The risk factors and predictive model for cardiac valve calcification in patients on maintenance peritoneal dialysis: a single-center retrospective study
Yuxi Wang,
Quanquan Shen,
Junni Wang,
Shilong Xiang,
Yaomin Wang,
Xiaohui Zhang,
Jianghua Chen,
Fei Han
Affiliations
Yuxi Wang
Kidney Disease Center, The First Affiliated Hospital, Zhejiang University School of Medicine; Institute of Nephrology, Zhejiang University; Key Laboratory of Kidney Disease Prevention and Control Technology, Zhejiang Province; Zhejiang Clinical Research Center of Kidney and Urinary System Disease, Hangzhou, Zhejiang, China
Quanquan Shen
Kidney Disease Center, The First Affiliated Hospital, Zhejiang University School of Medicine; Institute of Nephrology, Zhejiang University; Key Laboratory of Kidney Disease Prevention and Control Technology, Zhejiang Province; Zhejiang Clinical Research Center of Kidney and Urinary System Disease, Hangzhou, Zhejiang, China
Junni Wang
Kidney Disease Center, The First Affiliated Hospital, Zhejiang University School of Medicine; Institute of Nephrology, Zhejiang University; Key Laboratory of Kidney Disease Prevention and Control Technology, Zhejiang Province; Zhejiang Clinical Research Center of Kidney and Urinary System Disease, Hangzhou, Zhejiang, China
Shilong Xiang
Kidney Disease Center, The First Affiliated Hospital, Zhejiang University School of Medicine; Institute of Nephrology, Zhejiang University; Key Laboratory of Kidney Disease Prevention and Control Technology, Zhejiang Province; Zhejiang Clinical Research Center of Kidney and Urinary System Disease, Hangzhou, Zhejiang, China
Yaomin Wang
Kidney Disease Center, The First Affiliated Hospital, Zhejiang University School of Medicine; Institute of Nephrology, Zhejiang University; Key Laboratory of Kidney Disease Prevention and Control Technology, Zhejiang Province; Zhejiang Clinical Research Center of Kidney and Urinary System Disease, Hangzhou, Zhejiang, China
Xiaohui Zhang
Kidney Disease Center, The First Affiliated Hospital, Zhejiang University School of Medicine; Institute of Nephrology, Zhejiang University; Key Laboratory of Kidney Disease Prevention and Control Technology, Zhejiang Province; Zhejiang Clinical Research Center of Kidney and Urinary System Disease, Hangzhou, Zhejiang, China
Jianghua Chen
Kidney Disease Center, The First Affiliated Hospital, Zhejiang University School of Medicine; Institute of Nephrology, Zhejiang University; Key Laboratory of Kidney Disease Prevention and Control Technology, Zhejiang Province; Zhejiang Clinical Research Center of Kidney and Urinary System Disease, Hangzhou, Zhejiang, China
Fei Han
Kidney Disease Center, The First Affiliated Hospital, Zhejiang University School of Medicine; Institute of Nephrology, Zhejiang University; Key Laboratory of Kidney Disease Prevention and Control Technology, Zhejiang Province; Zhejiang Clinical Research Center of Kidney and Urinary System Disease, Hangzhou, Zhejiang, China
Background Cardiovascular calcification includes cardiac valve calcification (CVC) and vascular calcification. We aimed to analyze risk factors for CVC, and construct a predictive model in maintenance peritoneal dialysis (MPD) patients.Methods We retrospectively analyzed MPD patients who began peritoneal dialysis between January 2014 and September 2021. Patients were randomly assigned to the derivation cohort and validation cohort in a 7:3 ratio. The patients in the derivation cohort were divided into the CVC group and non-CVC group. Logistic regression was used to analyze risk factors, then the rms package in R language was used to construct a nomogram model to predict CVC.Results 1,035 MPD patients were included, with the age of 50.0 ± 14.2 years and 632 males (61.1%). Their median follow-up time was 25 (12, 46) months. The new-onset CVC occurred in 128 patients (12.4%). In the derivation cohort, multivariate logistic regression indicated old age, female, high systolic blood pressure (SBP), high calcium-phosphorus product (Ca × P), high Charlson comorbidity index (CCI) and long dialysis time were independent risk factors for CVC (p < 0.05). We constructed a nomogram model for predicting CVC in the derivation cohort, with a C index of 0.845 (95% CI 0.803–0.886). This model was validated with a C index of 0.845 (95%CI 0.781–0.909) in the validation cohort.Conclusion We constructed a nomogram model for CVC in MPD patients, using independent risk factors including age, sex, SBP, Ca × P, CCI and dialysis time. This model achieved high efficiency in CVC prediction.
