Therapeutic apheresis and dialysis : official peer-reviewed journal of the International Society for Apheresis, the Japanese Society for Apheresis, the Japanese Society for Dialysis Therapy · 2026
Predicting the Future Burden of Renal Replacement Therapy in Türkiye Using National Registry Data and Comparative Modeling Approaches.
СтатьяСравнительное исследованиеНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Akgül Arzu, Koç Neriman Sıla, Aylı Berrak Itır
Аннотация
BACKGROUND: Chronic kidney disease is a growing public health problem worldwide, and the number of patients requiring renal replacement therapy is steadily increasing. Türkiye has experienced a similar rise in both the incidence and prevalence of renal replacement therapy over the past decades; however, national-level projections of future renal replacement therapy demand are lacking.
AIMS: This study aimed to forecast the future burden of renal replacement therapy in Türkiye by modeling historical incidence and prevalence trends using national registry data and demographic projections.
METHODS: National renal replacement therapy registry data from 2012 to 2024 were analyzed together with demographic projections from the Turkish Statistical Institute extending to 2035. Four models were constructed separately for incidence and prevalence. Model performance was compared using the coefficient of determination, root mean square error, and Akaike information criterion. Leave-one-out cross-validation was used to assess model robustness. The modeling was further validated on data from Japan and the United Kingdom.
RESULTS: All models indicated a consistent upward trend in renal replacement therapy incidence in Türkiye over the next decade. By 2035, the prevalence of renal replacement therapy in Türkiye is projected to reach approximately 1150-1250 per million population. The models demonstrated strong goodness of fit, and cross-validation supported good internal consistency, although the machine-learning model showed some overfitting for prevalence.
CONCLUSION: The national demand for renal replacement therapy in Türkiye is projected to continue increasing through 2035. Incorporating predictive modeling into national registry surveillance can help guide proactive capacity planning and fair resource distribution for dialysis and transplantation services. At the same time, public health strategies aimed at preventing and slowing the progression of chronic kidney disease remain essential to mitigate the growing burden of end-stage renal disease.
AIMS: This study aimed to forecast the future burden of renal replacement therapy in Türkiye by modeling historical incidence and prevalence trends using national registry data and demographic projections.
METHODS: National renal replacement therapy registry data from 2012 to 2024 were analyzed together with demographic projections from the Turkish Statistical Institute extending to 2035. Four models were constructed separately for incidence and prevalence. Model performance was compared using the coefficient of determination, root mean square error, and Akaike information criterion. Leave-one-out cross-validation was used to assess model robustness. The modeling was further validated on data from Japan and the United Kingdom.
RESULTS: All models indicated a consistent upward trend in renal replacement therapy incidence in Türkiye over the next decade. By 2035, the prevalence of renal replacement therapy in Türkiye is projected to reach approximately 1150-1250 per million population. The models demonstrated strong goodness of fit, and cross-validation supported good internal consistency, although the machine-learning model showed some overfitting for prevalence.
CONCLUSION: The national demand for renal replacement therapy in Türkiye is projected to continue increasing through 2035. Incorporating predictive modeling into national registry surveillance can help guide proactive capacity planning and fair resource distribution for dialysis and transplantation services. At the same time, public health strategies aimed at preventing and slowing the progression of chronic kidney disease remain essential to mitigate the growing burden of end-stage renal disease.