Development and validation of a prediction model for all-cause mortality in maintenance dialysis patients: a multicenter retrospective cohort study

Abstract

Background

The mortality risk varies considerably among individual dialysis patients. This study aimed to develop a user-friendly predictive model for predicting all-cause mortality among dialysis patients.

Methods

Retrospective data regarding dialysis patients were obtained from two hospitals. Patients in training cohort (N = 1421) were recruited from the Fifth Affiliated Hospital of Sun Yat-sen University, and patients in external validation cohort (N = 429) were recruited from the First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine. The follow-up endpoint event was all-cause death. Variables were selected by LASSO-Cox regression, and the model was constructed by Cox regression, which was presented in the form of nomogram and web-based tool. The discrimination and accuracy of the prediction model were assessed using C-indexes and calibration curves, while the clinical value was assessed by decision curve analysis (DCA).

Results

The best predictors of 1-, 3-, and 5-year all-cause mortality contained nine independent factors, including age, body mass index (BMI), diabetes mellitus (DM), cardiovascular disease (CVD), cancer, urine volume, hemoglobin (HGB), albumin (ALB), and pleural effusion (PE). The 1-, 3-, and 5-year C-indexes in the training set (0.840, 0.866, and 0.846, respectively) and validation set (0.746, 0.783, and 0.741, respectively) were consistent with comparable performance. According to the calibration curve, the nomogram predicted survival accurately matched the actual survival rate. The DCA showed the nomogram got more clinical net benefit in both the training and validation sets.

Conclusions

The effective and convenient nomogram may help clinicians quantify the risk of mortality in maintenance dialysis patients.

Keywords:

• Maintenance dialysis
• all-cause mortality
• pleural effusion
• prediction model

Acknowledgements

We would like to thank all the patients and their families for participating in this study.

Author contributions

Cheng Wang and Jingcan Wu were the guarantors of this paper. Jingcan Wu was responsible for study concept and design. Jingcan Wu wrote the manuscript. Jingcan Wu, Xuehong Li, Hong Zhang, and Lin Lin were responsible for acquisition of data. Man Li and Jingcan Wu were responsible for statistical analysis and interpretation of data. Cheng Wang and Gangyi Chen were responsible for supervision or mentorship. All authors discussed the results and contributed to the final manuscript. Jingcan Wu, Xuehong Li, and Hong Zhang contributed equally to this work.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data underlying this article will be shared on reasonable request to the first author and corresponding author.

Additional information

Funding

The authors declare no funding was received for this study.