Optimizing anemia management using artificial intelligence for patients undergoing hemodialysis

Abstract

Patients with end-stage kidney disease (ESKD) frequently experience anemia, and maintaining hemoglobin (Hb) levels within a targeted range using erythropoiesis-stimulating agents (ESAs) is challenging. This study introduces a gated recurrent unit-attention-based module (GAM) for efficient anemia management among patients undergoing chronic dialysis and proposes a novel alert system for anticipating the need for red blood cell transfusions. Data on demographic characteristics, dialysis metrics, drug administration, laboratory tests, and transfusion history were retrospectively collected from patients undergoing hemodialysis at Kangwon National University Hospital between 2017 and 2022. After preprocessing, a final dataset of 252 patients was used for model training. Our model functions in two major phases: (1) Hb level prediction and ESA dose recommendation and (2) transfusion alert framework. The GAM model outperformed traditional machine learning algorithms, including linear regression, XGBoost, and multilayer perceptron, in predicting Hb levels (R-squared value = 0.60). The model also demonstrated a recommendation accuracy of 0.78 compared to that of clinical experts, indicating a high degree of concordance with the ESA dosing recommendations. Additionally, the model exhibited considerably high accuracy (0.99) for transfusion alarms. Thus, the GAM model holds promise for improving anemia management in patients with ESKD by optimizing ESA dosages and providing timely transfusion alerts.

Keywords: Anemia; Artificial intelligence; End-stage kidney disease; Erythropoiesis-stimulating agents; Transfusion alert.

Fig. 1

Overview of the data preparation process. Commencing with the basic preprocessing of hemodialysis patient data spanning approximately 6 years, we utilized clinical knowledge to select relevant features. Following this, the dataset was partitioned into the training, validation, and test sets, which were then scaled appropriately. Finally, the data were transformed into a sequential data format suitable for analysis. CKD, chronic kidney disease; Hb, hemoglobin; WBC, white blood cell.

Fig. 2

Overall architecture of the proposed model (GRU-attention-based module; GAM). Patient data, including demographic characteristics, dialysis, drug use, laboratory tests, and transfusion, were input simultaneously into the gated recurrent unit (GRU) and multi-head attention module. The GRU captures a patient’s long-term condition, whereas the multi-head attention module captures essential information about state changes. These two datasets are combined to create a patient embedding containing historical information.

Fig. 3

The framework of hemoglobin (Hb) level prediction process. At first, the gated recurrent unit (GRU)-attention-based module (GAM) network is used to process sequential data from patients, generating patient embedding. Following this, prediction of the Hb level for the next time point is carried out using fully connected and dropout layers. CKD, chronic kidney disease; ESA, erythropoiesis-stimulating agent.

Fig. 4

The framework of erythropoiesis-stimulating agent (ESA) dose recommendation and transfusion alarm process. Patient data from time points t to t + 2 passes through the gated recurrent unit (GRU)-attention-based module (GAM) network, generating patient embeddings. Subsequently, the hemoglobin (Hb) level, transfusion volume, and supplementary data at t + 3 are combined with the optimal Hb level data at t + 4, followed by the application of a fully connected layer and dropout. The outcomes for the ESA dose recommendation task are categorized as “more,” “similar,” and “less,” whereas the transfusion alarm task provides results indicating “necessary” or “non-necessary.” CKD, chronic kidney disease.