An international observational study suggests that artificial intelligence for clinical decision support optimizes anemia management in hemodialysis patients.
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.
Fresenius Medical Care, Bad Homburg, Germany. Electronic address: Carlo.Barbieri@fmc-ag.com.
Carlo
C
Barbieri
Servicio de Nefrologia, Hospital Universitario Santa Lucía, Cartagena, Spain.
Manuel
M
Molina
Fresenius Medical Care-Dialysis Center Lumiar, Lisbon, Portugal.
Pedro
P
Ponce
Fresenius Medical Care-Dialysis Center Motol, Prague, Czech Republic.
Monika
M
Tothova
Fresenius Medical Care, Bad Homburg, Germany.
Isabella
I
Cattinelli
Fresenius Medical Care, Bad Homburg, Germany.
Jasmine
J
Ion Titapiccolo
Fresenius Medical Care, Bad Homburg, Germany.
Flavio
F
Mari
Fresenius Medical Care, Bad Homburg, Germany.
Claudia
C
Amato
Fresenius Medical Care, Bad Homburg, Germany.
Frank
F
Leipold
Fresenius Medical Care, Bad Homburg, Germany.
Wolfgang
W
Wehmeyer
Fresenius Medical Care, Bad Homburg, Germany.
Stefano
S
Stuard
Fresenius Medical Care, Bad Homburg, Germany.
Andrea
A
Stopper
Fresenius Medical Care, Bad Homburg, Germany; Montpellier University I, UFR Medicine, Montpellier, France.
Bernard
B
Canaud
D000368:Aged; D000740:Anemia / Q000188:drug therapy*; D001185:Artificial Intelligence; D000066491:Clinical Decision-Making / Q000379:methods*; D000068256:Darbepoetin alfa / Q000008:administration & dosage / Q000627:therapeutic use*; D020000:Decision Support Systems, Clinical; D005260:Female; D006397:Hematinics / Q000008:administration & dosage / Q000627:therapeutic use*; D006454:Hemoglobins / Q000032:analysis*; D006801:Humans; D007676:Kidney Failure, Chronic / Q000150:complications* / Q000628:therapy; D008297:Male; D008875:Middle Aged; D006435:Renal Dialysis; D012189:Retrospective Studies
D006397:Hematinics; D006454:Hemoglobins; D000068256:Darbepoetin alfa
anemia; chronic kidney disease; erythropoietin; hemodialysis