Телемедицинское наблюдение и дистанционное консультирование пациентов с неконтролируемой артериальной гипертензией
Телемедицинское наблюдение и дистанционное консультирование пациентов с неконтролируемой артериальной гипертензией
Ионов М.В., Жукова О.В., Звартау Н.Э., Конради А.О. Телемедицинское наблюдение и дистанционное консультирование пациентов с неконтролируемой артериальной гипертензией. Терапевтический архив. 2021; 93 (1): 30–40. DOI: 10.26442/00403660.2021.01.200590
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Аннотация
Телемедицинское наблюдение является ценным дополнением в лечении артериальной гипертензии (АГ). Несмотря на очевидные краткосрочные клинические преимущества, отдаленная перспектива и экономическая эффективность подобных вмешательств изучены недостаточно.
Цель. Дать прогноз клинических исходов в долгосрочной перспективе с параллельным определением экономической эффективности телемониторирования артериального давления (АД) и телемедицинского дистанционного консультирования (ТМДК) у пациентов с АГ.
Материалы и методы. На основании 3-месячного наблюдения 240 пациентов (160 в группе ТМДК и 80 в группе контроля) с неосложненной АГ построена марковская модель для предсказательного моделирования и экстраполяции результатов на 10 лет с годичным циклом для гипотетической когорты пациентов (n=1000). Пациенты находились в начальном состоянии АГ и могли переходить в иные ее осложнения. В конце каждого цикла вероятности перехода между состояниями транслированы в число пациентов, находящихся в каждом из них. Оценивали показатель сохраненных лет жизни (LYG) с поправкой на ее качество (QALY). Затраты оценивались с позиции системы здравоохранения.
Результаты. В результате моделирования число умерших пациентов – 91 в группе контроля и 67 в группе ТМДК. Показатели LYG составили в группе контроля 9,6 года, в группе ТМДК – 9,71 года. QALY составил 7,82 года в группе контроля против 8,31 года в группе ТМДК. Суммарные затраты на лечение АГ и возможных осложнений на конец исследуемого горизонта планирования составили 145 237 700 руб. в группе контроля и 102 508 000 руб. в группе ТМДК, а стоимость 1 QALY в последнем случае составила 27 5178,98 руб. (134 837,70 руб./0,49).
Заключение. Согласно результатам моделирования, можно говорить о долгосрочной клинико-экономической эффективности внедрения ТМДК в стандартную практику лечения пациентов с АГ.
Ключевые слова: артериальная гипертензия, телемедицина, телемониторинг, экономическая эффективность, анализ «затраты–полезность», сохраненные годы жизни, сохраненные годы жизни с поправкой на качество, модель Маркова
Aim. Predictive modeling of long-term clinical and social outcomes and the cost-effectiveness analysis of blood pressure (BP) telemonitoring and remote counseling (BPTM) in patients with HTN.
Materials and methods. A Markov cohort-based (1000 patients in each study arm) model was developed and adopted a 10-year time horizon with 12-month time cycles. Cost and outcome data collected from the three-month study of 240 patients (160 in BPTM group and 80 controls, 48 y.o.). All patients started at a non-complicated HTN “well” state with a certain possibility of disease progression in a number of health states over a discrete time period. BPTM was compared with usual care in terms of 10-year healthcare costs, quality adjusted life years (QALY) using a Ministry of Health of Russian Federation perspective.
Results. In the long-term run when compared with usual care BPTM was more effective in terms mortality (67 versus 91 patients lost and 9.6 versus 9.71 life years gained) and costs (cost of illness 102 508 000 RUR versus 145 237 700 RUR). Taking quality of life measures into account, the effect of BPTM was also more pronounced (8.31 versus 7.82 QALYs gained). The resultant incremental cost-utility ratio for BPTM was 275 178.98 RUR/1 QALY gained/1 patient (134 837.70 RUR/0.49 QALY/1 patient).
Conclusion. According to the results of predictive modeling, implementation of BPTM into clinical practice is likely lead to reduced cardiovascular morbidity and mortality in a cost-effective way.
Keywords: hypertension, telemedicine, telemonitoring, cost-effectiveness, cost-utility analysis, life years gained, quality-adjusted life years, Markov modeling
Цель. Дать прогноз клинических исходов в долгосрочной перспективе с параллельным определением экономической эффективности телемониторирования артериального давления (АД) и телемедицинского дистанционного консультирования (ТМДК) у пациентов с АГ.
Материалы и методы. На основании 3-месячного наблюдения 240 пациентов (160 в группе ТМДК и 80 в группе контроля) с неосложненной АГ построена марковская модель для предсказательного моделирования и экстраполяции результатов на 10 лет с годичным циклом для гипотетической когорты пациентов (n=1000). Пациенты находились в начальном состоянии АГ и могли переходить в иные ее осложнения. В конце каждого цикла вероятности перехода между состояниями транслированы в число пациентов, находящихся в каждом из них. Оценивали показатель сохраненных лет жизни (LYG) с поправкой на ее качество (QALY). Затраты оценивались с позиции системы здравоохранения.
Результаты. В результате моделирования число умерших пациентов – 91 в группе контроля и 67 в группе ТМДК. Показатели LYG составили в группе контроля 9,6 года, в группе ТМДК – 9,71 года. QALY составил 7,82 года в группе контроля против 8,31 года в группе ТМДК. Суммарные затраты на лечение АГ и возможных осложнений на конец исследуемого горизонта планирования составили 145 237 700 руб. в группе контроля и 102 508 000 руб. в группе ТМДК, а стоимость 1 QALY в последнем случае составила 27 5178,98 руб. (134 837,70 руб./0,49).
Заключение. Согласно результатам моделирования, можно говорить о долгосрочной клинико-экономической эффективности внедрения ТМДК в стандартную практику лечения пациентов с АГ.
Ключевые слова: артериальная гипертензия, телемедицина, телемониторинг, экономическая эффективность, анализ «затраты–полезность», сохраненные годы жизни, сохраненные годы жизни с поправкой на качество, модель Маркова
________________________________________________
Aim. Predictive modeling of long-term clinical and social outcomes and the cost-effectiveness analysis of blood pressure (BP) telemonitoring and remote counseling (BPTM) in patients with HTN.
Materials and methods. A Markov cohort-based (1000 patients in each study arm) model was developed and adopted a 10-year time horizon with 12-month time cycles. Cost and outcome data collected from the three-month study of 240 patients (160 in BPTM group and 80 controls, 48 y.o.). All patients started at a non-complicated HTN “well” state with a certain possibility of disease progression in a number of health states over a discrete time period. BPTM was compared with usual care in terms of 10-year healthcare costs, quality adjusted life years (QALY) using a Ministry of Health of Russian Federation perspective.
Results. In the long-term run when compared with usual care BPTM was more effective in terms mortality (67 versus 91 patients lost and 9.6 versus 9.71 life years gained) and costs (cost of illness 102 508 000 RUR versus 145 237 700 RUR). Taking quality of life measures into account, the effect of BPTM was also more pronounced (8.31 versus 7.82 QALYs gained). The resultant incremental cost-utility ratio for BPTM was 275 178.98 RUR/1 QALY gained/1 patient (134 837.70 RUR/0.49 QALY/1 patient).
Conclusion. According to the results of predictive modeling, implementation of BPTM into clinical practice is likely lead to reduced cardiovascular morbidity and mortality in a cost-effective way.
Keywords: hypertension, telemedicine, telemonitoring, cost-effectiveness, cost-utility analysis, life years gained, quality-adjusted life years, Markov modeling
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Авторы
М.В. Ионов1,2, О.В. Жукова3, Н.Э. Звартау1,2, А.О. Конради1,2
1 ФГБУ «Национальный̆ медицинский исследовательский центр им. В.А. Алмазова» Минздрава России, Санкт-Петербург, Россия;
2 ФГАОУ ВО «Национальный̆ исследовательский̆ университет информационных технологий, механики и оптики»,
Санкт-Петербург, Россия;
3 ФГБОУ ВО «Приволжский̆ исследовательский̆ медицинский университет» Минздрава России, Нижний̆ Новгород, Россия
1 Almazov National Medical Research Centre, Saint Petersburg, Russia;
2 National Research University of Information Technologies, Mechanics and Optics, Saint Petersburg, Russia;
3 Privolzhsky Research Medical University, Nizhny Novgorod, Russia
1 ФГБУ «Национальный̆ медицинский исследовательский центр им. В.А. Алмазова» Минздрава России, Санкт-Петербург, Россия;
2 ФГАОУ ВО «Национальный̆ исследовательский̆ университет информационных технологий, механики и оптики»,
Санкт-Петербург, Россия;
3 ФГБОУ ВО «Приволжский̆ исследовательский̆ медицинский университет» Минздрава России, Нижний̆ Новгород, Россия
________________________________________________
1 Almazov National Medical Research Centre, Saint Petersburg, Russia;
2 National Research University of Information Technologies, Mechanics and Optics, Saint Petersburg, Russia;
3 Privolzhsky Research Medical University, Nizhny Novgorod, Russia
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