitus. Diabetes Obes Metab. 2017; 19 (5): 628-634.McEwan P., Bennett H., Qin L., et al. An alternative approach to modelling HbA1c trajectories in patients with type 2 diabetes mellitus. Diabetes Obes Metab. 2017; 19 (5): 628-634.Kind P., Lafata J. E., Matuszewski K. et al. The Use of QALYs in Clinical and Patient Decision-Making: Issues and Prospects. Value Health. 2009; 12 (1): 27-30.Kind P., Lafata J. E., Matuszewski K. et al. The Use of QALYs in Clinical and Patient Decision-Making: Issues and Prospects. Value Health. 2009; 12 (1): 27-30.Дедов И. И., Шестакова М. В., Галстян Г. Р. Распространенность сахарного диабета 2 типа у взрослого населения России (исследование NATION). Сахарный диабет. 2016; 19 (2): 104-112.Dedov I. I., Shestakova M. V., Galstyan G. R. Rasprostranennost’ sakharnogo diabeta 2 tipa u vzroslogo naseleniya Rossii (issledovanie NATION). Sakharnyi diabet (in Russian). 2016; 19 (2): 104-112.Шестакова М. В., Чазова И. Е., Шестакова Е. А. Российское многоцентровое скрининговое исследование по выявлению недиагностированного сахарного диабета 2 типа у пациентов с сердечно-сосудистой патологией. Сахарный диабет. 2016; 19 (1): 24-29.Shestakova M. V., Chazova I. E., Shestakova E. A. Rossiiskoe mnogotsentrovoe skriningovoe issledovanie po vyyavleniyu nediagnostirovannogo sakharnogo diabeta 2 tipa u patsientov s serdechnososudistoi patologiei. Sakharnyi diabet (in Russian). 2016; 19 (1): 24-29.Yamagishi S. I., Nakamura N., Matsui T. Glycation and cardiovascular disease in diabetes: A perspective on the concept of metabolic memory. J Diabetes. 2017; 9 (2): 141-148.Yamagishi S. I., Nakamura N., Matsui T. Glycation and cardiovascular disease in diabetes: A perspective on the concept of metabolic memory. J Diabetes. 2017; 9 (2): 141-148.McEwan P., Foos V., Lamotte M., Evans M. Quantifying the health economic benefit of key therapeutic outcomes in the management of type 2 diabetes and assessing their inter-relationship. Value Health. 2016; 19 (3): A88.McEwan P., Foos V., Lamotte M., Evans M. Quantifying the health economic benefit of key therapeutic outcomes in the management of type 2 diabetes and assessing their inter-relationship. Value Health. 2016; 19 (3): A88.McEwan P., Lamotte M., Grant D., et al. Assessing The Consistency Of Absolute Cardiovascular Risk Prediction And Relative Risk Reduction In Type-2 Diabetes Mellitus. Value Health. 2016; 19 (3): A165.McEwan P., Lamotte M., Grant D., et al. Assessing The Consistency Of Absolute Cardiovascular Risk Prediction And Relative Risk Reduction In Type-2 Diabetes Mellitus. Value Health. 2016; 19 (3): A165.IMS Core Diabetes Model. URL: http://www.core-diabetes.com/. Дата обращения: 19.07.2017.IMS Core Diabetes Model. URL: http://www.core-diabetes.com/. Accessed: 19.07.2017.Foos V., Grant D., Palmer J. L., et al. The Role of Simulation Modeling in Planning Long-Term Clinical Trials in Type 2 Diabetes. Value Health. 2013; 16 (7): A587-8.Foos V., Grant D., Palmer J. L., et al. The Role of Simulation Modeling in Planning Long-Term Clinical Trials in Type 2 Diabetes. Value Health. 2013; 16 (7): A587-8.Clarke P. M., Gray A. M., Briggs A. et al. A model to estimate the lifetime health outcomes of patients with type 2 diabetes: the United Kingdom Prospective Diabetes Study (UKPDS) outcomes model (UKPDS 68). Diabetologia. 2004; 47: 1747-1759.Clarke P. M., Gray A. M., Briggs A. et al. A model to estimate the lifetime health outcomes of patients with type 2 diabetes: the United Kingdom Prospective Diabetes Study (UKPDS) outcomes model (UKPDS 68). Diabetologia (in Russian). 2004; 47: 1747-1759.McEwan P., Foos V., Palmer J. L. et al. Therapy Escalation Thresholds and the Potential for Biased Cost Effectiveness Analysis When Failing to Sample Baseline HbA1c in Type 2 Diabetes. Value Health. 2013; 16 (7): A592.McEwan P., Foos V., Palmer J. L. et al. Therapy Escalation Thresholds and the Potential for Biased Cost Effectiveness Analysis When Failing to Sample Baseline HbA1c in Type 2 Diabetes. Value Health. 2013; 16 (7): A592.National Institute for Health and Care Excellence. Type 2 diabetes in adults: management. NICE guidelines [NG28]. URL: https://www. nice.org.uk/guidance/ng28. Дата обращения: 19.07.2017.National Institute for Health and Care Excellence. Type 2 diabetes in adults: management. NICE guidelines [NG28]. URL: https://www. nice.org.uk/guidance/ng28. Accessed: 19.07.2017.Дедов И. И. Алгоритмы специализированной медицинской помощи больным сахарным диабетом. Под редакцией И. И. Дедова, М. В. Шестаковой, А. Ю. Майорова. 8-й выпуск. Сахарный диабет. 2017; 20(1S): 1-121.Dedov I. I. Algoritmy spetsializirovannoi meditsinskoi pomoshchi bol’nym sakharnym diabetom. Pod redaktsiei I. I. Dedova, M. V. Shestakovoi, A. Yu. Maiorova. 8-i vypusk. Sakharnyi diabet (in Russian). 2017; 20 (1S): 1-121.Mata-Cases M., Franch-Nadal J., Real J. et al. Therapeutic Inertia in Patients Treated With Two or More Antidiabetics in Primary Care: Factors Predicting Intensification of Treatment. Diabetes Obes Metab. 2017 [Epub ahead of print].Mata-Cases M., Franch-Nadal J., Real J. et al. Therapeutic Inertia in Patients Treated With Two or More Antidiabetics in Primary Care: Factors Predicting Intensification of Treatment. Diabetes Obes Metab. 2017 [Epub ahead of print].McEwan P., Foos V., Lamotte M. The Impact of Baseline Hba1c and Hba1c Trajectories on Time to Therapy Escalation In Type 2 Diabetes Mellitus. Value Health. 2015; 18 (7): A698.McEwan P., Foos V., Lamotte M. The Impact of Baseline Hba1c and Hba1c Trajectories on Time to Therapy Escalation In Type 2 Diabetes Mellitus. Value Health. 2015; 18 (7): A698.Электронный калькулятор для прогнозирования изменения уровня гликированного гемоглобина у пациентов с сахарным диабетом 2 типа. URL: http://onlinelibrary.wiley.com/doi/10.1111/ dom.12865/full. Дата обращения: 19.07.2017.Elektronnyi kal’kulyator dlya prognozirovaniya izmeneniya urovnya glikirovannogo gemoglobina u patsientov s sakharnym diabetom 2 tipa. URL: http://onlinelibrary.wiley.com/doi/10.1111/ dom.12865/full. Data obrashcheniya: 19.07.2017.Bennett H., McEwan P., Bergenheim K., Gordon J. Assessment of unmet clinical need in type 2 diabetic patients on conventional therapy in the UK. Diabetes Ther. 2014; 5 (2): 567-578.Bennett H., McEwan P., Bergenheim K., Gordon J. Assessment of unmet clinical need in type 2 diabetic patients on conventional therapy in the UK. Diabetes Ther. 2014; 5 (2): 567-578.Willis M., Asseburg C., Nilsson A. et al. Multivariate Prediction Equations for HbA1c Lowering, Weight Change, and Hypoglycemic Events Associated with Insulin Rescue Medication in Type 2 Diabetes Mellitus: Informing Economic Modeling. Value Health. 2017; 20 (3): 357-371.Willis M., Asseburg C., Nilsson A. et al. Multivariate Prediction Equations for HbA1c Lowering, Weight Change, and Hypoglycemic Events Associated with Insulin Rescue Medication in Type 2 Diabetes Mellitus: Informing Economic Modeling. Value Health. 2017; 20 (3): 357-371.Glasgow Diabetes Managed Clinical Network. Guidelines for Insulin Initiation and Adjustment in Primary Care in patients with Type 2 Diabetes: for the guidance of Diabetes Specialist Nurses. 2012. 36 p.Glasgow Diabetes Managed Clinical Network. Guidelines for Insulin Initiation and Adjustment in Primary Care in patients with Type 2 Diabetes: for the guidance of Diabetes Specialist Nurses. 2012. 36 p.Petznick A. Insulin management of type 2 diabetes mellitus. Am Fam Physician. 2011; 84 (2): 183-90.Petznick A. Insulin management of type 2 diabetes mellitus. Am Fam Physician. 2011; 84 (2): 183-90.AACE/ACE comprehensive diabetes management algorithm. 2015. Endocr Pract. 2015; 21: 438-447.AACE/ACE comprehensive diabetes management algorithm. 2015. Endocr Pract. 2015; 21: 438-447.Diabetes Control and Complications Trial. Epidemiology of severe Hypoglycemia in the Diabetes Control and Complications Trial. The DCCT Research Group. Am J Med. 1991; 90: 450-9.Diabetes Control and Complications Trial. Epidemiology of severe Hypoglycemia in the Diabetes Control and Complications Trial. The DCCT Research Group. Am J Med. 1991; 90: 450-9.DeFronzo R.A., Stonehouse A. H., Han J., Wintle ME. Relationship of baseline HbA1c and efficacy of current glucoselowering therapies: a meta-analysis of randomized clinical trials. Diabet Med 2010; 27: 309-17.DeFronzo R.A., Stonehouse A. H., Han J., Wintle ME. Relationship of baseline HbA1c and efficacy of current glucose-lowering therapies: a meta-analysis of randomized clinical trials. Diabet Med. 2010; 27: 309-17.Riddle M. C., Vlajnic A., Zhou R., Rosenstock J. Baseline HbA1c predicts attainment of 7.0% HbA1c target with structured titration of insulin glargine in type 2 diabetes: a patient-level analysis of 12 studies. Diabetes Obes Metab. 2013; 15: 819-25.Riddle M. C., Vlajnic A., Zhou R., Rosenstock J. Baseline HbA1c predicts attainment of 7.0% HbA1c target with structured titration of insulin glargine in type 2 diabetes: a patient-level analysis of 12 studies. Diabetes Obes Metab. 2013; 15: 819-25.Home P. D., Shen C., Hasan M. I. et al. Predictive and explanatory factors of change in HbA1c in a 24-week observational study of 66,726 people with type 2 diabetes starting insulin analogs. Diabetes Care. 2014; 37: 1237-45Home P. D., Shen C., Hasan M. I. et al. Predictive and explanatory factors of change in HbA1c in a 24-week observational study of 66,726 people with type 2 diabetes starting insulin analogs. Diabetes Care. 2014; 37: 1237-45McEwan P., Foos V., Grant D., et al. Predicting the frequency of severe and non-severe hypoglycaemia in insulin treated type-2 diabetes subjects. Value Health. 2013; 16: A435.McEwan P., Foos V., Grant D., et al. Predicting the frequency of severe and non-severe hypoglycaemia in insulin treated type-2 diabetes subjects. Value Health. 2013; 16: A435.Foos V., Lamotte M., McEwan P. The comparison of cardiovascular incidence predictions in Type 1 diabetes utilizing alternative risk prediction models. Value Health. 2016; 19 (3): A86.Foos V., Lamotte M., McEwan P. The comparison of cardiovascular incidence predictions in Type 1 diabetes utilizing alternative risk prediction models. Value Health. 2016; 19 (3): A86.De Ferranti S. D., de Boer I. H., Fonseca V. et al. Type 1 diabetes mellitus and cardiovascular disease: a scientific statement from the American Heart Association and American Diabetes Association. Diabetes Care. 2014; 37 (10): 2843-63.De Ferranti S. D., de Boer I. H., Fonseca V. et al. Type 1 diabetes mellitus and cardiovascular disease: a scientific statement from the American Heart Association and American Diabetes Association. Diabetes Care. 2014; 37 (10): 2843-63.McEwan P., Foos V., Lamotte M. Contrasting Predictions of Cardiovascular Incidence Derived From Alternative Risk Prediction Models In Type 1 Diabetes. Value Health. 2015; 18 (7): A695.McEwan P., Foos V., Lamotte M. Contrasting Predictions of Cardiovascular Incidence Derived From Alternative Risk Prediction Models In Type 1 Diabetes. Value Health. 2015; 18 (7): A695.The Mount Hood 4 Modeling Group. Computer Modeling of Diabetes and Its Complications. Diabetes Care. 2007; 30 (6): 1638-1646.The Mount Hood 4 Modeling Group. Computer Modeling of Diabetes and Its Complications. Diabetes Care. 2007; 30 (6): 16381646.Pambianco G., Costacou T., Ellis D., et al. The 30-year natural history of type 1 diabetes complications: the Pittsburgh Epidemiology of Diabetes Complications Study experience. Diabetes. 2006; 55 (5): 1463-9.Pambianco G., Costacou T., Ellis D., et al. The 30-year natural history of type 1 diabetes complications: the Pittsburgh Epidemiology of Diabetes Complications Study experience. Diabetes. 2006; 55 (5): 1463-9.Cederholm J., Eeg-Olofsson K., Eliasson B. et al. A new model for 5-year risk of cardiovascular disease in Type 1 diabetes; from the Swedish National Diabetes Register (NDR). Diabet Med. 2011; 28 (10): 1213-20.Cederholm J., Eeg-Olofsson K., Eliasson B. et al. A new model for 5-year risk of cardiovascular disease in Type 1 diabetes; from the Swedish National Diabetes Register (NDR). Diabet Med. 2011; 28 (10): 1213-20.Alex C. Michalos (Ed.) Encyclopedia of Quality of Life and WellBeing Research Springer, Dordrecht, Netherlands, Springer Reference Series. 2014; 5320-5321.Alex C. Michalos (Ed.) Encyclopedia of Quality of Life and WellBeing Research Springer, Dordrecht, Netherlands, Springer Reference Series. 2014; 5320-5321.McEwan P., Foos V., Grant D. et al. Drivers Of Cost-Effectiveness In Type-2 Diabetes Mellitus. Value Health. 2013; 16 (3): A165.McEwan P., Foos V., Grant D. et al. Drivers Of Cost-Effectiveness In Type-2 Diabetes Mellitus. Value Health. 2013; 16 (3): A165.Bennett H., Bergenheim K., McEwan P. Understanding The Inter-Relationship Between Improved Glycaemic Control, Hypoglycaemia and Weight Change Within A Type 1 Diabetic Population. Value Health. 2015; 18 (7): A610-611.Bennett H., Bergenheim K., McEwan P. Understanding The Inter-Relationship Between Improved Glycaemic Control, Hypoglycaemia and Weight Change Within A Type 1 Diabetic Population. Value Health. 2015; 18 (7): A610-611.Nordon C., Karcher H., Groenwold R. H. et al. GetReal consortium. The “Efficacy-Effectiveness Gap”: historical background and current conceptualization. Value Health. 2016; 19 (1): 75-81.Nordon C., Karcher H., Groenwold R. H. et al. GetReal consortium. The “Efficacy-Effectiveness Gap”: historical background and current conceptualization. Value Health. 2016; 19 (1): 75-81.Ankarfeldt M. Z., Adalsteinsson E., Groenwold R. H.H. et al. A systematic literature review on the efficacy-effectiveness gap: comparison of randomized controlled trials and observational studies of glucose-lowering drugs. Clinical Epidemiology. 2017; 9: 41-51.Ankarfeldt M. Z., Adalsteinsson E., Groenwold R. H.H. et al. A systematic literature review on the efficacy-effectiveness gap: comparison of randomized controlled trials and observational studies of glucose-lowering drugs. Clinical Epidemiology. 2017; 9: 41-51.Bajaj H., Zinman B. Diabetes: Steno-2 – a small study with a big heart. Nat Rev Endocrinol. 2016; 12 (12): 692-694.Bajaj H., Zinman B. Diabetes: Steno-2 – a small study with a big heart. Nat Rev Endocrinol. 2016; 12 (12): 692-694.Perreault L. EMPA-REG OUTCOME: The Endocrinologist’s Point of View. Am J Cardiol. 2017; 120 (1S): 48-52.Perreault L. EMPA-REG OUTCOME: The Endocrinologist’s Point of View. Am J Cardiol. 2017; 120 (1S): 48-52.Pham S. V., Chilton R. J. EMPA-REG OUTCOME: The Cardiologist’s Point of View. Am J Cardiol. 2017; 120 (1S): 53-58.Pham S. V., Chilton R. J. EMPA-REG OUTCOME: The Cardiologist’s Point of View. Am J Cardiol. 2017; 120 (1S): 53-58.Wanner C. EMPA-REG OUTCOME: The Nephrologist’s Point of View. Am J Cardiol. 2017; 120 (1S): 59-67.Wanner C. EMPA-REG OUTCOME: The Nephrologist’s Point of View. Am J Cardiol. 2017; 120 (1S): 59-67.Stam F., van Guldener C., Becker A. et al. Endothelial dysfunction contributes to renal function-associated cardiovascular mortality in a population with mild renal insufficiency: the Hoorn study. J Am Soc Nephrol. 2006; 17 (2): 537-45.Stam F., van Guldener C., Becker A. et al. Endothelial dysfunction contributes to renal function-associated cardiovascular mortality in a population with mild renal insufficiency: the Hoorn study. J Am Soc Nephrol. 2006; 17 (2): 537-45.Ragot S., Saulnier P. J., Velho G. et al.; SURDIAGENE and DIABHYCAR Study Groups. Dynamic changes in renal function are associated with major cardiovascular events in patients with type 2 diabetes. Diabetes Care. 2016; 39: 1259-1266.Ragot S., Saulnier P. J., Velho G. et al.; SURDIAGENE and DIABHYCAR Study Groups. Dynamic changes in renal function are associated with major cardiovascular events in patients with type 2 diabetes. Diabetes Care. 2016; 39: 1259-1266.Herbst R., Bolton W., Shariff A., Green J. B. Cardiovascular Outcome Trial Update in Diabetes: New Evidence, Remaining Questions. Curr Diab Rep. 2017; 17 (9): 67.Herbst R., Bolton W., Shariff A., Green J. B. Cardiovascular Outcome Trial Update in Diabetes: New Evidence, Remaining Questions. Curr Diab Rep. 2017; 17 (9): 67.Bajaj H. S., Zinman B., Verma S. Antihyperglycemic agents and cardiovascular outcomes: recent insights. Curr Opin Cardiol. 2017. [Epub ahead of print]Bajaj H. S., Zinman B., Verma S. Antihyperglycemic agents and cardiovascular outcomes: recent insights. Curr Opin Cardiol. 2017. [Epub ahead of print]MacEwan J.P., Sheehan J. J., Yin W., et al. The relationship between adherence and total spending among Medicare beneficiaries with type 2 diabetes. Am J Manag Care. 2017; 23 (4): 248-252.MacEwan J.P., Sheehan J. J., Yin W., et al. The relationship between adherence and total spending among Medicare beneficiaries with type 2 diabetes. Am J Manag Care. 2017; 23 (4): 248-252.Salas M., Hughes D., Zuluaga A. et al. Costs of medication nonadherence in patients with diabetes mellitus: a systematic review and critical analysis of the literature. Value Health. 2009; 12 (6): 915-22.Salas M., Hughes D., Zuluaga A. et al. Costs of medication nonadherence in patients with diabetes mellitus: a systematic review and critical analysis of the literature. Value Health. 2009; 12 (6): 91522.

The authors declare that there are no conflicts of interest present.