Related Experiment Video
Updated: Jul 12, 2025
A Semi-Automated and Reproducible Biological-Based Method to Quantify Calcium Deposition In Vitro
Published on: June 2, 2022
The risk factors and predictive model for cardiac valve calcification in patients on maintenance peritoneal dialysis:
Yuxi Wang1, Quanquan Shen1,2, Junni Wang1
1Kidney 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.
Insights
This study identified key risk factors for cardiac valve calcification (CVC) in maintenance peritoneal dialysis (MPD) patients. A predictive nomogram model incorporating age, sex, blood pressure, and dialysis parameters demonstrated high accuracy in predicting CVC risk.
Area of Science:
- Nephrology
- Cardiology
- Medical Informatics
Background:
- Cardiovascular calcification, encompassing cardiac valve calcification (CVC) and vascular calcification, is a significant concern in patients with chronic kidney disease.
- Maintenance peritoneal dialysis (MPD) patients are particularly susceptible to cardiovascular complications, including CVC.
Purpose of the Study:
- To identify independent risk factors associated with the development of CVC in MPD patients.
- To develop and validate a predictive nomogram model for CVC in this patient population.
Main Methods:
- A retrospective analysis of 1,035 MPD patients was conducted, with data collected between January 2014 and September 2021.
- Patients were randomly allocated to derivation (70%) and validation (30%) cohorts.
- Logistic regression analysis identified risk factors, and a nomogram was constructed using the rms package in R for CVC prediction.
Main Results:
- The incidence of new-onset CVC was 12.4% (128 patients) over a median follow-up of 25 months.
- Independent risk factors for CVC included older age, female sex, high systolic blood pressure (SBP), elevated calcium-phosphorus product (Ca×P), high Charlson comorbidity index (CCI), and longer dialysis duration.
- The nomogram demonstrated strong predictive performance with a C-index of 0.845 in the derivation cohort and 0.845 in the validation cohort.
Conclusions:
- A nomogram model effectively predicts CVC in MPD patients.
- The model integrates readily available clinical parameters: age, sex, SBP, Ca×P, CCI, and dialysis time.
- This tool can aid in early identification and management of CVC risk in MPD patients.
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.
More Related Videos
04:30Investigating Aortic Valve Calcification via Isolation and Culture of T Lymphocytes using Feeder Cells from Irradiated Buffy Coat
Published on: February 4, 2021
05:47Isolation of Mouse Interstitial Valve Cells to Study the Calcification of the Aortic Valve In Vitro
Published on: May 10, 2021
Related Concept Videos
01:25Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT
01:25Peritoneal Dialysis II: Peritoneal Dialysis Systems and Complications
01:28Urinary Tract Calculi I: Introduction
01:27Urinary Tract Calculi IV: Nutrition Therapy and Prevention
01:20Imaging Studies for Cardiovascular System III: X-Ray
Definition and Purpose
An X-ray, or radiograph, is a non-invasive method that uses ionizing radiation to take images of internal structures. It is mainly used in cardiac imaging to examine the heart, lungs, and major blood vessels, aiming to identify abnormalities in the heart's size, shape, and position, such as heart failure, congenital defects, and vascular...
01:25Peritoneal Dialysis III: Nursing Management