An international observational study suggests that artificial intelligence for clinical decision support optimizes anemia management in hemodialysis patients

Abstract

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.

Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation

“…AI-based clinical decision support systems (CDSS) can be implemented employing the expert system strategy, data-driven approach, or an ensemble approach by coupling both. An expert system consolidates a knowledge base containing a set of rules for specific clinical scenarios, and the initial rule set may be acquired from domain experts or learned from data through machine learning algorithms [72,[78][79][80] AI has recently been adopted for the prediction, diagnosis, and treatment of kidney diseases [76,[81][82][83][84][85], as shown in Table 2. For example, a prediction model based on the combination of a machine learning algorithm and survival analysis has recently developed and can stratify risk for kidney disease progression among patients with IgA Nephropathy [86].…”

Model‐Informed Artificial Intelligence: Reinforcement Learning for Precision Dosing

“…Komorowski et al and Barbieri et al have recently applied such an approach for the treatment of patients with sepsis and anemia, respectively. 14,15 The computer learned the experience of many previously treated patients so that it could achieve very good results in new patients. As expected, the amount of data needed for such methods to perform is high.…”

Using artificial intelligence to assess clinicians’ communication skills

“…When clinicians enter a diagnosis for their patient into a system, they might expect guidance about confirmatory tests and reasonable treatment options 678. AI has been used to guide decisions such as the safety of combining a β blocker with a drug for arrhythmia9 and can help clinicians diagnose late onset sepsis in premature infants 10Box 1.…”