Risk factor analysis and personalized prevention strategies for renal anemia in hemodialysis patients with end-stage kidney disease
- VernacularTitle:终末期肾脏病血液透析患者肾性贫血的危险因素分析及个性化预防策略探讨
- Author:
Yu FENG
1
;
Kaigui ZHANG
;
Maocai ZHU
;
Zhaohua ZOU
;
Wei QING
Author Information
- Publication Type:Research Article
- Keywords: end-stage kidney disease; hemodialysis; renal anemia; hypoproliferative anemia; risk factors; personalized prevention
- From: Journal of Clinical Medicine in Practice 2024;28(24):103-109
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the risk factors for renal anemia in hemodialysis patients with end-stage kidney disease (ESKD). Methods A total of 48 ESKD patients undergoing hemodialysis in our hospital from December 2021 to December 2022 were selected as study objects. They were divided into renal anemia group (86 cases) and non-anemia group (62 cases) based on hemoglobin (Hb) levels and diagnostic criteria for renal anemia. Clinical data between the two groups were compared. Random forest algorithm and multivariate Logistic regression analysis were used to screen for factors influencing renal anemia, and a multivariate Logistic regression model was established. Cross-validation was also employed to verify the stability of the model. A risk stratification system was developed, and patients were stratified based on cut-off values obtained from X-Tile software. The areas under the receiver operating characteristic (ROC) curves were used to evaluate the discrimination ability of the multivariate Logistic regression model and the risk stratification system. Results Among 148 ESKD patients, the incidence of renal anemia was 58.11% (86/148). Hypertension, increased neutrophil-to-lymphocyte ratio (NLR), elevated C-reactive protein (CRP), increased serum ferritin (SF), and elevated intact parathyroid hormone (iPTH) were identified as risk factors for renal anemia (
P < 0.05). Administration of α-calcidol, increased erythropoietin (EPO), elevated triglyceride (TG), and increased serum albumin (ALB) were protective factors (P < 0.05). The goodness-of-fit test for the multivariate Logistic regression model showed a Nagelkerke R2 of 0.593. Based on the cut-off values for risk scores obtained from X-Tile software, patients were stratified into low-risk group (< 4 points, 29 cases), medium-risk group (4 to < 7 points, 64 cases), and high-risk group (≥7 points, 55 cases), with renal anemia incidence rates of 24.14%, 57.81%, and 76.36%, respectively. The difference in renal anemia incidence rates among the three groups was statistically significant (P < 0.001). In the training set, the areas under the ROC curves for the multivariate Logistic regression model and the risk stratification system were 0.826 and 0.811, respectively, and were 0.804 and 0.789, respectively in the validation set. Conclusion The occurrence of renal anemia in hemodialysis patients with ESKD is influenced by various factors, including hypertension, NLR, CRP, etc.
