Yu, D, Cai, Y, Chen, Y, Chen, T, Qin, R, Simmons, D and Zhao, Z (2018) Development and validation of risk prediction models for cardiovascular mortality in Chinese people initialising peritoneal dialysis: a cohort study. Scientific Reports, 8 (1). ISSN 2045-2322

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Cardiovascular disease is the leading cause of death among patients receiving peritoneal dialysis. We aimed to develop and validate a risk prediction model for cardiovascular death within 2 years after the initiation of peritoneal dialysis (PD). A cohort including all patients registered with the Henan Peritoneal Dialysis Registry (HPDR) between 2007 and 2014. Multivariate logistic regression analysis was used to develop the risk prediction model. The HPDR data was randomly divided into two cohorts with 60% (1,835 patients) for model derivation, and 40% (1,219 patients) for model validation. The absolute rate of cardiovascular mortality was 14.2% and 14.4 in the derivation and validation cohort, respectively. Age, body mass index, blood pressure, serum lipids, fasting glucose, sodium, albumin, total protein, and phosphorus were the strongest predictors of cardiovascular mortality in the final model. Discrimination of the model was similar in both cohorts, with a C statistic above 0.70, with good calibration of observed and predicted risks. The new prediction model that has been developed and validated with clinical measurements that are available at the point of initiation of PD and could serve as a tool to screen for patients at high risk of cardiovascular death and tailor more intensive cardioprotective care.

Item Type: Article
Additional Information: This is the final published version of the article, originally published by Nature Publishing Group and available from http://dx.doi.org/10.1038/s41598-018-20160-3- please refer to any applicable terms of use of the publisher.
Subjects: R Medicine > RC Internal medicine > RC666 Diseases of the circulatory (Cardiovascular) system
Divisions: Faculty of Medicine and Health Sciences > Primary Care Health Sciences
Depositing User: Symplectic
Date Deposited: 02 Feb 2018 16:45
Last Modified: 06 Jul 2018 13:22
URI: https://eprints.keele.ac.uk/id/eprint/4420

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