Abstract

Background: Patients on maintenance hemodialysis (MHD) face a dramatically elevated risk of cardiovascular death, which is 10 – 20 times higher than in the general population. To address this high risk, we developed and validated a prediction model to accurately estimate cardiovascular disease (CVD) mortality and guide preemptive clinical management.
Materials and methods: This study retrospectively collected data from MHD patients at the First Affiliated Hospital of Chengdu Medical College from 2016 to 2021 (Approval No. CYFYEC-C-005), including demographic characteristics, medical history, biochemical indicators, and echocardiogram indices. Variables were screened using univariate logistic regression and stepwise regression to construct a nomogram model. The dataset was randomly divided (6 : 4) into training and validation sets, and a classification and regression tree (CART) decision tree model was also constructed. Both models’ discrimination, calibration, and clinical utility were evaluated.
Results: The nomogram identified systolic blood pressure, uric acid, total cholesterol, diabetes, myoglobin, serum albumin, and procalcitonin as predictors, with an AUC of 0.947 (95% CI: 0.903 – 0.991) and good clinical applicability. The CART model identified serum albumin, procalcitonin, and myoglobin as predictors, categorizing the population into four groups. AUC values were 0.933 (95% CI: 0.851 – 1.000) in the training set and 0.774 (95% CI: 0.612 – 0.936) in the validation set.
Conclusion: In conclusion, this study consistently identified serum albumin, procalcitonin, and myoglobin as key factors associated with CVD mortality risk in MHD patients. Both models demonstrated promising predictive performance in our cohort. These findings suggest the potential of such models to inform clinical risk assessment. However, external validation in larger, multi-center studies is necessary before these tools can be considered for direct clinical decision-making.

*Xiaona He and Xu Zhang contributed equally to this paper and are thus co-first authors.

Author Details

Authors

• Xiaona He^{1, 2}*
• Xu Zhang^{2, 3}*
• Nan Mao^{2, 3}
• Yalan Zhang^{2, 3}
• Xin Ma^{2, 3}

Departments

• ¹ Department of Nephrology, Dazhou Dachuan District People’s Hospital (Dazhou Third People’s Hospital), Dazhou, Sichuan,
• ² Department of Nephrology, The First Affiliated Hospital of Chengdu Medical College, and
• ³ Department of Clinical Medicine, School of Clinical Medicine, Chengdu Medical College, Chengdu, China

Address

Dr. Xin Ma, MD
or
Yalan Zhang
Department of Nephrology
The First Affiliated Hospital of Chengdu Medical College
Chengdu, 610500, China,
Email: [email protected]; [email protected]

Citation