کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6160948 | 1249358 | 2016 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
An international observational study suggests that artificial intelligence for clinical decision support optimizes anemia management in hemodialysis patients
ترجمه فارسی عنوان
یک مطالعه بین المللی مشاهدات نشان می دهد که هوش مصنوعی برای پشتیبانی تصمیم گیری بالینی، مدیریت کم خونی را در بیماران همودیالیزی بهینه سازی می کند
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کلمات کلیدی
کم خونی بیماری مزمن کلیوی، اریتروپویتین، همودیالیز،
موضوعات مرتبط
علوم پزشکی و سلامت
پزشکی و دندانپزشکی
بیماریهای کلیوی
چکیده انگلیسی
Managing anemia in hemodialysis patients can be challenging because of competing therapeutic targets and individual variability. Because therapy recommendations provided by a decision support system can benefit both patients and doctors, we evaluated the impact of an artificial intelligence decision support system, the Anemia Control Model (ACM), on anemia outcomes. Based on patient profiles, the ACM was built to recommend suitable erythropoietic-stimulating agent doses. Our retrospective study consisted of a 12-month control phase (standard anemia care), followed by a 12-month observation phase (ACM-guided care) encompassing 752 patients undergoing hemodialysis therapy in 3 NephroCare clinics located in separate countries. The percentage of hemoglobin values on target, the median darbepoetin dose, and individual hemoglobin fluctuation (estimated from the intrapatient hemoglobin standard deviation) were deemed primary outcomes. In the observation phase, median darbepoetin consumption significantly decreased from 0.63 to 0.46 μg/kg/month, whereas on-target hemoglobin values significantly increased from 70.6% to 76.6%, reaching 83.2% when the ACM suggestions were implemented. Moreover, ACM introduction led to a significant decrease in hemoglobin fluctuation (intrapatient standard deviation decreased from 0.95 g/dl to 0.83 g/dl). Thus, ACM support helped improve anemia outcomes of hemodialysis patients, minimizing erythropoietic-stimulating agent use with the potential to reduce the cost of treatment.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Kidney International - Volume 90, Issue 2, August 2016, Pages 422-429
Journal: Kidney International - Volume 90, Issue 2, August 2016, Pages 422-429
نویسندگان
Carlo Barbieri, Manuel Molina, Pedro Ponce, Monika Tothova, Isabella Cattinelli, Jasmine Ion Titapiccolo, Flavio Mari, Claudia Amato, Frank Leipold, Wolfgang Wehmeyer, Stefano Stuard, Andrea Stopper, Bernard Canaud,