Limited research exists on identifying risk factors for preeclampsia (PE) in the chronic kidney disease (CKD) population, especially across different patient sources. This study aimed to address this gap by analyzing clinical data from CKD pregnant women admitted to Peking University Third Hospital from January 2012 to December 2022. Logistic regression analysis identified independent risk factors for PE in the CKD population and assessed variations among patients from different sources. Additionally, a predictive model for PE was established using data from the registered group. The study included 524 CKD patients. Hypertension, proteinuria, fibrinogen >4 g/L, serum albumin ≤30 g/L, and uric acid >260 μmol/L were independent risk factors for PE in the overall CKD population. Subgroup analysis revealed that hypertension, serum albumin ≤30 g/L, and uric acid >260 μmol/L were independent risk factors in the referred group, while hypertension, uric acid >260 μmol/L, and fibrinogen >4 g/L were independent risk factors in the registered group. The prediction model based on registered group risk factors showed good predictive efficiency, with the area under the curve of 0.774 in the training set and 0.714 in the validation set. In conclusion, this study revealed that hypertension and elevated uric acid are independent risk factors for PE in CKD patients regardless of patient source, while serum albumin and fibrinogen levels are associated with PE risk in specific patient subgroups. Our predictive model enables clinicians to quickly identify the risk of PE in CKD patients, and early intervention treatment to improve pregnancy outcomes.
Keywords: Chronic kidney disease; Prediction model; Preeclampsia; Risk factors.
© 2024. The Author(s), under exclusive licence to The Japanese Society of Hypertension.