Knowledge-based clinical decision support system for the automated classification of anemia in hemodialysis patients

Anemia is a frequent complication in patients with chronic kidney disease undergoing hemodialysis and is associated with increased morbidity, mortality, and healthcare burden. Accurate classification is essential to optimize treatment with intravenous iron and erythropoiesis-stimulating agents. Rule-based clinical decision support systems (CDSS) provide a strategy to standardize this process.

To describe the development and implementation of a knowledge-based clinical decision support system for the automated classification of anemia in hemodialysis patients using laboratory data.

This retrospective observational study included 883 adult patients receiving prevalent hemodialysis during 2023. An algorithm was developed based on established clinical guidelines [Sociedad Latinoamericana de Nefrología e Hipertensión (SLANH)], KDIGO, NICE to classify patients with hemoglobin below 12 g/dl into three categories: absolute iron deficiency, functional iron deficiency, and candidates for therapeutic trial with intravenous iron. The system also flagged cases with suspected severe secondary hyperparathyroidism (PTH > 800 pg/ml). Data was obtained from the laboratory information system and the clinical decision support system. We applied a descriptive statistical analysis.

The clinical decision support system automatically classified patients into the following categories: functional iron deficiency (39.2%), severe hyperparathyroidism (26.7%), absolute iron deficiency (17.7%), and candidates for intravenous iron trial (16.4%). A subgroup (9.5% within the functional iron deficiency group) also showed elevated PTH levels, suggesting potential resistance to erythropoiesis-stimulating agents. Distinct clinical profiles were observed across the groups.

The clinical decision support system enabled automated and standardized classification of anemia in hemodialysis patients, supporting evidence-based clinical decision-making. Its implementation represents a digital health innovation with the potential to improve the quality and safety of anemia management in chronic kidney disease.