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. 2023 Jul 28:18:1175-1190.
doi: 10.2147/CIA.S416421. eCollection 2023.

Development and Validation of Prediction Models for All-Cause Mortality and Cardiovascular Mortality in Patients on Hemodialysis: A Retrospective Cohort Study in China

Min Yang #  1   2 , Yaqin Yang #  1   2 , Yuntong Xu  1   2 , Yuchi Wu  1   2 , Jiarong Lin  1   2 , Jianling Mai  1   2 , Kunyang Fang  1   2 , Xiangxia Ma  1   2 , Chuan Zou  1   2 , Qizhan Lin  1   2
Affiliations

Development and Validation of Prediction Models for All-Cause Mortality and Cardiovascular Mortality in Patients on Hemodialysis: A Retrospective Cohort Study in China

Min Yang et al. Clin Interv Aging. .

Abstract

Purpose: This study aimed to develop two predictive nomograms for the assessment of long-term survival status in hemodialysis (HD) patients by examining the prognostic factors for all-cause mortality and cardiovascular (CVD) event mortality.

Patients and methods: A total of 551 HD patients with an average age of over 60 were included in this study. The patients' medical records were collected from our hospital and randomly allocated to two cohorts: the training cohort (n=385) and the validation cohort (n=166). We employed multivariate Cox assessments and fine-gray proportional hazards models to explore the predictive factors for both all-cause mortality and cardiovascular event mortality risk in HD patients. Two nomograms were established based on predictive factors to forecast patients' likelihood of survival for 3, 5, and 8 years. The performance of both models was evaluated using the area under the curve (AUC), calibration plots, and decision curve analysis.

Results: The nomogram for all-cause mortality prediction included seven factors: age ≥ 60, sex (male), history of diabetes and coronary artery disease, diastolic blood pressure, total triglycerides (TG), and total cholesterol (TC). The nomogram for cardiovascular event mortality prediction included three factors: history of diabetes and coronary artery disease, and total cholesterol (TC). Both models demonstrated good discrimination, with AUC values of 0.716, 0.722 and 0.725 for all-cause mortality at 3, 5, and 8 years, respectively, and 0.702, 0.695, and 0.677 for cardiovascular event mortality, respectively. The calibration plots indicated a good agreement between the predictions and the decision curve analysis demonstrated a favorable clinical utility of the nomograms.

Conclusion: Our nomograms were well-calibrated and exhibited significant estimation efficiency, providing a valuable predictive tool to forecast prognosis in HD patients.

Keywords: all-cause; cardiovascular; hemodialysis; model; mortality; nomogram.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1
Comprehensive study design flowchart: Allocation of Candidates for all-cause and CVD mortality analysis.
Figure 2
Figure 2
Nomogram of model 1 for predicting risk of all cause mortality in HD Patients.
Figure 3
Figure 3
Nomogram of model 2 for predicting risk of CVD events mortality in HD Patients.
Figure 4
Figure 4
Discrimination performance of model 1 in predicting all-cause mortality: AUC in the training and validation cohorts over 3-, 5-, and 8-year periods (3-, 5-, 8-from left to right).
Figure 5
Figure 5
Discrimination performance of model 2 in predicting CVD events mortality: AUC over 3-, 5-, and 8-year periods (3-, 5-, 8-from left to right).
Figure 6
Figure 6
Calibration Performance of model 1’s training and validation cohorts for 3-, 5-, and 8-year periods (3-, 5-, 8-from left to right).
Figure 7
Figure 7
Calibration performance of model 2 for 3-, 5-, and 8-year periods (3-, 5-, 8-from left to right).
Figure 8
Figure 8
Decision curve analysis of model 1’s clinical utility for 3-, 5-, and 8-year all cause mortality prediction (3-, 5-, 8-from left to right).
Figure 9
Figure 9
Decision curve analysis of model 2’s clinical utility for 3-, 5-, and 8-year CVD events mortality prediction (3-, 5-, 8-from left to right).
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